research objectives in research methodology

Formulating Research Aims and Objectives

Formulating research aim and objectives in an appropriate manner is one of the most important aspects of your thesis. This is because research aim and objectives determine the scope, depth and the overall direction of the research. Research question is the central question of the study that has to be answered on the basis of research findings.

Research aim emphasizes what needs to be achieved within the scope of the research, by the end of the research process. Achievement of research aim provides answer to the research question.

Research objectives divide research aim into several parts and address each part separately. Research aim specifies WHAT needs to be studied and research objectives comprise a number of steps that address HOW research aim will be achieved.

As a rule of dumb, there would be one research aim and several research objectives. Achievement of each research objective will lead to the achievement of the research aim.

Consider the following as an example:

Research title: Effects of organizational culture on business profitability: a case study of Virgin Atlantic

Research aim: To assess the effects of Virgin Atlantic organizational culture on business profitability

Following research objectives would facilitate the achievement of this aim:

• Analyzing the nature of organizational culture at Virgin Atlantic by September 1, 2022
• Identifying factors impacting Virgin Atlantic organizational culture by September 16, 2022
• Analyzing impacts of Virgin Atlantic organizational culture on employee performances by September 30, 2022
• Providing recommendations to Virgin Atlantic strategic level management in terms of increasing the level of effectiveness of organizational culture by October 5, 2022

Figure below illustrates additional examples in formulating research aims and objectives:

Formulation of research question, aim and objectives

Common mistakes in the formulation of research aim relate to the following:

1. Choosing the topic too broadly . This is the most common mistake. For example, a research title of “an analysis of leadership practices” can be classified as too broad because the title fails to answer the following questions:

a) Which aspects of leadership practices? Leadership has many aspects such as employee motivation, ethical behaviour, strategic planning, change management etc. An attempt to cover all of these aspects of organizational leadership within a single research will result in an unfocused and poor work.

b) An analysis of leadership practices in which country? Leadership practices tend to be different in various countries due to cross-cultural differences, legislations and a range of other region-specific factors. Therefore, a study of leadership practices needs to be country-specific.

c) Analysis of leadership practices in which company or industry? Similar to the point above, analysis of leadership practices needs to take into account industry-specific and/or company-specific differences, and there is no way to conduct a leadership research that relates to all industries and organizations in an equal manner.

Accordingly, as an example “a study into the impacts of ethical behaviour of a leader on the level of employee motivation in US healthcare sector” would be a more appropriate title than simply “An analysis of leadership practices”.

2. Setting an unrealistic aim . Formulation of a research aim that involves in-depth interviews with Apple strategic level management by an undergraduate level student can be specified as a bit over-ambitious. This is because securing an interview with Apple CEO Tim Cook or members of Apple Board of Directors might not be easy. This is an extreme example of course, but you got the idea. Instead, you may aim to interview the manager of your local Apple store and adopt a more feasible strategy to get your dissertation completed.

3. Choosing research methods incompatible with the timeframe available . Conducting interviews with 20 sample group members and collecting primary data through 2 focus groups when only three months left until submission of your dissertation can be very difficult, if not impossible. Accordingly, timeframe available need to be taken into account when formulating research aims and objectives and selecting research methods.

Moreover, research objectives need to be formulated according to SMART principle,

 where the abbreviation stands for specific, measurable, achievable, realistic, and time-bound.

Examples of SMART research objectives

At the conclusion part of your research project you will need to reflect on the level of achievement of research aims and objectives. In case your research aims and objectives are not fully achieved by the end of the study, you will need to discuss the reasons. These may include initial inappropriate formulation of research aims and objectives, effects of other variables that were not considered at the beginning of the research or changes in some circumstances during the research process.

John Dudovskiy

• How it works

Published by Nicolas at March 21st, 2024 , Revised On March 12, 2024

The Ultimate Guide To Research Methodology

Research methodology is a crucial aspect of any investigative process, serving as the blueprint for the entire research journey. If you are stuck in the methodology section of your research paper , then this blog will guide you on what is a research methodology, its types and how to successfully conduct one. 

Table of Contents

What Is Research Methodology?

Research methodology can be defined as the systematic framework that guides researchers in designing, conducting, and analyzing their investigations. It encompasses a structured set of processes, techniques, and tools employed to gather and interpret data, ensuring the reliability and validity of the research findings. 

Research methodology is not confined to a singular approach; rather, it encapsulates a diverse range of methods tailored to the specific requirements of the research objectives.

Here is why Research methodology is important in academic and professional settings.

Facilitating Rigorous Inquiry

Research methodology forms the backbone of rigorous inquiry. It provides a structured approach that aids researchers in formulating precise thesis statements , selecting appropriate methodologies, and executing systematic investigations. This, in turn, enhances the quality and credibility of the research outcomes.

Ensuring Reproducibility And Reliability

In both academic and professional contexts, the ability to reproduce research outcomes is paramount. A well-defined research methodology establishes clear procedures, making it possible for others to replicate the study. This not only validates the findings but also contributes to the cumulative nature of knowledge.

Guiding Decision-Making Processes

In professional settings, decisions often hinge on reliable data and insights. Research methodology equips professionals with the tools to gather pertinent information, analyze it rigorously, and derive meaningful conclusions.

This informed decision-making is instrumental in achieving organizational goals and staying ahead in competitive environments.

Contributing To Academic Excellence

For academic researchers, adherence to robust research methodology is a hallmark of excellence. Institutions value research that adheres to high standards of methodology, fostering a culture of academic rigour and intellectual integrity. Furthermore, it prepares students with critical skills applicable beyond academia.

Enhancing Problem-Solving Abilities

Research methodology instills a problem-solving mindset by encouraging researchers to approach challenges systematically. It equips individuals with the skills to dissect complex issues, formulate hypotheses , and devise effective strategies for investigation.

Understanding Research Methodology

In the pursuit of knowledge and discovery, understanding the fundamentals of research methodology is paramount. 

Basics Of Research

Research, in its essence, is a systematic and organized process of inquiry aimed at expanding our understanding of a particular subject or phenomenon. It involves the exploration of existing knowledge, the formulation of hypotheses, and the collection and analysis of data to draw meaningful conclusions. 

Research is a dynamic and iterative process that contributes to the continuous evolution of knowledge in various disciplines.

Types of Research

Research takes on various forms, each tailored to the nature of the inquiry. Broadly classified, research can be categorized into two main types:

• Quantitative Research: This type involves the collection and analysis of numerical data to identify patterns, relationships, and statistical significance. It is particularly useful for testing hypotheses and making predictions.
• Qualitative Research: Qualitative research focuses on understanding the depth and details of a phenomenon through non-numerical data. It often involves methods such as interviews, focus groups, and content analysis, providing rich insights into complex issues.

Components Of Research Methodology

To conduct effective research, one must go through the different components of research methodology. These components form the scaffolding that supports the entire research process, ensuring its coherence and validity.

Research Design

Research design serves as the blueprint for the entire research project. It outlines the overall structure and strategy for conducting the study. The three primary types of research design are:

• Exploratory Research: Aimed at gaining insights and familiarity with the topic, often used in the early stages of research.
• Descriptive Research: Involves portraying an accurate profile of a situation or phenomenon, answering the ‘what,’ ‘who,’ ‘where,’ and ‘when’ questions.
• Explanatory Research: Seeks to identify the causes and effects of a phenomenon, explaining the ‘why’ and ‘how.’

Data Collection Methods

Choosing the right data collection methods is crucial for obtaining reliable and relevant information. Common methods include:

• Surveys and Questionnaires: Employed to gather information from a large number of respondents through standardized questions.
• Interviews: In-depth conversations with participants, offering qualitative insights.
• Observation: Systematic watching and recording of behaviour, events, or processes in their natural setting.

Data Analysis Techniques

Once data is collected, analysis becomes imperative to derive meaningful conclusions. Different methodologies exist for quantitative and qualitative data:

• Quantitative Data Analysis: Involves statistical techniques such as descriptive statistics, inferential statistics, and regression analysis to interpret numerical data.
• Qualitative Data Analysis: Methods like content analysis, thematic analysis, and grounded theory are employed to extract patterns, themes, and meanings from non-numerical data.

The research paper we write have:

• Precision and Clarity
• Zero Plagiarism
• High-level Encryption
• Authentic Sources

Choosing a Research Method

Selecting an appropriate research method is a critical decision in the research process. It determines the approach, tools, and techniques that will be used to answer the research questions. 

Quantitative Research Methods

Quantitative research involves the collection and analysis of numerical data, providing a structured and objective approach to understanding and explaining phenomena.

Experimental Research

Experimental research involves manipulating variables to observe the effect on another variable under controlled conditions. It aims to establish cause-and-effect relationships.

Key Characteristics:

• Controlled Environment: Experiments are conducted in a controlled setting to minimize external influences.
• Random Assignment: Participants are randomly assigned to different experimental conditions.
• Quantitative Data: Data collected is numerical, allowing for statistical analysis.

Applications: Commonly used in scientific studies and psychology to test hypotheses and identify causal relationships.

Survey Research

Survey research gathers information from a sample of individuals through standardized questionnaires or interviews. It aims to collect data on opinions, attitudes, and behaviours.

• Structured Instruments: Surveys use structured instruments, such as questionnaires, to collect data.
• Large Sample Size: Surveys often target a large and diverse group of participants.
• Quantitative Data Analysis: Responses are quantified for statistical analysis.

Applications: Widely employed in social sciences, marketing, and public opinion research to understand trends and preferences.

Descriptive Research

Descriptive research seeks to portray an accurate profile of a situation or phenomenon. It focuses on answering the ‘what,’ ‘who,’ ‘where,’ and ‘when’ questions.

• Observation and Data Collection: This involves observing and documenting without manipulating variables.
• Objective Description: Aim to provide an unbiased and factual account of the subject.
• Quantitative or Qualitative Data: T his can include both types of data, depending on the research focus.

Applications: Useful in situations where researchers want to understand and describe a phenomenon without altering it, common in social sciences and education.

Qualitative Research Methods

Qualitative research emphasizes exploring and understanding the depth and complexity of phenomena through non-numerical data.

A case study is an in-depth exploration of a particular person, group, event, or situation. It involves detailed, context-rich analysis.

• Rich Data Collection: Uses various data sources, such as interviews, observations, and documents.
• Contextual Understanding: Aims to understand the context and unique characteristics of the case.
• Holistic Approach: Examines the case in its entirety.

Applications: Common in social sciences, psychology, and business to investigate complex and specific instances.

Ethnography

Ethnography involves immersing the researcher in the culture or community being studied to gain a deep understanding of their behaviours, beliefs, and practices.

• Participant Observation: Researchers actively participate in the community or setting.
• Holistic Perspective: Focuses on the interconnectedness of cultural elements.
• Qualitative Data: In-depth narratives and descriptions are central to ethnographic studies.

Applications: Widely used in anthropology, sociology, and cultural studies to explore and document cultural practices.

Grounded Theory

Grounded theory aims to develop theories grounded in the data itself. It involves systematic data collection and analysis to construct theories from the ground up.

• Constant Comparison: Data is continually compared and analyzed during the research process.
• Inductive Reasoning: Theories emerge from the data rather than being imposed on it.
• Iterative Process: The research design evolves as the study progresses.

Applications: Commonly applied in sociology, nursing, and management studies to generate theories from empirical data.

Research design is the structural framework that outlines the systematic process and plan for conducting a study. It serves as the blueprint, guiding researchers on how to collect, analyze, and interpret data.

Exploratory, Descriptive, And Explanatory Designs

Exploratory design.

Exploratory research design is employed when a researcher aims to explore a relatively unknown subject or gain insights into a complex phenomenon.

• Flexibility: Allows for flexibility in data collection and analysis.
• Open-Ended Questions: Uses open-ended questions to gather a broad range of information.
• Preliminary Nature: Often used in the initial stages of research to formulate hypotheses.

Applications: Valuable in the early stages of investigation, especially when the researcher seeks a deeper understanding of a subject before formalizing research questions.

Descriptive Design

Descriptive research design focuses on portraying an accurate profile of a situation, group, or phenomenon.

• Structured Data Collection: Involves systematic and structured data collection methods.
• Objective Presentation: Aims to provide an unbiased and factual account of the subject.
• Quantitative or Qualitative Data: Can incorporate both types of data, depending on the research objectives.

Applications: Widely used in social sciences, marketing, and educational research to provide detailed and objective descriptions.

Explanatory Design

Explanatory research design aims to identify the causes and effects of a phenomenon, explaining the ‘why’ and ‘how’ behind observed relationships.

• Causal Relationships: Seeks to establish causal relationships between variables.
• Controlled Variables : Often involves controlling certain variables to isolate causal factors.
• Quantitative Analysis: Primarily relies on quantitative data analysis techniques.

Applications: Commonly employed in scientific studies and social sciences to delve into the underlying reasons behind observed patterns.

Cross-Sectional Vs. Longitudinal Designs

Cross-sectional design.

Cross-sectional designs collect data from participants at a single point in time.

• Snapshot View: Provides a snapshot of a population at a specific moment.
• Efficiency: More efficient in terms of time and resources.
• Limited Temporal Insights: Offers limited insights into changes over time.

Applications: Suitable for studying characteristics or behaviours that are stable or not expected to change rapidly.

Longitudinal Design

Longitudinal designs involve the collection of data from the same participants over an extended period.

• Temporal Sequence: Allows for the examination of changes over time.
• Causality Assessment: Facilitates the assessment of cause-and-effect relationships.
• Resource-Intensive: Requires more time and resources compared to cross-sectional designs.

Applications: Ideal for studying developmental processes, trends, or the impact of interventions over time.

Experimental Vs Non-experimental Designs

Experimental design.

Experimental designs involve manipulating variables under controlled conditions to observe the effect on another variable.

• Causality Inference: Enables the inference of cause-and-effect relationships.
• Quantitative Data: Primarily involves the collection and analysis of numerical data.

Applications: Commonly used in scientific studies, psychology, and medical research to establish causal relationships.

Non-Experimental Design

Non-experimental designs observe and describe phenomena without manipulating variables.

• Natural Settings: Data is often collected in natural settings without intervention.
• Descriptive or Correlational: Focuses on describing relationships or correlations between variables.
• Quantitative or Qualitative Data: This can involve either type of data, depending on the research approach.

Applications: Suitable for studying complex phenomena in real-world settings where manipulation may not be ethical or feasible.

Effective data collection is fundamental to the success of any research endeavour. 

Designing Effective Surveys

Objective Design:

• Clearly define the research objectives to guide the survey design.
• Craft questions that align with the study’s goals and avoid ambiguity.

Structured Format:

• Use a structured format with standardized questions for consistency.
• Include a mix of closed-ended and open-ended questions for detailed insights.

Pilot Testing:

• Conduct pilot tests to identify and rectify potential issues with survey design.
• Ensure clarity, relevance, and appropriateness of questions.

Sampling Strategy:

• Develop a robust sampling strategy to ensure a representative participant group.
• Consider random sampling or stratified sampling based on the research goals.

Conducting Interviews

Establishing Rapport:

• Build rapport with participants to create a comfortable and open environment.
• Clearly communicate the purpose of the interview and the value of participants’ input.

Open-Ended Questions:

• Frame open-ended questions to encourage detailed responses.
• Allow participants to express their thoughts and perspectives freely.

Active Listening:

• Practice active listening to understand areas and gather rich data.
• Avoid interrupting and maintain a non-judgmental stance during the interview.

Ethical Considerations:

• Obtain informed consent and assure participants of confidentiality.
• Be transparent about the study’s purpose and potential implications.

Observation

1. participant observation.

Immersive Participation:

• Actively immerse yourself in the setting or group being observed.
• Develop a deep understanding of behaviours, interactions, and context.

Field Notes:

• Maintain detailed and reflective field notes during observations.
• Document observed patterns, unexpected events, and participant reactions.

Ethical Awareness:

• Be conscious of ethical considerations, ensuring respect for participants.
• Balance the role of observer and participant to minimize bias.

2. Non-participant Observation

Objective Observation:

• Maintain a more detached and objective stance during non-participant observation.
• Focus on recording behaviours, events, and patterns without direct involvement.

Data Reliability:

• Enhance the reliability of data by reducing observer bias.
• Develop clear observation protocols and guidelines.

Contextual Understanding:

• Strive for a thorough understanding of the observed context.
• Consider combining non-participant observation with other methods for triangulation.

Archival Research

1. using existing data.

Identifying Relevant Archives:

• Locate and access archives relevant to the research topic.
• Collaborate with institutions or repositories holding valuable data.

Data Verification:

• Verify the accuracy and reliability of archived data.
• Cross-reference with other sources to ensure data integrity.

Ethical Use:

• Adhere to ethical guidelines when using existing data.
• Respect copyright and intellectual property rights.

2. Challenges and Considerations

Incomplete or Inaccurate Archives:

• Address the possibility of incomplete or inaccurate archival records.
• Acknowledge limitations and uncertainties in the data.

Temporal Bias:

• Recognize potential temporal biases in archived data.
• Consider the historical context and changes that may impact interpretation.

Access Limitations:

• Address potential limitations in accessing certain archives.
• Seek alternative sources or collaborate with institutions to overcome barriers.

Common Challenges in Research Methodology

Conducting research is a complex and dynamic process, often accompanied by a myriad of challenges. Addressing these challenges is crucial to ensure the reliability and validity of research findings.

Sampling Issues

Sampling bias:.

• The presence of sampling bias can lead to an unrepresentative sample, affecting the generalizability of findings.
• Employ random sampling methods and ensure the inclusion of diverse participants to reduce bias.

Sample Size Determination:

• Determining an appropriate sample size is a delicate balance. Too small a sample may lack statistical power, while an excessively large sample may strain resources.
• Conduct a power analysis to determine the optimal sample size based on the research objectives and expected effect size.

Data Quality And Validity

Measurement error:.

• Inaccuracies in measurement tools or data collection methods can introduce measurement errors, impacting the validity of results.
• Pilot test instruments, calibrate equipment, and use standardized measures to enhance the reliability of data.

Construct Validity:

• Ensuring that the chosen measures accurately capture the intended constructs is a persistent challenge.
• Use established measurement instruments and employ multiple measures to assess the same construct for triangulation.

Time And Resource Constraints

Timeline pressures:.

• Limited timeframes can compromise the depth and thoroughness of the research process.
• Develop a realistic timeline, prioritize tasks, and communicate expectations with stakeholders to manage time constraints effectively.

Resource Availability:

• Inadequate resources, whether financial or human, can impede the execution of research activities.
• Seek external funding, collaborate with other researchers, and explore alternative methods that require fewer resources.

Managing Bias in Research

Selection bias:.

• Selecting participants in a way that systematically skews the sample can introduce selection bias.
• Employ randomization techniques, use stratified sampling, and transparently report participant recruitment methods.

Confirmation Bias:

• Researchers may unintentionally favour information that confirms their preconceived beliefs or hypotheses.
• Adopt a systematic and open-minded approach, use blinded study designs, and engage in peer review to mitigate confirmation bias.

Tips On How To Write A Research Methodology

Conducting successful research relies not only on the application of sound methodologies but also on strategic planning and effective collaboration. Here are some tips to enhance the success of your research methodology:

Tip 1. Clear Research Objectives

Well-defined research objectives guide the entire research process. Clearly articulate the purpose of your study, outlining specific research questions or hypotheses.

Tip 2. Comprehensive Literature Review

A thorough literature review provides a foundation for understanding existing knowledge and identifying gaps. Invest time in reviewing relevant literature to inform your research design and methodology.

Tip 3. Detailed Research Plan

A detailed plan serves as a roadmap, ensuring all aspects of the research are systematically addressed. Develop a detailed research plan outlining timelines, milestones, and tasks.

Tip 4. Ethical Considerations

Ethical practices are fundamental to maintaining the integrity of research. Address ethical considerations early, obtain necessary approvals, and ensure participant rights are safeguarded.

Tip 5. Stay Updated On Methodologies

Research methodologies evolve, and staying updated is essential for employing the most effective techniques. Engage in continuous learning by attending workshops, conferences, and reading recent publications.

Tip 6. Adaptability In Methods

Unforeseen challenges may arise during research, necessitating adaptability in methods. Be flexible and willing to modify your approach when needed, ensuring the integrity of the study.

Tip 7. Iterative Approach

Research is often an iterative process, and refining methods based on ongoing findings enhance the study’s robustness. Regularly review and refine your research design and methods as the study progresses.

Frequently Asked Questions

What is the research methodology.

Research methodology is the systematic process of planning, executing, and evaluating scientific investigation. It encompasses the techniques, tools, and procedures used to collect, analyze, and interpret data, ensuring the reliability and validity of research findings.

What are the methodologies in research?

Research methodologies include qualitative and quantitative approaches. Qualitative methods involve in-depth exploration of non-numerical data, while quantitative methods use statistical analysis to examine numerical data. Mixed methods combine both approaches for a comprehensive understanding of research questions.

How to write research methodology?

To write a research methodology, clearly outline the study’s design, data collection, and analysis procedures. Specify research tools, participants, and sampling methods. Justify choices and discuss limitations. Ensure clarity, coherence, and alignment with research objectives for a robust methodology section.

How to write the methodology section of a research paper?

In the methodology section of a research paper, describe the study’s design, data collection, and analysis methods. Detail procedures, tools, participants, and sampling. Justify choices, address ethical considerations, and explain how the methodology aligns with research objectives, ensuring clarity and rigour.

What is mixed research methodology?

Mixed research methodology combines both qualitative and quantitative research approaches within a single study. This approach aims to enhance the details and depth of research findings by providing a more comprehensive understanding of the research problem or question.

You May Also Like

The central idea of this excerpt revolves around the exploration of key themes, offering insights that illuminate the concepts within the text.

Common topics in Botany papers include taxonomy, plant physiology, ecology and biodiversity, plant pathology, and genetics.

Craft a compelling scholarship motivation letter by showcasing your passion, achievements, and future goals concisely and impactfully.

Ready to place an order?

USEFUL LINKS

Learning resources, company details.

• How It Works

Automated page speed optimizations for fast site performance

• Privacy Policy

Home » Research Methodology – Types, Examples and writing Guide

Research Methodology – Types, Examples and writing Guide

Table of Contents

Research Methodology

Definition:

Research Methodology refers to the systematic and scientific approach used to conduct research, investigate problems, and gather data and information for a specific purpose. It involves the techniques and procedures used to identify, collect , analyze , and interpret data to answer research questions or solve research problems . Moreover, They are philosophical and theoretical frameworks that guide the research process.

Structure of Research Methodology

Research methodology formats can vary depending on the specific requirements of the research project, but the following is a basic example of a structure for a research methodology section:

I. Introduction

• Provide an overview of the research problem and the need for a research methodology section
• Outline the main research questions and objectives

II. Research Design

• Explain the research design chosen and why it is appropriate for the research question(s) and objectives
• Discuss any alternative research designs considered and why they were not chosen
• Describe the research setting and participants (if applicable)

III. Data Collection Methods

• Describe the methods used to collect data (e.g., surveys, interviews, observations)
• Explain how the data collection methods were chosen and why they are appropriate for the research question(s) and objectives
• Detail any procedures or instruments used for data collection

IV. Data Analysis Methods

• Describe the methods used to analyze the data (e.g., statistical analysis, content analysis )
• Explain how the data analysis methods were chosen and why they are appropriate for the research question(s) and objectives
• Detail any procedures or software used for data analysis

V. Ethical Considerations

• Discuss any ethical issues that may arise from the research and how they were addressed
• Explain how informed consent was obtained (if applicable)
• Detail any measures taken to ensure confidentiality and anonymity

VI. Limitations

• Identify any potential limitations of the research methodology and how they may impact the results and conclusions

VII. Conclusion

• Summarize the key aspects of the research methodology section
• Explain how the research methodology addresses the research question(s) and objectives

Research Methodology Types

Types of Research Methodology are as follows:

Quantitative Research Methodology

This is a research methodology that involves the collection and analysis of numerical data using statistical methods. This type of research is often used to study cause-and-effect relationships and to make predictions.

Qualitative Research Methodology

This is a research methodology that involves the collection and analysis of non-numerical data such as words, images, and observations. This type of research is often used to explore complex phenomena, to gain an in-depth understanding of a particular topic, and to generate hypotheses.

Mixed-Methods Research Methodology

This is a research methodology that combines elements of both quantitative and qualitative research. This approach can be particularly useful for studies that aim to explore complex phenomena and to provide a more comprehensive understanding of a particular topic.

Case Study Research Methodology

This is a research methodology that involves in-depth examination of a single case or a small number of cases. Case studies are often used in psychology, sociology, and anthropology to gain a detailed understanding of a particular individual or group.

Action Research Methodology

This is a research methodology that involves a collaborative process between researchers and practitioners to identify and solve real-world problems. Action research is often used in education, healthcare, and social work.

Experimental Research Methodology

This is a research methodology that involves the manipulation of one or more independent variables to observe their effects on a dependent variable. Experimental research is often used to study cause-and-effect relationships and to make predictions.

Survey Research Methodology

This is a research methodology that involves the collection of data from a sample of individuals using questionnaires or interviews. Survey research is often used to study attitudes, opinions, and behaviors.

Grounded Theory Research Methodology

This is a research methodology that involves the development of theories based on the data collected during the research process. Grounded theory is often used in sociology and anthropology to generate theories about social phenomena.

Research Methodology Example

An Example of Research Methodology could be the following:

Research Methodology for Investigating the Effectiveness of Cognitive Behavioral Therapy in Reducing Symptoms of Depression in Adults

Introduction:

The aim of this research is to investigate the effectiveness of cognitive-behavioral therapy (CBT) in reducing symptoms of depression in adults. To achieve this objective, a randomized controlled trial (RCT) will be conducted using a mixed-methods approach.

Research Design:

The study will follow a pre-test and post-test design with two groups: an experimental group receiving CBT and a control group receiving no intervention. The study will also include a qualitative component, in which semi-structured interviews will be conducted with a subset of participants to explore their experiences of receiving CBT.

Participants:

Participants will be recruited from community mental health clinics in the local area. The sample will consist of 100 adults aged 18-65 years old who meet the diagnostic criteria for major depressive disorder. Participants will be randomly assigned to either the experimental group or the control group.

Intervention :

The experimental group will receive 12 weekly sessions of CBT, each lasting 60 minutes. The intervention will be delivered by licensed mental health professionals who have been trained in CBT. The control group will receive no intervention during the study period.

Data Collection:

Quantitative data will be collected through the use of standardized measures such as the Beck Depression Inventory-II (BDI-II) and the Generalized Anxiety Disorder-7 (GAD-7). Data will be collected at baseline, immediately after the intervention, and at a 3-month follow-up. Qualitative data will be collected through semi-structured interviews with a subset of participants from the experimental group. The interviews will be conducted at the end of the intervention period, and will explore participants’ experiences of receiving CBT.

Data Analysis:

Quantitative data will be analyzed using descriptive statistics, t-tests, and mixed-model analyses of variance (ANOVA) to assess the effectiveness of the intervention. Qualitative data will be analyzed using thematic analysis to identify common themes and patterns in participants’ experiences of receiving CBT.

Ethical Considerations:

This study will comply with ethical guidelines for research involving human subjects. Participants will provide informed consent before participating in the study, and their privacy and confidentiality will be protected throughout the study. Any adverse events or reactions will be reported and managed appropriately.

Data Management:

All data collected will be kept confidential and stored securely using password-protected databases. Identifying information will be removed from qualitative data transcripts to ensure participants’ anonymity.

Limitations:

One potential limitation of this study is that it only focuses on one type of psychotherapy, CBT, and may not generalize to other types of therapy or interventions. Another limitation is that the study will only include participants from community mental health clinics, which may not be representative of the general population.

Conclusion:

This research aims to investigate the effectiveness of CBT in reducing symptoms of depression in adults. By using a randomized controlled trial and a mixed-methods approach, the study will provide valuable insights into the mechanisms underlying the relationship between CBT and depression. The results of this study will have important implications for the development of effective treatments for depression in clinical settings.

How to Write Research Methodology

Writing a research methodology involves explaining the methods and techniques you used to conduct research, collect data, and analyze results. It’s an essential section of any research paper or thesis, as it helps readers understand the validity and reliability of your findings. Here are the steps to write a research methodology:

• Start by explaining your research question: Begin the methodology section by restating your research question and explaining why it’s important. This helps readers understand the purpose of your research and the rationale behind your methods.
• Describe your research design: Explain the overall approach you used to conduct research. This could be a qualitative or quantitative research design, experimental or non-experimental, case study or survey, etc. Discuss the advantages and limitations of the chosen design.
• Discuss your sample: Describe the participants or subjects you included in your study. Include details such as their demographics, sampling method, sample size, and any exclusion criteria used.
• Describe your data collection methods : Explain how you collected data from your participants. This could include surveys, interviews, observations, questionnaires, or experiments. Include details on how you obtained informed consent, how you administered the tools, and how you minimized the risk of bias.
• Explain your data analysis techniques: Describe the methods you used to analyze the data you collected. This could include statistical analysis, content analysis, thematic analysis, or discourse analysis. Explain how you dealt with missing data, outliers, and any other issues that arose during the analysis.
• Discuss the validity and reliability of your research : Explain how you ensured the validity and reliability of your study. This could include measures such as triangulation, member checking, peer review, or inter-coder reliability.
• Acknowledge any limitations of your research: Discuss any limitations of your study, including any potential threats to validity or generalizability. This helps readers understand the scope of your findings and how they might apply to other contexts.
• Provide a summary: End the methodology section by summarizing the methods and techniques you used to conduct your research. This provides a clear overview of your research methodology and helps readers understand the process you followed to arrive at your findings.

When to Write Research Methodology

Research methodology is typically written after the research proposal has been approved and before the actual research is conducted. It should be written prior to data collection and analysis, as it provides a clear roadmap for the research project.

The research methodology is an important section of any research paper or thesis, as it describes the methods and procedures that will be used to conduct the research. It should include details about the research design, data collection methods, data analysis techniques, and any ethical considerations.

The methodology should be written in a clear and concise manner, and it should be based on established research practices and standards. It is important to provide enough detail so that the reader can understand how the research was conducted and evaluate the validity of the results.

Applications of Research Methodology

Here are some of the applications of research methodology:

• To identify the research problem: Research methodology is used to identify the research problem, which is the first step in conducting any research.
• To design the research: Research methodology helps in designing the research by selecting the appropriate research method, research design, and sampling technique.
• To collect data: Research methodology provides a systematic approach to collect data from primary and secondary sources.
• To analyze data: Research methodology helps in analyzing the collected data using various statistical and non-statistical techniques.
• To test hypotheses: Research methodology provides a framework for testing hypotheses and drawing conclusions based on the analysis of data.
• To generalize findings: Research methodology helps in generalizing the findings of the research to the target population.
• To develop theories : Research methodology is used to develop new theories and modify existing theories based on the findings of the research.
• To evaluate programs and policies : Research methodology is used to evaluate the effectiveness of programs and policies by collecting data and analyzing it.
• To improve decision-making: Research methodology helps in making informed decisions by providing reliable and valid data.

Purpose of Research Methodology

Research methodology serves several important purposes, including:

• To guide the research process: Research methodology provides a systematic framework for conducting research. It helps researchers to plan their research, define their research questions, and select appropriate methods and techniques for collecting and analyzing data.
• To ensure research quality: Research methodology helps researchers to ensure that their research is rigorous, reliable, and valid. It provides guidelines for minimizing bias and error in data collection and analysis, and for ensuring that research findings are accurate and trustworthy.
• To replicate research: Research methodology provides a clear and detailed account of the research process, making it possible for other researchers to replicate the study and verify its findings.
• To advance knowledge: Research methodology enables researchers to generate new knowledge and to contribute to the body of knowledge in their field. It provides a means for testing hypotheses, exploring new ideas, and discovering new insights.
• To inform decision-making: Research methodology provides evidence-based information that can inform policy and decision-making in a variety of fields, including medicine, public health, education, and business.

Advantages of Research Methodology

Research methodology has several advantages that make it a valuable tool for conducting research in various fields. Here are some of the key advantages of research methodology:

• Systematic and structured approach : Research methodology provides a systematic and structured approach to conducting research, which ensures that the research is conducted in a rigorous and comprehensive manner.
• Objectivity : Research methodology aims to ensure objectivity in the research process, which means that the research findings are based on evidence and not influenced by personal bias or subjective opinions.
• Replicability : Research methodology ensures that research can be replicated by other researchers, which is essential for validating research findings and ensuring their accuracy.
• Reliability : Research methodology aims to ensure that the research findings are reliable, which means that they are consistent and can be depended upon.
• Validity : Research methodology ensures that the research findings are valid, which means that they accurately reflect the research question or hypothesis being tested.
• Efficiency : Research methodology provides a structured and efficient way of conducting research, which helps to save time and resources.
• Flexibility : Research methodology allows researchers to choose the most appropriate research methods and techniques based on the research question, data availability, and other relevant factors.
• Scope for innovation: Research methodology provides scope for innovation and creativity in designing research studies and developing new research techniques.

Research Methodology Vs Research Methods

About the author.

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

How to Cite Research Paper – All Formats and...

Data Collection – Methods Types and Examples

Delimitations in Research – Types, Examples and...

Research Paper Format – Types, Examples and...

Research Process – Steps, Examples and Tips

Research Design – Types, Methods and Examples

Reference management. Clean and simple.

What is research methodology?

The basics of research methodology

Why do you need a research methodology, what needs to be included, why do you need to document your research method, what are the different types of research instruments, qualitative / quantitative / mixed research methodologies, how do you choose the best research methodology for you, frequently asked questions about research methodology, related articles.

When you’re working on your first piece of academic research, there are many different things to focus on, and it can be overwhelming to stay on top of everything. This is especially true of budding or inexperienced researchers.

If you’ve never put together a research proposal before or find yourself in a position where you need to explain your research methodology decisions, there are a few things you need to be aware of.

Once you understand the ins and outs, handling academic research in the future will be less intimidating. We break down the basics below:

A research methodology encompasses the way in which you intend to carry out your research. This includes how you plan to tackle things like collection methods, statistical analysis, participant observations, and more.

You can think of your research methodology as being a formula. One part will be how you plan on putting your research into practice, and another will be why you feel this is the best way to approach it. Your research methodology is ultimately a methodological and systematic plan to resolve your research problem.

In short, you are explaining how you will take your idea and turn it into a study, which in turn will produce valid and reliable results that are in accordance with the aims and objectives of your research. This is true whether your paper plans to make use of qualitative methods or quantitative methods.

The purpose of a research methodology is to explain the reasoning behind your approach to your research - you'll need to support your collection methods, methods of analysis, and other key points of your work.

Think of it like writing a plan or an outline for you what you intend to do.

When carrying out research, it can be easy to go off-track or depart from your standard methodology.

Tip: Having a methodology keeps you accountable and on track with your original aims and objectives, and gives you a suitable and sound plan to keep your project manageable, smooth, and effective.

With all that said, how do you write out your standard approach to a research methodology?

As a general plan, your methodology should include the following information:

• Your research method.  You need to state whether you plan to use quantitative analysis, qualitative analysis, or mixed-method research methods. This will often be determined by what you hope to achieve with your research.
• Explain your reasoning. Why are you taking this methodological approach? Why is this particular methodology the best way to answer your research problem and achieve your objectives?
• Explain your instruments.  This will mainly be about your collection methods. There are varying instruments to use such as interviews, physical surveys, questionnaires, for example. Your methodology will need to detail your reasoning in choosing a particular instrument for your research.
• What will you do with your results?  How are you going to analyze the data once you have gathered it?
• Advise your reader.  If there is anything in your research methodology that your reader might be unfamiliar with, you should explain it in more detail. For example, you should give any background information to your methods that might be relevant or provide your reasoning if you are conducting your research in a non-standard way.
• How will your sampling process go?  What will your sampling procedure be and why? For example, if you will collect data by carrying out semi-structured or unstructured interviews, how will you choose your interviewees and how will you conduct the interviews themselves?
• Any practical limitations?  You should discuss any limitations you foresee being an issue when you’re carrying out your research.

In any dissertation, thesis, or academic journal, you will always find a chapter dedicated to explaining the research methodology of the person who carried out the study, also referred to as the methodology section of the work.

A good research methodology will explain what you are going to do and why, while a poor methodology will lead to a messy or disorganized approach.

You should also be able to justify in this section your reasoning for why you intend to carry out your research in a particular way, especially if it might be a particularly unique method.

Having a sound methodology in place can also help you with the following:

• When another researcher at a later date wishes to try and replicate your research, they will need your explanations and guidelines.
• In the event that you receive any criticism or questioning on the research you carried out at a later point, you will be able to refer back to it and succinctly explain the how and why of your approach.
• It provides you with a plan to follow throughout your research. When you are drafting your methodology approach, you need to be sure that the method you are using is the right one for your goal. This will help you with both explaining and understanding your method.
• It affords you the opportunity to document from the outset what you intend to achieve with your research, from start to finish.

A research instrument is a tool you will use to help you collect, measure and analyze the data you use as part of your research.

The choice of research instrument will usually be yours to make as the researcher and will be whichever best suits your methodology.

There are many different research instruments you can use in collecting data for your research.

Generally, they can be grouped as follows:

• Interviews (either as a group or one-on-one). You can carry out interviews in many different ways. For example, your interview can be structured, semi-structured, or unstructured. The difference between them is how formal the set of questions is that is asked of the interviewee. In a group interview, you may choose to ask the interviewees to give you their opinions or perceptions on certain topics.
• Surveys (online or in-person). In survey research, you are posing questions in which you ask for a response from the person taking the survey. You may wish to have either free-answer questions such as essay-style questions, or you may wish to use closed questions such as multiple choice. You may even wish to make the survey a mixture of both.
• Focus Groups.  Similar to the group interview above, you may wish to ask a focus group to discuss a particular topic or opinion while you make a note of the answers given.
• Observations.  This is a good research instrument to use if you are looking into human behaviors. Different ways of researching this include studying the spontaneous behavior of participants in their everyday life, or something more structured. A structured observation is research conducted at a set time and place where researchers observe behavior as planned and agreed upon with participants.

These are the most common ways of carrying out research, but it is really dependent on your needs as a researcher and what approach you think is best to take.

It is also possible to combine a number of research instruments if this is necessary and appropriate in answering your research problem.

There are three different types of methodologies, and they are distinguished by whether they focus on words, numbers, or both.

➡️ Want to learn more about the differences between qualitative and quantitative research, and how to use both methods? Check out our guide for that!

If you've done your due diligence, you'll have an idea of which methodology approach is best suited to your research.

It’s likely that you will have carried out considerable reading and homework before you reach this point and you may have taken inspiration from other similar studies that have yielded good results.

Still, it is important to consider different options before setting your research in stone. Exploring different options available will help you to explain why the choice you ultimately make is preferable to other methods.

If proving your research problem requires you to gather large volumes of numerical data to test hypotheses, a quantitative research method is likely to provide you with the most usable results.

If instead you’re looking to try and learn more about people, and their perception of events, your methodology is more exploratory in nature and would therefore probably be better served using a qualitative research methodology.

It helps to always bring things back to the question: what do I want to achieve with my research?

Once you have conducted your research, you need to analyze it. Here are some helpful guides for qualitative data analysis:

➡️  How to do a content analysis

➡️  How to do a thematic analysis

➡️  How to do a rhetorical analysis

Research methodology refers to the techniques used to find and analyze information for a study, ensuring that the results are valid, reliable and that they address the research objective.

Data can typically be organized into four different categories or methods: observational, experimental, simulation, and derived.

Writing a methodology section is a process of introducing your methods and instruments, discussing your analysis, providing more background information, addressing your research limitations, and more.

Your research methodology section will need a clear research question and proposed research approach. You'll need to add a background, introduce your research question, write your methodology and add the works you cited during your data collecting phase.

The research methodology section of your study will indicate how valid your findings are and how well-informed your paper is. It also assists future researchers planning to use the same methodology, who want to cite your study or replicate it.

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• v.53(4); 2010 Aug

Research questions, hypotheses and objectives

Patricia farrugia.

* Michael G. DeGroote School of Medicine, the

Bradley A. Petrisor

† Division of Orthopaedic Surgery and the

Forough Farrokhyar

‡ Departments of Surgery and

§ Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ont

Mohit Bhandari

There is an increasing familiarity with the principles of evidence-based medicine in the surgical community. As surgeons become more aware of the hierarchy of evidence, grades of recommendations and the principles of critical appraisal, they develop an increasing familiarity with research design. Surgeons and clinicians are looking more and more to the literature and clinical trials to guide their practice; as such, it is becoming a responsibility of the clinical research community to attempt to answer questions that are not only well thought out but also clinically relevant. The development of the research question, including a supportive hypothesis and objectives, is a necessary key step in producing clinically relevant results to be used in evidence-based practice. A well-defined and specific research question is more likely to help guide us in making decisions about study design and population and subsequently what data will be collected and analyzed. 1

Objectives of this article

In this article, we discuss important considerations in the development of a research question and hypothesis and in defining objectives for research. By the end of this article, the reader will be able to appreciate the significance of constructing a good research question and developing hypotheses and research objectives for the successful design of a research study. The following article is divided into 3 sections: research question, research hypothesis and research objectives.

Research question

Interest in a particular topic usually begins the research process, but it is the familiarity with the subject that helps define an appropriate research question for a study. 1 Questions then arise out of a perceived knowledge deficit within a subject area or field of study. 2 Indeed, Haynes suggests that it is important to know “where the boundary between current knowledge and ignorance lies.” 1 The challenge in developing an appropriate research question is in determining which clinical uncertainties could or should be studied and also rationalizing the need for their investigation.

Increasing one’s knowledge about the subject of interest can be accomplished in many ways. Appropriate methods include systematically searching the literature, in-depth interviews and focus groups with patients (and proxies) and interviews with experts in the field. In addition, awareness of current trends and technological advances can assist with the development of research questions. 2 It is imperative to understand what has been studied about a topic to date in order to further the knowledge that has been previously gathered on a topic. Indeed, some granting institutions (e.g., Canadian Institute for Health Research) encourage applicants to conduct a systematic review of the available evidence if a recent review does not already exist and preferably a pilot or feasibility study before applying for a grant for a full trial.

In-depth knowledge about a subject may generate a number of questions. It then becomes necessary to ask whether these questions can be answered through one study or if more than one study needed. 1 Additional research questions can be developed, but several basic principles should be taken into consideration. 1 All questions, primary and secondary, should be developed at the beginning and planning stages of a study. Any additional questions should never compromise the primary question because it is the primary research question that forms the basis of the hypothesis and study objectives. It must be kept in mind that within the scope of one study, the presence of a number of research questions will affect and potentially increase the complexity of both the study design and subsequent statistical analyses, not to mention the actual feasibility of answering every question. 1 A sensible strategy is to establish a single primary research question around which to focus the study plan. 3 In a study, the primary research question should be clearly stated at the end of the introduction of the grant proposal, and it usually specifies the population to be studied, the intervention to be implemented and other circumstantial factors. 4

Hulley and colleagues 2 have suggested the use of the FINER criteria in the development of a good research question ( Box 1 ). The FINER criteria highlight useful points that may increase the chances of developing a successful research project. A good research question should specify the population of interest, be of interest to the scientific community and potentially to the public, have clinical relevance and further current knowledge in the field (and of course be compliant with the standards of ethical boards and national research standards).

FINER criteria for a good research question

Adapted with permission from Wolters Kluwer Health. 2

Whereas the FINER criteria outline the important aspects of the question in general, a useful format to use in the development of a specific research question is the PICO format — consider the population (P) of interest, the intervention (I) being studied, the comparison (C) group (or to what is the intervention being compared) and the outcome of interest (O). 3 , 5 , 6 Often timing (T) is added to PICO ( Box 2 ) — that is, “Over what time frame will the study take place?” 1 The PICOT approach helps generate a question that aids in constructing the framework of the study and subsequently in protocol development by alluding to the inclusion and exclusion criteria and identifying the groups of patients to be included. Knowing the specific population of interest, intervention (and comparator) and outcome of interest may also help the researcher identify an appropriate outcome measurement tool. 7 The more defined the population of interest, and thus the more stringent the inclusion and exclusion criteria, the greater the effect on the interpretation and subsequent applicability and generalizability of the research findings. 1 , 2 A restricted study population (and exclusion criteria) may limit bias and increase the internal validity of the study; however, this approach will limit external validity of the study and, thus, the generalizability of the findings to the practical clinical setting. Conversely, a broadly defined study population and inclusion criteria may be representative of practical clinical practice but may increase bias and reduce the internal validity of the study.

PICOT criteria 1

A poorly devised research question may affect the choice of study design, potentially lead to futile situations and, thus, hamper the chance of determining anything of clinical significance, which will then affect the potential for publication. Without devoting appropriate resources to developing the research question, the quality of the study and subsequent results may be compromised. During the initial stages of any research study, it is therefore imperative to formulate a research question that is both clinically relevant and answerable.

Research hypothesis

The primary research question should be driven by the hypothesis rather than the data. 1 , 2 That is, the research question and hypothesis should be developed before the start of the study. This sounds intuitive; however, if we take, for example, a database of information, it is potentially possible to perform multiple statistical comparisons of groups within the database to find a statistically significant association. This could then lead one to work backward from the data and develop the “question.” This is counterintuitive to the process because the question is asked specifically to then find the answer, thus collecting data along the way (i.e., in a prospective manner). Multiple statistical testing of associations from data previously collected could potentially lead to spuriously positive findings of association through chance alone. 2 Therefore, a good hypothesis must be based on a good research question at the start of a trial and, indeed, drive data collection for the study.

The research or clinical hypothesis is developed from the research question and then the main elements of the study — sampling strategy, intervention (if applicable), comparison and outcome variables — are summarized in a form that establishes the basis for testing, statistical and ultimately clinical significance. 3 For example, in a research study comparing computer-assisted acetabular component insertion versus freehand acetabular component placement in patients in need of total hip arthroplasty, the experimental group would be computer-assisted insertion and the control/conventional group would be free-hand placement. The investigative team would first state a research hypothesis. This could be expressed as a single outcome (e.g., computer-assisted acetabular component placement leads to improved functional outcome) or potentially as a complex/composite outcome; that is, more than one outcome (e.g., computer-assisted acetabular component placement leads to both improved radiographic cup placement and improved functional outcome).

However, when formally testing statistical significance, the hypothesis should be stated as a “null” hypothesis. 2 The purpose of hypothesis testing is to make an inference about the population of interest on the basis of a random sample taken from that population. The null hypothesis for the preceding research hypothesis then would be that there is no difference in mean functional outcome between the computer-assisted insertion and free-hand placement techniques. After forming the null hypothesis, the researchers would form an alternate hypothesis stating the nature of the difference, if it should appear. The alternate hypothesis would be that there is a difference in mean functional outcome between these techniques. At the end of the study, the null hypothesis is then tested statistically. If the findings of the study are not statistically significant (i.e., there is no difference in functional outcome between the groups in a statistical sense), we cannot reject the null hypothesis, whereas if the findings were significant, we can reject the null hypothesis and accept the alternate hypothesis (i.e., there is a difference in mean functional outcome between the study groups), errors in testing notwithstanding. In other words, hypothesis testing confirms or refutes the statement that the observed findings did not occur by chance alone but rather occurred because there was a true difference in outcomes between these surgical procedures. The concept of statistical hypothesis testing is complex, and the details are beyond the scope of this article.

Another important concept inherent in hypothesis testing is whether the hypotheses will be 1-sided or 2-sided. A 2-sided hypothesis states that there is a difference between the experimental group and the control group, but it does not specify in advance the expected direction of the difference. For example, we asked whether there is there an improvement in outcomes with computer-assisted surgery or whether the outcomes worse with computer-assisted surgery. We presented a 2-sided test in the above example because we did not specify the direction of the difference. A 1-sided hypothesis states a specific direction (e.g., there is an improvement in outcomes with computer-assisted surgery). A 2-sided hypothesis should be used unless there is a good justification for using a 1-sided hypothesis. As Bland and Atlman 8 stated, “One-sided hypothesis testing should never be used as a device to make a conventionally nonsignificant difference significant.”

The research hypothesis should be stated at the beginning of the study to guide the objectives for research. Whereas the investigators may state the hypothesis as being 1-sided (there is an improvement with treatment), the study and investigators must adhere to the concept of clinical equipoise. According to this principle, a clinical (or surgical) trial is ethical only if the expert community is uncertain about the relative therapeutic merits of the experimental and control groups being evaluated. 9 It means there must exist an honest and professional disagreement among expert clinicians about the preferred treatment. 9

Designing a research hypothesis is supported by a good research question and will influence the type of research design for the study. Acting on the principles of appropriate hypothesis development, the study can then confidently proceed to the development of the research objective.

Research objective

The primary objective should be coupled with the hypothesis of the study. Study objectives define the specific aims of the study and should be clearly stated in the introduction of the research protocol. 7 From our previous example and using the investigative hypothesis that there is a difference in functional outcomes between computer-assisted acetabular component placement and free-hand placement, the primary objective can be stated as follows: this study will compare the functional outcomes of computer-assisted acetabular component insertion versus free-hand placement in patients undergoing total hip arthroplasty. Note that the study objective is an active statement about how the study is going to answer the specific research question. Objectives can (and often do) state exactly which outcome measures are going to be used within their statements. They are important because they not only help guide the development of the protocol and design of study but also play a role in sample size calculations and determining the power of the study. 7 These concepts will be discussed in other articles in this series.

From the surgeon’s point of view, it is important for the study objectives to be focused on outcomes that are important to patients and clinically relevant. For example, the most methodologically sound randomized controlled trial comparing 2 techniques of distal radial fixation would have little or no clinical impact if the primary objective was to determine the effect of treatment A as compared to treatment B on intraoperative fluoroscopy time. However, if the objective was to determine the effect of treatment A as compared to treatment B on patient functional outcome at 1 year, this would have a much more significant impact on clinical decision-making. Second, more meaningful surgeon–patient discussions could ensue, incorporating patient values and preferences with the results from this study. 6 , 7 It is the precise objective and what the investigator is trying to measure that is of clinical relevance in the practical setting.

The following is an example from the literature about the relation between the research question, hypothesis and study objectives:

Study: Warden SJ, Metcalf BR, Kiss ZS, et al. Low-intensity pulsed ultrasound for chronic patellar tendinopathy: a randomized, double-blind, placebo-controlled trial. Rheumatology 2008;47:467–71.

Research question: How does low-intensity pulsed ultrasound (LIPUS) compare with a placebo device in managing the symptoms of skeletally mature patients with patellar tendinopathy?

Research hypothesis: Pain levels are reduced in patients who receive daily active-LIPUS (treatment) for 12 weeks compared with individuals who receive inactive-LIPUS (placebo).

Objective: To investigate the clinical efficacy of LIPUS in the management of patellar tendinopathy symptoms.

The development of the research question is the most important aspect of a research project. A research project can fail if the objectives and hypothesis are poorly focused and underdeveloped. Useful tips for surgical researchers are provided in Box 3 . Designing and developing an appropriate and relevant research question, hypothesis and objectives can be a difficult task. The critical appraisal of the research question used in a study is vital to the application of the findings to clinical practice. Focusing resources, time and dedication to these 3 very important tasks will help to guide a successful research project, influence interpretation of the results and affect future publication efforts.

Tips for developing research questions, hypotheses and objectives for research studies

• Perform a systematic literature review (if one has not been done) to increase knowledge and familiarity with the topic and to assist with research development.
• Learn about current trends and technological advances on the topic.
• Seek careful input from experts, mentors, colleagues and collaborators to refine your research question as this will aid in developing the research question and guide the research study.
• Use the FINER criteria in the development of the research question.
• Ensure that the research question follows PICOT format.
• Develop a research hypothesis from the research question.
• Develop clear and well-defined primary and secondary (if needed) objectives.
• Ensure that the research question and objectives are answerable, feasible and clinically relevant.

FINER = feasible, interesting, novel, ethical, relevant; PICOT = population (patients), intervention (for intervention studies only), comparison group, outcome of interest, time.

Competing interests: No funding was received in preparation of this paper. Dr. Bhandari was funded, in part, by a Canada Research Chair, McMaster University.

The Importance Of Research Objectives

Imagine you’re a student planning a vacation in a foreign country. You’re on a tight budget and need to draw…

Imagine you’re a student planning a vacation in a foreign country. You’re on a tight budget and need to draw up a pocket-friendly plan. Where do you begin? The first step is to do your research.

Before that, you make a mental list of your objectives—finding reasonably-priced hotels, traveling safely and finding ways of communicating with someone back home. These objectives help you focus sharply during your research and be aware of the finer details of your trip.

More often than not, research is a part of our daily lives. Whether it’s to pick a restaurant for your next birthday dinner or to prepare a presentation at work, good research is the foundation of effective learning. Read on to understand the meaning, importance and examples of research objectives.

Why Do We Need Research?

What are the objectives of research, what goes into a research plan.

Research is a careful and detailed study of a particular problem or concern, using scientific methods. An in-depth analysis of information creates space for generating new questions, concepts and understandings. The main objective of research is to explore the unknown and unlock new possibilities. It’s an essential component of success.

Over the years, businesses have started emphasizing the need for research. You’ve probably noticed organizations hiring research managers and analysts. The primary purpose of business research is to determine the goals and opportunities of an organization. It’s critical in making business decisions and appropriately allocating available resources.

Here are a few benefits of research that’ll explain why it is a vital aspect of our professional lives:

Expands Your Knowledge Base

One of the greatest benefits of research is to learn and gain a deeper understanding. The deeper you dig into a topic, the more well-versed you are. Furthermore, research has the power to help you build on any personal experience you have on the subject.

Keeps You Up To Date

Research encourages you to discover the most recent information available. Updated information prevents you from falling behind and helps you present accurate information. You’re better equipped to develop ideas or talk about a topic when you’re armed with the latest inputs.

Builds Your Credibility

Research provides you with a good foundation upon which you can develop your thoughts and ideas. People take you more seriously when your suggestions are backed by research. You can speak with greater confidence because you know that the information is accurate.

Sparks Connections

Take any leading nonprofit organization, you’ll see how they have a strong research arm supported by real-life stories. Research also becomes the base upon which real-life connections and impact can be made. It even helps you communicate better with others and conveys why you’re pursuing something.

Encourages Curiosity

As we’ve already established, research is mostly about using existing information to create new ideas and opinions. In the process, it sparks curiosity as you’re encouraged to explore and gain deeper insights into a subject. Curiosity leads to higher levels of positivity and lower levels of anxiety.

Well-defined objectives of research are an essential component of successful research engagement. If you want to drive all aspects of your research methodology such as data collection, design, analysis and recommendation, you need to lay down the objectives of research methodology. In other words, the objectives of research should address the underlying purpose of investigation and analysis. It should outline the steps you’d take to achieve desirable outcomes. Research objectives help you stay focused and adjust your expectations as you progress.

The objectives of research should be closely related to the problem statement, giving way to specific and achievable goals. Here are the four types of research objectives for you to explore:

General Objective

Also known as secondary objectives, general objectives provide a detailed view of the aim of a study. In other words, you get a general overview of what you want to achieve by the end of your study. For example, if you want to study an organization’s contribution to environmental sustainability, your general objective could be: a study of sustainable practices and the use of renewable energy by the organization.

Specific Objectives

Specific objectives define the primary aim of the study. Typically, general objectives provide the foundation for identifying specific objectives. In other words, when general objectives are broken down into smaller and logically connected objectives, they’re known as specific objectives. They help define the who, what, why, when and how aspects of your project. Once you identify the main objective of research, it’s easier to develop and pursue a plan of action.

Let’s take the example of ‘a study of an organization’s contribution to environmental sustainability’ again. The specific objectives will look like this:

To determine through history how the organization has changed its practices and adopted new solutions

To assess how the new practices, technology and strategies will contribute to the overall effectiveness

Once you’ve identified the objectives of research, it’s time to organize your thoughts and streamline your research goals. Here are a few effective tips to develop a powerful research plan and improve your business performance.

Set SMART Goals

Your research objectives should be SMART—Specific, Measurable, Achievable, Realistic and Time-constrained. When you focus on utilizing available resources and setting realistic timeframes and milestones, it’s easier to prioritize objectives. Continuously track your progress and check whether you need to revise your expectations or targets. This way, you’re in greater control over the process.

Create A Plan

Create a plan that’ll help you select appropriate methods to collect accurate information. A well-structured plan allows you to use logical and creative approaches towards problem-solving. The complexity of information and your skills are bound to influence your plan, which is why you need to make room for flexibility. The availability of resources will also play a big role in influencing your decisions.

Collect And Collate

After you’ve created a plan for the research process, make a list of the data you’re going to collect and the methods you’ll use. Not only will it help make sense of your insights but also keep track of your approach. The information you collect should be:

Logical, rigorous and objective

Can be reproduced by other people working on the same subject

Free of errors and highlighting necessary details

Current and updated

Includes everything required to support your argument/suggestions

Analyze And Keep Ready

Data analysis is the most crucial part of the process and there are many ways in which the information can be utilized. Four types of data analysis are often seen in a professional environment. While they may be divided into separate categories, they’re linked to each other.

Descriptive Analysis:

The most commonly used data analysis, descriptive analysis simply summarizes past data. For example, Key Performance Indicators (KPIs) use descriptive analysis. It establishes certain benchmarks after studying how someone has been performing in the past.

Diagnostic Analysis:

The next step is to identify why something happened. Diagnostic analysis uses the information gathered through descriptive analysis and helps find the underlying causes of an outcome. For example, if a marketing initiative was successful, you deep-dive into the strategies that worked.

Predictive Analysis:

It attempts to answer ‘what’s likely to happen’. Predictive analysis makes use of past data to predict future outcomes. However, the accuracy of predictions depends on the quality of the data provided. Risk assessment is an ideal example of using predictive analysis.

Prescriptive Analysis: 

The most sought-after type of data analysis, prescriptive analysis combines the insights of all of the previous analyses. It’s a huge organizational commitment as it requires plenty of effort and resources. A great example of prescriptive analysis is Artificial Intelligence (AI), which consumes large amounts of data. You need to be prepared to commit to this type of analysis.

Review And Interpret

Once you’ve collected and collated your data, it’s time to review it and draw accurate conclusions. Here are a few ways to improve the review process:

Identify the fundamental issues, opportunities and problems and make note of recurring trends if any

Make a list of your insights and check which is the most or the least common. In short, keep track of the frequency of each insight

Conduct a SWOT analysis and identify the strengths, weaknesses, opportunities and threats

Write down your conclusions and recommendations of the research

When we think about research, we often associate it with academicians and students. but the truth is research is for everybody who is willing to learn and enhance their knowledge. If you want to master the art of strategically upgrading your knowledge, Harappa Education’s Learning Expertly course has all the answers. Not only will it help you look at things from a fresh perspective but also show you how to acquire new information with greater efficiency. The Growth Mindset framework will teach you how to believe in your abilities to grow and improve. The Learning Transfer framework will help you apply your learnings from one context to another. Begin the journey of tactful learning and self-improvement today!

Explore Harappa Diaries to learn more about topics related to the THINK Habit such as  Learning From Experience ,  Critical Thinking  & What is  Brainstorming  to think clearly and rationally.

Get science-backed answers as you write with Paperpal's Research feature

What is Research Methodology? Definition, Types, and Examples

Research methodology 1,2 is a structured and scientific approach used to collect, analyze, and interpret quantitative or qualitative data to answer research questions or test hypotheses. A research methodology is like a plan for carrying out research and helps keep researchers on track by limiting the scope of the research. Several aspects must be considered before selecting an appropriate research methodology, such as research limitations and ethical concerns that may affect your research.

The research methodology section in a scientific paper describes the different methodological choices made, such as the data collection and analysis methods, and why these choices were selected. The reasons should explain why the methods chosen are the most appropriate to answer the research question. A good research methodology also helps ensure the reliability and validity of the research findings. There are three types of research methodology—quantitative, qualitative, and mixed-method, which can be chosen based on the research objectives.

What is research methodology ?

A research methodology describes the techniques and procedures used to identify and analyze information regarding a specific research topic. It is a process by which researchers design their study so that they can achieve their objectives using the selected research instruments. It includes all the important aspects of research, including research design, data collection methods, data analysis methods, and the overall framework within which the research is conducted. While these points can help you understand what is research methodology, you also need to know why it is important to pick the right methodology.

Why is research methodology important?

Having a good research methodology in place has the following advantages: 3

• Helps other researchers who may want to replicate your research; the explanations will be of benefit to them.
• You can easily answer any questions about your research if they arise at a later stage.
• A research methodology provides a framework and guidelines for researchers to clearly define research questions, hypotheses, and objectives.
• It helps researchers identify the most appropriate research design, sampling technique, and data collection and analysis methods.
• A sound research methodology helps researchers ensure that their findings are valid and reliable and free from biases and errors.
• It also helps ensure that ethical guidelines are followed while conducting research.
• A good research methodology helps researchers in planning their research efficiently, by ensuring optimum usage of their time and resources.

Writing the methods section of a research paper? Let Paperpal help you achieve perfection

Types of research methodology.

There are three types of research methodology based on the type of research and the data required. 1

• Quantitative research methodology focuses on measuring and testing numerical data. This approach is good for reaching a large number of people in a short amount of time. This type of research helps in testing the causal relationships between variables, making predictions, and generalizing results to wider populations.
• Qualitative research methodology examines the opinions, behaviors, and experiences of people. It collects and analyzes words and textual data. This research methodology requires fewer participants but is still more time consuming because the time spent per participant is quite large. This method is used in exploratory research where the research problem being investigated is not clearly defined.
• Mixed-method research methodology uses the characteristics of both quantitative and qualitative research methodologies in the same study. This method allows researchers to validate their findings, verify if the results observed using both methods are complementary, and explain any unexpected results obtained from one method by using the other method.

What are the types of sampling designs in research methodology?

Sampling 4 is an important part of a research methodology and involves selecting a representative sample of the population to conduct the study, making statistical inferences about them, and estimating the characteristics of the whole population based on these inferences. There are two types of sampling designs in research methodology—probability and nonprobability.

• Probability sampling

In this type of sampling design, a sample is chosen from a larger population using some form of random selection, that is, every member of the population has an equal chance of being selected. The different types of probability sampling are:

• Systematic —sample members are chosen at regular intervals. It requires selecting a starting point for the sample and sample size determination that can be repeated at regular intervals. This type of sampling method has a predefined range; hence, it is the least time consuming.
• Stratified —researchers divide the population into smaller groups that don’t overlap but represent the entire population. While sampling, these groups can be organized, and then a sample can be drawn from each group separately.
• Cluster —the population is divided into clusters based on demographic parameters like age, sex, location, etc.
• Convenience —selects participants who are most easily accessible to researchers due to geographical proximity, availability at a particular time, etc.
• Purposive —participants are selected at the researcher’s discretion. Researchers consider the purpose of the study and the understanding of the target audience.
• Snowball —already selected participants use their social networks to refer the researcher to other potential participants.
• Quota —while designing the study, the researchers decide how many people with which characteristics to include as participants. The characteristics help in choosing people most likely to provide insights into the subject.

What are data collection methods?

During research, data are collected using various methods depending on the research methodology being followed and the research methods being undertaken. Both qualitative and quantitative research have different data collection methods, as listed below.

Qualitative research 5

• One-on-one interviews: Helps the interviewers understand a respondent’s subjective opinion and experience pertaining to a specific topic or event
• Document study/literature review/record keeping: Researchers’ review of already existing written materials such as archives, annual reports, research articles, guidelines, policy documents, etc.
• Focus groups: Constructive discussions that usually include a small sample of about 6-10 people and a moderator, to understand the participants’ opinion on a given topic.
• Qualitative observation : Researchers collect data using their five senses (sight, smell, touch, taste, and hearing).

Quantitative research 6

• Sampling: The most common type is probability sampling.
• Interviews: Commonly telephonic or done in-person.
• Observations: Structured observations are most commonly used in quantitative research. In this method, researchers make observations about specific behaviors of individuals in a structured setting.
• Document review: Reviewing existing research or documents to collect evidence for supporting the research.
• Surveys and questionnaires. Surveys can be administered both online and offline depending on the requirement and sample size.

Let Paperpal help you write the perfect research methods section. Start now!

What are data analysis methods.

The data collected using the various methods for qualitative and quantitative research need to be analyzed to generate meaningful conclusions. These data analysis methods 7 also differ between quantitative and qualitative research.

Quantitative research involves a deductive method for data analysis where hypotheses are developed at the beginning of the research and precise measurement is required. The methods include statistical analysis applications to analyze numerical data and are grouped into two categories—descriptive and inferential.

Descriptive analysis is used to describe the basic features of different types of data to present it in a way that ensures the patterns become meaningful. The different types of descriptive analysis methods are:

• Measures of frequency (count, percent, frequency)
• Measures of central tendency (mean, median, mode)
• Measures of dispersion or variation (range, variance, standard deviation)
• Measure of position (percentile ranks, quartile ranks)

Inferential analysis is used to make predictions about a larger population based on the analysis of the data collected from a smaller population. This analysis is used to study the relationships between different variables. Some commonly used inferential data analysis methods are:

• Correlation: To understand the relationship between two or more variables.
• Cross-tabulation: Analyze the relationship between multiple variables.
• Regression analysis: Study the impact of independent variables on the dependent variable.
• Frequency tables: To understand the frequency of data.
• Analysis of variance: To test the degree to which two or more variables differ in an experiment.

Qualitative research involves an inductive method for data analysis where hypotheses are developed after data collection. The methods include:

• Content analysis: For analyzing documented information from text and images by determining the presence of certain words or concepts in texts.
• Narrative analysis: For analyzing content obtained from sources such as interviews, field observations, and surveys. The stories and opinions shared by people are used to answer research questions.
• Discourse analysis: For analyzing interactions with people considering the social context, that is, the lifestyle and environment, under which the interaction occurs.
• Grounded theory: Involves hypothesis creation by data collection and analysis to explain why a phenomenon occurred.
• Thematic analysis: To identify important themes or patterns in data and use these to address an issue.

How to choose a research methodology?

Here are some important factors to consider when choosing a research methodology: 8

• Research objectives, aims, and questions —these would help structure the research design.
• Review existing literature to identify any gaps in knowledge.
• Check the statistical requirements —if data-driven or statistical results are needed then quantitative research is the best. If the research questions can be answered based on people’s opinions and perceptions, then qualitative research is most suitable.
• Sample size —sample size can often determine the feasibility of a research methodology. For a large sample, less effort- and time-intensive methods are appropriate.
• Constraints —constraints of time, geography, and resources can help define the appropriate methodology.

Got writer’s block? Kickstart your research paper writing with Paperpal now!

How to write a research methodology .

A research methodology should include the following components: 3,9

• Research design —should be selected based on the research question and the data required. Common research designs include experimental, quasi-experimental, correlational, descriptive, and exploratory.
• Research method —this can be quantitative, qualitative, or mixed-method.
• Reason for selecting a specific methodology —explain why this methodology is the most suitable to answer your research problem.
• Research instruments —explain the research instruments you plan to use, mainly referring to the data collection methods such as interviews, surveys, etc. Here as well, a reason should be mentioned for selecting the particular instrument.
• Sampling —this involves selecting a representative subset of the population being studied.
• Data collection —involves gathering data using several data collection methods, such as surveys, interviews, etc.
• Data analysis —describe the data analysis methods you will use once you’ve collected the data.
• Research limitations —mention any limitations you foresee while conducting your research.
• Validity and reliability —validity helps identify the accuracy and truthfulness of the findings; reliability refers to the consistency and stability of the results over time and across different conditions.
• Ethical considerations —research should be conducted ethically. The considerations include obtaining consent from participants, maintaining confidentiality, and addressing conflicts of interest.

Streamline Your Research Paper Writing Process with Paperpal

The methods section is a critical part of the research papers, allowing researchers to use this to understand your findings and replicate your work when pursuing their own research. However, it is usually also the most difficult section to write. This is where Paperpal can help you overcome the writer’s block and create the first draft in minutes with Paperpal Copilot, its secure generative AI feature suite.  

With Paperpal you can get research advice, write and refine your work, rephrase and verify the writing, and ensure submission readiness, all in one place. Here’s how you can use Paperpal to develop the first draft of your methods section.  

• Generate an outline: Input some details about your research to instantly generate an outline for your methods section 
• Develop the section: Use the outline and suggested sentence templates to expand your ideas and develop the first draft.  
• P araph ras e and trim : Get clear, concise academic text with paraphrasing that conveys your work effectively and word reduction to fix redundancies. 
• Choose the right words: Enhance text by choosing contextual synonyms based on how the words have been used in previously published work.  
• Check and verify text : Make sure the generated text showcases your methods correctly, has all the right citations, and is original and authentic. .   

You can repeat this process to develop each section of your research manuscript, including the title, abstract and keywords. Ready to write your research papers faster, better, and without the stress? Sign up for Paperpal and start writing today!

Frequently Asked Questions

Q1. What are the key components of research methodology?

A1. A good research methodology has the following key components:

• Research design
• Data collection procedures
• Data analysis methods
• Ethical considerations

Q2. Why is ethical consideration important in research methodology?

A2. Ethical consideration is important in research methodology to ensure the readers of the reliability and validity of the study. Researchers must clearly mention the ethical norms and standards followed during the conduct of the research and also mention if the research has been cleared by any institutional board. The following 10 points are the important principles related to ethical considerations: 10

• Participants should not be subjected to harm.
• Respect for the dignity of participants should be prioritized.
• Full consent should be obtained from participants before the study.
• Participants’ privacy should be ensured.
• Confidentiality of the research data should be ensured.
• Anonymity of individuals and organizations participating in the research should be maintained.
• The aims and objectives of the research should not be exaggerated.
• Affiliations, sources of funding, and any possible conflicts of interest should be declared.
• Communication in relation to the research should be honest and transparent.
• Misleading information and biased representation of primary data findings should be avoided.

Q3. What is the difference between methodology and method?

A3. Research methodology is different from a research method, although both terms are often confused. Research methods are the tools used to gather data, while the research methodology provides a framework for how research is planned, conducted, and analyzed. The latter guides researchers in making decisions about the most appropriate methods for their research. Research methods refer to the specific techniques, procedures, and tools used by researchers to collect, analyze, and interpret data, for instance surveys, questionnaires, interviews, etc.

Research methodology is, thus, an integral part of a research study. It helps ensure that you stay on track to meet your research objectives and answer your research questions using the most appropriate data collection and analysis tools based on your research design.

Accelerate your research paper writing with Paperpal. Try for free now!

• Research methodologies. Pfeiffer Library website. Accessed August 15, 2023. https://library.tiffin.edu/researchmethodologies/whatareresearchmethodologies
• Types of research methodology. Eduvoice website. Accessed August 16, 2023. https://eduvoice.in/types-research-methodology/
• The basics of research methodology: A key to quality research. Voxco. Accessed August 16, 2023. https://www.voxco.com/blog/what-is-research-methodology/
• Sampling methods: Types with examples. QuestionPro website. Accessed August 16, 2023. https://www.questionpro.com/blog/types-of-sampling-for-social-research/
• What is qualitative research? Methods, types, approaches, examples. Researcher.Life blog. Accessed August 15, 2023. https://researcher.life/blog/article/what-is-qualitative-research-methods-types-examples/
• What is quantitative research? Definition, methods, types, and examples. Researcher.Life blog. Accessed August 15, 2023. https://researcher.life/blog/article/what-is-quantitative-research-types-and-examples/
• Data analysis in research: Types & methods. QuestionPro website. Accessed August 16, 2023. https://www.questionpro.com/blog/data-analysis-in-research/#Data_analysis_in_qualitative_research
• Factors to consider while choosing the right research methodology. PhD Monster website. Accessed August 17, 2023. https://www.phdmonster.com/factors-to-consider-while-choosing-the-right-research-methodology/
• What is research methodology? Research and writing guides. Accessed August 14, 2023. https://paperpile.com/g/what-is-research-methodology/
• Ethical considerations. Business research methodology website. Accessed August 17, 2023. https://research-methodology.net/research-methodology/ethical-considerations/

Paperpal is a comprehensive AI writing toolkit that helps students and researchers achieve 2x the writing in half the time. It leverages 21+ years of STM experience and insights from millions of research articles to provide in-depth academic writing, language editing, and submission readiness support to help you write better, faster.  

Get accurate academic translations, rewriting support, grammar checks, vocabulary suggestions, and generative AI assistance that delivers human precision at machine speed. Try for free or upgrade to Paperpal Prime starting at US$19 a month to access premium features, including consistency, plagiarism, and 30+ submission readiness checks to help you succeed.  

Experience the future of academic writing – Sign up to Paperpal and start writing for free!  

Related Reads:

• Dangling Modifiers and How to Avoid Them in Your Writing 
• Webinar: How to Use Generative AI Tools Ethically in Your Academic Writing
• Research Outlines: How to Write An Introduction Section in Minutes with Paperpal Copilot
• How to Paraphrase Research Papers Effectively

Language and Grammar Rules for Academic Writing

Climatic vs. climactic: difference and examples, you may also like, how to write a high-quality conference paper, how paperpal is enhancing academic productivity and accelerating..., academic editing: how to self-edit academic text with..., 4 ways paperpal encourages responsible writing with ai, what are scholarly sources and where can you..., how to write a hypothesis types and examples , what is academic writing: tips for students, what is hedging in academic writing  , how to use ai to enhance your college..., how to use paperpal to generate emails &....

• USC Libraries
• Research Guides

Organizing Your Social Sciences Research Paper

• 6. The Methodology
• Purpose of Guide
• Design Flaws to Avoid
• Independent and Dependent Variables
• Glossary of Research Terms
• Reading Research Effectively
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
• Academic Writing Style
• Applying Critical Thinking
• Choosing a Title
• Making an Outline
• Paragraph Development
• Research Process Video Series
• Executive Summary
• The C.A.R.S. Model
• Background Information
• The Research Problem/Question
• Theoretical Framework
• Citation Tracking
• Content Alert Services
• Evaluating Sources
• Primary Sources
• Secondary Sources
• Tiertiary Sources
• Scholarly vs. Popular Publications
• Qualitative Methods
• Quantitative Methods
• Insiderness
• Using Non-Textual Elements
• Limitations of the Study
• Common Grammar Mistakes
• Writing Concisely
• Avoiding Plagiarism
• Footnotes or Endnotes?
• Further Readings
• Generative AI and Writing
• USC Libraries Tutorials and Other Guides
• Bibliography

The methods section describes actions taken to investigate a research problem and the rationale for the application of specific procedures or techniques used to identify, select, process, and analyze information applied to understanding the problem, thereby, allowing the reader to critically evaluate a study’s overall validity and reliability. The methodology section of a research paper answers two main questions: How was the data collected or generated? And, how was it analyzed? The writing should be direct and precise and always written in the past tense.

Kallet, Richard H. "How to Write the Methods Section of a Research Paper." Respiratory Care 49 (October 2004): 1229-1232.

Importance of a Good Methodology Section

You must explain how you obtained and analyzed your results for the following reasons:

• Readers need to know how the data was obtained because the method you chose affects the results and, by extension, how you interpreted their significance in the discussion section of your paper.
• Methodology is crucial for any branch of scholarship because an unreliable method produces unreliable results and, as a consequence, undermines the value of your analysis of the findings.
• In most cases, there are a variety of different methods you can choose to investigate a research problem. The methodology section of your paper should clearly articulate the reasons why you have chosen a particular procedure or technique.
• The reader wants to know that the data was collected or generated in a way that is consistent with accepted practice in the field of study. For example, if you are using a multiple choice questionnaire, readers need to know that it offered your respondents a reasonable range of answers to choose from.
• The method must be appropriate to fulfilling the overall aims of the study. For example, you need to ensure that you have a large enough sample size to be able to generalize and make recommendations based upon the findings.
• The methodology should discuss the problems that were anticipated and the steps you took to prevent them from occurring. For any problems that do arise, you must describe the ways in which they were minimized or why these problems do not impact in any meaningful way your interpretation of the findings.
• In the social and behavioral sciences, it is important to always provide sufficient information to allow other researchers to adopt or replicate your methodology. This information is particularly important when a new method has been developed or an innovative use of an existing method is utilized.

Bem, Daryl J. Writing the Empirical Journal Article. Psychology Writing Center. University of Washington; Denscombe, Martyn. The Good Research Guide: For Small-Scale Social Research Projects . 5th edition. Buckingham, UK: Open University Press, 2014; Lunenburg, Frederick C. Writing a Successful Thesis or Dissertation: Tips and Strategies for Students in the Social and Behavioral Sciences . Thousand Oaks, CA: Corwin Press, 2008.

Structure and Writing Style

I.  Groups of Research Methods

There are two main groups of research methods in the social sciences:

• The e mpirical-analytical group approaches the study of social sciences in a similar manner that researchers study the natural sciences . This type of research focuses on objective knowledge, research questions that can be answered yes or no, and operational definitions of variables to be measured. The empirical-analytical group employs deductive reasoning that uses existing theory as a foundation for formulating hypotheses that need to be tested. This approach is focused on explanation.
• The i nterpretative group of methods is focused on understanding phenomenon in a comprehensive, holistic way . Interpretive methods focus on analytically disclosing the meaning-making practices of human subjects [the why, how, or by what means people do what they do], while showing how those practices arrange so that it can be used to generate observable outcomes. Interpretive methods allow you to recognize your connection to the phenomena under investigation. However, the interpretative group requires careful examination of variables because it focuses more on subjective knowledge.

II.  Content

The introduction to your methodology section should begin by restating the research problem and underlying assumptions underpinning your study. This is followed by situating the methods you used to gather, analyze, and process information within the overall “tradition” of your field of study and within the particular research design you have chosen to study the problem. If the method you choose lies outside of the tradition of your field [i.e., your review of the literature demonstrates that the method is not commonly used], provide a justification for how your choice of methods specifically addresses the research problem in ways that have not been utilized in prior studies.

The remainder of your methodology section should describe the following:

• Decisions made in selecting the data you have analyzed or, in the case of qualitative research, the subjects and research setting you have examined,
• Tools and methods used to identify and collect information, and how you identified relevant variables,
• The ways in which you processed the data and the procedures you used to analyze that data, and
• The specific research tools or strategies that you utilized to study the underlying hypothesis and research questions.

In addition, an effectively written methodology section should:

• Introduce the overall methodological approach for investigating your research problem . Is your study qualitative or quantitative or a combination of both (mixed method)? Are you going to take a special approach, such as action research, or a more neutral stance?
• Indicate how the approach fits the overall research design . Your methods for gathering data should have a clear connection to your research problem. In other words, make sure that your methods will actually address the problem. One of the most common deficiencies found in research papers is that the proposed methodology is not suitable to achieving the stated objective of your paper.
• Describe the specific methods of data collection you are going to use , such as, surveys, interviews, questionnaires, observation, archival research. If you are analyzing existing data, such as a data set or archival documents, describe how it was originally created or gathered and by whom. Also be sure to explain how older data is still relevant to investigating the current research problem.
• Explain how you intend to analyze your results . Will you use statistical analysis? Will you use specific theoretical perspectives to help you analyze a text or explain observed behaviors? Describe how you plan to obtain an accurate assessment of relationships, patterns, trends, distributions, and possible contradictions found in the data.
• Provide background and a rationale for methodologies that are unfamiliar for your readers . Very often in the social sciences, research problems and the methods for investigating them require more explanation/rationale than widely accepted rules governing the natural and physical sciences. Be clear and concise in your explanation.
• Provide a justification for subject selection and sampling procedure . For instance, if you propose to conduct interviews, how do you intend to select the sample population? If you are analyzing texts, which texts have you chosen, and why? If you are using statistics, why is this set of data being used? If other data sources exist, explain why the data you chose is most appropriate to addressing the research problem.
• Provide a justification for case study selection . A common method of analyzing research problems in the social sciences is to analyze specific cases. These can be a person, place, event, phenomenon, or other type of subject of analysis that are either examined as a singular topic of in-depth investigation or multiple topics of investigation studied for the purpose of comparing or contrasting findings. In either method, you should explain why a case or cases were chosen and how they specifically relate to the research problem.
• Describe potential limitations . Are there any practical limitations that could affect your data collection? How will you attempt to control for potential confounding variables and errors? If your methodology may lead to problems you can anticipate, state this openly and show why pursuing this methodology outweighs the risk of these problems cropping up.

NOTE :   Once you have written all of the elements of the methods section, subsequent revisions should focus on how to present those elements as clearly and as logically as possibly. The description of how you prepared to study the research problem, how you gathered the data, and the protocol for analyzing the data should be organized chronologically. For clarity, when a large amount of detail must be presented, information should be presented in sub-sections according to topic. If necessary, consider using appendices for raw data.

ANOTHER NOTE : If you are conducting a qualitative analysis of a research problem , the methodology section generally requires a more elaborate description of the methods used as well as an explanation of the processes applied to gathering and analyzing of data than is generally required for studies using quantitative methods. Because you are the primary instrument for generating the data [e.g., through interviews or observations], the process for collecting that data has a significantly greater impact on producing the findings. Therefore, qualitative research requires a more detailed description of the methods used.

YET ANOTHER NOTE :   If your study involves interviews, observations, or other qualitative techniques involving human subjects , you may be required to obtain approval from the university's Office for the Protection of Research Subjects before beginning your research. This is not a common procedure for most undergraduate level student research assignments. However, i f your professor states you need approval, you must include a statement in your methods section that you received official endorsement and adequate informed consent from the office and that there was a clear assessment and minimization of risks to participants and to the university. This statement informs the reader that your study was conducted in an ethical and responsible manner. In some cases, the approval notice is included as an appendix to your paper.

III.  Problems to Avoid

Irrelevant Detail The methodology section of your paper should be thorough but concise. Do not provide any background information that does not directly help the reader understand why a particular method was chosen, how the data was gathered or obtained, and how the data was analyzed in relation to the research problem [note: analyzed, not interpreted! Save how you interpreted the findings for the discussion section]. With this in mind, the page length of your methods section will generally be less than any other section of your paper except the conclusion.

Unnecessary Explanation of Basic Procedures Remember that you are not writing a how-to guide about a particular method. You should make the assumption that readers possess a basic understanding of how to investigate the research problem on their own and, therefore, you do not have to go into great detail about specific methodological procedures. The focus should be on how you applied a method , not on the mechanics of doing a method. An exception to this rule is if you select an unconventional methodological approach; if this is the case, be sure to explain why this approach was chosen and how it enhances the overall process of discovery.

Problem Blindness It is almost a given that you will encounter problems when collecting or generating your data, or, gaps will exist in existing data or archival materials. Do not ignore these problems or pretend they did not occur. Often, documenting how you overcame obstacles can form an interesting part of the methodology. It demonstrates to the reader that you can provide a cogent rationale for the decisions you made to minimize the impact of any problems that arose.

Literature Review Just as the literature review section of your paper provides an overview of sources you have examined while researching a particular topic, the methodology section should cite any sources that informed your choice and application of a particular method [i.e., the choice of a survey should include any citations to the works you used to help construct the survey].

It’s More than Sources of Information! A description of a research study's method should not be confused with a description of the sources of information. Such a list of sources is useful in and of itself, especially if it is accompanied by an explanation about the selection and use of the sources. The description of the project's methodology complements a list of sources in that it sets forth the organization and interpretation of information emanating from those sources.

Azevedo, L.F. et al. "How to Write a Scientific Paper: Writing the Methods Section." Revista Portuguesa de Pneumologia 17 (2011): 232-238; Blair Lorrie. “Choosing a Methodology.” In Writing a Graduate Thesis or Dissertation , Teaching Writing Series. (Rotterdam: Sense Publishers 2016), pp. 49-72; Butin, Dan W. The Education Dissertation A Guide for Practitioner Scholars . Thousand Oaks, CA: Corwin, 2010; Carter, Susan. Structuring Your Research Thesis . New York: Palgrave Macmillan, 2012; Kallet, Richard H. “How to Write the Methods Section of a Research Paper.” Respiratory Care 49 (October 2004):1229-1232; Lunenburg, Frederick C. Writing a Successful Thesis or Dissertation: Tips and Strategies for Students in the Social and Behavioral Sciences . Thousand Oaks, CA: Corwin Press, 2008. Methods Section. The Writer’s Handbook. Writing Center. University of Wisconsin, Madison; Rudestam, Kjell Erik and Rae R. Newton. “The Method Chapter: Describing Your Research Plan.” In Surviving Your Dissertation: A Comprehensive Guide to Content and Process . (Thousand Oaks, Sage Publications, 2015), pp. 87-115; What is Interpretive Research. Institute of Public and International Affairs, University of Utah; Writing the Experimental Report: Methods, Results, and Discussion. The Writing Lab and The OWL. Purdue University; Methods and Materials. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper. Department of Biology. Bates College.

Writing Tip

Statistical Designs and Tests? Do Not Fear Them!

Don't avoid using a quantitative approach to analyzing your research problem just because you fear the idea of applying statistical designs and tests. A qualitative approach, such as conducting interviews or content analysis of archival texts, can yield exciting new insights about a research problem, but it should not be undertaken simply because you have a disdain for running a simple regression. A well designed quantitative research study can often be accomplished in very clear and direct ways, whereas, a similar study of a qualitative nature usually requires considerable time to analyze large volumes of data and a tremendous burden to create new paths for analysis where previously no path associated with your research problem had existed.

To locate data and statistics, GO HERE .

Another Writing Tip

Knowing the Relationship Between Theories and Methods

There can be multiple meaning associated with the term "theories" and the term "methods" in social sciences research. A helpful way to delineate between them is to understand "theories" as representing different ways of characterizing the social world when you research it and "methods" as representing different ways of generating and analyzing data about that social world. Framed in this way, all empirical social sciences research involves theories and methods, whether they are stated explicitly or not. However, while theories and methods are often related, it is important that, as a researcher, you deliberately separate them in order to avoid your theories playing a disproportionate role in shaping what outcomes your chosen methods produce.

Introspectively engage in an ongoing dialectic between the application of theories and methods to help enable you to use the outcomes from your methods to interrogate and develop new theories, or ways of framing conceptually the research problem. This is how scholarship grows and branches out into new intellectual territory.

Reynolds, R. Larry. Ways of Knowing. Alternative Microeconomics . Part 1, Chapter 3. Boise State University; The Theory-Method Relationship. S-Cool Revision. United Kingdom.

Yet Another Writing Tip

Methods and the Methodology

Do not confuse the terms "methods" and "methodology." As Schneider notes, a method refers to the technical steps taken to do research . Descriptions of methods usually include defining and stating why you have chosen specific techniques to investigate a research problem, followed by an outline of the procedures you used to systematically select, gather, and process the data [remember to always save the interpretation of data for the discussion section of your paper].

The methodology refers to a discussion of the underlying reasoning why particular methods were used . This discussion includes describing the theoretical concepts that inform the choice of methods to be applied, placing the choice of methods within the more general nature of academic work, and reviewing its relevance to examining the research problem. The methodology section also includes a thorough review of the methods other scholars have used to study the topic.

Bryman, Alan. "Of Methods and Methodology." Qualitative Research in Organizations and Management: An International Journal 3 (2008): 159-168; Schneider, Florian. “What's in a Methodology: The Difference between Method, Methodology, and Theory…and How to Get the Balance Right?” PoliticsEastAsia.com. Chinese Department, University of Leiden, Netherlands.

• << Previous: Scholarly vs. Popular Publications
• Next: Qualitative Methods >>
• Last Updated: May 20, 2024 9:47 AM
• URL: https://libguides.usc.edu/writingguide

How To Choose Your Research Methodology

Qualitative vs quantitative vs mixed methods.

By: Derek Jansen (MBA). Expert Reviewed By: Dr Eunice Rautenbach | June 2021

Without a doubt, one of the most common questions we receive at Grad Coach is “ How do I choose the right methodology for my research? ”. It’s easy to see why – with so many options on the research design table, it’s easy to get intimidated, especially with all the complex lingo!

In this post, we’ll explain the three overarching types of research – qualitative, quantitative and mixed methods – and how you can go about choosing the best methodological approach for your research.

Overview: Choosing Your Methodology

Understanding the options – Qualitative research – Quantitative research – Mixed methods-based research

Choosing a research methodology – Nature of the research – Research area norms – Practicalities

1. Understanding the options

Before we jump into the question of how to choose a research methodology, it’s useful to take a step back to understand the three overarching types of research – qualitative , quantitative and mixed methods -based research. Each of these options takes a different methodological approach.

Qualitative research utilises data that is not numbers-based. In other words, qualitative research focuses on words , descriptions , concepts or ideas – while quantitative research makes use of numbers and statistics. Qualitative research investigates the “softer side” of things to explore and describe, while quantitative research focuses on the “hard numbers”, to measure differences between variables and the relationships between them.

Importantly, qualitative research methods are typically used to explore and gain a deeper understanding of the complexity of a situation – to draw a rich picture . In contrast to this, quantitative methods are usually used to confirm or test hypotheses . In other words, they have distinctly different purposes. The table below highlights a few of the key differences between qualitative and quantitative research – you can learn more about the differences here.

• Uses an inductive approach
• Is used to build theories
• Takes a subjective approach
• Adopts an open and flexible approach
• The researcher is close to the respondents
• Interviews and focus groups are oftentimes used to collect word-based data.
• Generally, draws on small sample sizes
• Uses qualitative data analysis techniques (e.g. content analysis , thematic analysis , etc)
• Uses a deductive approach
• Is used to test theories
• Takes an objective approach
• Adopts a closed, highly planned approach
• The research is disconnected from respondents
• Surveys or laboratory equipment are often used to collect number-based data.
• Generally, requires large sample sizes
• Uses statistical analysis techniques to make sense of the data

Mixed methods -based research, as you’d expect, attempts to bring these two types of research together, drawing on both qualitative and quantitative data. Quite often, mixed methods-based studies will use qualitative research to explore a situation and develop a potential model of understanding (this is called a conceptual framework), and then go on to use quantitative methods to test that model empirically.

In other words, while qualitative and quantitative methods (and the philosophies that underpin them) are completely different, they are not at odds with each other. It’s not a competition of qualitative vs quantitative. On the contrary, they can be used together to develop a high-quality piece of research. Of course, this is easier said than done, so we usually recommend that first-time researchers stick to a single approach , unless the nature of their study truly warrants a mixed-methods approach.

The key takeaway here, and the reason we started by looking at the three options, is that it’s important to understand that each methodological approach has a different purpose – for example, to explore and understand situations (qualitative), to test and measure (quantitative) or to do both. They’re not simply alternative tools for the same job. 

Right – now that we’ve got that out of the way, let’s look at how you can go about choosing the right methodology for your research.

2. How to choose a research methodology

To choose the right research methodology for your dissertation or thesis, you need to consider three important factors . Based on these three factors, you can decide on your overarching approach – qualitative, quantitative or mixed methods. Once you’ve made that decision, you can flesh out the finer details of your methodology, such as the sampling , data collection methods and analysis techniques (we discuss these separately in other posts ).

The three factors you need to consider are:

• The nature of your research aims, objectives and research questions
• The methodological approaches taken in the existing literature
• Practicalities and constraints

Let’s take a look at each of these.

Factor #1: The nature of your research

As I mentioned earlier, each type of research (and therefore, research methodology), whether qualitative, quantitative or mixed, has a different purpose and helps solve a different type of question. So, it’s logical that the key deciding factor in terms of which research methodology you adopt is the nature of your research aims, objectives and research questions .

But, what types of research exist?

Broadly speaking, research can fall into one of three categories:

• Exploratory – getting a better understanding of an issue and potentially developing a theory regarding it
• Confirmatory – confirming a potential theory or hypothesis by testing it empirically
• A mix of both – building a potential theory or hypothesis and then testing it

As a rule of thumb, exploratory research tends to adopt a qualitative approach , whereas confirmatory research tends to use quantitative methods . This isn’t set in stone, but it’s a very useful heuristic. Naturally then, research that combines a mix of both, or is seeking to develop a theory from the ground up and then test that theory, would utilize a mixed-methods approach.

Let’s look at an example in action.

If your research aims were to understand the perspectives of war veterans regarding certain political matters, you’d likely adopt a qualitative methodology, making use of interviews to collect data and one or more qualitative data analysis methods to make sense of the data.

If, on the other hand, your research aims involved testing a set of hypotheses regarding the link between political leaning and income levels, you’d likely adopt a quantitative methodology, using numbers-based data from a survey to measure the links between variables and/or constructs .

So, the first (and most important thing) thing you need to consider when deciding which methodological approach to use for your research project is the nature of your research aims , objectives and research questions. Specifically, you need to assess whether your research leans in an exploratory or confirmatory direction or involves a mix of both.

The importance of achieving solid alignment between these three factors and your methodology can’t be overstated. If they’re misaligned, you’re going to be forcing a square peg into a round hole. In other words, you’ll be using the wrong tool for the job, and your research will become a disjointed mess.

If your research is a mix of both exploratory and confirmatory, but you have a tight word count limit, you may need to consider trimming down the scope a little and focusing on one or the other. One methodology executed well has a far better chance of earning marks than a poorly executed mixed methods approach. So, don’t try to be a hero, unless there is a very strong underpinning logic.

Need a helping hand?

Factor #2: The disciplinary norms

Choosing the right methodology for your research also involves looking at the approaches used by other researchers in the field, and studies with similar research aims and objectives to yours. Oftentimes, within a discipline, there is a common methodological approach (or set of approaches) used in studies. While this doesn’t mean you should follow the herd “just because”, you should at least consider these approaches and evaluate their merit within your context.

A major benefit of reviewing the research methodologies used by similar studies in your field is that you can often piggyback on the data collection techniques that other (more experienced) researchers have developed. For example, if you’re undertaking a quantitative study, you can often find tried and tested survey scales with high Cronbach’s alphas. These are usually included in the appendices of journal articles, so you don’t even have to contact the original authors. By using these, you’ll save a lot of time and ensure that your study stands on the proverbial “shoulders of giants” by using high-quality measurement instruments .

Of course, when reviewing existing literature, keep point #1 front of mind. In other words, your methodology needs to align with your research aims, objectives and questions. Don’t fall into the trap of adopting the methodological “norm” of other studies just because it’s popular. Only adopt that which is relevant to your research.

Factor #3: Practicalities

When choosing a research methodology, there will always be a tension between doing what’s theoretically best (i.e., the most scientifically rigorous research design ) and doing what’s practical , given your constraints . This is the nature of doing research and there are always trade-offs, as with anything else.

But what constraints, you ask?

When you’re evaluating your methodological options, you need to consider the following constraints:

• Data access
• Equipment and software
• Your knowledge and skills

Let’s look at each of these.

Constraint #1: Data access

The first practical constraint you need to consider is your access to data . If you’re going to be undertaking primary research , you need to think critically about the sample of respondents you realistically have access to. For example, if you plan to use in-person interviews , you need to ask yourself how many people you’ll need to interview, whether they’ll be agreeable to being interviewed, where they’re located, and so on.

If you’re wanting to undertake a quantitative approach using surveys to collect data, you’ll need to consider how many responses you’ll require to achieve statistically significant results. For many statistical tests, a sample of a few hundred respondents is typically needed to develop convincing conclusions.

So, think carefully about what data you’ll need access to, how much data you’ll need and how you’ll collect it. The last thing you want is to spend a huge amount of time on your research only to find that you can’t get access to the required data.

Constraint #2: Time

The next constraint is time. If you’re undertaking research as part of a PhD, you may have a fairly open-ended time limit, but this is unlikely to be the case for undergrad and Masters-level projects. So, pay attention to your timeline, as the data collection and analysis components of different methodologies have a major impact on time requirements . Also, keep in mind that these stages of the research often take a lot longer than originally anticipated.

Another practical implication of time limits is that it will directly impact which time horizon you can use – i.e. longitudinal vs cross-sectional . For example, if you’ve got a 6-month limit for your entire research project, it’s quite unlikely that you’ll be able to adopt a longitudinal time horizon. 

Constraint #3: Money

As with so many things, money is another important constraint you’ll need to consider when deciding on your research methodology. While some research designs will cost near zero to execute, others may require a substantial budget .

Some of the costs that may arise include:

• Software costs – e.g. survey hosting services, analysis software, etc.
• Promotion costs – e.g. advertising a survey to attract respondents
• Incentive costs – e.g. providing a prize or cash payment incentive to attract respondents
• Equipment rental costs – e.g. recording equipment, lab equipment, etc.
• Travel costs
• Food & beverages

These are just a handful of costs that can creep into your research budget. Like most projects, the actual costs tend to be higher than the estimates, so be sure to err on the conservative side and expect the unexpected. It’s critically important that you’re honest with yourself about these costs, or you could end up getting stuck midway through your project because you’ve run out of money.

Constraint #4: Equipment & software

Another practical consideration is the hardware and/or software you’ll need in order to undertake your research. Of course, this variable will depend on the type of data you’re collecting and analysing. For example, you may need lab equipment to analyse substances, or you may need specific analysis software to analyse statistical data. So, be sure to think about what hardware and/or software you’ll need for each potential methodological approach, and whether you have access to these.

Constraint #5: Your knowledge and skillset

The final practical constraint is a big one. Naturally, the research process involves a lot of learning and development along the way, so you will accrue knowledge and skills as you progress. However, when considering your methodological options, you should still consider your current position on the ladder.

Some of the questions you should ask yourself are:

• Am I more of a “numbers person” or a “words person”?
• How much do I know about the analysis methods I’ll potentially use (e.g. statistical analysis)?
• How much do I know about the software and/or hardware that I’ll potentially use?
• How excited am I to learn new research skills and gain new knowledge?
• How much time do I have to learn the things I need to learn?

Answering these questions honestly will provide you with another set of criteria against which you can evaluate the research methodology options you’ve shortlisted.

So, as you can see, there is a wide range of practicalities and constraints that you need to take into account when you’re deciding on a research methodology. These practicalities create a tension between the “ideal” methodology and the methodology that you can realistically pull off. This is perfectly normal, and it’s your job to find the option that presents the best set of trade-offs.

Recap: Choosing a methodology

In this post, we’ve discussed how to go about choosing a research methodology. The three major deciding factors we looked at were:

• Exploratory
• Confirmatory
• Combination
• Research area norms
• Hardware and software
• Your knowledge and skillset

If you have any questions, feel free to leave a comment below. If you’d like a helping hand with your research methodology, check out our 1-on-1 research coaching service , or book a free consultation with a friendly Grad Coach.

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

You Might Also Like:

Very useful and informative especially for beginners

Nice article! I’m a beginner in the field of cybersecurity research. I am a Telecom and Network Engineer and Also aiming for PhD scholarship.

I find the article very informative especially for my decitation it has been helpful and an eye opener.

Hi I am Anna ,

I am a PHD candidate in the area of cyber security, maybe we can link up

The Examples shows by you, for sure they are really direct me and others to knows and practices the Research Design and prepration.

I found the post very informative and practical.

I struggle so much with designs of the research for sure!

I’m the process of constructing my research design and I want to know if the data analysis I plan to present in my thesis defense proposal possibly change especially after I gathered the data already.

Thank you so much this site is such a life saver. How I wish 1-1 coaching is available in our country but sadly it’s not.

Submit a Comment Cancel reply

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.

• Print Friendly

Research Writing and Analysis

• NVivo Group and Study Sessions
• SPSS This link opens in a new window
• Statistical Analysis Group sessions
• Using Qualtrics
• Dissertation and Data Analysis Group Sessions
• Defense Schedule - Commons Calendar This link opens in a new window
• Research Process Flow Chart
• Research Alignment Chapter 1 This link opens in a new window
• Step 1: Seek Out Evidence
• Step 2: Explain
• Step 3: The Big Picture
• Step 4: Own It
• Step 5: Illustrate
• Annotated Bibliography
• Literature Review This link opens in a new window
• Systematic Reviews & Meta-Analyses
• How to Synthesize and Analyze
• Synthesis and Analysis Practice
• Synthesis and Analysis Group Sessions
• Problem Statement
• Purpose Statement
• Conceptual Framework
• Theoretical Framework
• Quantitative Research Questions
• Qualitative Research Questions
• Trustworthiness of Qualitative Data
• Analysis and Coding Example- Qualitative Data
• Thematic Data Analysis in Qualitative Design
• Dissertation to Journal Article This link opens in a new window
• International Journal of Online Graduate Education (IJOGE) This link opens in a new window
• Journal of Research in Innovative Teaching & Learning (JRIT&L) This link opens in a new window

Jump to DSE Guide

Purpose statement overview.

The purpose statement succinctly explains (on no more than 1 page) the objectives of the research study. These objectives must directly address the problem and help close the stated gap. Expressed as a formula:

Good purpose statements:

• Flow from the problem statement and actually address the proposed problem
• Are concise and clear
• Answer the question ‘Why are you doing this research?’
• Match the methodology (similar to research questions)
• Have a ‘hook’ to get the reader’s attention
• Set the stage by clearly stating, “The purpose of this (qualitative or quantitative) study is to ...

In PhD studies, the purpose usually involves applying a theory to solve the problem. In other words, the purpose tells the reader what the goal of the study is, and what your study will accomplish, through which theoretical lens. The purpose statement also includes brief information about direction, scope, and where the data will come from.

A problem and gap in combination can lead to different research objectives, and hence, different purpose statements. In the example from above where the problem was severe underrepresentation of female CEOs in Fortune 500 companies and the identified gap related to lack of research of male-dominated boards; one purpose might be to explore implicit biases in male-dominated boards through the lens of feminist theory. Another purpose may be to determine how board members rated female and male candidates on scales of competency, professionalism, and experience to predict which candidate will be selected for the CEO position. The first purpose may involve a qualitative ethnographic study in which the researcher observes board meetings and hiring interviews; the second may involve a quantitative regression analysis. The outcomes will be very different, so it’s important that you find out exactly how you want to address a problem and help close a gap!

The purpose of the study must not only align with the problem and address a gap; it must also align with the chosen research method. In fact, the DP/DM template requires you to name the  research method at the very beginning of the purpose statement. The research verb must match the chosen method. In general, quantitative studies involve “closed-ended” research verbs such as determine , measure , correlate , explain , compare , validate , identify , or examine ; whereas qualitative studies involve “open-ended” research verbs such as explore , understand , narrate , articulate [meanings], discover , or develop .

A qualitative purpose statement following the color-coded problem statement (assumed here to be low well-being among financial sector employees) + gap (lack of research on followers of mid-level managers), might start like this:

In response to declining levels of employee well-being, the purpose of the qualitative phenomenology was to explore and understand the lived experiences related to the well-being of the followers of novice mid-level managers in the financial services industry. The levels of follower well-being have been shown to correlate to employee morale, turnover intention, and customer orientation (Eren et al., 2013). A combined framework of Leader-Member Exchange (LMX) Theory and the employee well-being concept informed the research questions and supported the inquiry, analysis, and interpretation of the experiences of followers of novice managers in the financial services industry.

A quantitative purpose statement for the same problem and gap might start like this:

In response to declining levels of employee well-being, the purpose of the quantitative correlational study was to determine which leadership factors predict employee well-being of the followers of novice mid-level managers in the financial services industry. Leadership factors were measured by the Leader-Member Exchange (LMX) assessment framework  by Mantlekow (2015), and employee well-being was conceptualized as a compound variable consisting of self-reported turnover-intent and psychological test scores from the Mental Health Survey (MHS) developed by Johns Hopkins University researchers.

Both of these purpose statements reflect viable research strategies and both align with the problem and gap so it’s up to the researcher to design a study in a manner that reflects personal preferences and desired study outcomes. Note that the quantitative research purpose incorporates operationalized concepts  or variables ; that reflect the way the researcher intends to measure the key concepts under study; whereas the qualitative purpose statement isn’t about translating the concepts under study as variables but instead aim to explore and understand the core research phenomenon.  

Best Practices for Writing your Purpose Statement

Always keep in mind that the dissertation process is iterative, and your writing, over time, will be refined as clarity is gradually achieved. Most of the time, greater clarity for the purpose statement and other components of the Dissertation is the result of a growing understanding of the literature in the field. As you increasingly master the literature you will also increasingly clarify the purpose of your study.

The purpose statement should flow directly from the problem statement. There should be clear and obvious alignment between the two and that alignment will get tighter and more pronounced as your work progresses.

The purpose statement should specifically address the reason for conducting the study, with emphasis on the word specifically. There should not be any doubt in your readers’ minds as to the purpose of your study. To achieve this level of clarity you will need to also insure there is no doubt in your mind as to the purpose of your study.

Many researchers benefit from stopping your work during the research process when insight strikes you and write about it while it is still fresh in your mind. This can help you clarify all aspects of a dissertation, including clarifying its purpose.

Your Chair and your committee members can help you to clarify your study’s purpose so carefully attend to any feedback they offer.

The purpose statement should reflect the research questions and vice versa. The chain of alignment that began with the research problem description and continues on to the research purpose, research questions, and methodology must be respected at all times during dissertation development. You are to succinctly describe the overarching goal of the study that reflects the research questions. Each research question narrows and focuses the purpose statement. Conversely, the purpose statement encompasses all of the research questions.

Identify in the purpose statement the research method as quantitative, qualitative or mixed (i.e., “The purpose of this [qualitative/quantitative/mixed] study is to ...)

Avoid the use of the phrase “research study” since the two words together are redundant.

Follow the initial declaration of purpose with a brief overview of how, with what instruments/data, with whom and where (as applicable) the study will be conducted. Identify variables/constructs and/or phenomenon/concept/idea. Since this section is to be a concise paragraph, emphasis must be placed on the word brief. However, adding these details will give your readers a very clear picture of the purpose of your research.

Developing the purpose section of your dissertation is usually not achieved in a single flash of insight. The process involves a great deal of reading to find out what other scholars have done to address the research topic and problem you have identified. The purpose section of your dissertation could well be the most important paragraph you write during your academic career, and every word should be carefully selected. Think of it as the DNA of your dissertation. Everything else you write should emerge directly and clearly from your purpose statement. In turn, your purpose statement should emerge directly and clearly from your research problem description. It is good practice to print out your problem statement and purpose statement and keep them in front of you as you work on each part of your dissertation in order to insure alignment.

It is helpful to collect several dissertations similar to the one you envision creating. Extract the problem descriptions and purpose statements of other dissertation authors and compare them in order to sharpen your thinking about your own work.  Comparing how other dissertation authors have handled the many challenges you are facing can be an invaluable exercise. Keep in mind that individual universities use their own tailored protocols for presenting key components of the dissertation so your review of these purpose statements should focus on content rather than form.

Once your purpose statement is set it must be consistently presented throughout the dissertation. This may require some recursive editing because the way you articulate your purpose may evolve as you work on various aspects of your dissertation. Whenever you make an adjustment to your purpose statement you should carefully follow up on the editing and conceptual ramifications throughout the entire document.

In establishing your purpose you should NOT advocate for a particular outcome. Research should be done to answer questions not prove a point. As a researcher, you are to inquire with an open mind, and even when you come to the work with clear assumptions, your job is to prove the validity of the conclusions reached. For example, you would not say the purpose of your research project is to demonstrate that there is a relationship between two variables. Such a statement presupposes you know the answer before your research is conducted and promotes or supports (advocates on behalf of) a particular outcome. A more appropriate purpose statement would be to examine or explore the relationship between two variables.

Your purpose statement should not imply that you are going to prove something. You may be surprised to learn that we cannot prove anything in scholarly research for two reasons. First, in quantitative analyses, statistical tests calculate the probability that something is true rather than establishing it as true. Second, in qualitative research, the study can only purport to describe what is occurring from the perspective of the participants. Whether or not the phenomenon they are describing is true in a larger context is not knowable. We cannot observe the phenomenon in all settings and in all circumstances.

Writing your Purpose Statement

It is important to distinguish in your mind the differences between the Problem Statement and Purpose Statement.

The Problem Statement is why I am doing the research

The Purpose Statement is what type of research I am doing to fit or address the problem

The Purpose Statement includes:

• Method of Study
• Specific Population

Remember, as you are contemplating what to include in your purpose statement and then when you are writing it, the purpose statement is a concise paragraph that describes the intent of the study, and it should flow directly from the problem statement.  It should specifically address the reason for conducting the study, and reflect the research questions.  Further, it should identify the research method as qualitative, quantitative, or mixed.  Then provide a brief overview of how the study will be conducted, with what instruments/data collection methods, and with whom (subjects) and where (as applicable). Finally, you should identify variables/constructs and/or phenomenon/concept/idea.

Qualitative Purpose Statement

Creswell (2002) suggested for writing purpose statements in qualitative research include using deliberate phrasing to alert the reader to the purpose statement. Verbs that indicate what will take place in the research and the use of non-directional language that do not suggest an outcome are key. A purpose statement should focus on a single idea or concept, with a broad definition of the idea or concept. How the concept was investigated should also be included, as well as participants in the study and locations for the research to give the reader a sense of with whom and where the study took place. 

Creswell (2003) advised the following script for purpose statements in qualitative research:

“The purpose of this qualitative_________________ (strategy of inquiry, such as ethnography, case study, or other type) study is (was? will be?) to ________________ (understand? describe? develop? discover?) the _________________(central phenomenon being studied) for ______________ (the participants, such as the individual, groups, organization) at __________(research site). At this stage in the research, the __________ (central phenomenon being studied) will be generally defined as ___________________ (provide a general definition)” (pg. 90).

Quantitative Purpose Statement

Creswell (2003) offers vast differences between the purpose statements written for qualitative research and those written for quantitative research, particularly with respect to language and the inclusion of variables. The comparison of variables is often a focus of quantitative research, with the variables distinguishable by either the temporal order or how they are measured. As with qualitative research purpose statements, Creswell (2003) recommends the use of deliberate language to alert the reader to the purpose of the study, but quantitative purpose statements also include the theory or conceptual framework guiding the study and the variables that are being studied and how they are related. 

Creswell (2003) suggests the following script for drafting purpose statements in quantitative research:

“The purpose of this _____________________ (experiment? survey?) study is (was? will be?) to test the theory of _________________that _________________ (compares? relates?) the ___________(independent variable) to _________________________(dependent variable), controlling for _______________________ (control variables) for ___________________ (participants) at _________________________ (the research site). The independent variable(s) _____________________ will be generally defined as _______________________ (provide a general definition). The dependent variable(s) will be generally defined as _____________________ (provide a general definition), and the control and intervening variables(s), _________________ (identify the control and intervening variables) will be statistically controlled in this study” (pg. 97).

Sample Purpose Statements

• The purpose of this qualitative study was to determine how participation in service-learning in an alternative school impacted students academically, civically, and personally.  There is ample evidence demonstrating the failure of schools for students at-risk; however, there is still a need to demonstrate why these students are successful in non-traditional educational programs like the service-learning model used at TDS.  This study was unique in that it examined one alternative school’s approach to service-learning in a setting where students not only serve, but faculty serve as volunteer teachers.  The use of a constructivist approach in service-learning in an alternative school setting was examined in an effort to determine whether service-learning participation contributes positively to academic, personal, and civic gain for students, and to examine student and teacher views regarding the overall outcomes of service-learning.  This study was completed using an ethnographic approach that included observations, content analysis, and interviews with teachers at The David School.
• The purpose of this quantitative non-experimental cross-sectional linear multiple regression design was to investigate the relationship among early childhood teachers’ self-reported assessment of multicultural awareness as measured by responses from the Teacher Multicultural Attitude Survey (TMAS) and supervisors’ observed assessment of teachers’ multicultural competency skills as measured by the Multicultural Teaching Competency Scale (MTCS) survey. Demographic data such as number of multicultural training hours, years teaching in Dubai, curriculum program at current school, and age were also examined and their relationship to multicultural teaching competency. The study took place in the emirate of Dubai where there were 14,333 expatriate teachers employed in private schools (KHDA, 2013b).
• The purpose of this quantitative, non-experimental study is to examine the degree to which stages of change, gender, acculturation level and trauma types predicts the reluctance of Arab refugees, aged 18 and over, in the Dearborn, MI area, to seek professional help for their mental health needs. This study will utilize four instruments to measure these variables: University of Rhode Island Change Assessment (URICA: DiClemente & Hughes, 1990); Cumulative Trauma Scale (Kira, 2012); Acculturation Rating Scale for Arabic Americans-II Arabic and English (ARSAA-IIA, ARSAA-IIE: Jadalla & Lee, 2013), and a demographic survey. This study will examine 1) the relationship between stages of change, gender, acculturation levels, and trauma types and Arab refugees’ help-seeking behavior, 2) the degree to which any of these variables can predict Arab refugee help-seeking behavior.  Additionally, the outcome of this study could provide researchers and clinicians with a stage-based model, TTM, for measuring Arab refugees’ help-seeking behavior and lay a foundation for how TTM can help target the clinical needs of Arab refugees. Lastly, this attempt to apply the TTM model to Arab refugees’ condition could lay the foundation for future research to investigate the application of TTM to clinical work among refugee populations.
• The purpose of this qualitative, phenomenological study is to describe the lived experiences of LLM for 10 EFL learners in rural Guatemala and to utilize that data to determine how it conforms to, or possibly challenges, current theoretical conceptions of LLM. In accordance with Morse’s (1994) suggestion that a phenomenological study should utilize at least six participants, this study utilized semi-structured interviews with 10 EFL learners to explore why and how they have experienced the motivation to learn English throughout their lives. The methodology of horizontalization was used to break the interview protocols into individual units of meaning before analyzing these units to extract the overarching themes (Moustakas, 1994). These themes were then interpreted into a detailed description of LLM as experienced by EFL students in this context. Finally, the resulting description was analyzed to discover how these learners’ lived experiences with LLM conformed with and/or diverged from current theories of LLM.
• The purpose of this qualitative, embedded, multiple case study was to examine how both parent-child attachment relationships are impacted by the quality of the paternal and maternal caregiver-child interactions that occur throughout a maternal deployment, within the context of dual-military couples. In order to examine this phenomenon, an embedded, multiple case study was conducted, utilizing an attachment systems metatheory perspective. The study included four dual-military couples who experienced a maternal deployment to Operation Iraqi Freedom (OIF) or Operation Enduring Freedom (OEF) when they had at least one child between 8 weeks-old to 5 years-old.  Each member of the couple participated in an individual, semi-structured interview with the researcher and completed the Parenting Relationship Questionnaire (PRQ). “The PRQ is designed to capture a parent’s perspective on the parent-child relationship” (Pearson, 2012, para. 1) and was used within the proposed study for this purpose. The PRQ was utilized to triangulate the data (Bekhet & Zauszniewski, 2012) as well as to provide some additional information on the parents’ perspective of the quality of the parent-child attachment relationship in regards to communication, discipline, parenting confidence, relationship satisfaction, and time spent together (Pearson, 2012). The researcher utilized the semi-structured interview to collect information regarding the parents' perspectives of the quality of their parental caregiver behaviors during the deployment cycle, the mother's parent-child interactions while deployed, the behavior of the child or children at time of reunification, and the strategies or behaviors the parents believe may have contributed to their child's behavior at the time of reunification. The results of this study may be utilized by the military, and by civilian providers, to develop proactive and preventive measures that both providers and parents can implement, to address any potential adverse effects on the parent-child attachment relationship, identified through the proposed study. The results of this study may also be utilized to further refine and understand the integration of attachment theory and systems theory, in both clinical and research settings, within the field of marriage and family therapy.

Was this resource helpful?

• << Previous: Problem Statement
• Next: Conceptual Framework >>
• Last Updated: May 16, 2024 8:25 AM
• URL: https://resources.nu.edu/researchtools

Aims and Objectives of Research Methodology

Meaning of research methodology.

Research methodology simply refers to the procedure or plan of action for conducting a research. It defines techniques and tools used to collect, process and analyze data regarding the research topic.

Research methodologies tell the systematic method for acquiring data and studying it for deriving out crucial findings. This is an important process that helps in solving problems and making business decisions. It enables management for properly organizing their efforts in a right direction for generating an idea.

Methodology of research indicates and influences the overall validity and reliability of whole research to be conducted. Methodology answers mainly two questions regarding research that are how the data used for study was acquired and how it was analyzed to derive out the findings.

Research methodologies are broadly classified into two main categories: Quantitative research methods and Qualitative research methods. Quantitative research is one which is based on quantitative terms and involves collection of numerical data, analyzing it and drawing conclusions using numbers. Qualitative research on other hand, is one which is done using non-numerical and unquantifiable elements like feelings, emotion, sound etc.

Aims and Objectives of Research methodology

Develops better Insight into Topic

Research methodology provides better familiarity with the research topic by properly explaining each concept associated with it. It aims at the proper analysis of every aspect and accurately portrays all findings of the project. 

Provides Systematic Structure

Research methodology eases the process of whole research to be done. It clearly defines the tools and techniques to be used for collecting, analyzing and interpreting the data to find out the solutions.

Enhance the Research Quality

It determines the reliability and validity of the whole research work. Research methodology tells accurate sources from where data should be taken for studying purpose which thereby improves the quality of research done.

Derive Better Solutions

Research methodology helps in deriving crucial findings for solving business problems. It performs an in-depth study of various projects, develops a better understanding and detects all problems.

Aids in Decision Making

Decision making is another important role played by research methodology. It supports management in organizing their efforts in generating a new idea. Research methodology by providing direction for various activities of the project helps managers for efficient decision making.

Inculcates logical and systematic Thinking

It develops the logical thinking ability of individuals. Research methodology evaluates every element of the project and highlights them in detail. It represents every aspect in a simplified manner which improves logical thinking.

Related posts:

• Project Report: Meaning, Contents, Objectives, Characteristics, Need
• Steps in Project Planning
• Advantages and Disadvantages of Decision Making
• Characteristics of Research Methodology
• Methodology of Operation Research
• Aims and Objectives of Business Organisation

Add CommerceMates to your Homescreen!

School climate: Using a person–environment fit perspective to inform school improvement

• Original Paper
• Open access
• Published: 19 May 2024

Cite this article

You have full access to this open access article

• Jill M. Aldridge   ORCID: orcid.org/0000-0003-0742-0473 1 ,
• Meghan J. Blackstock 2 &
• Felicity I. McLure   ORCID: orcid.org/0000-0003-3664-9146 3  

Strong and consistent findings suggest that a positive school climate is related to improved student outcomes. However, assessment of the school climate rarely considers the environmental fit (or misfit) between individuals' actual or lived experiences and their preferred environment. This study drew on a person-environment fit perspective to examine whether: students’ experiences of the school climate (actual environment) differed from their views of their ideal school climate (preferred environment); the views of the actual and preferred environment differed between schools; and the actual–preferred discrepancy (as a measure of the environmental fit) was related to student wellbeing, resilience and reports of bullying. The results from the analysis of data collected from 993 upper primary school students suggest that outcomes were enhanced when the perceived environment more closely matched the preferred environment. Our study’s findings support using a person-environment fit perspective alongside a socio-ecological approach to inform strategic decisions for school improvement efforts.

Avoid common mistakes on your manuscript.

The psychosocial school climate refers to the overall atmosphere of a school, including social, emotional and interpersonal aspects. The school climate quantifies the lived experiences of school members, which are shaped by the unspoken ethos, norms, values and beliefs that pervade a school (Cohen et al., 2009 ). The school climate is reflected in the relationships and daily interactions between school members, the rituals and traditions of school life, feelings of safety and inclusion and contextual factors that might impact student learning (Cohen, 2013 ).

The critical role the school climate plays in student wellbeing, academic success and personal development is gaining growing recognition. Research findings suggest that the school climate provides the protective factors needed to guard against adverse student experiences (Doumas et al., 2017 ; Keane & Evans, 2022 ) and promote a range of outcomes, including life satisfaction (Aldridge et al., 2020 ), mental health (e.g., a review of literature by Aldridge & McChesney, 2018 ) and overall wellbeing (Varela et al., 2019 ). Further, positive school climates have been found to have an inverse relationship with adverse outcomes, such as school violence (Booren et al., 2011 ; Espelage & Hong, 2019 ; Steffgen et al., 2013 ), bullying (Acosta et al., 2019 ; Marchante et al., 2022 ; Varela et al., 2021 ) and antisocial (Manzano-Sánchez et al., 2021 ; O’Brennan et al., 2014 ), delinquent (Akman, 2021 ; Aldridge et al., 2018 ; Klein et al., 2012 ; Kohl et al., 2013 ) and aggressive behaviours (e.g., Low et al., 2014 ). School climate factors have also been found to influence the development of social skills, such as prosocial behaviours (González Moreno & Molero Jurado, 2022 ; Luengo et al., 2017 ; Patalay & Fitzsimons, 2016 ; Thapa et al., 2013 ), personality (Roberts & Robins, 2004 ) and resilience (Aldridge et al., 2016 ; Cohen, 2013 ; Kutsyuruba et al., 2015 ); all of which are essential precursors to bullying prevention (Acosta et al., 2019 ; Cohen, 2013 ; Cohen & Freiberg, 2013 ; Wang et al., 2013a , 2013b ).

Of relevance to this study, is that a school’s climate plays an essential role in school improvement efforts (Thapa et al., 2013 ). This makes sense given that students in schools with positive school climates are not only more motivated to actively participate in their learning (e.g., Eccles et al., 1991 ), but are also more likely to attend regularly (Daily et al., 2020 ) and less likely to be suspended (Lee et al., 2011 ). Further, positive school climates have the potential to improve learning outcomes and academic achievement (Shindler et al., 2016 ), reduce achievement gaps and increase career prospects for students from less advantaged backgrounds (Berkowtz, 2022 ; Hopson & Lee, 2011 ).

Growing recognition of the importance of a positive school climate has led to increasing interest in its measurement at both the school and education system levels (Cohen et al., 2009 ). However, traditionally, measures of school climate have relied on assessing students’ experiences of the actual environment, without consideration of their needs. Whilst assessment of the actual environment is meaningful, given the strong and consistent relationship between school climate factors and student outcomes (Aldridge & McChesney, 2018 ; Thapa et al., 2013 ), we contend that consideration of the match (or misfit) between the person and the environment is integral to effective strategic decision-making and school improvement efforts. Therefore, in our study, we drew on a person-environment fit perspective to examine whether a focus on reducing a lack of congruence between students' perceived and desired experiences could improve their outcomes. To examine this overarching aim, three research objectives were addressed:

To examine differences in the perceived (actual) and preferred school climate.

To investigate whether students’ views of the perceived and preferred school climate were similar for students in the same school but different from those in other schools.

To examine the relationships between students' reports of resilience, wellbeing and bullying, and:

Their perceived (actual) school climate.

The actual–preferred discrepancy (size of the environmental misfit).

Assessing school climate

Within the field of learning environments, surveys have been developed to assess classroom-level and school-level environments. The pioneering work of Walberg and Anderson ( 1968 ) and Moos and Trickett ( 1974 ) saw the development of the first learning environment surveys. These instruments were designed to assess students’ perceptions of classroom-level factors, such as the relationships between students and their peers and their teachers (Fraser, 2013 ). Initially, measurement of the school-level environment was associated with the field of educational administration (Anderson, 1982 ; Stewart 1979 ) and focused on aspects related to an organisational climate. However, during the 1960s, a shift in education in the United States that sought to address equity and inclusion issues, saw the emergence of the first school climate surveys. Since this time, numerous instruments have been developed to assess the school climate from the perspectives of different school members (González et al., 2022 ), including teachers (e.g., Aldridge & Fraser, 2016 ; Bear et al., 2014 ), parents (e.g., Aldridge & McChesney  2018 ) and students (e.g., Aldridge and Ala'i, 2013 ; Bear et al., 2011 ).

It is widely agreed that school climate is a multidimensional construct (Thapa et al., 2013 ; Wang & Degol, 2016 ). Despite a lack of consensus regarding which aspects of the school climate should be included in a measure (Cohen et al., 2009 ; Wang & Degol, 2016 ), there is a growing recognition that coverage of four broad categories is important (e.g., Thapa et al., 2013 ; Wang & Degol, 2016 ), these being: safety (assessing the physical and emotional safety provided by the school), community (assessing the quality of the relationships within the school), academic atmosphere (assessing the quality of the instruction and learning support) and institutional environment (assessing how the shared beliefs contribute to overall sense of belonging or inclusion). Although the institutional environment can also assess the quality of the physical environment, our survey did not include this aspect.

As well as being multidimensional, factors that contribute to school climate are considered to be malleable, unlike those outside schools (such as family). That is, measures of school climate should provide information about school-related factors that educators can, to some extent, change or control (García-Carrión et al., 2019 ; Long et al., 2020 ). The malleable nature of the school climate is an important consideration in school improvement efforts as it allows school leaders and education systems to target the improvement of environmental factors that are highly correlated with desired student outcomes (Wang & Degol, 2016 ).

• Person–environment fit

The notion of person-environment fit originates from the work of French et al. ( 1974 ), which was heavily influenced by Lewin’s ( 1935 ) field theory. Lewin’s ( 1936 ) equation B =  f (P, E) proposes that a person’s behaviour (B) is a function of (rather than distinct from) their personal characteristics (P) and the environment (E), highlighting the importance of the environment in understanding behaviour.

A person-environment fit perspective focuses on the interactions between individuals and their environment, with the knowledge that one constantly influences the other (Edwards et al., 1998 ). Underpinning a person-environment fit perspective is the innate desire for individuals to fit in their environment, which includes a need to belong (Deci & Ryan, 2000 ; van Vianen, 2018 ), have autonomy over their life (Hutchings & Chaplin, 2017 ; Yu & Davis, 2016 ), reduce uncertainty and increase consistency (Yu, 2013 ). The fit (or misfit) between the person and the environment causes satisfaction or dissatisfaction and affects an individual’s behaviours, and mental and physical health (Caplan & Harrison, 1993 ; Greguras et al., 2014 ). Further, a misfit between what a person desires and the actual environment has been highly correlated to psychological (e.g.,anxiety or stress) and physiological (e.g., physical wellbeing (Dahm et al., 2015 ) and elevated blood pressure (Edwards & Cooper, 1998 ; Edwards et al., 1998 ; Nagai & Dasari, 2023 )) outcomes.

The two components of person-environment fit, the individual and the environment, can be described objectively or subjectively (Bohndick et al., 2018 ; Caplan, 1987 ). In our study, we used a subjective perspective in which self-ratings (subjectively perceived) of personal qualities (as opposed to the consensual judgement of peers or educators) and (subjective) perceptions of the environment (as opposed to consensual judgments or the concrete environment) were used. The latter (perceptions of the environment) involved students’ views of their actual school climate (the subjective resources provided by the school) and their preferred school climate (subjective needs).

The work of Moos ( 1987 ), which examined the social environments in a range of milieu, examined how the degree of fit between the perceived and desired environment might influence outcomes. Moos ( 1979 ) pioneered the use of both actual and preferred versions of social climate surveys, in which the actual version assessed a person’s perceptions of the environment and the preferred version assessed the environment a person would like (ideal environment). Using both an actual and preferred version allowed the examination of the congruence between what a person needs and what the environment provides. The magnitude of the actual–preferred difference provides information about the environmental fit or misfit. Historically, the two versions (actual and preferred) were administered separately about one week apart. The different versions had corresponding items and although the content was similar for each, the preferred version used a conditional tense (e.g., would). For example, a statement in the actual version would read “Students in this class like me”; in the preferred version, the same item would read “Students in this class would like me”. More recent research has used a more economical side-by-side format to capture the two responses simultaneously (Aldridge et al., 1999 ). Students are instructed to respond to each item twice to report how often the statement takes place and how often they would like it to occur (a wish list if you will). Our study used a side-by-side format.

In keeping with a person-environment perspective, this research drew on the premise that outcomes would improve when a person’s perceptions of the environment were more aligned with their preferences. Past research at the classroom level has suggested that more positive outcomes result when the learning environment is better matched to the student’s needs (see Fraser & Fisher, 1983a , 1983b ). Of the limited number of studies examining whether person-environment fit influenced student outcomes, the majority of these were carried out at the college or university levels. These studies found that a greater person-environment fit improved relationship building with instructors (Deng & Yaim, 2020 ) and students’ performance (Pawlowska et al., 2014 ), satisfaction (Rocconi et al., 2020 ) and wellbeing (Gilbreath et al., 2011 ). Past research examining person-environment fit in schools is limited. Only a handful of studies have been reported, all of which were carried out at the classroom level (see Fraser & Fisher, 1983a , 1983b ). These early classroom-level studies provide evidence to suggest that the actual–preferred differences reported by students could influence their outcomes at the school level.

Despite the limited research available to support the efficacy of using actual–preferred differences at the school level, we hypothesised that improvement efforts to reduce these differences could promote improved outcomes. To our knowledge, past research has not examined relationships between the actual–preferred differences and student resilience, wellbeing and bullying; therefore, our research helps to fill this gap.

The sample was drawn from 12 primary schools across three Australian states. To increase generalisation, the schools were co-educational and located in metropolitan ( n  = 9) and regional ( n  = 3) areas.

In each school, the surveys were administered to all students who volunteered to participate and were present on the day of administration. This provided a total of 1002 cases. During data cleaning, nine cases (approximately 1.01%) were removed as the responses indicated these students were disengaged (standard deviation of 0) or provided the incorrect year level.

Of the remaining 993 cases, 493 (49.6%) respondents identified as male and 500 (50.4%) respondents identified as female. The students, aged between 11 to 12 years of age, were enrolled in years five 5 ( n  = 478, 48.1%) and six 6 ( n  = 515, 51.9%). The distribution of students across metropolitan ( n  = 742) and regional ( n  = 251) schools generally reflected the differences in school sizes in these areas.

Instruments

The collection of data for the study involved the administration of two surveys, one to assess students’ perception of the school climate and the other to assess students’ self-reports of wellbeing, resilience and bullying.

Perceptions of the school climate

The What’s Happening In This School—Primary (WHITS-P; Aldridge & Blackstock, 2024 ) was used to assess students’ perceptions of the school climate. The WHITS-P was based largely on the secondary school version of the What’s Happening In This School (WHITS; Aldridge & Ala’i, 2013 ; Riekie & Aldridge, 2017 ). Development of the WHITS-P involved extrapolating and modifying items (which included simplifying the language and reducing the number of items) to make them suitable for use with younger students. To improve reliability and comprehensibility, items belonging to a scale were grouped together and a child-friendly header was provided at the beginning of each group as a contextual cue. To reduce confusion, all items were worded positively.

To reflect the multidimensional nature of the school climate, the WHITS-P included seven scales to provide coverage of the four broad categories identified by Wang and Degol ( 2016 ). Two scales, teacher support and peer connectedness, assessed the quality of the interpersonal relationships in the school (community). Two scales, rule clarity and reporting and seeking help, assessed the quality of the processes, procedures and other mechanisms used to support school safety (safety). Two scales, support for learning and high expectations, assessed the quality of the learning support (academic atmosphere). Finally, one scale, school connectedness, assessed the extent to which the norms, values and policies gave students a sense of belonging and being valued (institutional environment).

Apart from high expectations, which was assessed using three items, the six remaining scales were assessed using four items. Each item was responded to using a simplified five-point frequency-response format that was developed over multiple trials. The response format included three major anchor points labelled ‘almost never’, ‘sometimes’, and ‘almost always’. An emoji face accompanied these major anchor points to provide a visual cue. In addition to the three major anchor points, two additional anchor points were included, one between the response alternatives of ‘almost never’ and ‘sometimes’ and another between ‘sometimes' and ‘almost always’. A side-by-side format was used to collect the actual and preferred responses simultaneously. Using this format, students responded twice for each statement: once for how often the statement actually happened and again for how often they would like it to happen. Table 1 provides the broad category, a brief description and a sample item for each WHITS-P scale.

Student outcomes

Three scales were used to assess student self-reports of resilience, wellbeing and bullying. All were comprised of four items and used the same five-point response format to measure the school climate. The scales were modified (by reducing the number of items and simplifying the language where appropriate) from existing instruments developed for use in secondary schools. The resilience scale was adapted from a scale initially developed by Wagnild and Young ( 1993 ) and later adapted for use with secondary students (Riekie & Aldridge, 2017 ). The scale demonstrated sound psychometric properties in previous studies (e.g., Aldridge et al., 2016 ) and sought to examine concepts of perseverance and self-reliance. The wellbeing scale was modified from the WHO-Five (WHO, 1998 ) to assess students’ positive wellbeing. When used with secondary students, a modified version reported good psychometric properties (e.g., Riekie et al., 2017 ). Finally, the bullying scale was modified from a survey developed initially by Bandyopadhyay et al. ( 2009 ) to assess the extent to which students felt they were victims of bullying. When responding to outcomes scales, students were asked to consider how often each item occurred over the previous two weeks.

Analysis of the data, described below, was carried out using SPSS (version 29).

For the first research objective, descriptive statistics, including means and standard deviations, were used to compare student responses to the actual and preferred version of the WHITS-P. To examine whether the actual-preferred differences were statistically significant, paired samples t- tests were used. Finally, to examine the magnitude of the differences, the effect sizes were calculated for each scale using the following formula:

For the second research objective, a one-way analysis of variance (ANOVA) with school membership as the main effect was used to examine whether students’ responses to actual and preferred versions of the WHITS-P differed across the 12 schools. Two indices related to the ANOVA results were examined, the significance level and eta 2 statistic (the proportion of ‘between’ to ‘total’ sums of squares), to provide a measure of the degree of association between student responses and the dependent variable to examine the variance explained by school membership (Field, 2009 ),

For the third research objective, simple correlation and multiple regression analysis were used to examine the relationships between the outcome variables (resilience, wellbeing and bullying) and a) students’ perceptions of the school climate and b) the size of the actual–preferred discrepancy. Simple correlations (Pearson’s correlation coefficient) were used to summarise the strength and degree of the relationships. Multiple regression analysis was used to help to understand whether a school climate scale contributed to the student outcomes over and above the contributions made by other school climate variables. Beta coefficients were used to examine the predictive ability of each variable.

Although the analysis was the same for both parts of research objective three, the data used differed. To examine the relationships between the three outcomes and students’ perceptions of the school climate (part 1 of research objective 3), the aggregated responses to items in the actual version of each WHITS-P scale were used. To investigate the relationships between the three outcomes and the degree of fit or misfit (part 2 of research objective 3), the absolute value for the difference between a student's actual and preferred responses was calculated and then used to aggregate the scores of the items in each scale.

Research objective 1: actual–preferred comparisons

The first research objective compared students’ responses to the actual and preferred versions of the WHITS-P. The average item means, portrayed graphically in Fig.  1 and reported in Table  2 , indicate that students’ responses to the preferred version were higher than the actual version for all but one WHITS-P scale, high expectations. Except for the high expectations scale, the range of responses to the preferred version was narrower (with standard deviations ranging from 0.505 to 0.743) than for responses to the actual version (with standard deviations ranging from 0.705 to 0.908). The high expectations scale was the only one for which the preferred responses were lower than the actual responses. Whilst the difference was small and statistically non-significant, this result suggests that students would like less than they receive.

Average item means for students’ responses to actual and preferred versions of WHITS-P scales

The t -test results, reported to the right of Table  2 , suggest that the differences were statistically significant ( p  < 0.01) for all scales except high expectations. The effect sizes, calculated to estimate the magnitude of the differences, ranged from approximately one-third (effect size = 0.395) to over three-quarters (effect size = 0.834) of a standard deviation for scales with a statistically significant difference.

Research objective 2: differences across schools

One-way ANOVA was used to examine whether students’ mean responses to the actual and preferred version of the WHITS-P differed based on school membership. The results, reported in Table  3 , indicate that, for responses to both the actual and preferred versions, there was a statistically significant ( p  < 0.05) difference for all WHITS-P scales. The eta 2 statistic for different WHITS-P scales ranged from 0.023 to 0.050 for the actual version and from 0.051 to 0.113 for the preferred version. The F -values were all greater than 1, ranging from 2.108 to 4.673 for the actual version and from 4.771 to 11.338 for the preferred version. These results (the F -value and p -value) suggest that students in the same school viewed the school climate similarly but differed from those in other schools.

Research objective 3: relationships

Simple correlation and multiple regression analysis were used to investigate relationships between the three student outcomes and (a) the school climate and (b) the actual–preferred discrepancy.

School climate–outcome relationships

First, the results of simple correlation analysis involving students’ responses to the actual version, reported in Table  4 , suggest that the relationships between all seven WHITS-P scales and student reports of resilience and wellbeing were positive and statistically significant ( p  <  0.0 1). These results imply that when school climate factors (as assessed using the WHITS-P) are experienced more positively, students report increased resilience and better wellbeing. Conversely, the correlations between the WHITS-P scales and reports of bullying were all negative and statistically significant ( p  <  0.0 5), suggesting that, when students experienced the school climate more positively, they reported fewer experiences of bullying.

Multiple regression analysis was used to determine which school climate factors predicted student outcomes. The overall regression, reported in Table  4 , was statistically significant for all three outcomes: resilience (multiple R  = 0.595, R 2  = 0.353 , p  < 0.01), wellbeing (multiple R  = 0.769, R 2  = 0.591, p  < 0.01), and bullying (multiple R  = 0.460, R 2  = 0.212, p  < 0.01). The results of multiple regression analyses revealed that all WHITS-P scales except teacher support positively and statistically significantly ( p  < 0.01) predicted student resilience; five scales (peer connectedness, reporting and seeking help, rule clarity, support for learning and school connectedness) statistically significantly (p < 0.01) and positively predicted student wellbeing; four scales (peer connectedness, reporting and seeking help, support for learning and school connectedness) statistically significantly ( p  < 0.05) and negatively predicted reports of bullying and one scale, high expectations, statistically significantly ( p  < 0.01) and positively predicted reports of bullying.

Relationships between preferred–actual congruence and outcomes

Whereas the previous analyses examined relationships between students’ lived experiences of school climate and their outcomes, this section reports relationships between the actual–preferred discrepancy for each WHITS-P scale and the student outcomes.

The results of simple correlation analyses, used to examine the bivariate relationship between the actual–preferred discrepancy and each outcome, reported in Table  4 , indicate that, without exception, the correlations were statistically significant ( p  <  0.0 1). All relationships were negative for resilience and wellbeing and positive for bullying, suggesting that, when the actual–preferred gap is smaller, students report greater resilience and wellbeing and less bullying.

Multiple regression analysis was used to evaluate the relationships between the actual–preferred discrepancy for a school climate scale and a student outcome while controlling for the effect of the other scales. The results, reported in Table  4 , suggest that the overall regression was statistically significant for all three outcomes: resilience (multiple R  = 0.431, R 2  = 0.186, p  < 0.01), wellbeing (multiple R  = 0.572, R 2  = 0.327, p  < 0.01) and reports of bullying (multiple R  = 0.406, R 2  = 0.165, p  < 0.01). Examination of the p -values and beta values suggest that, for resilience, the actual–preferred discrepancy for all WHITS-P scales, except the support for learning scale, were negative and statistically significantly ( p  < 0.05). For wellbeing, the actual–preferred discrepancy for five WHITS-P scales were negative and statistically significant ( p  < 0.01), the exceptions being reporting and seeking help and support for learning (which were nonsignificant). Finally, for reports of bullying, the actual–preferred discrepancy for five WHITS-P scales was statistically significant ( p  < 0.01) and positive; the exceptions, support for learning and high expectations, were nonsignificant.

A person-environment fit perspective is focused on the interactions between an individual and the environment and assumes that a good match between the two promotes positive outcomes. Using this premise, the study reported in this article examined whether smaller actual–preferred discrepancies promoted student resilience and wellbeing and reduced bullying.

First, we examined whether students’ views of the actual and preferred environment differed. The statistically significant t- test results and effect sizes for all but one WHITS-P scale (high expectations) suggest that students would prefer the school climate features to occur more often. These findings are consistent with research at the classroom level, which suggests that students desire a more positive environment than the one they experience (e.g., Fraser, 1982 ; Magen-Nagar & Steinberger, 2017 ).

For the exception, high expectations, there was only a slight (statistically non-significant) actual–preferred discrepancy. It is noteworthy, however, that students’ responses indicates their actual experiences exceeded their preferences. To our knowledge, few, if any, studies in the field of learning environment report discrepancies in this direction. Moos ( 1987 ) warns, however, that when the environment exceeds a person's preference, dysfunction can occur because personal characteristics (such as self-esteem) influence the interplay between personal and environmental factors. It is recommended, therefore, that future studies examine the extent to which dysfunction occurs when the environment exceeds a person’s preference and that educators seek causal explanations.

Second, the ANOVA results were interpreted to determine whether the mean responses for students in the same school were similar but different from those of students in other schools. The statistically significant results for responses to the actual version for all WHITS-P scales, suggesting the perceptions of students can be differentiated between schools, corroborate those of past studies (e.g., Johnson et al., 2007 ; Riekie & Aldridge, 2017 ). This finding makes sense given that a school's climate is influenced by a range of factors, such as interpersonal relationships, making each one unique (Tomaszewski et al., 2023 ).

The statistically significant ANOVA results for students’ responses to the preferred version for all WHITS-P scales were also notable. These findings suggest that not only is a school's climate unique, but the needs of students within a school (as reported in the preferred version) also differs between schools. From a cultural capital perspective (Davies & Rizk, 2018 ), this finding reflects the intrinsic relationship between the community in which it is situated (e.g., socio-economic demographics), the school’s organisational practices and the school culture (Tarabini et al., 2017 ). These findings support the need for school improvement efforts that are culturally responsive and consider the sociocultural context (e.g., Antrop-Gozales & De Jesus, 2006 ; McLure & Aldridge, 2022 ).

We then examined the relationships the relationships between students’ reports of resilience, wellbeing and bullying and responses to, first, the actual version of the WHITS-P and, second, the actual–preferred discrepancy. The relationships between the actual version of the WHITS-P and the three outcomes, suggest that positive school climates could promote students' resilience and wellbeing and reduce bullying. These findings corroborate those of past research that examined the influence of school climate factors on emotional wellbeing (e.g., Aldridge & McChesney, 2018 ; Aldridge et al., 2016 ; Kutsyuruba et al., 2015 ; Lester & Cross, 2015 ; Riekie & Aldridge, 2017 ), resilience (e.g., Aldridge et al., 2016 ; Cohen, 2013 ; Ebbart & Luthar, 2021 ) and reports of bullying (Aldridge et al., 2018 , 2020 ). Our findings suggest that developing a positive school climate could provide the protective factors needed to promote wellbeing and equip students to handle stressors and setbacks. Further, the negative relationships between the WHITS-P scales and students’ reports of bullying, highlight the critical role that a positive school climate plays in preventing bullying (e.g., Cohen & Frieberg, 2013 ; Low & Van Ryzin, 2014 ; Wang et al., 2013a , 2013b ).

Finally, we drew on a person-environment perspective to examine whether the degree of misfit reported by students was inversely related to their outcomes. Our findings indicate that when the actual–preferred discrepancies were smaller, student resilience and wellbeing were improved and reports of bullying reduced. These findings support Caplan’s ( 1987 ) theory of person-environment fit and earlier studies that found outcomes were improved when environmental misfits were reduced (Fraser & Fisher, 1983a , 1983b ; Moos, 1987 ). Further, it is worth noting that the correlation between students’ actual perceptions of teacher support and their outcomes was statistically non-significant, while the relationship between the actual-–preferred discrepancy in student responses to teacher support and student outcomes (resilience, wellbeing and reductions in bullying) was statistically significant. This finding suggests that including information about actual and preferred experiences could provide a more nuanced understanding of students' needs, which can be used to promote student outcomes more effectively when compared with relying on actual data alone.

Implications for schools

Given that traditional models used to guide interventions aimed at improving student outcomes often target change efforts at individual students, our findings provide important implications for schools seeking to improve outcomes across the entire school. Our findings draw attention to the interaction between students and their environment and to the interconnectedness between the context and the environment. Given the malleable nature of the school climate factors, these findings provide important implications to schools as outlined below.

Our finding, that students in different schools have different perceptions and preferences, support the premise that students are embedded within larger social systems and acknowledges that multiple levels of influence exist (Bronfenbrenner, 1979 ). These findings suggest that schools would benefit from examining the interconnections between students and larger social systems to determine how changes at different system levels can support their needs. This information could help educators to select and shape the school climate for person–environment matching (Moos, 1996 ).

Our results suggest that examining ways to reduce actual–preferred discrepancy could improve student outcomes. For example, one of our findings suggest that, when students perceived their teachers as friendly and caring as they would like them to be (e.g., the actual–preferred discrepancy was reduced), they were more resilient, had better wellbeing and experienced less bullying. Given that high-quality relationships are typically associated with positive student outcomes, it would make sense, in this case, for schools to consider ways to foster interpersonal rapport and relational trust between teachers and their students.

Our findings also support the usefulness of a socio-ecological approach to promoting student outcomes. Using a social-ecological approach recognises the social variables (e.g., teacher or peer support) and the multiple levels of influence on student development and behaviour. Although it may not be practical to influence all aspects of the environment, considering students as part of an ecosystem may help focus school improvement efforts. For example, students are influenced by the level of teacher support (beliefs about whether teachers value them) at both the microsystem and macrosystem levels. Teachers contribute to the school climate at the microsystem level (in the way they interact with individuals e.g., by being caring, friendly and dependable), at the mesosystem levels (e.g., through the way they interact, involve or engage parents in the educational process) and at the macrosystem level (e.g., through the norms and rules that guide student social behaviour and explicit messages and rules regarding interactions between peers (see, for example, Patrick et al., 2001 ). Although the benefits of using a social-ecological perspective to promote outcomes and address inequities have been widely reported in healthcare settings (see for example, Cramer & Kapusta, 2017 ; Baron et al., 2014 ; Golden et al., 2015 ; Goodwin et al., 2022 ), similar efforts appear to be limited in school improvement literature. However, our findings suggest that, given the importance of the school context, using a social-ecological approach to guide school improvement interventions could be beneficial.  

Finally, for schools using actual–preferred discrepancies to guide improvement efforts it could be worth  considering the results in light of Eccles and Midgley’s ( 1989 ) stage-environment fit perspective which suggests that, when the environment fit caters to students' developmental needs, individual functioning is maximised (Midgley et al., 2014 ). Drawing on a stage-environment fit perspective could encourage educators to examine whether misfits could be addressed by making changes to ensure that the environment is suited to the students’s developmental needs. Such changes could support a range of adaptive changes, such as personality development (Roberts & Robins, 2004 ) and increased motivation and academic performance (Eccles et al., 1991 ).

Recognition that the individual and the environment are not isolated entities but, rather, each shape and are shaped by the other supports possibilities of a schoolwide focus on improving the school climate and reducing actual–preferred differences to improve student outcomes.

However, despite the mounting research evidence supporting the critical role of school climate, as well as an increased demand for its measurement, schools are more likely to rely on achievement data or attendance rates to inform strategic decisions (Cohen et al., 2009 ). Our findings not only corroborate past research that suggests a focus on improving school climate will lead to improved outcomes, they also support the possibility of examining whether misfits occur, to guide decisions about school improvement efforts. We posit that future school improvement efforts would benefit from approaches that move away from a focus on individual-level solutions to ones that draw on a social-ecological perspective, ensuring a multi-level perspective that considers the school's context, to improve school climate and reduce actual–preferred discrepancies. 

Acosta, J., Chinman, M., Ebener, P., Malone, P. S., Phillips, A., & Wilks, A. (2019). Understanding the relationship between perceived school climate and bullying: A mediator analysis. Journal of School Violence, 18 (2), 200–215.

Article   Google Scholar  

Akman, Y. (2021). The relationship between school climate and students’ aggressive behaviors. International Journal of Progressive Education, 17 (2), 430–448.

Aldridge, J. M., & Ala’i, K. (2013). Assessing students' views of school climate: Developing and validating the What's happening in this school? (WHITS) questionnaire. Improving Schools, 16 (1), 47–66.

Aldridge, J. M., & Blackstock, M. J. (2024). Assessing students’ perceptions of school climate in primary schools. Learning Environments Research . https://doi.org/10.1007/s10984-024-09492-2

Aldridge, J. M., & Fraser, B. J. (2016). Teacher’s views of the school climate and its influence on teacher self-efficacy and work satisfaction. Learning Environment Research, 19 , 291–307. https://doi.org/10.1007/s10984-015-9198-x

Aldridge, J. M., Fraser, B. J., Fozdar, F., Ala’i, K., Earnest, J., & Afari, E. (2016). Students’ perceptions of school climate as determinants of wellbeing, resilience, and identity. Improving Schools, 19 (1), 5–26.

Aldridge, J. M., Fraser, B. J., & Huang, I. T. C. (1999). Investigating classroom environments in Taiwan and Australia with multiple research methods. The Journal of Educational Research, 93 (1), 48–62.

Aldridge, J. M., & McChesney, K. (2018). The relationships between school climate and adolescent mental health and wellbeing: A systematic literature review. International Journal of Educational Research, 88 , 121–145. https://doi.org/10.1016/j.ijer.2018.01.012

Aldridge, J. M., McChesney, K., & Afari, E. (2018). Relationships between school climate, bullying, and delinquent behaviours. Learning Environments Research, 21 , 153–172.

Aldridge, J. M., McChesney, K., & Afari, E. (2020). Associations between school climate and student life satisfaction: Resilience and bullying as mediating factors. Learning Environments Research, 23 , 129–150.

Anderson, C. S. (1982). The search for school climate: A review of research. Review of Educational Research, 52 , 368–420.

Antrop-González, R., & De Jesús, D. A. (2006). Toward a theory of critical care in urban small school reform: Examining structures and pedagogies of caring in two Latino community-based schools. International Journal of Qualitative Studies in Education, 19 (4), 409–433. https://doi.org/10.1080/09518390600773148

Bandura, A. (2001). Social cognitive theory: An agentic perspective. Annual Review of Psychology, 52 , 1–26.

Bandyopadhyay, S., Cornell, D., & Konold, T. (2009). Internal and external validity of three school climate scales from the School Climate Bullying Survey. School Psychology Review, 38 , 338–355.

Baron, S. L., Beard, S., Davis, L. K., et al. (2014). Promoting integrated approaches to reducing health inequities among low‐income workers: Applying a social-ecological framework. American Journal of Industrial Medicine, 57 (5), 539–556.

Bear, G. G., Gaskins, C., Blank, J., & Chen, F. F. (2011). Delaware school climate survey–student: Its factor structure, concurrent validity, and reliability. Journal of School Psychology, 49 , 157–174. https://doi.org/10.1016/j.jsp.2011.01.001

Bear, G. G., Yang, C., Pell, M., & Gaskins, C. (2014). Validation of a brief measure of teachers’ perceptions of school climate: Relations to student achievement and suspensions. Learning Environments Research, 17 , 339–354. https://doi.org/10.1007/s10984-014-9162-1

Berkowitz, R. (2022). School matters: The contribution of positive school climate to equal educational opportunities among ethnocultural minority students. Youth and Society, 54 (3), 372–296. https://doi.org/10.1177/0044118X20970235

Bohndick, C., Rosman, T., Kohlmeyer, S., et al. (2018). The interplay between subjective abilities and subjective demands and its relationship with academic success: An application of the person–environment fit theory. Higher Education, 75 , 839–854.

Booren, L. M., Handy, D. J., & Power, T. G. (2011). Examining perceptions of school safety strategies, school climate, and violence. Youth Vviolence and Jjuvenile Jjustice, 9 (2), 171–187. https://doi.org/10.1177/1541204010374297

Bronfenbrenner, U. (1979). The ecology of human development: Experiments by nature and design . Harvard University.

Book   Google Scholar  

Caplan, R. D. (1987). Person-environment fit theory and organisations: Commensurate dimensions, time perspectives and mechanisms. Journal of Vocational Behavior, 31 , 248–267.

Caplan, R. D., & Harrison, R. V. (1993). Person-environment fit theory: Some history, recent developments, and future directions. Journal of Social Issues, 49 (4), 253–275.

Cohen, J. (2013). Creating a positive school climate: A foundation for resilience. Handbook of Resilience in Children . https://doi.org/10.1007/978-1-4614-3661-4_24

Cohen, J., & Freiberg, J. A. (2013). School climate and bullying prevention (School Climate Practice Brief) . National School Climate Center.

Google Scholar  

Cohen, J., McCabe, E. M., Michelli, N. M., & Pickeral, T. (2009). School climate: Research, policy, practice, and teacher education. Teachers College Record, 111 (1), 180–213.

Cramer, R. J., & Kapusta, N. D. (2017). A social-ecological framework of theory, assessment, and prevention of suicide. Frontiers in Psychology, 8 , 1756.

Dahm, P., Glomb, T., Manchester, C., & Leroy, S. (2015). Work-family conflict and self-discrepant time allocation at work. Journal of Applied Psychology, 100 , 767–792. https://doi.org/10.1037/a0038542

Daily, S. M., Smith, M. L., Lilly, C. L., Davidov, D. M., Mann, M. J., & Kristjansson, A. L. (2020). Using school climate to improve attendance and grades: Understanding the importance of school satisfaction among middle and high school students. Journal of School Health, 90 (9), 683–693.

Davies, S., & Rizk, J. (2018). The three generations of cultural capital research: A narrative review. Review of Educational Research, 88 (3), 331–365.

Deci, E., & Ryan, R. M. (2000). The “what” and “why” of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11 , 227–268.

Deng, Y., & Yao, X. (2020). Person-environment fit and proactive socialization: Reciprocal relationships in an academic environment. Journal of Vocational Behavior, 120 , 103446. https://doi.org/10.1016/j.jvb.2020.103446

Doumas, D. M., Midgett, A., & Johnston, A. D. (2017). Substance use and bullying victimization among middle and high school students: Is positive school climate a protective factor? Journal of Addictions and Offender Counseling, 38 (1), 2–15. https://doi.org/10.1002/jaoc.12025

Ebbert, A. M., & Luthar, S. S. (2021). Influential domains of school climate fostering resilience in high achieving schools. International Journal of School and Educational Psychology, 9 (4), 305–317.

Eccles, J. S., & Midgley, C. (1989). Stage-environment fit: Developmentally appropriate classrooms for young adolescents. Research on Motivation in Education, 3 , 139–186.

Eccles, J. S., Buchanan, C. M., Flanagan, C., Fuligni, A., Midgley, C., & Yee, D. (1991). Control versus autonomy during early adolescence. Journal of Social Issues, 47 (4), 53–68. https://doi.org/10.1111/j.1540-4560.1991.tb01834.x

Edwards, J. R., Caplan, R. D., & Harrison, R. V. (1998). Person-environment fit theory: Conceptual foundations, empirical evidence, and directions for future research. In C. L. Cooper (Ed.), Theories of organizational stress (pp. 28–67). Oxford University Press.

Chapter   Google Scholar  

Espelage, D. L., & Hong, J. S. (2019). School climate, bullying, and school violence. In M. J. Mayer & S. R. Jimerson (Eds.), School safety and violence prevention: Science, practice, policy (pp. 45–69). American Psychological Association.

Field, A. (2009). Discovering statistics using SPSS . London, UK: Sage Publications.

Fraser, B. J. (2013). Classroom learning environments. In S. K Abell & N. G. Lederman (Eds.), Handbook of research on science education (pp. 103–124). Routledge.

Fraser, B. J. (2019). Milestones in the evolution of the learning environments field over the past three decades. In D. Zandvliet & B. Fraser (Eds.), Thirty years of learning environments. Brill.

Fraser, B. J. (1982). Differences between student and teacher perceptions of actual and preferred classroom learning environment. Educational Evaluation and Policy Analysis, 4 (4), 511–519.

Fraser, B. J., & Fisher, D. L. (1983a). Student achievement as a function of person–environment fit A regression surface analysis. British Journal of Educational Psychology, 53 (1), 89–99. https://doi.org/10.1111/j.2044-8279.1983.tb02538.x

Fraser, B. J., & Fisher, D. L. (1983b). Use of actual and preferred Classroom Environment Scales in person–environment fit research. Journal of Educational Psychology, 75 (2), 303–313. https://doi.org/10.1037/0022-0663.75.2.303

French, J. R. P., Jr., Rodgers, W., & Cobb, S. (1974). Adjustment as person-environment fit. In G. Coelho, D. Hamburg, & J. Adams (Eds.), Coping and Adaptation (pp. 316–333). Basic Books.

García-Carrión, R., Villarejo-Carballido, B., & Villardón-Gallego, L. (2019). Children and adolescents’ mental health: A systematic review of interaction-based interventions in schools and communities. Frontiers in Psychology, 10 , 918.

Gilbreath, B., Kim, T. Y., & Nichols, B. (2011). Person-environment fit and its effects on university students: A response surface methodology study. Research in Higher Education, 52 , 47–62. https://doi.org/10.1007/s11162-010-9182-3

Golden, S. D., McLeroy, K. R., Green, L. W., Earp, J. A. L., & Lieberman, L. D. (2015). Upending the social ecological model to guide health promotion efforts toward policy and environmental change. Health Education & Behavior, 42 (1_suppl), 8S–14S.

González Moreno, A., & Molero Jurado, M. d. M. (2022). The moderating role of family functionality in prosocial behaviour and school climate in adolescence. International Journal of Environmental Research and Public Health, 20 (1), 590. https://doi.org/10.3390/ijerph20010590

Gonzálvez, C., Bacon, V., & Kearney, C. A. (2022). Systematic and evaluative review of school climate instruments for students, teachers, and parents. Psychology in the Schools, 60 (6), 1781–1836. https://doi.org/10.1002/pits.22838

Goodwin, L., Jones, A., & Hunter, B. (2022). Addressing social inequity through improving relational care: A social–ecological model based on the experiences of migrant women and midwives in South Wales. Health Expectations, 25 (5), 2124–2133.

Greguras, G. J., Diefendorff, J. M., Carpenter, J., & Tröster, C. (2014). Person-environment fit and self-determination theory. In M. Gagne (Ed.), The oxford handbook of work engagement, motivation, and self determination theory . New York: NY: Oxford University Press.

Hopson, L. M., & Lee, E. (2011). Mitigating the effect of family poverty on academic and behavioral outcomes: The role of school climate in middle and high school. Children and Youth Services Review, 33 , 2221–2229. https://doi.org/10.1016/j.childyouth.2011.07.006

Hutchings, B. L., & Chaplin, E. (2017). The relationship of person–environment fit to perceptions of autonomy, competency and satisfaction among older adults with developmental disabilities. Journal of Policy and Practice in Intellectual Disabilities, 14 (3), 214–223. https://doi.org/10.1111/jppi.12175

Johnson, B., Stevens, J. J., & Zvoch, K. (2007). Teachers’ perceptions of school climate: A validity study of scores from the revised school level environment questionnaire. Educational and Ppsychological Mmeasurement, 67 (5), 833–844. https://doi.org/10.1177/0013164406299102

Keane, K., & Evans, R. R. (2022). The potential for teacher-student relationships and the whole school, whole community, whole child model to mitigate adverse childhood experiences. Journal of School Health, 92 (5), 504–513. https://doi.org/10.1111/josh.13154

Klein, J., Cornell, D., & Konold, T. (2012). Relationships between bullying, school climate, and student risk behaviors. School Psychology Quarterly, 27 (3), 154–169. https://doi.org/10.1037/a0029350

Kohl, D., Recchia, S., & Steffgen, G. (2013). Measuring school climate: An overview of measurement scales. Educational Research, 55 (4), 411–426. https://doi.org/10.1080/00131881.2013.844944

Kutsyuruba, B., Klinger, D. A., & Hussain, A. (2015). Relationships among school climate, school safety, and student achievement and well-being: A review of the literature. Review of Education, 3 (2), 103–135. https://doi.org/10.1002/rev3.3043

Lee, T., Cornell, D., Gregory, A., & Fan, X. (2011). High suspension schools and dropout rates for black and white students. Education and Treatment of Children , 167–192.

Lester, L., & Cross, D. (2015). The relationship between school climate and mental and emotional wellbeing over the transition from primary to secondary school. Psychology of Well-Being, 5 , 1–15. https://doi.org/10.1186/s13612-015-0037-8

Lewin, K. (1935). A dynamic theory of personality . McGraw-Hill.

Lewin, K. (1936). Principals of topological psychology . McGraw-Hill.

Long, E., Zucca, C., & Sweeting, H. (2021). School climate, peer relationships, and adolescent mental health: A social ecological perspective. Youth & society, 53 (8), 1400–1415.

Low, S., & Van Ryzin, M. (2014). The moderating effects of school climate on bullying prevention efforts. School Psychology Quarterly, 29 (3), 306–319. https://doi.org/10.1037/spq0000073

LuengoKanacri, B. P., Eisenberg, N., Thartori, E., Pastorelli, C., Uribe Tirado, L. M., Gerbino, M., & Caprara, G. V. (2017). Longitudinal relations among positivity, perceived positive school climate, and prosocial behavior in Colombian adolescents. Child Development, 88 (4), 1100–1114.

Magen-Nagar, N., & Steinberger, P. (2017). Characteristics of an innovative learning environment according to students’ perceptions: Actual versus preferred. Learning Environments Research, 20 , 307–323. https://doi.org/10.1007/s10984-017-9232-2

Manzano-Sánchez, D., Gómez-Mármol, A., Valero-Valenzuela, A., & Jiménez-Parra, J. F. (2021). School climate and responsibility as predictors of antisocial and prosocial behaviors and violence: A study towards self-determination theory. Behavioral Sciences, 11 (3), 36. https://doi.org/10.3390/bs11030036

Marchante, M., Coelho, V. A., & Romão, A. M. (2022). The influence of school climate in bullying and victimization behaviors during middle school transition. Contemporary Educational Psychology, 71 , 102–111. https://doi.org/10.1016/j.cedpsych.2022.102111

McLure, F. I., & Aldridge, J. M. (2022). A systematic literature review of barriers and supports: Initiating educational change at the system level. School Leadership & Management . https://doi.org/10.1080/13632434.2022.2113050

Midgley, C., Middleton, M. J., Gheen, M. H., & Kumar, R. (2014). Stage–environment fit revisited: A goal theory approach to examining school transitions. In C. Midgley (Ed.), Goals, goal structures, and patterns of adaptive learning (pp. 109–142). Routledge.

Moos, R. H. (1996). Understanding environments: The key to improving social processes and program outcomes. American Journal of Community Psychology, 24 (1), 193–201.

Moos, R. H. (1979). Evaluating educational environments: Procedures, measures, findings and policy implications . Jossey-Bass.

Moos, R. H. (1987). Peron-environment congruence in work, school, and healthcare settings. Journal of Vocational Behaviour, 31 , 231–247. https://doi.org/10.1016/0001-8791(87)90041-8

Moos, R. H., & Trickett, E. J. (1974). Classroom environment scale manual (1st ed.). Consulting Psychologist Press.

Nagai, M., & Dasari, T. W. (2023). Home blood pressure measurement of hypertensive patients with cognitive impairment—Is it “environmental pressure”? Hypertension Research . https://doi.org/10.1038/s41440-023-01526-1

O’Brennan, L. M., Bradshaw, C. P., & Furlong, M. J. (2014). Influence of classroom and school climate on teacher perceptions of student problem behavior. School Mmental Hhealth, 6 , 125–136. https://doi.org/10.1007/s12310-014-9118-8

Patalay, P., & Fitzsimons, E. (2016). Correlates of mental illness and wellbeing in children: Are they the same? Results from the UK Millennium Cohort Study. Journal of the American Academy of Child & Adolescent Psychiatry, 55 (9), 771–783. https://doi.org/10.1016/j.jaac.2016.05.019

Patrick, H., Anderman, L. H., Ryan, A. M., Edelin, K. C., & Midgley, C. (2001). Teachers’ communication of goal orientations in four fifth-grade classrooms. The Elementary School Journal, 102 (1), 35–58.

Pawlowska, D. K., Westerman, J. W., Bergman, S. M., & Huelsman, T. J. (2014). Student personality, classroom environment, and student outcomes: A person–environment fit analysis. Learning and Individual Differences, 36 , 180–193. https://doi.org/10.1016/j.lindif.2014.10.005

Riekie, H., & Aldridge, J. M. (2017). The role of the school climate in school students’ mental health and identity formation: A South Australian study. British Journal of Educational Research, 43 (1), 95–123. https://doi.org/10.1002/berj.3254

Roberts, B. W. R., & Robins, R. W. (2004). Person–environment fit and its implications for personality development: A longitudinal study. Journal of Personality, 72 (1), 89–110. https://doi.org/10.1111/j.0022-3506.2004.00257.x

Rocconi, L. M., Liu, X., & Pike, G. R. (2020). The impact of person-environment fit on grades, perceived gains, and satisfaction: An application of Holland’s theory. Higher Education, 80 , 857–874. https://doi.org/10.1007/s10734-020-00519-0

Shindler, J., Jones, A., Williams, A. D., Taylor, C., & Cardonas, H. (2016). The school climate-student achievement connection: If we want achievement gains, we need to begin by improving the climate. Journal of School Administration Research and Development, 1 , 9–16. https://doi.org/10.32674/jsard.v1i1.1905

Steffgen, G., Recchia, S., & Viechtbauer, W. (2013). The link between school climate and violence in school: A meta-analytic review. Aggression and Violent Behavior, 18 (2), 300–309. https://doi.org/10.1016/j.avb.2012.12.001

Stewart, D. (1979). A critique of school climate: What it is, how it can be improved and some general recommendations. Journal of Educational Administration, 27 , 148–159.

Tarabini, A., Curran, M., & Fontdevila, C. (2017). Institutional habitus in context: Implementation, development and impacts in two compulsory secondary schools in Barcelona. British Journal of Sociology of Education, 38 (8), 1177–1189. https://doi.org/10.1080/01425692.2016.1251306

Thapa, A., Cohen, J., Guffey, S., & Higgins-D’Alessandro, A. (2013). A review of school climate research. Review of Educational Research, 83 (3), 357–385. https://doi.org/10.3102/0034654313483907

Tomaszewski, W., Xiang, N., & Huang, Y. (2023). School climate, student engagement and academic achievement across school sectors in Australia. Australian Educational Researcher . https://doi.org/10.1007/s13384-023-00618-8

Van Vianen, A. E. M. (2018). Person-environment fit: A review of its basic tenets. Annual Review of Organizational Psychology and Organisational Behaviour, 5 , 75–101. https://doi.org/10.1146/annurev-orgpsych-032117-104702

Varela, J. J., Sánchez, P. A., De Tezanos-Pinto, P., Chuecas, J., & Benavente, M. (2021). School climate, bullying, and mental health among Chilean adolescents. Child Indicators Research, 14 (6), 2249–2264. https://doi.org/10.1007/12187-019-09631-9

Varela, J. J., Sirlopú, D., Melipillán, R., et al. (2019). Exploring the influence of school climate on the relationship between school violence and adolescent subjective well-being. Child Indicators Research, 12 , 2095–2110. https://doi.org/10.1007/s12187-019-09631-9

Wagnild, G. M., & Young, H. M. (1993). Development and psychometric evaluation of the resilience scale. Journal of Nursing Measurement, 1 , 165–178.

Walberg, H. J., & Anderson, G. J. (1968). Classroom climate and individual learning. Journal of Educational Psychology, 59 (6), 414–419. https://doi.org/10.1037/h0026490

Wang, C., Berry, B., & Swearer, S. M. (2013a). The critical role of school climate in effective bullying prevention. Theory into Ppractice, 52 (4), 296–302. https://doi.org/10.1080/00405841.2013.829735

Wang, M., Brinkworth, M., & Eccles, J. (2013b). Moderating effects of teacher-student relationship in adolescent trajectories of emotional and behavioural adjustment. Developmental Psychology, 49 (4), 690–705. https://doi.org/10.1037/a0027916

Wang, M.-T., & Degol, J. L. (2016). School climate: A review of the construct, measurement, and impact on student outcomes. Educational Psychology Review, 28 , 315–152. https://doi.org/10.1007/s10648-015-9319-1

World Health Organisation (WHO). (1998). Wellbeing measures in primary health care/the DepCare Project: Report on a WHO meeting in Stockholm, Sweden 12–13 February 1998. Copenhagen, Denmark: WHO Regional Office for Europe.

Yu, K. Y. T. (2013). A motivational model of person-environment fit: Psychological motives as drivers of change. In A. L. Kristof-Brown & J. Billsberry (Eds.), Organizational fit: Key issues and new directions. Wiley-Blackwell.

Yu, K. Y. T., & Davis, H. M. (2016). Autonomy’s impact on newcomer proactive behaviour and socialization: A needs-supplies fit perspective. Journal of Occupational Organisation Psychology, 89 , 172–97. https://doi.org/10.1111/joop.12116

Download references

Open Access funding enabled and organized by CAUL and its Member Institutions.

Author information

Authors and affiliations.

School of Education, Curtin University, GPO Box U1987, Perth, 6845, Australia

Jill M. Aldridge

National School Improvement Partnerships, Perth, WA, Australia

Meghan J. Blackstock

Faculty of Arts & Society, Charles Darwin University, Alice Springs, Australia

Felicity I. McLure

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Jill M. Aldridge .

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Aldridge, J.M., Blackstock, M.J. & McLure, F.I. School climate: Using a person–environment fit perspective to inform school improvement. Learning Environ Res (2024). https://doi.org/10.1007/s10984-023-09490-w

Download citation

Received : 11 September 2023

Accepted : 07 December 2023

Published : 19 May 2024

DOI : https://doi.org/10.1007/s10984-023-09490-w

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Data-informed decision-making
• Learning environment
• Perception data
• School climate
• School improvement
• Find a journal
• Publish with us
• Track your research

• Open access
• Published: 17 May 2024

Behavioral skills training for teaching safety skills to mental health service providers compared to training-as-usual: a pragmatic randomized control trial

• Elizabeth Lin 1 ,
• Mais Malhas 1 ,
• Emmanuel Bratsalis 1 ,
• Kendra Thomson 1 , 2 ,
• Fabienne Hargreaves 1 ,
• Kayle Donner 1 ,
• Heba Baig 1 ,
• Rhonda Boateng 1 ,
• Rajlaxmi Swain 1 ,
• Mary Benisha Benadict 1 &
• Louis Busch 1  

BMC Health Services Research volume  24 , Article number:  639 ( 2024 ) Cite this article

63 Accesses

Metrics details

Violence in the healthcare workplace has been a global concern for over two decades, with a high prevalence of violence towards healthcare workers reported. Workplace violence has become a healthcare quality indicator and embedded in quality improvement initiatives of many healthcare organizations. The Centre for Addiction and Mental Health (CAMH), Canada’s largest mental health hospital, provides all clinical staff with mandated staff safety training for self-protection and team-control skills. These skills are to be used as a last resort when a patient is at imminent risk of harm to self or others. The purpose of this study is to compare the effectiveness of two training methods of this mandated staff safety training for workplace violence in a large psychiatric hospital setting.

Using a pragmatic randomized control trial design, this study compares two approaches to teaching safety skills CAMH’s training-as-usual (TAU) using the 3D approach (description, demonstration and doing) and behavioural skills training (BST), from the field of applied behaviour analysis, using instruction, modeling, practice and feedback loop. Staff were assessed on three outcome measures (competency, mastery and confidence), across three time points: before training (baseline), immediately after training (post-training) and one month later (follow-up). This study was registered with the ISRCTN registry on 06/09/2023 (ISRCTN18133140).

With a sample size of 99 new staff, results indicate that BST was significantly better than TAU in improving observed performance of self-protection and team-control skills. Both methods were associated with improved skills and confidence. However, there was a decrease in skill performance levels at the one-month follow-up for both methods, with BST remaining higher than TAU scores across all three time points. The impact of training improved staff confidence in both training methods and remained high across all three time points.

Conclusions

The study findings suggest that BST is more effective than TAU in improving safety skills among healthcare workers. However, the retention of skills over time remains a concern, and therefore a single training session without on-the-job-feedback or booster sessions based on objective assessments of skill may not be sufficient. Further research is needed to confirm and expand upon these findings in different settings.

Peer Review reports

Introduction

Violence in the healthcare workplace has been a global concern for over two decades. In 2002, a joint task force of the International Labour Office (ILO), World Health Organization, Public Services International, and the International Council of Nurses created an initiative to address this issue [ 1 ]. One result was the documentation of a high international prevalence of violence towards healthcare workers showing that as many as half or more experienced physical or psychological violence in the previous year [ 2 , 3 ]. Since then, workplace violence has become a healthcare quality indicator and been embedded in the quality improvement initiatives of many healthcare organizations (for example, Health Quality Ontario [ 4 ]). Conceptually, it is also reflected in the expansion of the Triple Aim framework to the Quintuple Aim to include staff work-life experience [ 5 ].

Despite these efforts, the high prevalence of workplace violence in healthcare persists [ 6 ]. Two meta-analyses, representing 393,344 healthcare workers, found a 19.3% pooled prevalence of workplace violence in the past year among which 24.4% and 42.5% reported physical and psychological violence experiences, respectively [ 7 , 8 ]. The literature also highlighted that workers in mental health settings were at particular risk [ 8 , 9 ]. A systematic review of violence in U.S. psychiatric hospitals found between 25 to 85 percent of staff encountering physical aggression in the past year [ 10 ]. Partial explanations for this wide range include methodological, population, and setting differences. For example, Gerberich and colleagues [ 11 ] surveyed nearly 4,000 Minnesota nurses and found 13 percent reporting physical assault and 38 percent reporting verbal or other non-physical violence in the previous year. Further analyses showed that nurses on psychiatric or behavioral units were twice as likely as those on medical/surgical units to experience physical violence and nearly three times as likely to experience non-physical violence. Ridenour, et al., [ 12 ] in a hospital-record study of acute locked psychiatric wards in U.S. Veteran’s Hospitals found that 85 percent of nurses had experienced aggression in a 30-day period (85 percent verbal; 81 percent physical). And, in a prospective study of a Canadian psychiatric hospital, Cooper and Mendonca [ 13 ] found over 200 physical assaults on nurses within 27 months. While they do not indicate what percentage of nurses were assaulted, their results are consistent with a frequency of between 1 and 2 assaults per week.

Workplace violence has been associated with negative psychological, physical, emotional, financial, and social consequences which impact staff’s ability to provide care and function at work [ 14 , 15 , 16 ]. A 7-year, population-based, follow-up study in Denmark highlighted the long-term impact of physical and psychological health issues owing to physical workplace violence [ 17 ]. Two studies, one in Italy [ 18 ] and one in Pakistan [ 19 ], have linked workplace violence to demoralization and declining quality of healthcare delivery and job satisfaction among healthcare workers.

Building on these efforts, the ILO published a 2020 report recommending the need for national and organizational work environment policies and workplace training “…on the identified hazards and risks of violence and harassment and the associated prevention and protection measures….” ([ 20 ], p. 55). Consequently, many countries [ 21 , 22 , 23 ] have committed to creating a safe work environment. In Ontario, Canada, the government has provided guidelines for preventing workplace violence in healthcare [ 4 , 24 ], and our institution, the Centre for Addiction and Mental Health, launched a major initiative in 2018 to address the physical and psychological safety of patients and staff [ 25 ]. A priority component of this initiative is mandatory training for all new clinical staff on trauma-informed crisis prevention, de-escalation skills, and, in particular, safe physical intervention skills [ 26 , 27 ].

However, the effects of such training, especially for managing aggressive behaviour, are only partially understood. A 2015 systematic review on training for mental health staff [ 28 ] and a more recent Cochrane review on training for healthcare staff [ 29 ] reported remarkably similar findings. Both noted the inconsistent evidence (due to methodological issues, small numbers of studies, heterogenous results) which made definitive conclusions about the merits and efficacy of training difficult. The more consistent impacts found by Price and colleagues [ 28 ] were improved knowledge and staff confidence in their ability to manage aggression. There was some evidence of improved de-escalation skills including the ability to deal with physical aggression [ 30 , 31 ] and verbal abuse [ 32 ]. However, these studies were limited because they used unvalidated scales or simulated, rather than real-world, scenarios. For outcomes such as assault rates, injuries, the incidence of aggressive events, and the use of physical restraints, the findings were mixed or difficult to generalize due to the inconsistent evidence.

Similarly, Geoffrion and colleagues [ 29 ] found some positive effect of skills-training on knowledge and attitudes, at least short-term, but noted that support for longer-term effects was less sure. The evidence for impacts on skills or the incidence of aggressive behaviour was even more uncertain. They also noted that the literature was limited because it focused largely on nurses. They concluded, “education combined with training may not have an effect on workplace aggression directed toward healthcare workers, even though education and training may increase personal knowledge and positive attitudes” ([ 29 ], p. 2). Among their recommendations were the need to evaluate training in higher-risk settings such as mental healthcare, include other healthcare professionals who also have direct patient contact in addition to nurses, and use more robust study designs. In addition, the literature evaluating training procedures focussed on self-reported rather than objective measures of performance.

Given the concerns with demonstrating effectiveness, the violence prevention literature has tended to focus on training modalities and immediate post-training assessment rather than on skill retention over time. In a systematic review of prevention interventions in the emergency room, Wirth et al. [ 21 ] found only five out of 15 included studies that noted any kind of evaluation in the period after training (generally two to nine months post-training) while Geoffrion, et al. [ 29 ] identified only two among the nine studies in their meta-analysis that had follow-up skills assessments. However, for both of these reviews, the studies doing follow-up evaluations focused on subjective, self-reported outcomes (empathy, confidence, self-reported knowledge) with no objective behavioral skills measures. Both Wirth et al. [ 21 ] and Leach et al. [ 33 ] cite studies noting a loss of effectiveness of prevention skills (between three to six months post-training), but specific percentages of retention were not provided.

The present study sought to address these gaps by comparing two approaches to teaching safety skills for managing aggressive patient/client behaviour. The setting was a large psychiatric teaching hospital; the sample was drawn from all new clinical staff attending their mandated on-boarding training; and we used a pragmatic randomized control trial design. In addition, we added a 1-month post-training assessment to evaluate skill retention. Our control intervention was the current training-as-usual (TAU) in which trainers “describe” and “demonstrate”, and trainees “do” by practicing the demonstrated skill but without objective checklist-guided performance assessment by the trainer. Our test intervention was behavioural skills training (BST) [ 34 , 35 ] drawn from the field of applied behaviour analysis [ 36 ]. BST is a performance- and competency-based training model that uses an instructional, modeling, practice, and feedback loop to teach targeted skills to a predetermined performance level. Checklists guide the instructional sequence and the determination of whether or not the predetermined performance threshold has been reached. Considerable evidence indicates that BST can yield significant improvement in skills post-training, over time, and across different settings [ 37 , 38 , 39 ]. It has been used to train a wide range of participants, including behavior analysts, parents, and educators, to build safety-related skills and manage aggressive behavior [ 37 , 40 , 41 ].

As previously described [ 42 ], our objective was to compare the effectiveness of TAU against BST. Our hypotheses, stated in null form, were that these methods would not differ significantly in:

Observer assessment of self-protection and team-control physical skills.

Self-assessed confidence in using those skills.

Study participants were recruited from all newly-hired clinical staff attending a mandatory two-week orientation. Staff were required to register beforehand for a half-day, in-person, physical safety skills session. They were randomized to a session at the time of registration, and the sessions were then randomized to TAU or BST. All randomization was performed by RB using GraphPad software [ 43 ].

The physical skills training was scheduled for a 3.5 h session on one day of the mandatory onboarding. At the end of the previous day, attendees were introduced to the study (including the fact that it was a randomized study) and asked for consent to email them a copy of the informed consent. On the morning of the physical skills training, a research team member met with attendees to answer questions and then meet privately with each individual to ascertain if they wished to participate and sign the informed consent. The trainers and session attendees were thus unaware of who was or was not in the study. Recruitment began January 2021, after ethics approval, and continued until September 2021 when the target of at least 40 study participants completing all assessments for each training condition was reached. The target sample size was chosen to allow 80-percent power to detect a medium to large effect size [ 44 ].

Both methods taught the same 11 target skills for safely responding to patients/clients that may exhibit harm to self or others (e.g., aggressive behaviour) during their hospital admission. These skills, defined by the hospital as mandatory for all newly hired staff, included six self-protection and five team-control (physical restraint) skills (see Appendix A ). Each target skill had defined components and a specific sequence in which they were taught as outlined on performance checklists (see Appendix B for a checklist example).

The two methods differed in how these sequences were administered. For BST, the trainers used the performance checklists to guide the training sequence (instruction, modeling, rehearsal, and feedback) and to indicate when the trainee was ready to move on to the next skill [ 34 ] (see Appendix C for BST sequence). In BST, common practice is to define successful performance criteria a priori (e.g., up to three correct, consecutive executions at 100% [ 45 ]). However, because the physical skills training session in our study had to be completed within the scheduled 3.5 h, the criterion was lowered for practical reasons to one correct performance (defined as 80% of the components comprising that skill) with the added goal of aiming for up to 5 times in a row if time allowed before moving on to the next skill. In contrast, while TAU included elements of modeling, practice, and feedback, it did not systematically assess skill acquisition nor impose any specific level of success before proceeding to the next skill.

There were three outcome measures, two observer-based assessments of skill acquisition (competence and mastery) and one self-reported confidence measure. Competence was defined as the percentage of components comprising an individual skill that were correctly executed (e.g., if a skill had 10 components and only six were executed properly, the competence score for that skill would be 60%). Mastery was the threshold defining when a competence score was felt to indicate successful achievement of a skill and to indicate some degree of the durability of the skill acquisition [ 46 ]. For our study, we expanded mastery to apply to the two categories of self-protection and team-control (rather than to each individual skill) using the average competence scores for the skills within each category. Mastery was pre-defined as 80-percent, a commonly used threshold [ 28 , 47 ].

The outcome measures were assessed at three time points: immediately before training (baseline), immediately after training (post-training), and one month later (follow-up). The hospital provided limited descriptive information (professional role, department) for all registrants for administrative purposes but for confidentiality reasons did not provide personal information such as age or gender/sex. The research team elected not to collect personal information for two reasons. First, the primary study concern was to evaluate the main effect of training method rather than developing predictive models, and the expected result of the randomization process was that potential covariates would not be systematically biased in the two study groups. Second, we would not be able to use this information to compare participants with non-participants to identify biases in who consented to be in the study. We were able to compare them on department role and profession by subtracting the aggregated study-participant information from the aggregated hospital-provided information – the only form of the hospital-provided information available to the research team (see Table 1  below). In addition, since degree of patient contact was an important factor in the likelihood of needing to exercise safety skills, the research team also created an algorithm estimating which combinations of professional role and department were likely to have direct, less direct, or rare/low patient contact.

Participants were also asked at baseline and follow-up how many events they encountered in the previous month that required the use of these skills. This information was collected because of our interest in testing a post-hoc hypothesis that those with actual experience would score higher than those who did not.

All assessments were carried out following a standardized protocol. To ensure that registrants remained blinded to which colleagues were in the study, each registrant’s skill acquisition was assessed privately by a research team member at baseline and post-training using the performance checklists. Only assessments for those consenting to participate were videotaped. Study participants were then asked to return one month later for a follow-up assessment which was also videotaped. For the purposes of post-hoc analyses, participants completing all three assessments were defined as ‘completers’ while those completing baseline and post-training assessments but not the one-month follow-up were ‘non-completers.’

The same performance checklists used by the BST trainers were then used by trained observers blinded to the participant’s training method to assess the videotapes. As described previously [ 42 ], interobserver agreement (IOA) was routinely evaluated throughout the study with the final value being 96% across the 33% of the performance assessment videos scored for the IOA calculation.

Skill acquisition outcomes were calculated using the checklist-based observer assessments of the videotapes. The percentage of correctly executed components for each target skill was established. Then, these percentages were averaged across the six self-protection target skills and across the five team-control target skills to create competence scores. Finally, the predefined threshold of 80% was applied to the competence scores to determine which participants met the mastery threshold [ 47 , 48 ].

Self-reported confidence was assessed on a 10-point Likert scale (‘not at all’ to ‘extremely’ confident) using a version of our institution’s standard assessment questions adapted for this study (See Appendix D ).

Statistical analysis

R software was used to generate descriptive statistics (frequencies, percentages) and test our hypotheses [ 49 ]. Generalized linear mixed models (GLMM) were used to test nested main and interaction effects using likelihood-ratio chi-square statistics for the post-training and follow-up results as there were no baseline differences. GLMM was also used to evaluate BST-TAU differences at the three study time points [ 50 , 51 ]. For the BST-TAU comparisons, we used Cohen’s d as a guide for evaluating the practical significance of the differences for the continuous measures (competence, confidence). We used Cohen’s suggested thresholds [ 52 ] of 0.2, 0.5, and 0.8 for small, medium, and large effect sizes conservatively by applying them to both the point estimates and 95% confidence intervals. Thus, for example, a Cohen’s d where the confidence interval went below 0.2 would be interpreted as non-meaningful. For the categorical measure of mastery, we used BST-TAU risk ratios. Confidence intervals for all effect size measures were obtained using bootstrapping. Independent-samples t -tests were used for the post-hoc analyses and, along with chi-square tests, to compare the completers and non-completers.

One hundred ninety-nine staff consented to participate in the study out of a total of 360 session attendees (55%). Of these, 108 (54%) had been randomly assigned to a BST session and 91 (46%) to a TAU session. Half ( n  = 99) completed assessments at all three time points (44% TAU; 55% BST). These 99 (hereafter ‘study completers’) constituted 28 percent of all session attendees.

Among the non-completers, 53 had been assigned to BST and 47 to TAU. Eight were classified as incomplete because of technical software issues when video-recording one of their assessments and one (the first participant) because the IOA process prompted substantive changes to the assessment checklist. The primary reason for the remaining non-completers was missing the follow-up assessment (91 individuals: 50/53 BST, 41/47 TAU) largely due to difficulties scheduling a non-mandatory event during the pandemic (e.g., units restricting staff from leaving because of clinical staff shortages or patient outbreaks, staff illness).

Descriptive information for the expected degree of patient contact and for hospital department is shown in Table  1 for study participants (completers, non-completers), non-participants, and the total group of session attendees. No significant differences were found when comparing participants versus non-participants or study completers versus non-completers in terms of expected patient contact ( χ 2 (2) = 0.36, n.s.; χ 2 (2) = 2.22, n.s.; respectively) or department type ( χ 2 (3) = 4.40; ( χ 2 (3) = 1.00, n.s.; respectively).

Figure  1 depicts the self-protection and team-control competence scores for the study completers (left and right sides, respectively). The hypothesis-testing results showed a significant difference by training Method (self-protection: χ 2 (1) = 34.46, p  < 0.001; team-control: χ 2 (1) = 50.42, p  < 0.001). There was also a significant decline between post-training and follow-up (Time) for both skill categories independent of Method (self-protection: χ 2 (1) = 81.29, p  < 0.001; team-control: χ 2 (1) = 56.51, p  < 0.001), and a significant Method-by-Time interaction independent of Method and Time for team-control skills ( χ 2 (1) = 17.41, p  < 0.001). BST-TAU comparisons showed no difference at baseline for either type of skill (not shown). However, BST was significantly better than TAU at both post-training (self-protection: Cohen’s d  = 1.45 [1.02, 1.87], large effect size; team-control: Cohen’s d  = 2.55 [2.08, 3.02]; large effect size) and follow-up (respectively – Cohen’s d  = 0.82 [0.40, 1.23]; Cohen’s d  = 0.62 [0.21, 1.03], both small effect sizes). For both methods, competence scores dropped between post-training and follow-up although not to the original baseline levels.

Observer-rated self-protection and team-control competence skills in TAU and BST across time-points

The skill mastery results for the study completers are shown in Fig.  2 . The mastery patterns paralleled the competence patterns in that BST was significantly better than TAU (self protection: χ 2 (1) = 28.82, p  < 0.001; team-control: χ 2 (1) = 72.87, p  < 0.001). There was also a significant Time effect independent of Method (self-protection: χ 2 (1) = 27.54, p  < 0.001; team-control: χ 2 (1) = 33.03, p  < 0.001). There were no significant interactions for either type of skill once the effects of Method and Time were accounted for. BST-TAU comparisons showed no difference in percent achieving Mastery at baseline (not shown) but large risk ratios at both post-training (self-protection: 13.43 [4.01, > 1000]; team-control: 31.24 [8.45, > 1000] and follow-up [self-protection: 12.30 [1.58, > 1000]; team-control: 30.60 [6.75. > 1000]).

Observer-rated self-protection and team-control mastery (Predefined as 80% or better competence) by TAU and BST across time-points

Confidence scores for the study completers are shown in Fig.  3 . The only significant main effect was for Time (self-protection: χ 2 (1) = 36.87, p  < 0.001; team-control: χ 2 (1) = 21.08, p  < 0.001). For both skill categories, the scores increased between baseline and post-training and then dropped at follow-up but not to the original baseline levels.

Self-rated self-protection and team-control confidence in TAU and BST across time-points

To assess what impact the high no-show rate for the one-month follow-up could have had, we compared the completers and the non-completers on the six post-training outcomes (competence, mastery, and confidence for self-protection and for team-control). Non-completers had slightly lower scores than completers except for the two confidence measures where their self-assessments were higher (not shown). However, the only significant difference between the two groups was for self-protection competence means (0.70 vs 0.63, completers vs non- completers, t (195) = 2.40, p  = 0.017).

In terms of past-month experience, few study completers reported events requiring self-protection (19 at baseline, 9 at follow-up) or team-control skills (14 at baseline, 14 at follow-up). Consequently, we only examined the presence or absence of experience without breaking it down by training method. We found non-significant results for both competence and mastery (not shown) but a potential impact on confidence for self-protection skills at follow-up and for team-control skills at baseline and post-training (Fig.  4 ).

Self-rated self-protection and team-control confidence by occasion to use skills in the past month across time-points

4. Summary and discussion.

Our strongest finding was that BST was significantly better than TAU in improving the observed performance of self-protection and team-control skills. While follow-up scores decreased for both methods, BST scores remained higher than TAU scores. The impact of training on staff confidence differs from these patterns in that confidence scores improved noticeably at post-training and remained relatively high at follow-up. Further, our post-hoc analyses suggested that recent experience using safety skills might have a greater impact on confidence than on observed skill performance. We also found that training, regardless of method, was independently associated with improved observer-scored skills and self-reported confidence.

The better performance of BST is consistent with the fact that it incorporates training elements that are supported both by current educational and learning theories and evidence of effectiveness [ 46 , 53 , 54 , 55 ]. While both BST and TAU can be considered ‘outcomes based’ [ 54 ], the key difference is the BST’s use of the checklist. Based directly on the desired behavioral outcomes, this tool simultaneously creates a common understanding because it is shared with the trainees, ensures consistent and systematic training across all BST trainees, pinpoints where immediate and personalized feedback is needed to either correct or reinforce performance, and tracks the number of correct repetitions required to meet mastery criteria as well as support retention [ 46 , 56 , 57 ]. By contrast, TAU does not use a checklist and the kind and amount of feedback or practice repetitions is left to the trainer’s discretion.

However, there are at least two questions regarding whether BST produced the expected results. The BST framework requires continued rehearsal and feedback until a specified performance criterion is reached [ 34 ]. However, our mandatory safety training had practical, unmodifiable constraints. The institution required the safety-training sessions be completed in 3.5 h which meant that BST trainers were limited in their ability to use the more stringent performance criteria described in the literature. For example, it was not practical to set the performance criterion at higher than 80 percent. In addition, all BST completers were able to demonstrate 80-percent correct performance for each skill at least once, but not all were able to demonstrate five consecutive, correct executions within the allotted time. If the requirement of five in a row at 80% or higher had been implemented, then the post-training scores (and potentially the 1-month follow-up scores) for the BST completers could have been higher.

A second question is what level of skill retention should be expected at follow-up. The BST scores at one-month follow-up constituted 66% and 73% of the competence scores at post-training (self-protection and team-control, respectively) and 30% and 41% of the mastery percentages at post-training (self-protection and team-control, respectively). Although BST and elements of performance feedback models have been found to be effective in staff training with successful retention over time [ 58 , 59 , 60 , 61 , 62 ], finding appropriate comparators for our study was challenging because there are no studies where BST has been used for training such a large and diverse group of staff. Further, as noted above, the body of workplace violence prevention literature has not consistently focussed on retention. However, the broader training and education literature does suggest that our results are consistent with or somewhat lower than those from other studies. Offiah et al. [ 63 ] found that 45 percent of medical students retained the full set of clinical skills 18 months after completing simulation training, and Bruno and colleagues [ 64 ] found published retention rates ranging between 75 and 85 percent across time periods between four to 24 months and across diverse disciplinary fields. Regardless of the comparators, the loss in skill performance after one-month post-training is a concern.

Our interpretation is that reliance on a single session, even with highly structured and competency-based methods, is not adequate particularly in the context of managing distressing events. Efforts should be made to allow for flexibility with respect to setting higher thresholds for success despite organizational restraints for staff training. Furthermore, settings that require these skills to be performed more reliably for both patient and staff safety (e.g., emergency departments, acute care settings, security services) should consider on-the-job feedback or booster sessions based on objective assessments of skill rather than on pre-set amounts of time (e.g. annual refresher). This would be more consistent with the BST literature, as on-the-job training should occur based on an evidence-based approach.

Our finding of a differential impact of training on confidence versus demonstrable skills is consistent with a long-standing, substantial body of research examining the relationship between self-assessment and objective measures of learning [ 28 , 65 , 66 ]. The pattern of non-existent, weak, or even inverse relationships between the two has been shown for a variety of medical staff trainee and education learner groups [ 28 , 29 , 67 , 68 , 69 , 70 , 71 , 72 ]. Consequently, many researchers recommend either not using self-assessments at all or at least ensuring that objective measures are also collected (e.g.,[ 64 , 65 ]).

The literature does offer some hypotheses for why this discrepancy occurs and, further, why self-assessment continues to be used in medical education and training despite the robust evidence that it does not accurately reflect learning. Katowa-Mukwato and Banda [ 70 ] in a study of Zambian medical students suggest that fear of revealing their weaknesses led to a negative correlation between self- and objective-ratings. Persky, et al. [ 69 ] reference the theory of ‘metacognition’ – defined as ‘thinking about thinking’ (p. 993, [ 69 ] – and the ‘Dunning-Kruger’ effect that the ability to recognize competence (i.e., accurate metacognition) is unevenly distributed. There is also discussion as to why these measures continue to be used and suggestions of how best to use them. Yates et al. [ 65 ] suggest that ease of collecting this information is a factor. More complex and nuanced explanations are offered by Lu, et al. [ 66 ] and Tavares, et al. [ 73 ] who note that self-assessment is an important component in theories of learning and evaluation and that self-perception and self-reflection (particularly when objective findings are shared) are critical ingredients for supporting medical and continuing profession education in a self-regulating profession.

Because the goal of our study was to assess the effectiveness of two training methods, we did not collect information or have the opportunity to explore any of these potential reasons for why self-reported and objective measures are discrepant or to evaluate the best use of that discrepancy. The modest contributions that our study adds are that selecting a higher-risk setting, including non-nursing healthcare professionals, using a more rigorous study design (as recommended by Geoffrion, et al. [ 29 ]), and attempting to account for recent experience do not appear to alter this pattern.

The major strength of our study is its design. Currently, we have identified only one other study evaluating the impact of BST training for clinical staff using a randomized control trial design [ 41 ]. Other strengths are our inclusion of a large percentage of non-nursing, direct-care staff, our use of both self-reported and observer-assessed outcome measures, and our findings regarding retention. These strengths allow us to add to the evidence base already established in the literature.

However, interpretation of our results should consider several limitations. Conducting a research study on full-time clinical staff during a pandemic meant that a high percentage of those consenting to be in the study did not complete their 1-month follow-up assessment. The reported reasons for missing the third assessment (unit restrictions or short staffing because of the pandemic) are consistent with the demographic differences between completers and non-completers in that they were more likely to be nurses or working on inpatient units. Our comparison of the post-training scores of the completers and non-completers suggested that the no-shows had slightly lower post-training observed skill performance (but slightly better confidence ratings). If we had managed to assess the non-completers at follow-up, our reported findings may have been diluted although it is unlikely that this would have completely negated the large effect sizes.

The time constraints on the mandatory training meant that we were unable to fully apply either the BST mastery criteria commonly reported in the literature (i.e., three correct, consecutive executions [ 28 , 47 ] or the one we would have preferred (i.e., five correct executions). While this type of limitation is consistent with the pragmatic nature of our design, it likely had an impact on our findings in terms of potentially lowering the post-training BST competency and mastery scores and, perhaps more importantly, contributing to the lower retention rates at 1-month follow-up [ 56 ].

The 45-percent refusal rate by the training registrants is another concerning issue. Anecdotal reports from the training team were that the response rate was very low at the start of the study because many of the new hires were nervous about being videotaped (a specific comment reported was that it reminded some of the new graduates of ‘nursing school.’) and were unsure of the purpose of the study. The team then changed to a more informal, conversational introduction describing the need for the study as well as reassuring attendees that it was the training, not the participants, that was being evaluated. The team’s impression was that this improved the participation rate. The participants and non-participants were not statistically different in terms of their expected patient contact and department role. However, we cannot preclude that there may have been systematic biases for other unmeasured characteristics.

Another limitation, as identified by Price, et al. [ 28 ], is that we used artificial training scenarios, though this may be unavoidable given the low frequency of aggressive events and the ethics of deliberately exposing staff to these events. Also, we only measured the skills directly related to handling client/patient events. We were not able to access information on event frequency or severity, staff distress and complaints, or institutional-level measures such as lost workdays due to sick leave, staff turnover, or expenditures [ 29 , 33 ]. A further gap, which is important but difficult to assess, is whether there is any impact of staff safety training on the clients or patients who are involved.

Given these strengths and limitations, we see our study as adding one piece of evidence that needs to be a) confirmed or disconfirmed by other researchers in both the same and different settings and b) understood as part of a complex mix of ingredients. Specific areas for further research arising directly out of our findings include evaluating whether less constrained training time would improve attainment of skill mastery, exploration and evaluation of methods to increase skill retention over time, and, most importantly but also more difficult to assess, the impact on patients and clients of staff safety skills training. More evidence on these fronts will hopefully contribute to maintaining and improving workplace safety.

Availability of data and materials

The dataset generated and analysed during the current study is not publicly available due to the fact that it is part of a larger internal administrative data collection but is available from the corresponding author on reasonable request.

International Labour Organization. Joint Programme Launches New Initiative Against Workplace Violence in the Health Sector. 2002. Available from: https://www.ilo.org/global/about-the-ilo/newsroom/news/WCMS_007817/lang--en/index.htm . Cited 2022 Aug 5.

Di Martino V. Workplace violence in the health sector. Country case studies Brazil, Bulgaria, Lebanon, Portugal, South Africa, Thailand and an Additional Australian Study. Geneva: ILO/ICN/WHO/PSI Joint Programme on Workplace Violence in the Health Sector; 2002.

Needham I, Kingma M, O’Brien-Pallas L, McKenna K, Tucker R, Oud N, editors. Workplace Violence in the Health Sector. In: Proceedings of the First International Conference on Workplace Violence in the Health Sector - Together, Creating a Safe Work Environment. The Netherlands: Kavanah; 2008. p. 383.

Health Quality Ontario. Quality Improvement Plan Guidance: Workplace Violence Prevention. 2019.

Google Scholar  

Bodenheimer T, Sinsky C. From triple to quadruple aim: care of the patient requires care of the provider. Ann Fam Med. 2014;12(6):573–6.

Article   PubMed   PubMed Central   Google Scholar  

Somani R, Muntaner C, Hillan E, Velonis AJ, Smith P. A Systematic review: effectiveness of interventions to de-escalate workplace violence against nurses in healthcare settings. Saf Health Work. 2021;12(3):289–95. https://doi.org/10.1016/j.shaw.2021.04.004 .

Li Y, Li RQ, Qiu D, Xiao SY. Prevalence of workplace physical violence against health care professionals by patients and visitors: a systematic review and meta-analysis. Int J Environ Res Public Health. 2020;17(1):299.

Liu J, Gan Y, Jiang H, Li L, Dwyer R, Lu K, et al. Prevalence of workplace violence against healthcare workers: a systematic review and meta-analysis. Occup Environ Med. 2019;76(12):927–37.

Article   PubMed   Google Scholar  

O’Rourke M, Wrigley C, Hammond S. Violence within mental health services: how to enhance risk management. Risk Manag Healthc Policy. 2018;11:159–67.

Odes R, Chapman S, Harrison R, Ackerman S, Hong OS. Frequency of violence towards healthcare workers in the United States’ inpatient psychiatric hospitals: a systematic review of literature. Int J Ment Health Nurs. 2021;30(1):27–46.

Gerberich SG, Church TR, McGovern PM, Hansen HE, Nachreiner NM, Geisser MS, et al. An epidemiological study of the magnitude and consequences of work related violence: the minnesota nurses’ study. Occup Environ Med. 2004;61(6):495–503.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Ridenour M, Lanza M, Hendricks S, Hartley D, Rierdan J, Zeiss R, et al. Incidence and risk factors of workplace violence on psychiatric staff. Work. 2015;51(1):19–28.

Cooper AJ, Mendonca JD. A prospective study of patient assaults on nurses in a provincial psychiatric hospital in Canada. Acta Psychiatr Scand. 1991;84(2):163–6.

Article   CAS   PubMed   Google Scholar  

Hiebert BJ, Care WD, Udod SA, Waddell CM. Psychiatric nurses’ lived experiences of workplace violence in acute care psychiatric Units in Western Canada. Issues Ment Health Nurs. 2022;43(2):146–53.

Lim M, Jeffree M, Saupin S, Giloi N, Lukman K. Workplace violence in healthcare settings: the risk factors, implications and collaborative preventive measures. Ann Med Surg. 2022;78:103727.

Article   Google Scholar  

Lanctôt N, Guay S. The aftermath of workplace violence among healthcare workers: a systematic literature review of the consequences. Aggress Violent Behav. 2014;19(5):492–501.

Friis K, Pihl-Thingvad J, Larsen FB, Christiansen J, Lasgaard M. Long-term adverse health outcomes of physical workplace violence: a 7-year population-based follow-up study. Eur J Work Organ Psychol. 2019;28(1):101–9.

Berlanda S, Pedrazza M, Fraizzoli M, de Cordova F. Addressing risks of violence against healthcare staff in emergency departments: the effects of job satisfaction and attachment style. Biomed Res Int. 2019;28(5430870):1–12.

Baig LA, Ali SK, Shaikh S, Polkowski MM. Multiple dimensions of violence against healthcare providers in Karachi: results from a multicenter study from Karachi. J Pakistani Med Assoc. 2018;68(8):1157–65.

International Labour Organization. Safe and healthy working environments free from violence and harassment. International Labour Organization. Geneva: International Labour Organization; 2020. Available from: https://www.ilo.org/global/topics/safety-and-health-at-work/resources-library/publications/WCMS_751832/lang--en/index.htm .

Wirth T, Peters C, Nienhaus A, Schablon A. Interventions for workplace violence prevention in emergency departments: a systematic review. Int J Environ Res Public Health. 2021;18(16):8459.

U.S. Congress. Workplace Violence Prevention for Health Care and Social Service Workers Act. U.S.A.; 2020. Available from: https://www.congress.gov/bill/116th-congress/house-bill/1309 .

Lu A, Ren S, Xu Y, Lai J, Hu J, Lu J, et al. China legislates against violence to medical workers. TneLancet Psychiatry. 2020;7(3):E9.

PubMed   Google Scholar  

Government of Ontario Ministry of Labour T and SD. Preventing workplace violence in the health care sector | ontario.ca. 2020. Available from: https://www.ontario.ca/page/preventing-workplace-violence-health-care-sector . Cited 2020 Jul 28.

Safe and Well Committee. Safe & Well Newsletter. 2018. Available from: http://insite.camh.net/files/SafeWell_Newsletter_October2018_107833.pdf .

Patel MX, Sethi FN, Barnes TR, Dix R, Dratcu L, Fox B, et al. Joint BAP NAPICU evidence-based consensus guidelines for the clinical management of acute disturbance: de-escalation and rapid tranquillisation. J Psychiatr Intensive Care. 2018;14(2):89–132.

Heckemann B, Zeller A, Hahn S, Dassen T, Schols JMGA, Halfens RJG. The effect of aggression management training programmes for nursing staff and students working in an acute hospital setting. A narrative review of current literature. Nurse Educ Today. 2015;35(1):212–9.

Price O, Baker J, Bee P, Lovell K. Learning and performance outcomes of mental health staff training in de-escalation techniques for the management of violence and aggression. Br J Psychiatry. 2015;206:447–55.

Geoffrion S, Hills DJ, Ross HM, Pich J, Hill AT, Dalsbø TK, et al. Education and training for preventing and minimizing workplace aggression directed toward healthcare workers. Cochrane Database Syst Rev. 2020;9(Art. No.: CD011860). Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8094156/ . Cited 2021 Dec 3.

Paterson B, Turnbull J, Aitken I. An evaluation of a training course in the short-term management of violence. Nurse Educ Today. 1992;12(5):368–75.

Rice ME, Helzel MF, Varney GW, Quinsey VL. Crisis prevention and intervention training for psychiatric hospital staff. Am J Community Psychol. 1985;13(3):289–304.

Wondrak R, Dolan B. Dealing with verbal abuse: evaluation of the efficacy of a workshop for student nurses. Nurse Educ Today. 1992;12(2):108–15.

Leach B, Gloinson ER, Sutherland A, Whitmore M. Reviewing the evidence base for de-escalation training: a rapid evidence assessment. RAND research reports. RAND Corporation; 2019. Available from: https://www.rand.org/pubs/research_reports/RR3148.html . Cited 2021 Nov 26.

Parsons MB, Rollyson JH, Reid DH. Evidence-based staff training: a guide for practitioners. Behav Anal Pract. 2012;5(2):2–11.

Miltenberger RG, Flessner C, Gatheridge B, Johnson B, Satterlund M, Egemo K. Evaluation of behavioral skills training to prevent gun play in children. J Appl Bahavior Anal. 2004;37:513–6.

Baer DM, Wolf MM, Risley TR. Some still-current dimensions of applied behavior analysis. J Appl Behav Anal. 1987;20(4):313–27.

Dillenburger K. Staff training. Handbook of treatments for autism spectrum disorder. In: Matson JL, editor. Handbook of treatments for autism spectrum disorder. Switzerland: Springer Nature; 2017. p. 95–107.

Kirkpatrick M, Akers J, Rivera G. Use of behavioral skills training with teachers: a systematic review. J Behav Educ. 2019;28(3):344–61.

Sun X. Behavior skills training for family caregivers of people with intellectual or developmental disabilities: a systematic review of literature. Int J Dev Disabil. 2020:68(3):247-73.

Davis S, Thomson K, Magnacca C. Evaluation of a caregiver training program to teach help-seeking behavior to children with autism spectrum disorder. Int J Dev Disailities. 2020;66(5):348–57.

Gormley L, Healy O, O’Sullivan B, O’Regan D, Grey I, Bracken M. The impact of behavioural skills training on the knowledge, skills and well-being of front line staff in the intellectual disability sector: a clustered randomised control trial. J Intellect Disabil Res. 2019;63(11):1291–304.

Lin E, Malhas M, Bratsalis E, Thomson K, Boateng R, Hargreaves F, et al. Behavioural skills training for teaching safety skills to mental health clinicians: A protocol for a pragmatic randomized control trial. JMIR Res Protoc. 2022;11(12):e39672.

GraphPad. Randomly assign subjects to treatment groups. Available from: https://www.graphpad.com/quickcalcs/randomize1.cfm . Cited 2023 June 6.

Van Voorhis CRW, Morgan BL. Understanding power and rules of thumb for determining sample sizes. Tutor Quant Methods Psychol. 2007;3(2):43–50.

Erath TG, DiGennaro Reed FD, Sundermeyer HW, Brand D, Novak MD, Harbison MJ, et al. Enhancing the training integrity of human service staff using pyramidal behavioral skills training. J Appl Behav Anal. 2020;53(1):449–64.

Wong KK, Fienup DM, Richling SM, Keen A, Mackay K. Systematic review of acquisition mastery criteria and statistical analysis of associations with response maintenance and generalization. Behav Interv. 2022;37(4):993–1012.

Richling SM, Williams WL, Carr JE. The effects of different mastery criteria on the skill maintenance of children with developmental disabilities. J Appl Behav Anal. 2019;52(3):701–17 (Available from: https://pubmed.ncbi.nlm.nih.gov/31155708/ ). Cited 2022 Oct 14.

Pitts L, Hoerger ML. Mastery criteria and the maintenance of skills in children with developmental disabilities. Behav Interv. 2021;36(2):522–31. Available from: https://doi.org/10.1002/bin.1778 . Cited 2022 Oct 17.

R Core Team. R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2021.

Pinheiro J, Bates D. Mixed-effects models in S and S-PLUS. New York: Springer Science+Business Media; 2006.

Bosker R, Snijders TA. Multilevel analysis: An introduction to basic and advanced multilevel modeling. In: Analysis M, editor. London. UK: Sage Publishers; 2012. p. 1–368.

Cohen J. Statistical power analysis for the behavioral sciences. 2nd ed. Hillsdale, NJ: Lawrence Erlbaum Associates; 1988.

Taylor DCM, Hamdy H. Adult learning theories: Implications for learningand teaching in medical education: AMEEGuide No. 83. Med Teach. 2013;35(11):e1561-72.

Nodine TR. How did we get here? A brief history of competency-based higher education in the United States. Competency Based Educ. 2016;1:5–11.

Novak MD, Reed FDD, Erath TG, Blackman AL, Ruby SA, Pellegrino AJ. Evidence-Based Performance Management: Applying behavioral science to support practitioners. Perspect Behav Sci. 2019;42:955–72.

Fienup DM, Broadsky J. Effects of mastery criterion on the emergence of derived equivalence relations. J Appl Behav Anal. 2017;40:843–8.

Fuller JL, Fienup DM. A preliminary analysis of mastery criterion level: effects on response maintenance. Behav Anal Pract. 2018;11(1):1–8.

Alavosius MP, Sulzer-Azaroff B. The effects of performance feedback on the safety of client lifting and transfer. J Appl Behav Anal. 1986;19:261–7.

Hogan A, Knez N, Kahng S. Evaluating the use of behavioral skills training to improve school staffs’ implementation of behavior intervention plans. J Behav Educ. 2015;24(2):242–54.

Nabeyama B, Sturmey P. Using behavioral skills training to promote safe and correct staff guarding and ambulation distance of students with multiple physical disabilities. J Appl Behav Anal. 2010;43(2):341–5.

Parsons MB, Rollyson JH, Reid DH. Teaching practitioners to conduct behavioral skills training: a pyramidal approach for training multiple human service staff. Behav Anal Pract. 2013;6(2):4–16.

Sarakoff RA, Sturmey P. The effects of behavioral skills training on staff implementation of discrete-trial teaching. J Appl Behav Anal. 2004;37(4):535–8.

Offiah G, Ekpotu LP, Murphy S, Kane D, Gordon A, O’Sullivan M, et al. Evaluation of medical student retention of clinical skills following simulation training. BMC Med Educ. 2019;19:263.

Bruno P, Ongaro A, Fraser I. Long-term retention of material taught and examined in chiropractic curricula: its relevance to education and clinical practice. J Can Chiropr Assoc. 2007;51(1):14–8.

PubMed   PubMed Central   Google Scholar  

Yates N, Gough S, Brazil V. Self-assessment: With all its limitations, why are we still measuring and teaching it? Lessons from a scoping review. Med Teach. 2022;44(11):1296–302. https://doi.org/10.1080/0142159X.2022.2093704 .

Lu FI, Takahashi SG, Kerr C. Myth or reality: self-assessment is central to effective curriculum in anatomical pathology graduate medical education. Acad Pathol. 2021;8:23742895211013530.

Magnacca C, Thomson K, Marcinkiewicz A, Davis S, Steel L, Lunsky Y, et al. A telecommunication model to teach facilitators to deliver acceptance and commitment training. Behav Anal Pract. 2022;15(3):752–752.

Naughton CA, Friesner DL. Comparison of pharmacy students’ perceived and actual knowledge using the pharmacy curricular outcomes assessment. Am J Pharm Educ. 2012;76(4):63.

Persky AM, Ee E, Schlesselman LS. Perception of learning versus performance as outcome measures of educational research. Am J Pharm. 2020;84(7):993–1000.

Katowa-Mukwato P, Sekelani SB. Self-perceived versus objectively measured competence in performing clinical pratical procedures by final medical students. Int J Med Educ. 2016;7:122–9.

Barsuk JH, Cohen ER, Feinglass J, McGaghie WC, Wayne DB. Residents’ procedural experience does not ensure competence: a research synthesis. J Grad Med Educ. 2017;9(2):201–8.

Choudhry NK, Fletcher RH, Soumerai SB. Systematic review: the relationship between clinical experience and quality of health care. Ann Intern Med. 2005;142(4):260–73.

Tavares W, Sockalingam S, Valanci S, Giuliani M, Davis D, Campbell C, et al. Performance Data Advocacy for Continuing Professional Development in Health Professions. Acad Med. 2024;99(2):153-8. https://doi.org/10.1097/ACM.0000000000005490 .

Download references

Acknowledgements

We thank Sanjeev Sockalingam, Asha Maharaj, Katie Hodgson, Erin Ledrew, Sophie Soklaridis, and Stephanie Sliekers for their guidance and for dedicating the human and financial resources needed to support this study. We also want to express our sincere gratitude to the following individuals for facilitating physical skills sessions and for volunteering as actors in the physical skills demonstrations: Kate Van den Borre, Steven Hughes, Paul Martin Demers, Ross Violo, Genevieve Poulin, Stacy de Souza, Narendra Deonauth, Joanna Zygmunt, Tessa Donnelly, Lawren Taylor, and Bobby Bonner. Finally, we are grateful to Marcos Sanchez for statistical consultation and Quincy Vaz for research support.

This research was funded internally by the Centre for Addiction and Mental Health.

Author information

Authors and affiliations.

Department of Education, Centre for Addiction and Mental Health, Toronto, ON, Canada

Elizabeth Lin, Mais Malhas, Emmanuel Bratsalis, Kendra Thomson, Fabienne Hargreaves, Kayle Donner, Heba Baig, Rhonda Boateng, Rajlaxmi Swain, Mary Benisha Benadict & Louis Busch

Department of Applied Disability Studies, Brock University, St. Catharines, ON, Canada

Kendra Thomson

You can also search for this author in PubMed   Google Scholar

Contributions

All authors were involved in the study design, monitoring and implementing the study, and review of manuscript drafts. EL was responsible for the original study design and drafting of the full manuscript. MM, EB, and FH led the implementation of the training sessions. EB, FH, HB, KT, and LB were involved in the reliability assessments (IOA). KD and HB were primarily responsible for data analysis. HB and RB monitored the data collection and the ongoing study procedures. RS and MBB assisted in the literature review.

Corresponding author

Correspondence to Elizabeth Lin .

Ethics declarations

Ethics approval and consent to participate.

This study was approved by the Research Ethics Board of the Centre for Addiction and Mental Health (#101/2020). Informed consent was obtained from all subjects participating in the study. All interventions were performed in accordance with the Declaration of Helsinki. This study was registered with the ISRCTN registry on 06/01/2023 (ISRCTN18133140).

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1., additional file 2., rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Lin, E., Malhas, M., Bratsalis, E. et al. Behavioral skills training for teaching safety skills to mental health service providers compared to training-as-usual: a pragmatic randomized control trial. BMC Health Serv Res 24 , 639 (2024). https://doi.org/10.1186/s12913-024-10994-1

Download citation

Received : 06 September 2023

Accepted : 16 April 2024

Published : 17 May 2024

DOI : https://doi.org/10.1186/s12913-024-10994-1

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Workplace violence
• Violence prevention
• Behavioural skills training
• Performance and competency-based staff training

BMC Health Services Research

ISSN: 1472-6963

• General enquiries: [email protected]