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- v.37(16); 2022 Apr 25
A Practical Guide to Writing Quantitative and Qualitative Research Questions and Hypotheses in Scholarly Articles
Edward barroga.
1 Department of General Education, Graduate School of Nursing Science, St. Luke’s International University, Tokyo, Japan.
Glafera Janet Matanguihan
2 Department of Biological Sciences, Messiah University, Mechanicsburg, PA, USA.
The development of research questions and the subsequent hypotheses are prerequisites to defining the main research purpose and specific objectives of a study. Consequently, these objectives determine the study design and research outcome. The development of research questions is a process based on knowledge of current trends, cutting-edge studies, and technological advances in the research field. Excellent research questions are focused and require a comprehensive literature search and in-depth understanding of the problem being investigated. Initially, research questions may be written as descriptive questions which could be developed into inferential questions. These questions must be specific and concise to provide a clear foundation for developing hypotheses. Hypotheses are more formal predictions about the research outcomes. These specify the possible results that may or may not be expected regarding the relationship between groups. Thus, research questions and hypotheses clarify the main purpose and specific objectives of the study, which in turn dictate the design of the study, its direction, and outcome. Studies developed from good research questions and hypotheses will have trustworthy outcomes with wide-ranging social and health implications.
INTRODUCTION
Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses. 1 , 2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results. 3 , 4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the inception of novel studies and the ethical testing of ideas. 5 , 6
It is crucial to have knowledge of both quantitative and qualitative research 2 as both types of research involve writing research questions and hypotheses. 7 However, these crucial elements of research are sometimes overlooked; if not overlooked, then framed without the forethought and meticulous attention it needs. Planning and careful consideration are needed when developing quantitative or qualitative research, particularly when conceptualizing research questions and hypotheses. 4
There is a continuing need to support researchers in the creation of innovative research questions and hypotheses, as well as for journal articles that carefully review these elements. 1 When research questions and hypotheses are not carefully thought of, unethical studies and poor outcomes usually ensue. Carefully formulated research questions and hypotheses define well-founded objectives, which in turn determine the appropriate design, course, and outcome of the study. This article then aims to discuss in detail the various aspects of crafting research questions and hypotheses, with the goal of guiding researchers as they develop their own. Examples from the authors and peer-reviewed scientific articles in the healthcare field are provided to illustrate key points.
DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES
A research question is what a study aims to answer after data analysis and interpretation. The answer is written in length in the discussion section of the paper. Thus, the research question gives a preview of the different parts and variables of the study meant to address the problem posed in the research question. 1 An excellent research question clarifies the research writing while facilitating understanding of the research topic, objective, scope, and limitations of the study. 5
On the other hand, a research hypothesis is an educated statement of an expected outcome. This statement is based on background research and current knowledge. 8 , 9 The research hypothesis makes a specific prediction about a new phenomenon 10 or a formal statement on the expected relationship between an independent variable and a dependent variable. 3 , 11 It provides a tentative answer to the research question to be tested or explored. 4
Hypotheses employ reasoning to predict a theory-based outcome. 10 These can also be developed from theories by focusing on components of theories that have not yet been observed. 10 The validity of hypotheses is often based on the testability of the prediction made in a reproducible experiment. 8
Conversely, hypotheses can also be rephrased as research questions. Several hypotheses based on existing theories and knowledge may be needed to answer a research question. Developing ethical research questions and hypotheses creates a research design that has logical relationships among variables. These relationships serve as a solid foundation for the conduct of the study. 4 , 11 Haphazardly constructed research questions can result in poorly formulated hypotheses and improper study designs, leading to unreliable results. Thus, the formulations of relevant research questions and verifiable hypotheses are crucial when beginning research. 12
CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES
Excellent research questions are specific and focused. These integrate collective data and observations to confirm or refute the subsequent hypotheses. Well-constructed hypotheses are based on previous reports and verify the research context. These are realistic, in-depth, sufficiently complex, and reproducible. More importantly, these hypotheses can be addressed and tested. 13
There are several characteristics of well-developed hypotheses. Good hypotheses are 1) empirically testable 7 , 10 , 11 , 13 ; 2) backed by preliminary evidence 9 ; 3) testable by ethical research 7 , 9 ; 4) based on original ideas 9 ; 5) have evidenced-based logical reasoning 10 ; and 6) can be predicted. 11 Good hypotheses can infer ethical and positive implications, indicating the presence of a relationship or effect relevant to the research theme. 7 , 11 These are initially developed from a general theory and branch into specific hypotheses by deductive reasoning. In the absence of a theory to base the hypotheses, inductive reasoning based on specific observations or findings form more general hypotheses. 10
TYPES OF RESEARCH QUESTIONS AND HYPOTHESES
Research questions and hypotheses are developed according to the type of research, which can be broadly classified into quantitative and qualitative research. We provide a summary of the types of research questions and hypotheses under quantitative and qualitative research categories in Table 1 .
Quantitative research questions | Quantitative research hypotheses |
---|---|
Descriptive research questions | Simple hypothesis |
Comparative research questions | Complex hypothesis |
Relationship research questions | Directional hypothesis |
Non-directional hypothesis | |
Associative hypothesis | |
Causal hypothesis | |
Null hypothesis | |
Alternative hypothesis | |
Working hypothesis | |
Statistical hypothesis | |
Logical hypothesis | |
Hypothesis-testing | |
Qualitative research questions | Qualitative research hypotheses |
Contextual research questions | Hypothesis-generating |
Descriptive research questions | |
Evaluation research questions | |
Explanatory research questions | |
Exploratory research questions | |
Generative research questions | |
Ideological research questions | |
Ethnographic research questions | |
Phenomenological research questions | |
Grounded theory questions | |
Qualitative case study questions |
Research questions in quantitative research
In quantitative research, research questions inquire about the relationships among variables being investigated and are usually framed at the start of the study. These are precise and typically linked to the subject population, dependent and independent variables, and research design. 1 Research questions may also attempt to describe the behavior of a population in relation to one or more variables, or describe the characteristics of variables to be measured ( descriptive research questions ). 1 , 5 , 14 These questions may also aim to discover differences between groups within the context of an outcome variable ( comparative research questions ), 1 , 5 , 14 or elucidate trends and interactions among variables ( relationship research questions ). 1 , 5 We provide examples of descriptive, comparative, and relationship research questions in quantitative research in Table 2 .
Quantitative research questions | |
---|---|
Descriptive research question | |
- Measures responses of subjects to variables | |
- Presents variables to measure, analyze, or assess | |
What is the proportion of resident doctors in the hospital who have mastered ultrasonography (response of subjects to a variable) as a diagnostic technique in their clinical training? | |
Comparative research question | |
- Clarifies difference between one group with outcome variable and another group without outcome variable | |
Is there a difference in the reduction of lung metastasis in osteosarcoma patients who received the vitamin D adjunctive therapy (group with outcome variable) compared with osteosarcoma patients who did not receive the vitamin D adjunctive therapy (group without outcome variable)? | |
- Compares the effects of variables | |
How does the vitamin D analogue 22-Oxacalcitriol (variable 1) mimic the antiproliferative activity of 1,25-Dihydroxyvitamin D (variable 2) in osteosarcoma cells? | |
Relationship research question | |
- Defines trends, association, relationships, or interactions between dependent variable and independent variable | |
Is there a relationship between the number of medical student suicide (dependent variable) and the level of medical student stress (independent variable) in Japan during the first wave of the COVID-19 pandemic? |
Hypotheses in quantitative research
In quantitative research, hypotheses predict the expected relationships among variables. 15 Relationships among variables that can be predicted include 1) between a single dependent variable and a single independent variable ( simple hypothesis ) or 2) between two or more independent and dependent variables ( complex hypothesis ). 4 , 11 Hypotheses may also specify the expected direction to be followed and imply an intellectual commitment to a particular outcome ( directional hypothesis ) 4 . On the other hand, hypotheses may not predict the exact direction and are used in the absence of a theory, or when findings contradict previous studies ( non-directional hypothesis ). 4 In addition, hypotheses can 1) define interdependency between variables ( associative hypothesis ), 4 2) propose an effect on the dependent variable from manipulation of the independent variable ( causal hypothesis ), 4 3) state a negative relationship between two variables ( null hypothesis ), 4 , 11 , 15 4) replace the working hypothesis if rejected ( alternative hypothesis ), 15 explain the relationship of phenomena to possibly generate a theory ( working hypothesis ), 11 5) involve quantifiable variables that can be tested statistically ( statistical hypothesis ), 11 6) or express a relationship whose interlinks can be verified logically ( logical hypothesis ). 11 We provide examples of simple, complex, directional, non-directional, associative, causal, null, alternative, working, statistical, and logical hypotheses in quantitative research, as well as the definition of quantitative hypothesis-testing research in Table 3 .
Quantitative research hypotheses | |
---|---|
Simple hypothesis | |
- Predicts relationship between single dependent variable and single independent variable | |
If the dose of the new medication (single independent variable) is high, blood pressure (single dependent variable) is lowered. | |
Complex hypothesis | |
- Foretells relationship between two or more independent and dependent variables | |
The higher the use of anticancer drugs, radiation therapy, and adjunctive agents (3 independent variables), the higher would be the survival rate (1 dependent variable). | |
Directional hypothesis | |
- Identifies study direction based on theory towards particular outcome to clarify relationship between variables | |
Privately funded research projects will have a larger international scope (study direction) than publicly funded research projects. | |
Non-directional hypothesis | |
- Nature of relationship between two variables or exact study direction is not identified | |
- Does not involve a theory | |
Women and men are different in terms of helpfulness. (Exact study direction is not identified) | |
Associative hypothesis | |
- Describes variable interdependency | |
- Change in one variable causes change in another variable | |
A larger number of people vaccinated against COVID-19 in the region (change in independent variable) will reduce the region’s incidence of COVID-19 infection (change in dependent variable). | |
Causal hypothesis | |
- An effect on dependent variable is predicted from manipulation of independent variable | |
A change into a high-fiber diet (independent variable) will reduce the blood sugar level (dependent variable) of the patient. | |
Null hypothesis | |
- A negative statement indicating no relationship or difference between 2 variables | |
There is no significant difference in the severity of pulmonary metastases between the new drug (variable 1) and the current drug (variable 2). | |
Alternative hypothesis | |
- Following a null hypothesis, an alternative hypothesis predicts a relationship between 2 study variables | |
The new drug (variable 1) is better on average in reducing the level of pain from pulmonary metastasis than the current drug (variable 2). | |
Working hypothesis | |
- A hypothesis that is initially accepted for further research to produce a feasible theory | |
Dairy cows fed with concentrates of different formulations will produce different amounts of milk. | |
Statistical hypothesis | |
- Assumption about the value of population parameter or relationship among several population characteristics | |
- Validity tested by a statistical experiment or analysis | |
The mean recovery rate from COVID-19 infection (value of population parameter) is not significantly different between population 1 and population 2. | |
There is a positive correlation between the level of stress at the workplace and the number of suicides (population characteristics) among working people in Japan. | |
Logical hypothesis | |
- Offers or proposes an explanation with limited or no extensive evidence | |
If healthcare workers provide more educational programs about contraception methods, the number of adolescent pregnancies will be less. | |
Hypothesis-testing (Quantitative hypothesis-testing research) | |
- Quantitative research uses deductive reasoning. | |
- This involves the formation of a hypothesis, collection of data in the investigation of the problem, analysis and use of the data from the investigation, and drawing of conclusions to validate or nullify the hypotheses. |
Research questions in qualitative research
Unlike research questions in quantitative research, research questions in qualitative research are usually continuously reviewed and reformulated. The central question and associated subquestions are stated more than the hypotheses. 15 The central question broadly explores a complex set of factors surrounding the central phenomenon, aiming to present the varied perspectives of participants. 15
There are varied goals for which qualitative research questions are developed. These questions can function in several ways, such as to 1) identify and describe existing conditions ( contextual research question s); 2) describe a phenomenon ( descriptive research questions ); 3) assess the effectiveness of existing methods, protocols, theories, or procedures ( evaluation research questions ); 4) examine a phenomenon or analyze the reasons or relationships between subjects or phenomena ( explanatory research questions ); or 5) focus on unknown aspects of a particular topic ( exploratory research questions ). 5 In addition, some qualitative research questions provide new ideas for the development of theories and actions ( generative research questions ) or advance specific ideologies of a position ( ideological research questions ). 1 Other qualitative research questions may build on a body of existing literature and become working guidelines ( ethnographic research questions ). Research questions may also be broadly stated without specific reference to the existing literature or a typology of questions ( phenomenological research questions ), may be directed towards generating a theory of some process ( grounded theory questions ), or may address a description of the case and the emerging themes ( qualitative case study questions ). 15 We provide examples of contextual, descriptive, evaluation, explanatory, exploratory, generative, ideological, ethnographic, phenomenological, grounded theory, and qualitative case study research questions in qualitative research in Table 4 , and the definition of qualitative hypothesis-generating research in Table 5 .
Qualitative research questions | |
---|---|
Contextual research question | |
- Ask the nature of what already exists | |
- Individuals or groups function to further clarify and understand the natural context of real-world problems | |
What are the experiences of nurses working night shifts in healthcare during the COVID-19 pandemic? (natural context of real-world problems) | |
Descriptive research question | |
- Aims to describe a phenomenon | |
What are the different forms of disrespect and abuse (phenomenon) experienced by Tanzanian women when giving birth in healthcare facilities? | |
Evaluation research question | |
- Examines the effectiveness of existing practice or accepted frameworks | |
How effective are decision aids (effectiveness of existing practice) in helping decide whether to give birth at home or in a healthcare facility? | |
Explanatory research question | |
- Clarifies a previously studied phenomenon and explains why it occurs | |
Why is there an increase in teenage pregnancy (phenomenon) in Tanzania? | |
Exploratory research question | |
- Explores areas that have not been fully investigated to have a deeper understanding of the research problem | |
What factors affect the mental health of medical students (areas that have not yet been fully investigated) during the COVID-19 pandemic? | |
Generative research question | |
- Develops an in-depth understanding of people’s behavior by asking ‘how would’ or ‘what if’ to identify problems and find solutions | |
How would the extensive research experience of the behavior of new staff impact the success of the novel drug initiative? | |
Ideological research question | |
- Aims to advance specific ideas or ideologies of a position | |
Are Japanese nurses who volunteer in remote African hospitals able to promote humanized care of patients (specific ideas or ideologies) in the areas of safe patient environment, respect of patient privacy, and provision of accurate information related to health and care? | |
Ethnographic research question | |
- Clarifies peoples’ nature, activities, their interactions, and the outcomes of their actions in specific settings | |
What are the demographic characteristics, rehabilitative treatments, community interactions, and disease outcomes (nature, activities, their interactions, and the outcomes) of people in China who are suffering from pneumoconiosis? | |
Phenomenological research question | |
- Knows more about the phenomena that have impacted an individual | |
What are the lived experiences of parents who have been living with and caring for children with a diagnosis of autism? (phenomena that have impacted an individual) | |
Grounded theory question | |
- Focuses on social processes asking about what happens and how people interact, or uncovering social relationships and behaviors of groups | |
What are the problems that pregnant adolescents face in terms of social and cultural norms (social processes), and how can these be addressed? | |
Qualitative case study question | |
- Assesses a phenomenon using different sources of data to answer “why” and “how” questions | |
- Considers how the phenomenon is influenced by its contextual situation. | |
How does quitting work and assuming the role of a full-time mother (phenomenon assessed) change the lives of women in Japan? |
Qualitative research hypotheses | |
---|---|
Hypothesis-generating (Qualitative hypothesis-generating research) | |
- Qualitative research uses inductive reasoning. | |
- This involves data collection from study participants or the literature regarding a phenomenon of interest, using the collected data to develop a formal hypothesis, and using the formal hypothesis as a framework for testing the hypothesis. | |
- Qualitative exploratory studies explore areas deeper, clarifying subjective experience and allowing formulation of a formal hypothesis potentially testable in a future quantitative approach. |
Qualitative studies usually pose at least one central research question and several subquestions starting with How or What . These research questions use exploratory verbs such as explore or describe . These also focus on one central phenomenon of interest, and may mention the participants and research site. 15
Hypotheses in qualitative research
Hypotheses in qualitative research are stated in the form of a clear statement concerning the problem to be investigated. Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes. 2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed-methods research question can be developed. 1
FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES
Research questions followed by hypotheses should be developed before the start of the study. 1 , 12 , 14 It is crucial to develop feasible research questions on a topic that is interesting to both the researcher and the scientific community. This can be achieved by a meticulous review of previous and current studies to establish a novel topic. Specific areas are subsequently focused on to generate ethical research questions. The relevance of the research questions is evaluated in terms of clarity of the resulting data, specificity of the methodology, objectivity of the outcome, depth of the research, and impact of the study. 1 , 5 These aspects constitute the FINER criteria (i.e., Feasible, Interesting, Novel, Ethical, and Relevant). 1 Clarity and effectiveness are achieved if research questions meet the FINER criteria. In addition to the FINER criteria, Ratan et al. described focus, complexity, novelty, feasibility, and measurability for evaluating the effectiveness of research questions. 14
The PICOT and PEO frameworks are also used when developing research questions. 1 The following elements are addressed in these frameworks, PICOT: P-population/patients/problem, I-intervention or indicator being studied, C-comparison group, O-outcome of interest, and T-timeframe of the study; PEO: P-population being studied, E-exposure to preexisting conditions, and O-outcome of interest. 1 Research questions are also considered good if these meet the “FINERMAPS” framework: Feasible, Interesting, Novel, Ethical, Relevant, Manageable, Appropriate, Potential value/publishable, and Systematic. 14
As we indicated earlier, research questions and hypotheses that are not carefully formulated result in unethical studies or poor outcomes. To illustrate this, we provide some examples of ambiguous research question and hypotheses that result in unclear and weak research objectives in quantitative research ( Table 6 ) 16 and qualitative research ( Table 7 ) 17 , and how to transform these ambiguous research question(s) and hypothesis(es) into clear and good statements.
Variables | Unclear and weak statement (Statement 1) | Clear and good statement (Statement 2) | Points to avoid |
---|---|---|---|
Research question | Which is more effective between smoke moxibustion and smokeless moxibustion? | “Moreover, regarding smoke moxibustion versus smokeless moxibustion, it remains unclear which is more effective, safe, and acceptable to pregnant women, and whether there is any difference in the amount of heat generated.” | 1) Vague and unfocused questions |
2) Closed questions simply answerable by yes or no | |||
3) Questions requiring a simple choice | |||
Hypothesis | The smoke moxibustion group will have higher cephalic presentation. | “Hypothesis 1. The smoke moxibustion stick group (SM group) and smokeless moxibustion stick group (-SLM group) will have higher rates of cephalic presentation after treatment than the control group. | 1) Unverifiable hypotheses |
Hypothesis 2. The SM group and SLM group will have higher rates of cephalic presentation at birth than the control group. | 2) Incompletely stated groups of comparison | ||
Hypothesis 3. There will be no significant differences in the well-being of the mother and child among the three groups in terms of the following outcomes: premature birth, premature rupture of membranes (PROM) at < 37 weeks, Apgar score < 7 at 5 min, umbilical cord blood pH < 7.1, admission to neonatal intensive care unit (NICU), and intrauterine fetal death.” | 3) Insufficiently described variables or outcomes | ||
Research objective | To determine which is more effective between smoke moxibustion and smokeless moxibustion. | “The specific aims of this pilot study were (a) to compare the effects of smoke moxibustion and smokeless moxibustion treatments with the control group as a possible supplement to ECV for converting breech presentation to cephalic presentation and increasing adherence to the newly obtained cephalic position, and (b) to assess the effects of these treatments on the well-being of the mother and child.” | 1) Poor understanding of the research question and hypotheses |
2) Insufficient description of population, variables, or study outcomes |
a These statements were composed for comparison and illustrative purposes only.
b These statements are direct quotes from Higashihara and Horiuchi. 16
Variables | Unclear and weak statement (Statement 1) | Clear and good statement (Statement 2) | Points to avoid |
---|---|---|---|
Research question | Does disrespect and abuse (D&A) occur in childbirth in Tanzania? | How does disrespect and abuse (D&A) occur and what are the types of physical and psychological abuses observed in midwives’ actual care during facility-based childbirth in urban Tanzania? | 1) Ambiguous or oversimplistic questions |
2) Questions unverifiable by data collection and analysis | |||
Hypothesis | Disrespect and abuse (D&A) occur in childbirth in Tanzania. | Hypothesis 1: Several types of physical and psychological abuse by midwives in actual care occur during facility-based childbirth in urban Tanzania. | 1) Statements simply expressing facts |
Hypothesis 2: Weak nursing and midwifery management contribute to the D&A of women during facility-based childbirth in urban Tanzania. | 2) Insufficiently described concepts or variables | ||
Research objective | To describe disrespect and abuse (D&A) in childbirth in Tanzania. | “This study aimed to describe from actual observations the respectful and disrespectful care received by women from midwives during their labor period in two hospitals in urban Tanzania.” | 1) Statements unrelated to the research question and hypotheses |
2) Unattainable or unexplorable objectives |
a This statement is a direct quote from Shimoda et al. 17
The other statements were composed for comparison and illustrative purposes only.
CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES
To construct effective research questions and hypotheses, it is very important to 1) clarify the background and 2) identify the research problem at the outset of the research, within a specific timeframe. 9 Then, 3) review or conduct preliminary research to collect all available knowledge about the possible research questions by studying theories and previous studies. 18 Afterwards, 4) construct research questions to investigate the research problem. Identify variables to be accessed from the research questions 4 and make operational definitions of constructs from the research problem and questions. Thereafter, 5) construct specific deductive or inductive predictions in the form of hypotheses. 4 Finally, 6) state the study aims . This general flow for constructing effective research questions and hypotheses prior to conducting research is shown in Fig. 1 .
Research questions are used more frequently in qualitative research than objectives or hypotheses. 3 These questions seek to discover, understand, explore or describe experiences by asking “What” or “How.” The questions are open-ended to elicit a description rather than to relate variables or compare groups. The questions are continually reviewed, reformulated, and changed during the qualitative study. 3 Research questions are also used more frequently in survey projects than hypotheses in experiments in quantitative research to compare variables and their relationships.
Hypotheses are constructed based on the variables identified and as an if-then statement, following the template, ‘If a specific action is taken, then a certain outcome is expected.’ At this stage, some ideas regarding expectations from the research to be conducted must be drawn. 18 Then, the variables to be manipulated (independent) and influenced (dependent) are defined. 4 Thereafter, the hypothesis is stated and refined, and reproducible data tailored to the hypothesis are identified, collected, and analyzed. 4 The hypotheses must be testable and specific, 18 and should describe the variables and their relationships, the specific group being studied, and the predicted research outcome. 18 Hypotheses construction involves a testable proposition to be deduced from theory, and independent and dependent variables to be separated and measured separately. 3 Therefore, good hypotheses must be based on good research questions constructed at the start of a study or trial. 12
In summary, research questions are constructed after establishing the background of the study. Hypotheses are then developed based on the research questions. Thus, it is crucial to have excellent research questions to generate superior hypotheses. In turn, these would determine the research objectives and the design of the study, and ultimately, the outcome of the research. 12 Algorithms for building research questions and hypotheses are shown in Fig. 2 for quantitative research and in Fig. 3 for qualitative research.
EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES
- EXAMPLE 1. Descriptive research question (quantitative research)
- - Presents research variables to be assessed (distinct phenotypes and subphenotypes)
- “BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts.
- RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? ” 19
- EXAMPLE 2. Relationship research question (quantitative research)
- - Shows interactions between dependent variable (static postural control) and independent variable (peripheral visual field loss)
- “Background: Integration of visual, vestibular, and proprioceptive sensations contributes to postural control. People with peripheral visual field loss have serious postural instability. However, the directional specificity of postural stability and sensory reweighting caused by gradual peripheral visual field loss remain unclear.
- Research question: What are the effects of peripheral visual field loss on static postural control ?” 20
- EXAMPLE 3. Comparative research question (quantitative research)
- - Clarifies the difference among groups with an outcome variable (patients enrolled in COMPERA with moderate PH or severe PH in COPD) and another group without the outcome variable (patients with idiopathic pulmonary arterial hypertension (IPAH))
- “BACKGROUND: Pulmonary hypertension (PH) in COPD is a poorly investigated clinical condition.
- RESEARCH QUESTION: Which factors determine the outcome of PH in COPD?
- STUDY DESIGN AND METHODS: We analyzed the characteristics and outcome of patients enrolled in the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) with moderate or severe PH in COPD as defined during the 6th PH World Symposium who received medical therapy for PH and compared them with patients with idiopathic pulmonary arterial hypertension (IPAH) .” 21
- EXAMPLE 4. Exploratory research question (qualitative research)
- - Explores areas that have not been fully investigated (perspectives of families and children who receive care in clinic-based child obesity treatment) to have a deeper understanding of the research problem
- “Problem: Interventions for children with obesity lead to only modest improvements in BMI and long-term outcomes, and data are limited on the perspectives of families of children with obesity in clinic-based treatment. This scoping review seeks to answer the question: What is known about the perspectives of families and children who receive care in clinic-based child obesity treatment? This review aims to explore the scope of perspectives reported by families of children with obesity who have received individualized outpatient clinic-based obesity treatment.” 22
- EXAMPLE 5. Relationship research question (quantitative research)
- - Defines interactions between dependent variable (use of ankle strategies) and independent variable (changes in muscle tone)
- “Background: To maintain an upright standing posture against external disturbances, the human body mainly employs two types of postural control strategies: “ankle strategy” and “hip strategy.” While it has been reported that the magnitude of the disturbance alters the use of postural control strategies, it has not been elucidated how the level of muscle tone, one of the crucial parameters of bodily function, determines the use of each strategy. We have previously confirmed using forward dynamics simulations of human musculoskeletal models that an increased muscle tone promotes the use of ankle strategies. The objective of the present study was to experimentally evaluate a hypothesis: an increased muscle tone promotes the use of ankle strategies. Research question: Do changes in the muscle tone affect the use of ankle strategies ?” 23
EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES
- EXAMPLE 1. Working hypothesis (quantitative research)
- - A hypothesis that is initially accepted for further research to produce a feasible theory
- “As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness .” 24
- “In conclusion, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response . The difference in perceived safety of these agents in COVID-19 illness could be related to the more potent efficacy to reduce fever with ibuprofen compared to acetaminophen. Compelling data on the benefit of fever warrant further research and review to determine when to treat or withhold ibuprofen for early stage fever for COVID-19 and other related viral illnesses .” 24
- EXAMPLE 2. Exploratory hypothesis (qualitative research)
- - Explores particular areas deeper to clarify subjective experience and develop a formal hypothesis potentially testable in a future quantitative approach
- “We hypothesized that when thinking about a past experience of help-seeking, a self distancing prompt would cause increased help-seeking intentions and more favorable help-seeking outcome expectations .” 25
- “Conclusion
- Although a priori hypotheses were not supported, further research is warranted as results indicate the potential for using self-distancing approaches to increasing help-seeking among some people with depressive symptomatology.” 25
- EXAMPLE 3. Hypothesis-generating research to establish a framework for hypothesis testing (qualitative research)
- “We hypothesize that compassionate care is beneficial for patients (better outcomes), healthcare systems and payers (lower costs), and healthcare providers (lower burnout). ” 26
- Compassionomics is the branch of knowledge and scientific study of the effects of compassionate healthcare. Our main hypotheses are that compassionate healthcare is beneficial for (1) patients, by improving clinical outcomes, (2) healthcare systems and payers, by supporting financial sustainability, and (3) HCPs, by lowering burnout and promoting resilience and well-being. The purpose of this paper is to establish a scientific framework for testing the hypotheses above . If these hypotheses are confirmed through rigorous research, compassionomics will belong in the science of evidence-based medicine, with major implications for all healthcare domains.” 26
- EXAMPLE 4. Statistical hypothesis (quantitative research)
- - An assumption is made about the relationship among several population characteristics ( gender differences in sociodemographic and clinical characteristics of adults with ADHD ). Validity is tested by statistical experiment or analysis ( chi-square test, Students t-test, and logistic regression analysis)
- “Our research investigated gender differences in sociodemographic and clinical characteristics of adults with ADHD in a Japanese clinical sample. Due to unique Japanese cultural ideals and expectations of women's behavior that are in opposition to ADHD symptoms, we hypothesized that women with ADHD experience more difficulties and present more dysfunctions than men . We tested the following hypotheses: first, women with ADHD have more comorbidities than men with ADHD; second, women with ADHD experience more social hardships than men, such as having less full-time employment and being more likely to be divorced.” 27
- “Statistical Analysis
- ( text omitted ) Between-gender comparisons were made using the chi-squared test for categorical variables and Students t-test for continuous variables…( text omitted ). A logistic regression analysis was performed for employment status, marital status, and comorbidity to evaluate the independent effects of gender on these dependent variables.” 27
EXAMPLES OF HYPOTHESIS AS WRITTEN IN PUBLISHED ARTICLES IN RELATION TO OTHER PARTS
- EXAMPLE 1. Background, hypotheses, and aims are provided
- “Pregnant women need skilled care during pregnancy and childbirth, but that skilled care is often delayed in some countries …( text omitted ). The focused antenatal care (FANC) model of WHO recommends that nurses provide information or counseling to all pregnant women …( text omitted ). Job aids are visual support materials that provide the right kind of information using graphics and words in a simple and yet effective manner. When nurses are not highly trained or have many work details to attend to, these job aids can serve as a content reminder for the nurses and can be used for educating their patients (Jennings, Yebadokpo, Affo, & Agbogbe, 2010) ( text omitted ). Importantly, additional evidence is needed to confirm how job aids can further improve the quality of ANC counseling by health workers in maternal care …( text omitted )” 28
- “ This has led us to hypothesize that the quality of ANC counseling would be better if supported by job aids. Consequently, a better quality of ANC counseling is expected to produce higher levels of awareness concerning the danger signs of pregnancy and a more favorable impression of the caring behavior of nurses .” 28
- “This study aimed to examine the differences in the responses of pregnant women to a job aid-supported intervention during ANC visit in terms of 1) their understanding of the danger signs of pregnancy and 2) their impression of the caring behaviors of nurses to pregnant women in rural Tanzania.” 28
- EXAMPLE 2. Background, hypotheses, and aims are provided
- “We conducted a two-arm randomized controlled trial (RCT) to evaluate and compare changes in salivary cortisol and oxytocin levels of first-time pregnant women between experimental and control groups. The women in the experimental group touched and held an infant for 30 min (experimental intervention protocol), whereas those in the control group watched a DVD movie of an infant (control intervention protocol). The primary outcome was salivary cortisol level and the secondary outcome was salivary oxytocin level.” 29
- “ We hypothesize that at 30 min after touching and holding an infant, the salivary cortisol level will significantly decrease and the salivary oxytocin level will increase in the experimental group compared with the control group .” 29
- EXAMPLE 3. Background, aim, and hypothesis are provided
- “In countries where the maternal mortality ratio remains high, antenatal education to increase Birth Preparedness and Complication Readiness (BPCR) is considered one of the top priorities [1]. BPCR includes birth plans during the antenatal period, such as the birthplace, birth attendant, transportation, health facility for complications, expenses, and birth materials, as well as family coordination to achieve such birth plans. In Tanzania, although increasing, only about half of all pregnant women attend an antenatal clinic more than four times [4]. Moreover, the information provided during antenatal care (ANC) is insufficient. In the resource-poor settings, antenatal group education is a potential approach because of the limited time for individual counseling at antenatal clinics.” 30
- “This study aimed to evaluate an antenatal group education program among pregnant women and their families with respect to birth-preparedness and maternal and infant outcomes in rural villages of Tanzania.” 30
- “ The study hypothesis was if Tanzanian pregnant women and their families received a family-oriented antenatal group education, they would (1) have a higher level of BPCR, (2) attend antenatal clinic four or more times, (3) give birth in a health facility, (4) have less complications of women at birth, and (5) have less complications and deaths of infants than those who did not receive the education .” 30
Research questions and hypotheses are crucial components to any type of research, whether quantitative or qualitative. These questions should be developed at the very beginning of the study. Excellent research questions lead to superior hypotheses, which, like a compass, set the direction of research, and can often determine the successful conduct of the study. Many research studies have floundered because the development of research questions and subsequent hypotheses was not given the thought and meticulous attention needed. The development of research questions and hypotheses is an iterative process based on extensive knowledge of the literature and insightful grasp of the knowledge gap. Focused, concise, and specific research questions provide a strong foundation for constructing hypotheses which serve as formal predictions about the research outcomes. Research questions and hypotheses are crucial elements of research that should not be overlooked. They should be carefully thought of and constructed when planning research. This avoids unethical studies and poor outcomes by defining well-founded objectives that determine the design, course, and outcome of the study.
Disclosure: The authors have no potential conflicts of interest to disclose.
Author Contributions:
- Conceptualization: Barroga E, Matanguihan GJ.
- Methodology: Barroga E, Matanguihan GJ.
- Writing - original draft: Barroga E, Matanguihan GJ.
- Writing - review & editing: Barroga E, Matanguihan GJ.
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Quantitative Data Analysis
9 Presenting the Results of Quantitative Analysis
Mikaila Mariel Lemonik Arthur
This chapter provides an overview of how to present the results of quantitative analysis, in particular how to create effective tables for displaying quantitative results and how to write quantitative research papers that effectively communicate the methods used and findings of quantitative analysis.
Writing the Quantitative Paper
Standard quantitative social science papers follow a specific format. They begin with a title page that includes a descriptive title, the author(s)’ name(s), and a 100 to 200 word abstract that summarizes the paper. Next is an introduction that makes clear the paper’s research question, details why this question is important, and previews what the paper will do. After that comes a literature review, which ends with a summary of the research question(s) and/or hypotheses. A methods section, which explains the source of data, sample, and variables and quantitative techniques used, follows. Many analysts will include a short discussion of their descriptive statistics in the methods section. A findings section details the findings of the analysis, supported by a variety of tables, and in some cases graphs, all of which are explained in the text. Some quantitative papers, especially those using more complex techniques, will include equations. Many papers follow the findings section with a discussion section, which provides an interpretation of the results in light of both the prior literature and theory presented in the literature review and the research questions/hypotheses. A conclusion ends the body of the paper. This conclusion should summarize the findings, answering the research questions and stating whether any hypotheses were supported, partially supported, or not supported. Limitations of the research are detailed. Papers typically include suggestions for future research, and where relevant, some papers include policy implications. After the body of the paper comes the works cited; some papers also have an Appendix that includes additional tables and figures that did not fit into the body of the paper or additional methodological details. While this basic format is similar for papers regardless of the type of data they utilize, there are specific concerns relating to quantitative research in terms of the methods and findings that will be discussed here.
In the methods section, researchers clearly describe the methods they used to obtain and analyze the data for their research. When relying on data collected specifically for a given paper, researchers will need to discuss the sample and data collection; in most cases, though, quantitative research relies on pre-existing datasets. In these cases, researchers need to provide information about the dataset, including the source of the data, the time it was collected, the population, and the sample size. Regardless of the source of the data, researchers need to be clear about which variables they are using in their research and any transformations or manipulations of those variables. They also need to explain the specific quantitative techniques that they are using in their analysis; if different techniques are used to test different hypotheses, this should be made clear. In some cases, publications will require that papers be submitted along with any code that was used to produce the analysis (in SPSS terms, the syntax files), which more advanced researchers will usually have on hand. In many cases, basic descriptive statistics are presented in tabular form and explained within the methods section.
The findings sections of quantitative papers are organized around explaining the results as shown in tables and figures. Not all results are depicted in tables and figures—some minor or null findings will simply be referenced—but tables and figures should be produced for all findings to be discussed at any length. If there are too many tables and figures, some can be moved to an appendix after the body of the text and referred to in the text (e.g. “See Table 12 in Appendix A”).
Discussions of the findings should not simply restate the contents of the table. Rather, they should explain and interpret it for readers, and they should do so in light of the hypothesis or hypotheses that are being tested. Conclusions—discussions of whether the hypothesis or hypotheses are supported or not supported—should wait for the conclusion of the paper.
Creating Effective Tables
When creating tables to display the results of quantitative analysis, the most important goals are to create tables that are clear and concise but that also meet standard conventions in the field. This means, first of all, paring down the volume of information produced in the statistical output to just include the information most necessary for interpreting the results, but doing so in keeping with standard table conventions. It also means making tables that are well-formatted and designed, so that readers can understand what the tables are saying without struggling to find information. For example, tables (as well as figures such as graphs) need clear captions; they are typically numbered and referred to by number in the text. Columns and rows should have clear headings. Depending on the content of the table, formatting tools may need to be used to set off header rows/columns and/or total rows/columns; cell-merging tools may be necessary; and shading may be important in tables with many rows or columns.
Here, you will find some instructions for creating tables of results from descriptive, crosstabulation, correlation, and regression analysis that are clear, concise, and meet normal standards for data display in social science. In addition, after the instructions for creating tables, you will find an example of how a paper incorporating each table might describe that table in the text.
Descriptive Statistics
When presenting the results of descriptive statistics, we create one table with columns for each type of descriptive statistic and rows for each variable. Note, of course, that depending on level of measurement only certain descriptive statistics are appropriate for a given variable, so there may be many cells in the table marked with an — to show that this statistic is not calculated for this variable. So, consider the set of descriptive statistics below, for occupational prestige, age, highest degree earned, and whether the respondent was born in this country.
To display these descriptive statistics in a paper, one might create a table like Table 2. Note that for discrete variables, we use the value label in the table, not the value.
If we were then to discuss our descriptive statistics in a quantitative paper, we might write something like this (note that we do not need to repeat every single detail from the table, as readers can peruse the table themselves): This analysis relies on four variables from the 2021 General Social Survey: occupational prestige score, age, highest degree earned, and whether the respondent was born in the United States. Descriptive statistics for all four variables are shown in Table 2. The median occupational prestige score is 47, with a range from 16 to 80. 50% of respondents had occupational prestige scores scores between 35 and 59. The median age of respondents is 53, with a range from 18 to 89. 50% of respondents are between ages 37 and 66. Both variables have little skew. Highest degree earned ranges from less than high school to a graduate degree; the median respondent has earned an associate’s degree, while the modal response (given by 39.8% of the respondents) is a high school degree. 88.8% of respondents were born in the United States. CrosstabulationWhen presenting the results of a crosstabulation, we simplify the table so that it highlights the most important information—the column percentages—and include the significance and association below the table. Consider the SPSS output below.
Table 4 shows how a table suitable for include in a paper might look if created from the SPSS output in Table 3. Note that we use asterisks to indicate the significance level of the results: * means p < 0.05; ** means p < 0.01; *** means p < 0.001; and no stars mean p > 0.05 (and thus that the result is not significant). Also note than N is the abbreviation for the number of respondents.
If we were going to discuss the results of this crosstabulation in a quantitative research paper, the discussion might look like this: A crosstabulation of respondent’s class identification and their highest degree earned, with class identification as the independent variable, is significant, with a Spearman correlation of 0.419, as shown in Table 4. Among lower class and working class respondents, more than 50% had earned a high school degree. Less than 20% of poor respondents and less than 40% of working-class respondents had earned more than a high school degree. In contrast, the majority of middle class and upper class respondents had earned at least a bachelor’s degree. In fact, 50% of upper class respondents had earned a graduate degree. CorrelationWhen presenting a correlating matrix, one of the most important things to note is that we only present half the table so as not to include duplicated results. Think of the line through the table where empty cells exist to represent the correlation between a variable and itself, and include only the triangle of data either above or below that line of cells. Consider the output in Table 5.
Table 6 shows what the contents of Table 5 might look like when a table is constructed in a fashion suitable for publication.
If we were to discuss the results of this bivariate correlation analysis in a quantitative paper, the discussion might look like this: Bivariate correlations were run among variables measuring age, occupational prestige, the highest year of school respondents completed, and family income in constant 1986 dollars, as shown in Table 6. Correlations between age and highest year of school completed and between age and family income are not significant. All other correlations are positive and significant at the p<0.001 level. The correlation between age and occupational prestige is weak; the correlations between income and occupational prestige and between income and educational attainment are moderate, and the correlation between education and occupational prestige is strong. To present the results of a regression, we create one table that includes all of the key information from the multiple tables of SPSS output. This includes the R 2 and significance of the regression, either the B or the beta values (different analysts have different preferences here) for each variable, and the standard error and significance of each variable. Consider the SPSS output in Table 7.
The regression output in shown in Table 7 contains a lot of information. We do not include all of this information when making tables suitable for publication. As can be seen in Table 8, we include the Beta (or the B), the standard error, and the significance asterisk for each variable; the R 2 and significance for the overall regression; the degrees of freedom (which tells readers the sample size or N); and the constant; along with the key to p/significance values.
If we were to discuss the results of this regression in a quantitative paper, the results might look like this: Table 8 shows the results of a regression in which age, occupational prestige, and highest year of school completed are the independent variables and family income is the dependent variable. The regression results are significant, and all of the independent variables taken together explain 15.6% of the variance in family income. Age is not a significant predictor of income, while occupational prestige and educational attainment are. Educational attainment has a larger effect on family income than does occupational prestige. For every year of additional education attained, family income goes up on average by $3,988.545; for every one-unit increase in occupational prestige score, family income goes up on average by $522.887. [1]
Social Data Analysis Copyright © 2021 by Mikaila Mariel Lemonik Arthur is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.
Organizing Your Social Sciences Research Paper
Quantitative methods emphasize objective measurements and the statistical, mathematical, or numerical analysis of data collected through polls, questionnaires, and surveys, or by manipulating pre-existing statistical data using computational techniques . Quantitative research focuses on gathering numerical data and generalizing it across groups of people or to explain a particular phenomenon. Babbie, Earl R. The Practice of Social Research . 12th ed. Belmont, CA: Wadsworth Cengage, 2010; Muijs, Daniel. Doing Quantitative Research in Education with SPSS . 2nd edition. London: SAGE Publications, 2010. Need Help Locating Statistics?Resources for locating data and statistics can be found here: Statistics & Data Research Guide Characteristics of Quantitative ResearchYour goal in conducting quantitative research study is to determine the relationship between one thing [an independent variable] and another [a dependent or outcome variable] within a population. Quantitative research designs are either descriptive [subjects usually measured once] or experimental [subjects measured before and after a treatment]. A descriptive study establishes only associations between variables; an experimental study establishes causality. Quantitative research deals in numbers, logic, and an objective stance. Quantitative research focuses on numeric and unchanging data and detailed, convergent reasoning rather than divergent reasoning [i.e., the generation of a variety of ideas about a research problem in a spontaneous, free-flowing manner]. Its main characteristics are :
The overarching aim of a quantitative research study is to classify features, count them, and construct statistical models in an attempt to explain what is observed. Things to keep in mind when reporting the results of a study using quantitative methods :
NOTE: When using pre-existing statistical data gathered and made available by anyone other than yourself [e.g., government agency], you still must report on the methods that were used to gather the data and describe any missing data that exists and, if there is any, provide a clear explanation why the missing data does not undermine the validity of your final analysis. Babbie, Earl R. The Practice of Social Research . 12th ed. Belmont, CA: Wadsworth Cengage, 2010; Brians, Craig Leonard et al. Empirical Political Analysis: Quantitative and Qualitative Research Methods . 8th ed. Boston, MA: Longman, 2011; McNabb, David E. Research Methods in Public Administration and Nonprofit Management: Quantitative and Qualitative Approaches . 2nd ed. Armonk, NY: M.E. Sharpe, 2008; Quantitative Research Methods. Writing@CSU. Colorado State University; Singh, Kultar. Quantitative Social Research Methods . Los Angeles, CA: Sage, 2007. Basic Research Design for Quantitative StudiesBefore designing a quantitative research study, you must decide whether it will be descriptive or experimental because this will dictate how you gather, analyze, and interpret the results. A descriptive study is governed by the following rules: subjects are generally measured once; the intention is to only establish associations between variables; and, the study may include a sample population of hundreds or thousands of subjects to ensure that a valid estimate of a generalized relationship between variables has been obtained. An experimental design includes subjects measured before and after a particular treatment, the sample population may be very small and purposefully chosen, and it is intended to establish causality between variables. Introduction The introduction to a quantitative study is usually written in the present tense and from the third person point of view. It covers the following information:
Methodology The methods section of a quantitative study should describe how each objective of your study will be achieved. Be sure to provide enough detail to enable the reader can make an informed assessment of the methods being used to obtain results associated with the research problem. The methods section should be presented in the past tense.
Results The finding of your study should be written objectively and in a succinct and precise format. In quantitative studies, it is common to use graphs, tables, charts, and other non-textual elements to help the reader understand the data. Make sure that non-textual elements do not stand in isolation from the text but are being used to supplement the overall description of the results and to help clarify key points being made. Further information about how to effectively present data using charts and graphs can be found here .
Discussion Discussions should be analytic, logical, and comprehensive. The discussion should meld together your findings in relation to those identified in the literature review, and placed within the context of the theoretical framework underpinning the study. The discussion should be presented in the present tense.
Conclusion End your study by to summarizing the topic and provide a final comment and assessment of the study.
Black, Thomas R. Doing Quantitative Research in the Social Sciences: An Integrated Approach to Research Design, Measurement and Statistics . London: Sage, 1999; Gay,L. R. and Peter Airasain. Educational Research: Competencies for Analysis and Applications . 7th edition. Upper Saddle River, NJ: Merril Prentice Hall, 2003; Hector, Anestine. An Overview of Quantitative Research in Composition and TESOL . Department of English, Indiana University of Pennsylvania; Hopkins, Will G. “Quantitative Research Design.” Sportscience 4, 1 (2000); "A Strategy for Writing Up Research Results. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper." Department of Biology. Bates College; Nenty, H. Johnson. "Writing a Quantitative Research Thesis." International Journal of Educational Science 1 (2009): 19-32; Ouyang, Ronghua (John). Basic Inquiry of Quantitative Research . Kennesaw State University. Strengths of Using Quantitative MethodsQuantitative researchers try to recognize and isolate specific variables contained within the study framework, seek correlation, relationships and causality, and attempt to control the environment in which the data is collected to avoid the risk of variables, other than the one being studied, accounting for the relationships identified. Among the specific strengths of using quantitative methods to study social science research problems:
Babbie, Earl R. The Practice of Social Research . 12th ed. Belmont, CA: Wadsworth Cengage, 2010; Brians, Craig Leonard et al. Empirical Political Analysis: Quantitative and Qualitative Research Methods . 8th ed. Boston, MA: Longman, 2011; McNabb, David E. Research Methods in Public Administration and Nonprofit Management: Quantitative and Qualitative Approaches . 2nd ed. Armonk, NY: M.E. Sharpe, 2008; Singh, Kultar. Quantitative Social Research Methods . Los Angeles, CA: Sage, 2007. Limitations of Using Quantitative MethodsQuantitative methods presume to have an objective approach to studying research problems, where data is controlled and measured, to address the accumulation of facts, and to determine the causes of behavior. As a consequence, the results of quantitative research may be statistically significant but are often humanly insignificant. Some specific limitations associated with using quantitative methods to study research problems in the social sciences include:
Research TipFinding Examples of How to Apply Different Types of Research Methods SAGE publications is a major publisher of studies about how to design and conduct research in the social and behavioral sciences. Their SAGE Research Methods Online and Cases database includes contents from books, articles, encyclopedias, handbooks, and videos covering social science research design and methods including the complete Little Green Book Series of Quantitative Applications in the Social Sciences and the Little Blue Book Series of Qualitative Research techniques. The database also includes case studies outlining the research methods used in real research projects. This is an excellent source for finding definitions of key terms and descriptions of research design and practice, techniques of data gathering, analysis, and reporting, and information about theories of research [e.g., grounded theory]. The database covers both qualitative and quantitative research methods as well as mixed methods approaches to conducting research. SAGE Research Methods Online and Cases
How To Write The Results/Findings ChapterFor quantitative studies (dissertations & theses). By: Derek Jansen (MBA) | Expert Reviewed By: Kerryn Warren (PhD) | July 2021 So, you’ve completed your quantitative data analysis and it’s time to report on your findings. But where do you start? In this post, we’ll walk you through the results chapter (also called the findings or analysis chapter), step by step, so that you can craft this section of your dissertation or thesis with confidence. If you’re looking for information regarding the results chapter for qualitative studies, you can find that here . Overview: Quantitative Results Chapter
What exactly is the results chapter?The results chapter (also referred to as the findings or analysis chapter) is one of the most important chapters of your dissertation or thesis because it shows the reader what you’ve found in terms of the quantitative data you’ve collected. It presents the data using a clear text narrative, supported by tables, graphs and charts. In doing so, it also highlights any potential issues (such as outliers or unusual findings) you’ve come across. But how’s that different from the discussion chapter? Well, in the results chapter, you only present your statistical findings. Only the numbers, so to speak – no more, no less. Contrasted to this, in the discussion chapter , you interpret your findings and link them to prior research (i.e. your literature review), as well as your research objectives and research questions . In other words, the results chapter presents and describes the data, while the discussion chapter interprets the data. Let’s look at an example. In your results chapter, you may have a plot that shows how respondents to a survey responded: the numbers of respondents per category, for instance. You may also state whether this supports a hypothesis by using a p-value from a statistical test. But it is only in the discussion chapter where you will say why this is relevant or how it compares with the literature or the broader picture. So, in your results chapter, make sure that you don’t present anything other than the hard facts – this is not the place for subjectivity. It’s worth mentioning that some universities prefer you to combine the results and discussion chapters. Even so, it is good practice to separate the results and discussion elements within the chapter, as this ensures your findings are fully described. Typically, though, the results and discussion chapters are split up in quantitative studies. If you’re unsure, chat with your research supervisor or chair to find out what their preference is. What should you include in the results chapter?Following your analysis, it’s likely you’ll have far more data than are necessary to include in your chapter. In all likelihood, you’ll have a mountain of SPSS or R output data, and it’s your job to decide what’s most relevant. You’ll need to cut through the noise and focus on the data that matters. This doesn’t mean that those analyses were a waste of time – on the contrary, those analyses ensure that you have a good understanding of your dataset and how to interpret it. However, that doesn’t mean your reader or examiner needs to see the 165 histograms you created! Relevance is key. How do I decide what’s relevant? At this point, it can be difficult to strike a balance between what is and isn’t important. But the most important thing is to ensure your results reflect and align with the purpose of your study . So, you need to revisit your research aims, objectives and research questions and use these as a litmus test for relevance. Make sure that you refer back to these constantly when writing up your chapter so that you stay on track. As a general guide, your results chapter will typically include the following:
We’ll discuss each of these points in more detail in the next section. Importantly, your results chapter needs to lay the foundation for your discussion chapter . This means that, in your results chapter, you need to include all the data that you will use as the basis for your interpretation in the discussion chapter. For example, if you plan to highlight the strong relationship between Variable X and Variable Y in your discussion chapter, you need to present the respective analysis in your results chapter – perhaps a correlation or regression analysis. Need a helping hand?How do I write the results chapter?There are multiple steps involved in writing up the results chapter for your quantitative research. The exact number of steps applicable to you will vary from study to study and will depend on the nature of the research aims, objectives and research questions . However, we’ll outline the generic steps below. Step 1 – Revisit your research questionsThe first step in writing your results chapter is to revisit your research objectives and research questions . These will be (or at least, should be!) the driving force behind your results and discussion chapters, so you need to review them and then ask yourself which statistical analyses and tests (from your mountain of data) would specifically help you address these . For each research objective and research question, list the specific piece (or pieces) of analysis that address it. At this stage, it’s also useful to think about the key points that you want to raise in your discussion chapter and note these down so that you have a clear reminder of which data points and analyses you want to highlight in the results chapter. Again, list your points and then list the specific piece of analysis that addresses each point. Next, you should draw up a rough outline of how you plan to structure your chapter . Which analyses and statistical tests will you present and in what order? We’ll discuss the “standard structure” in more detail later, but it’s worth mentioning now that it’s always useful to draw up a rough outline before you start writing (this advice applies to any chapter). Step 2 – Craft an overview introductionAs with all chapters in your dissertation or thesis, you should start your quantitative results chapter by providing a brief overview of what you’ll do in the chapter and why . For example, you’d explain that you will start by presenting demographic data to understand the representativeness of the sample, before moving onto X, Y and Z. This section shouldn’t be lengthy – a paragraph or two maximum. Also, it’s a good idea to weave the research questions into this section so that there’s a golden thread that runs through the document. Step 3 – Present the sample demographic dataThe first set of data that you’ll present is an overview of the sample demographics – in other words, the demographics of your respondents. For example:
The purpose of this is to assess how representative the sample is of the broader population. This is important for the sake of the generalisability of the results. If your sample is not representative of the population, you will not be able to generalise your findings. This is not necessarily the end of the world, but it is a limitation you’ll need to acknowledge. Of course, to make this representativeness assessment, you’ll need to have a clear view of the demographics of the population. So, make sure that you design your survey to capture the correct demographic information that you will compare your sample to. But what if I’m not interested in generalisability? Well, even if your purpose is not necessarily to extrapolate your findings to the broader population, understanding your sample will allow you to interpret your findings appropriately, considering who responded. In other words, it will help you contextualise your findings . For example, if 80% of your sample was aged over 65, this may be a significant contextual factor to consider when interpreting the data. Therefore, it’s important to understand and present the demographic data. Step 4 – Review composite measures and the data “shape”.Before you undertake any statistical analysis, you’ll need to do some checks to ensure that your data are suitable for the analysis methods and techniques you plan to use. If you try to analyse data that doesn’t meet the assumptions of a specific statistical technique, your results will be largely meaningless. Therefore, you may need to show that the methods and techniques you’ll use are “allowed”. Most commonly, there are two areas you need to pay attention to: #1: Composite measures The first is when you have multiple scale-based measures that combine to capture one construct – this is called a composite measure . For example, you may have four Likert scale-based measures that (should) all measure the same thing, but in different ways. In other words, in a survey, these four scales should all receive similar ratings. This is called “ internal consistency ”. Internal consistency is not guaranteed though (especially if you developed the measures yourself), so you need to assess the reliability of each composite measure using a test. Typically, Cronbach’s Alpha is a common test used to assess internal consistency – i.e., to show that the items you’re combining are more or less saying the same thing. A high alpha score means that your measure is internally consistent. A low alpha score means you may need to consider scrapping one or more of the measures. #2: Data shape The second matter that you should address early on in your results chapter is data shape. In other words, you need to assess whether the data in your set are symmetrical (i.e. normally distributed) or not, as this will directly impact what type of analyses you can use. For many common inferential tests such as T-tests or ANOVAs (we’ll discuss these a bit later), your data needs to be normally distributed. If it’s not, you’ll need to adjust your strategy and use alternative tests. To assess the shape of the data, you’ll usually assess a variety of descriptive statistics (such as the mean, median and skewness), which is what we’ll look at next. Step 5 – Present the descriptive statisticsNow that you’ve laid the foundation by discussing the representativeness of your sample, as well as the reliability of your measures and the shape of your data, you can get started with the actual statistical analysis. The first step is to present the descriptive statistics for your variables. For scaled data, this usually includes statistics such as:
A large table that indicates all the above for multiple variables can be a very effective way to present your data economically. You can also use colour coding to help make the data more easily digestible. For categorical data, where you show the percentage of people who chose or fit into a category, for instance, you can either just plain describe the percentages or numbers of people who responded to something or use graphs and charts (such as bar graphs and pie charts) to present your data in this section of the chapter. When using figures, make sure that you label them simply and clearly , so that your reader can easily understand them. There’s nothing more frustrating than a graph that’s missing axis labels! Keep in mind that although you’ll be presenting charts and graphs, your text content needs to present a clear narrative that can stand on its own. In other words, don’t rely purely on your figures and tables to convey your key points: highlight the crucial trends and values in the text. Figures and tables should complement the writing, not carry it . Depending on your research aims, objectives and research questions, you may stop your analysis at this point (i.e. descriptive statistics). However, if your study requires inferential statistics, then it’s time to deep dive into those . Step 6 – Present the inferential statisticsInferential statistics are used to make generalisations about a population , whereas descriptive statistics focus purely on the sample . Inferential statistical techniques, broadly speaking, can be broken down into two groups . First, there are those that compare measurements between groups , such as t-tests (which measure differences between two groups) and ANOVAs (which measure differences between multiple groups). Second, there are techniques that assess the relationships between variables , such as correlation analysis and regression analysis. Within each of these, some tests can be used for normally distributed (parametric) data and some tests are designed specifically for use on non-parametric data. There are a seemingly endless number of tests that you can use to crunch your data, so it’s easy to run down a rabbit hole and end up with piles of test data. Ultimately, the most important thing is to make sure that you adopt the tests and techniques that allow you to achieve your research objectives and answer your research questions . In this section of the results chapter, you should try to make use of figures and visual components as effectively as possible. For example, if you present a correlation table, use colour coding to highlight the significance of the correlation values, or scatterplots to visually demonstrate what the trend is. The easier you make it for your reader to digest your findings, the more effectively you’ll be able to make your arguments in the next chapter. Step 7 – Test your hypothesesIf your study requires it, the next stage is hypothesis testing. A hypothesis is a statement , often indicating a difference between groups or relationship between variables, that can be supported or rejected by a statistical test. However, not all studies will involve hypotheses (again, it depends on the research objectives), so don’t feel like you “must” present and test hypotheses just because you’re undertaking quantitative research. The basic process for hypothesis testing is as follows:
Finally, if the aim of your study is to develop and test a conceptual framework , this is the time to present it, following the testing of your hypotheses. While you don’t need to develop or discuss these findings further in the results chapter, indicating whether the tests (and their p-values) support or reject the hypotheses is crucial. Step 8 – Provide a chapter summaryTo wrap up your results chapter and transition to the discussion chapter, you should provide a brief summary of the key findings . “Brief” is the keyword here – much like the chapter introduction, this shouldn’t be lengthy – a paragraph or two maximum. Highlight the findings most relevant to your research objectives and research questions, and wrap it up. Some final thoughts, tips and tricksNow that you’ve got the essentials down, here are a few tips and tricks to make your quantitative results chapter shine:
If you’ve got any questions about writing up the quantitative results chapter, please leave a comment below. If you’d like 1-on-1 assistance with your quantitative analysis and discussion, check out our hands-on coaching service , or book a free consultation with a friendly 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 ... Thank you. I will try my best to write my results. Awesome content 👏🏾 this was great explaination Submit a Comment Cancel replyYour 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.
Writing Quantitative Research Studies
1744 Accesses 1 Citations Summarizing quantitative data and its effective presentation and discussion can be challenging for students and researchers. This chapter provides a framework for adequately reporting findings from quantitative analysis in a research study for those contemplating to write a research paper. The rationale underpinning the reporting methods to maintain the credibility and integrity of quantitative studies is outlined. Commonly used terminologies in empirical studies are defined and discussed with suitable examples. Key elements that build consistency between different sections (background, methods, results, and the discussion) of a research study using quantitative methods in a journal article are explicated. Specifically, recommended standard guidelines for randomized controlled trials and observational studies for reporting and discussion of findings from quantitative studies are elaborated. Key aspects of methodology that include describing the study population, sampling strategy, data collection methods, measurements/variables, and statistical analysis which informs the quality of a study from the reviewer’s perspective are described. Effective use of references in the methods section to strengthen the rationale behind specific statistical techniques and choice of measures has been highlighted with examples. Identifying ways in which data can be most succinctly and effectively summarized in tables and graphs according to their suitability and purpose of information is also detailed in this chapter. Strategies to present and discuss the quantitative findings in a structured discussion section are also provided. Overall, the chapter provides the readers with a comprehensive set of tools to identify key strategies to be considered when reporting quantitative research. This is a preview of subscription content, log in via an institution to check access. Access this chapterSubscribe and save.
Tax calculation will be finalised at checkout Purchases are for personal use only Institutional subscriptions Similar content being viewed by othersQuantitative ResearchCase Study 3: Application of Quantitative MethodologyBhaumik S, Arora M, Singh A, Sargent JD. Impact of entertainment media smoking on adolescent smoking behaviours. Cochrane Database Syst Rev. 2015;6:1–12. https://doi.org/10.1002/14651858.CD011720 . Article Google Scholar Dickersin K, Manheimer E, Wieland S, Robinson KA, Lefebvre C, McDonald S. Development of the Cochrane Collaboration’s CENTRAL register of controlled clinical trials. Eval Health Prof. 2002;25(1):38–64. Google Scholar Docherty M, Smith R. The case for structuring the discussion of scientific papers: much the same as that for structuring abstracts. Br Med J. 1999;318(7193):1224–5. Greenland S, Pearl J, Robins JM. Causal diagrams for epidemiologic research. Epidemiology. 1999;10(1):37–48. Horton R. The rhetoric of research. Br Med J. 1995;310(6985):985–7. Kool B, Ziersch A, Robinson P, Wolfenden L, Lowe JB. The ‘Seven deadly sins’ of rejected papers. Aust N Z J Public Health. 2016;40(1):3–4. Mannocci A, Saulle R, Colamesta V, D’Aguanno S, Giraldi G, Maffongelli E, et al. What is the impact of reporting guidelines on public health journals in Europe? The case of STROBE, CONSORT and PRISMA. J Public Health. 2015;37(4):737–40. Rothwell PM. External validity of randomised controlled trials: “to whom do the results of this trial apply?”. Lancet. 2005;365(9453):82–93. Schulz KF, Altman DG, Moher D. CONSORT 2010 statement: updated guidelines for reporting parallel group randomised trials. PLoS Med. 2010;7(3):e1000251. Szklo M. Quality of scientific articles. Rev Saude Publica. 2006;40 Spec no:30–5. Vandenbroucke JP, von Elm E, Altman DG, Gotzsche PC, Mulrow CD, Pocock SJ, et al. Strengthening the reporting of observational studies in epidemiology (STROBE): explanation and elaboration. PLoS Med. 2007;4(10):e297. Weiss NS, Koepsell TD, Psaty BM. Generalizability of the results of randomized trials. Arch Intern Med. 2008;168(2):133–5. Singh A, Gupta A, Peres MA, Watt RG, Tsakos G, Mathur MR. Association between tooth loss and hypertension among a primarily rural middle aged and older Indian adult population. J Public Health Dent. 2016;76:198–205. Download references Author informationAuthors and affiliations. Centre for Health Equity, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia Ankur Singh School of Public Health, The University of Adelaide, Adelaide, SA, Australia Adyya Gupta Australian Research Centre for Population Oral Health (ARCPOH), Adelaide Dental School, The University of Adelaide, Adelaide, SA, Australia Karen G. Peres You can also search for this author in PubMed Google Scholar Corresponding authorCorrespondence to Ankur Singh . Editor informationEditors and affiliations. School of Science and Health, Western Sydney University, Penrith, NSW, Australia Pranee Liamputtong Rights and permissionsReprints and permissions Copyright information© 2019 Springer Nature Singapore Pte Ltd. About this entryCite this entry. Singh, A., Gupta, A., Peres, K.G. (2019). Writing Quantitative Research Studies. In: Liamputtong, P. (eds) Handbook of Research Methods in Health Social Sciences. Springer, Singapore. https://doi.org/10.1007/978-981-10-5251-4_117 Download citationDOI : https://doi.org/10.1007/978-981-10-5251-4_117 Published : 13 January 2019 Publisher Name : Springer, Singapore Print ISBN : 978-981-10-5250-7 Online ISBN : 978-981-10-5251-4 eBook Packages : Social Sciences Reference Module Humanities and Social Sciences Reference Module Business, Economics and Social Sciences Share this entryAnyone 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
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Educational resources and simple solutions for your research journey What is Quantitative Research? Definition, Methods, Types, and ExamplesIf you’re wondering what is quantitative research and whether this methodology works for your research study, you’re not alone. If you want a simple quantitative research definition , then it’s enough to say that this is a method undertaken by researchers based on their study requirements. However, to select the most appropriate research for their study type, researchers should know all the methods available. Selecting the right research method depends on a few important criteria, such as the research question, study type, time, costs, data availability, and availability of respondents. There are two main types of research methods— quantitative research and qualitative research. The purpose of quantitative research is to validate or test a theory or hypothesis and that of qualitative research is to understand a subject or event or identify reasons for observed patterns. Quantitative research methods are used to observe events that affect a particular group of individuals, which is the sample population. In this type of research, diverse numerical data are collected through various methods and then statistically analyzed to aggregate the data, compare them, or show relationships among the data. Quantitative research methods broadly include questionnaires, structured observations, and experiments. Here are two quantitative research examples:
Table of Contents What is quantitative research ? 1,2The steps shown in the figure can be grouped into the following broad steps:
Quantitative research characteristics 4
Quantitative research methods 5Quantitative research methods are classified into two types—primary and secondary. Primary quantitative research method:In this type of quantitative research , data are directly collected by the researchers using the following methods. – Survey research : Surveys are the easiest and most commonly used quantitative research method . They are of two types— cross-sectional and longitudinal. ->Cross-sectional surveys are specifically conducted on a target population for a specified period, that is, these surveys have a specific starting and ending time and researchers study the events during this period to arrive at conclusions. The main purpose of these surveys is to describe and assess the characteristics of a population. There is one independent variable in this study, which is a common factor applicable to all participants in the population, for example, living in a specific city, diagnosed with a specific disease, of a certain age group, etc. An example of a cross-sectional survey is a study to understand why individuals residing in houses built before 1979 in the US are more susceptible to lead contamination. ->Longitudinal surveys are conducted at different time durations. These surveys involve observing the interactions among different variables in the target population, exposing them to various causal factors, and understanding their effects across a longer period. These studies are helpful to analyze a problem in the long term. An example of a longitudinal study is the study of the relationship between smoking and lung cancer over a long period. – Descriptive research : Explains the current status of an identified and measurable variable. Unlike other types of quantitative research , a hypothesis is not needed at the beginning of the study and can be developed even after data collection. This type of quantitative research describes the characteristics of a problem and answers the what, when, where of a problem. However, it doesn’t answer the why of the problem and doesn’t explore cause-and-effect relationships between variables. Data from this research could be used as preliminary data for another study. Example: A researcher undertakes a study to examine the growth strategy of a company. This sample data can be used by other companies to determine their own growth strategy. – Correlational research : This quantitative research method is used to establish a relationship between two variables using statistical analysis and analyze how one affects the other. The research is non-experimental because the researcher doesn’t control or manipulate any of the variables. At least two separate sample groups are needed for this research. Example: Researchers studying a correlation between regular exercise and diabetes. – Causal-comparative research : This type of quantitative research examines the cause-effect relationships in retrospect between a dependent and independent variable and determines the causes of the already existing differences between groups of people. This is not a true experiment because it doesn’t assign participants to groups randomly. Example: To study the wage differences between men and women in the same role. For this, already existing wage information is analyzed to understand the relationship. – Experimental research : This quantitative research method uses true experiments or scientific methods for determining a cause-effect relation between variables. It involves testing a hypothesis through experiments, in which one or more independent variables are manipulated and then their effect on dependent variables are studied. Example: A researcher studies the importance of a drug in treating a disease by administering the drug in few patients and not administering in a few. The following data collection methods are commonly used in primary quantitative research :
The data collected can be analyzed in several ways in quantitative research , as listed below:
Secondary quantitative research methods :This method involves conducting research using already existing or secondary data. This method is less effort intensive and requires lesser time. However, researchers should verify the authenticity and recency of the sources being used and ensure their accuracy. The main sources of secondary data are:
When to use quantitative research 6Here are some simple ways to decide when to use quantitative research . Use quantitative research to:
A research case study to understand when to use quantitative research 7Context: A study was undertaken to evaluate a major innovation in a hospital’s design, in terms of workforce implications and impact on patient and staff experiences of all single-room hospital accommodations. The researchers undertook a mixed methods approach to answer their research questions. Here, we focus on the quantitative research aspect. Research questions : What are the advantages and disadvantages for the staff as a result of the hospital’s move to the new design with all single-room accommodations? Did the move affect staff experience and well-being and improve their ability to deliver high-quality care? Method: The researchers obtained quantitative data from three sources:
Results of quantitative research : The following observations were made based on quantitative data analysis:
Advantages of quantitative research 1,2When choosing the right research methodology, also consider the advantages of quantitative research and how it can impact your study.
Disadvantages of quantitative research 1,2Quantitative research may also be limiting; take a look at the disadvantages of quantitative research.
Frequently asked questions on quantitative researchQ: What is the difference between quantitative research and qualitative research? 1 A: The following table lists the key differences between quantitative research and qualitative research, some of which may have been mentioned earlier in the article.
Q: What is the difference between reliability and validity? 8,9 A: The term reliability refers to the consistency of a research study. For instance, if a food-measuring weighing scale gives different readings every time the same quantity of food is measured then that weighing scale is not reliable. If the findings in a research study are consistent every time a measurement is made, then the study is considered reliable. However, it is usually unlikely to obtain the exact same results every time because some contributing variables may change. In such cases, a correlation coefficient is used to assess the degree of reliability. A strong positive correlation between the results indicates reliability. Validity can be defined as the degree to which a tool actually measures what it claims to measure. It helps confirm the credibility of your research and suggests that the results may be generalizable. In other words, it measures the accuracy of the research. The following table gives the key differences between reliability and validity.
Q: What is mixed methods research? 10 A: A mixed methods approach combines the characteristics of both quantitative research and qualitative research 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. A mixed methods research design is useful in case of research questions that cannot be answered by either quantitative research or qualitative research alone. However, this method could be more effort- and cost-intensive because of the requirement of more resources. The figure 3 shows some basic mixed methods research designs that could be used. Thus, quantitative research is the appropriate method for testing your hypotheses and can be used either alone or in combination with qualitative research per your study requirements. We hope this article has provided an insight into the various facets of quantitative research , including its different characteristics, advantages, and disadvantages, and a few tips to quickly understand when to use this research method. References
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Example of a Quantitative Research Paper for Students & ResearchersThis example of a quantitative research paper is designed to help students and other r esearchers who are learning how to write about their work. The reported research obs erves the behaviour of restaurant customers, and example paragraphs are combined with instructions for logical argumentation. Authors are encouraged to observe a traditional structure for organising quantitative research papers, to formulate research que stions, working hypotheses and investigative tools, to report results accurately and thor oughly, and to present thoughtful interpretation and logical discussion of evidence. Related PapersJournal of Foodservice Christina Fjellström Rohit Taraporewala Noor Mustafa FAST FOOD OBESITY 16 Princess Moon Galindez Journal of Hospitality & Leisure Marketing Tajulurrus mohammad Food industry, the world over, is witnessing unprecedented increase in the number of multinational enterprises. These multinational enterprises, when deciding to expand their operations to a new country, have to make a choice between following uniform business strategies as in their home country or modify their strategies to suit the host country socioeconomic and political environment. Given the economic cost of modification of business strategies, the choice has widespread implications for the sustainability of multinational enterprises. The present paper argues that this decision-making is particularly critical in the case of multinational food enterprises because of large scale variability in food habits across countries and even within a country. Drawing from case studies of three multinational food enterprises in India, the paper points out that, in order to operate successfully in their host countries, the multinational food enterprises must adopt Glocalized strategies in marketing, product development, advertisement etc. Modern China Series,North American Business Press Robert Tian Food is an important aspect of social culture and has a close relationship with economic development. The Chinese food culture has the characteristics of inheritability and development, and throughout the history of Chinese food culture, it has maintained its momentum of development since its primitive society. Neither the change of dynasty nor the change of social system has had a profound influence on it, and the philosophy of supplying enough food to people and food being the top priority was very popular. Eating was a top priority for people in China. Long ago, Confucius said that the desire for food and sex is part of human nature. As such, in the Chinese culture food became the priority. Because of the attention to diet, Chinese people would, when they had leisure time or abundant raw materials, work out a variety of food. Chinese cooking is flexible, which is characterized by saying that there is no fixed taste and what is delicious is valued. The beauty of food is one of the important roots of Chinese aesthetics, which inspires people with the stimulation of eating. Triggering art inspiration is the inevitable result of Chinese food culture pursuing complete and beautiful color, fragrance, taste, shape, and utensils. It makes food culture a comprehensive art containing multiple cultural connotations of diet, diet mentality, beautiful utensils and etiquette, food enjoyment and eating. Chinese foods have not only exquisite craftsmanship and rich nutrition, but also elegant and graceful names, which are literary and romantic, poetic and fancy. Food functions to not only satiate people’s hunger; it has also become an integral aspect of life enjoyment, which represents an essential component of food anthropology. Food anthropologists stress that changes in people’s eating habits not only depend on the local food culture, which may be specific to a given region, but also varies with economic development in different regions. Food anthropology, as a sub branch of applied anthropology, adapts anthropological theories and methods to study food industry, food culture, food consumption and food commerce. Seminal work in this regard has been provided by scholars and consultants in the field of food anthropology. This book describes the anthropological studies on Chinese foodways, outlines the Chinese food anthropology basic theories and methods. Anthropology in China is still at its development stage in China, while food anthropology is just at its initial stages of development. Nevertheless, China’s economic and social development, especially in ethnic minority regions in Western China, needs the theoretical guidance of some disciplines, including food anthropology, economic anthropology and business anthropology. At the same time, it has provided opportunities to develop food anthropology with the Chinese characteristics. Therefore, when Chinese scholars are learning and adopting Western food anthropology theories and methodologies, they must innovate and develop the related theories and methodologies with Chinese characteristics, so that they can better serve the well-off of the entire society. MUHAMMAD IMAD UD DIN City & Community Petra Kuppinger Loading Preview Sorry, preview is currently unavailable. You can download the paper by clicking the button above. RELATED PAPERSGolden Arches East: McDonald's in East … Anuththara Wanaguru Adrian Paul Padilla Freya Higgins-Desbiolles , Gayathri Wijesinghe Jeroen Struben Harris Solomon Divina Seming AIMS Agriculture and Food Giuseppe Sortino , Pietro Columba Emmanuel Marillier Anshul Garg American Journal of Public Health Janelle Gunn Łukasz Korus Asmaliyana Ghani Denise Mainville Dayangku Nurul Asyiqin The 18 th Annual … Anil Bilgihan Celyrah B Castillo Asian Journal of Tourism Research Kathleen M Adams
Quantitative Research: Examples of Research Questions and SolutionsAre you ready to embark on a journey into the world of quantitative research? Whether you’re a seasoned researcher or just beginning your academic journey, understanding how to formulate effective research questions is essential for conducting meaningful studies. In this blog post, we’ll explore examples of quantitative research questions across various disciplines and discuss how StatsCamp.org courses can provide the tools and support you need to overcome any challenges you may encounter along the way. Understanding Quantitative Research Questions Quantitative research involves collecting and analyzing numerical data to answer research questions and test hypotheses. These questions typically seek to understand the relationships between variables, predict outcomes, or compare groups. Let’s explore some examples of quantitative research questions across different fields:
Stats Camp: Your Solution to Mastering Quantitative Research Methodologies At StatsCamp.org, we understand that navigating the complexities of quantitative research can be daunting. That’s why we offer a range of courses designed to equip you with the knowledge and skills you need to excel in your research endeavors. Whether you’re interested in learning about regression analysis, experimental design, or structural equation modeling, our experienced instructors are here to guide you every step of the way. Bringing Your Own Data One of the unique features of StatsCamp.org is the opportunity to bring your own data to the learning process. Our instructors provide personalized guidance and support to help you analyze your data effectively and overcome any roadblocks you may encounter. Whether you’re struggling with data cleaning, model specification, or interpretation of results, our team is here to help you succeed. Courses Offered at StatsCamp.org
As you embark on your journey into quantitative research, remember that StatsCamp.org is here to support you every step of the way. Whether you’re formulating research questions, analyzing data, or interpreting results, our courses provide the knowledge and expertise you need to succeed. Join us today and unlock the power of quantitative research! Follow Us On Social! Facebook | Instagram | X 933 San Mateo Blvd NE #500, Albuquerque, NM 87108 4414 82 nd Street #212-121 Lubbock, TX 79424 Monday – Friday: 9:00 AM – 5:00 PM © Copyright 2003 - 2024 | All Rights Reserved Stats Camp Foundation 501(c)(3) Non-Profit Organization. Quantitative Research ExamplesUpdated October 9, 2023 Quantitative Research Examples – IntroductionQuantitative research is a systematic approach to collecting and analyzing data from various sources. It uses statistical, computational, and mathematical methods to extract valuable findings and draw conclusions. In this article, you will see different quantitative research examples, explaining how to collect and analyze data in quantitative research. Start Your Free Investment Banking Course Download Corporate Valuation, Investment Banking, Accounting, CFA Calculator & others 7 Easy Quantitative Research ExamplesLet us first see a few simple hypothetical quantitative research examples (Example #1 to Example #4). Consider a researcher who conducted a quantitative survey among parents of children aged 1-8 years to study how many parents are fine with their children using phones. A total of 150 participated in the survey, where they rated their agreement on a 7-point scale.
Method: To find the average perspective of parents on giving mobile phones to children, the researcher finds the average of all 150 collected values (Sum of all values ÷ 150). Result : The results of the survey show the following insights:
We can see from the analyzed data that most parents are more likely to provide their children with mobile phones in today’s technological world. Suppose a startup company, BVN corporation, wants to test their employee’s satisfaction levels. The company divides the employees into six groups of 5 employees each. They then conduct a survey asking the following questions where the answers must range from 1 (lowest) to 10 (highest).
Result: The following image depicts the rating given by groups and the overall average rating. The interpretation of the results is as follows.
Let’s say a hospital performs quantitative research to analyze how efficient the hospital’s operations are. The hospital conducts a survey to collect data from both doctors and patients. The survey included questions such as:
Method: After getting all the information, the researcher determines the option that most people choose. For example, if 6 out of 10 people picked “<10 mins” for “How long the doctor spends with each patient?”, that’s what they consider as the average. Result: The following are the key results from the survey.
Let’s consider an NGO that wants to run an educational program in the village. Their aim is to improve the literacy rate in the village. However, before they launched the program, first, the organization first surveyed the entire village population (N=450) to know how many were likely to participate. Result: In the survey, the NGO found that Individuals aged 30-45 showed 60% interest, while those below 30 years showed 45% interest, and those above 45 years showed 40% interest. Finally, 50% (225) of the village population participated in the program. The four examples we just saw were simple hypothetical quantitative research examples. Now, let us see some real-life examples of quantitative research. In 2015 , researchers conducted an experimental study on the effect of lack of sleep on colds. The study was a two-part experiment conducted on 164 healthy individuals. Participants had to record their one-week bedtime in the first part. In the second part, researchers quarantine the participants in a hotel and give them nose drops containing virus-causing colds, i.e., rhinovirus. Data collection method: Participants recorded their bedtime, like sleeping and waking up time. Also, researchers used wrist actigraphy data to monitor sleep movement. Blood samples were collected to check the level (number) of rhinovirus antibodies. Tissues with mucus were used as a sign of illness, meaning if a participant used 10g or more tissues, they were sick. Method: The researchers used SPSS , a computer program, and logistic regression to predict which participants got colds and which didn’t. After that, they grouped the participants into categories based on how much they slept and, among those, how many people caught a cold. Result: A few highlights from the study were as follows:
The image below shows the correlation between the total % of participants who got the cold and their respective sleeping hours. In April 2020 , researchers conducted a cross-sectional survey in Bangladesh to explore the total sleep duration, night-time sleep, and daily naptime. 9,730 participants took a survey, including a questionnaire related to socio-demographic variables (age, gender, occupation), behavioral and health factors (smoking, alcohol consumption), depression, suicidal thoughts, night sleep duration, naptime duration, etc. Data collection method: In this study, researchers collected the data through online survey forms from participants aged 18–64 in Bangladesh. Analysis tools: SPSS 25.0, Stata 16, ArcGIS 10.7, etc. Method: The researchers made digital maps of Bangladesh using GIS mapping. They divided the maps into different sections to show nap times, how long people slept at night, and the total sleep duration. They also made another map that revealed how areas with COVID-19 cases related to the amount of sleep people got at night in those places. Result: Using the GIS maps, the researchers observed the following:
A study conducted in Kerman, Iran, in 2010-2011 , wanted to find the correlation between computer games and behavioral problems in adolescent boys. The study involved 384 male school students with a questionnaire and Achenbach’s Youth Self-Report (YSR) to assess their behavior problems. The YSR evaluates various issues, such as anxiety, depression, social problems, and more, comprising 10 categories. Data collection method: The students filled out the questionnaire form regarding computer game usage, including how likely they were to play those games and if they contained any violent content. Analysis tools: Bivariate regression, ANOVA, and SPSS 20.0. Method: In the questionnaire, participants listed their top five favorite video games and rated their frequency of play, the level of violent content, and the presence of violent images on a 7-point scale. To calculate the exposure score, the researchers added the content and image scores and multiplied the result by the play frequency divided by 5. In the YSR questionnaire, participants rated each game on a 5-point scale. To get dimension scores, the researchers totaled the scores for each item. Finally, they summed up the dimension scores to calculate the total score (all items combined). Result: The study found that:
Final ThoughtsQuantitative research examples rely on factual information, numerical data, and statistics. Its main advantage lies in the ease of predicting outcomes. Researchers gather information through different tools, equipment, surveys, questionnaires, quantified behaviors, and research methods, among other variables. Recommended ArticlesThis article is a complete guide to different quantitative research examples. You can also go through our other suggested articles to learn more.
*Please provide your correct email id. Login details for this Free course will be emailed to you By signing up, you agree to our Terms of Use and Privacy Policy . Corporate Valuation, Investment Banking, Accounting, CFA Calculator & others Forgot Password? This website or its third-party tools use cookies, which are necessary to its functioning and required to achieve the purposes illustrated in the cookie policy. By closing this banner, scrolling this page, clicking a link or continuing to browse otherwise, you agree to our Privacy Policy Explore 1000+ varieties of Mock tests View more Submit Next Question 🚀 Limited Time Offer! - 🎁 ENROLL NOW Qualitative vs Quantitative Research Methods & Data AnalysisSaul McLeod, PhD Editor-in-Chief for Simply Psychology BSc (Hons) Psychology, MRes, PhD, University of Manchester Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology. Learn about our Editorial Process Olivia Guy-Evans, MSc Associate Editor for Simply Psychology BSc (Hons) Psychology, MSc Psychology of Education Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors. On This Page: What is the difference between quantitative and qualitative?The main difference between quantitative and qualitative research is the type of data they collect and analyze. Quantitative research collects numerical data and analyzes it using statistical methods. The aim is to produce objective, empirical data that can be measured and expressed in numerical terms. Quantitative research is often used to test hypotheses, identify patterns, and make predictions. Qualitative research , on the other hand, collects non-numerical data such as words, images, and sounds. The focus is on exploring subjective experiences, opinions, and attitudes, often through observation and interviews. Qualitative research aims to produce rich and detailed descriptions of the phenomenon being studied, and to uncover new insights and meanings. Quantitative data is information about quantities, and therefore numbers, and qualitative data is descriptive, and regards phenomenon which can be observed but not measured, such as language. What Is Qualitative Research?Qualitative research is the process of collecting, analyzing, and interpreting non-numerical data, such as language. Qualitative research can be used to understand how an individual subjectively perceives and gives meaning to their social reality. Qualitative data is non-numerical data, such as text, video, photographs, or audio recordings. This type of data can be collected using diary accounts or in-depth interviews and analyzed using grounded theory or thematic analysis. Qualitative research is multimethod in focus, involving an interpretive, naturalistic approach to its subject matter. This means that qualitative researchers study things in their natural settings, attempting to make sense of, or interpret, phenomena in terms of the meanings people bring to them. Denzin and Lincoln (1994, p. 2) Interest in qualitative data came about as the result of the dissatisfaction of some psychologists (e.g., Carl Rogers) with the scientific study of psychologists such as behaviorists (e.g., Skinner ). Since psychologists study people, the traditional approach to science is not seen as an appropriate way of carrying out research since it fails to capture the totality of human experience and the essence of being human. Exploring participants’ experiences is known as a phenomenological approach (re: Humanism ). Qualitative research is primarily concerned with meaning, subjectivity, and lived experience. The goal is to understand the quality and texture of people’s experiences, how they make sense of them, and the implications for their lives. Qualitative research aims to understand the social reality of individuals, groups, and cultures as nearly as possible as participants feel or live it. Thus, people and groups are studied in their natural setting. Some examples of qualitative research questions are provided, such as what an experience feels like, how people talk about something, how they make sense of an experience, and how events unfold for people. Research following a qualitative approach is exploratory and seeks to explain ‘how’ and ‘why’ a particular phenomenon, or behavior, operates as it does in a particular context. It can be used to generate hypotheses and theories from the data. Qualitative MethodsThere are different types of qualitative research methods, including diary accounts, in-depth interviews , documents, focus groups , case study research , and ethnography. The results of qualitative methods provide a deep understanding of how people perceive their social realities and in consequence, how they act within the social world. The researcher has several methods for collecting empirical materials, ranging from the interview to direct observation, to the analysis of artifacts, documents, and cultural records, to the use of visual materials or personal experience. Denzin and Lincoln (1994, p. 14) Here are some examples of qualitative data: Interview transcripts : Verbatim records of what participants said during an interview or focus group. They allow researchers to identify common themes and patterns, and draw conclusions based on the data. Interview transcripts can also be useful in providing direct quotes and examples to support research findings. Observations : The researcher typically takes detailed notes on what they observe, including any contextual information, nonverbal cues, or other relevant details. The resulting observational data can be analyzed to gain insights into social phenomena, such as human behavior, social interactions, and cultural practices. Unstructured interviews : generate qualitative data through the use of open questions. This allows the respondent to talk in some depth, choosing their own words. This helps the researcher develop a real sense of a person’s understanding of a situation. Diaries or journals : Written accounts of personal experiences or reflections. Notice that qualitative data could be much more than just words or text. Photographs, videos, sound recordings, and so on, can be considered qualitative data. Visual data can be used to understand behaviors, environments, and social interactions. Qualitative Data AnalysisQualitative research is endlessly creative and interpretive. The researcher does not just leave the field with mountains of empirical data and then easily write up his or her findings. Qualitative interpretations are constructed, and various techniques can be used to make sense of the data, such as content analysis, grounded theory (Glaser & Strauss, 1967), thematic analysis (Braun & Clarke, 2006), or discourse analysis . For example, thematic analysis is a qualitative approach that involves identifying implicit or explicit ideas within the data. Themes will often emerge once the data has been coded . Key Features
Limitations of Qualitative Research
Advantages of Qualitative Research
What Is Quantitative Research?Quantitative research involves the process of objectively collecting and analyzing numerical data to describe, predict, or control variables of interest. The goals of quantitative research are to test causal relationships between variables , make predictions, and generalize results to wider populations. Quantitative researchers aim to establish general laws of behavior and phenomenon across different settings/contexts. Research is used to test a theory and ultimately support or reject it. Quantitative MethodsExperiments typically yield quantitative data, as they are concerned with measuring things. However, other research methods, such as controlled observations and questionnaires , can produce both quantitative information. For example, a rating scale or closed questions on a questionnaire would generate quantitative data as these produce either numerical data or data that can be put into categories (e.g., “yes,” “no” answers). Experimental methods limit how research participants react to and express appropriate social behavior. Findings are, therefore, likely to be context-bound and simply a reflection of the assumptions that the researcher brings to the investigation. There are numerous examples of quantitative data in psychological research, including mental health. Here are a few examples: Another example is the Experience in Close Relationships Scale (ECR), a self-report questionnaire widely used to assess adult attachment styles . The ECR provides quantitative data that can be used to assess attachment styles and predict relationship outcomes. Neuroimaging data : Neuroimaging techniques, such as MRI and fMRI, provide quantitative data on brain structure and function. This data can be analyzed to identify brain regions involved in specific mental processes or disorders. For example, the Beck Depression Inventory (BDI) is a clinician-administered questionnaire widely used to assess the severity of depressive symptoms in individuals. The BDI consists of 21 questions, each scored on a scale of 0 to 3, with higher scores indicating more severe depressive symptoms. Quantitative Data AnalysisStatistics help us turn quantitative data into useful information to help with decision-making. We can use statistics to summarize our data, describing patterns, relationships, and connections. Statistics can be descriptive or inferential. Descriptive statistics help us to summarize our data. In contrast, inferential statistics are used to identify statistically significant differences between groups of data (such as intervention and control groups in a randomized control study).
Limitations of Quantitative Research
Advantages of Quantitative Research
Antonius, R. (2003). Interpreting quantitative data with SPSS . Sage. Black, T. R. (1999). Doing quantitative research in the social sciences: An integrated approach to research design, measurement and statistics . Sage. Braun, V. & Clarke, V. (2006). Using thematic analysis in psychology . Qualitative Research in Psychology , 3, 77–101. Carr, L. T. (1994). The strengths and weaknesses of quantitative and qualitative research : what method for nursing? Journal of advanced nursing, 20(4) , 716-721. Denscombe, M. (2010). The Good Research Guide: for small-scale social research. McGraw Hill. Denzin, N., & Lincoln. Y. (1994). Handbook of Qualitative Research. Thousand Oaks, CA, US: Sage Publications Inc. Glaser, B. G., Strauss, A. L., & Strutzel, E. (1968). The discovery of grounded theory; strategies for qualitative research. Nursing research, 17(4) , 364. Minichiello, V. (1990). In-Depth Interviewing: Researching People. Longman Cheshire. Punch, K. (1998). Introduction to Social Research: Quantitative and Qualitative Approaches. London: Sage Further Information
9 Quantitative Research Methods With Real Client Examples
Share ArticleQuantitative research is essential to developing a clear understanding of consumer engagement and how to increase satisfaction. Primary Quantitative Research MethodsWhen it comes to quantitative research, many people often confuse this type of research with the methodology. The research type refers to style of research while the data collection method can be different. Research typesThese are the primary types of quantitative research used by businesses today.
Also read: 6 Factors Influencing Customer Behaviors in 2021 Data collection methodsLaunching the above research requires creating a plan to collect data. After all, quantitative research relies on data. Here are the common primary data collection methods for quantitative research.
Secondary research can be helpful when formulating a plan for obtaining primary quantitative data. It can help narrow areas of focus or illuminate key challenges. Secondary Quantitative Research MethodsSecondary data is information that is already collected and not necessarily exclusive to the company but still relevant when understanding overall industry and marketplace trends. Here are a few examples of secondary data:
Secondary research can be helpful when formulating a plan for obtaining primary quantitative data. It can help narrow areas of focus or illuminate key challenges. It can also help when it comes to interpreting primary data, especially when trying to understand the relationship between two variables of correlated data. Also read: The What, Why, & How of Customer Behavior Analysis Real Examples of Quantitative ResearchWe regularly use quantitative research to help our clients understand where they can best add value to increase customer engagement. Here are three examples of quantitative research in motion. Example 1: Leading food distribution companyWe helped a leading food distribution company identify changes in the needs and values of their restaurant clients as a result of COVID-19. This helped inform opportunities to become more valuable partners. The research plan involved creating a survey that was emailed to clients. The questions were specific and numeric. For example, respondents were asked what percentage of their weekly spend was used with the food distribution company. They were also asked to assign a percentage to the way their food ordering had changed during COVID-19 and to rate their satisfaction with the food distribution company. The results showed changes that had occurred for clients of the food distribution company as a result of the unique stressors of the pandemic. We were able to determine changes in weekly food supply and customer count as well as menu adaptations and purchase behavior. Example 2: Leading credit card companyOur work with a leading credit card company required us to understand what current travel card members valued about the rewards program and their preferred communication method for booking travel in order to create an omnichannel servicing strategy and ideal customer journey. Through an online survey of younger cardholders, the target demographic for this project, we asked questions such as length of card membership, total spend and the number of annual leisure trips in addition to more specific questions that showed how members get inspiration for trip planning and where they research. The results highlighted ways to overcome resistance to pricing by proving more value. It also illuminated ways to make the benefits of membership more tangible to card holders and how to influence travelers in the early stages of planning their journey. Example 3: Internal research reportWe’re in the business of drinking our own champagne, so to speak, which is why we conducted our own quantitative research aimed at understanding the consumer trends that were spurred by the pandemic and how these will transform behaviors in the future. There’s no question that new customer experiences emerged from the pandemic. Think of offerings such as “buy online, pickup in store (BOPIS),” or blended restaurant meals that are cooked at home. We wanted to understand how consumers truly felt about these new experiences and which they were likely to continue using even after restrictions were lifted. We also wanted to know more about the changing expectations for branded communication and how all of these pieces of the puzzle fit together to create consumer engagement. Our method of data collection was a survey. Our research led us to develop insights we could use to inform our customers in their decision making. For example, we found convenience is paramount for consumers who are seeking out hybrid experiences such as BOPIS to take the best of both worlds. We also found many of these changes are permanent as consumers embraced new experiences that made their lives easier. We regularly use quantitative research to help our clients understand where they can best add value to increase customer engagement. The Bottom LineQuantitative research is essential to developing a clear understanding of consumer engagement and how to increase satisfaction. Though online surveys are one of the most common methods for obtaining data, research isn’t limited to this strategy. It’s important to use whatever strategies are within your scope to constantly evaluate new trends and consumer behaviors that could significantly impact your offerings. The results can show you how to re-engage customers and drive loyalty. Interested in partnering with us to learn more about your customers needs, wants, and behaviors to inform future experience design? Contact us today !Tallwave Headquarters 6720 N. Scottsdale Road, Suite 140 Scottsdale, Arizona 85253 (602) 840-0400 For business inquiries, contact Ed Borromeo, Tallwave Partner at [email protected] ©2024 Tallwave LLC. All rights reserved. Terms of Use Bunger SteelDoing some things and making some impacts Quantitative ResearchAi generator. In conducting quantitative research, you need to make sure you have the right numbers and the correct values for specific variables. This is because quantitative research focuses more on numeric and logical results. Quantitative studies report and understand numerical data to make further analysis of a given phenomenon. This research organizes and computes statistics from current and prospect clients to make business forecasts for your company. Quantitative analysis examples also uses methods like polls, surveys, and sampling to gather information that can help complete your investigation. 31+ Quantitative Research ExamplesQuantitative research demands focus and precision from the researcher. If you need a guide in doing your research, here are 10+ Quantitative research examples you can use. 1. Free Quantitative Research Flowchart Example
Size: 80.2 KB Download 2. Free Quantitative Research Analyst Resume ExampleSize: 146 KB 3. Quantitative Research Review TemplateSize: 163 KB 4. Quantitative Research Plan TemplateSize: 152 KB 5. Quantitative Research Descriptive Analysis TemplateSize: 207 KB 6. Quantitative Research Checklist TemplateSize: 168 KB 7. Quantitative Research Survey TemplateSize: 182 KB 8. Quantitative Research Data Analysis TemplateSize: 145 KB 9. Quantitative Research Guide TemplateSize: 134 KB 10. Quantitative Research Proposal TemplateSize: 185 KB 11. Quantitative Research Question TemplateSize: 186 KB 12. Quantitative Research Literacy TemplateSize: 184 KB 13. Quantitative Research Correlation TemplateSize: 162 KB 14. Quantitative Research TemplateSize: 144 KB 15. Quantitative Research Report Template16. Simple Quantitative Research TemplateSize: 167 KB 17. Quantitative Research Paper TemplateSize: 173 KB 18. Example of Quantitative ResearchSize: 268 KB 19. Quantitative Research Design ExamplesSize: 30 KB 20. Quantitative Research Examples for StudentsSize: 938 KB 21. Impact of Social Media Reviews on Brands Perception ExampleSize: 1.5 MB In the age where likes, comments, and retweets measure the relevance of an entity online, brands make sure that their followers and customers have a positive perception of them on the web. The internet puts companies and individuals at a spot where the public eye sees reviews and comments about them. But how do these things affect the way people view a company’s branding? This quantitative study of the impact of social media reviews on brands perception answers that. Use this research as a guide in conducting your quantitative research. 22. Teacher Perceptions of Professional Learning Communities ExampleSize: 1.2 MB Educators lead young minds to great success. That is why there are training programs examples and models where teachers can collaborate and share how they can improve students’ learning. Saying this, some do question the effectiveness of models such as Professional Learning Communities. Research called “A Quantitative Study of Teacher Perceptions of Professional Learning Communities’ Context, Process, and Content,” looks into these queries. If you are conducting your quantitative research, you can use this research as an example for your study. Format your content like this investigation for a foolproof thesis paper. 23. Quantitative Research On The Level of Social Media Addiction ExampleSize: 658.2 KB The worldwide web is a being of wonder and mystery. That’s what makes it fascinating to young audiences. The internet helps them connect and interact with people through various social media platforms. With features and advancements that intrigue even the unexcited, addiction does become inevitable. An investigation in 2015 titled “A Quantitative Research on the Level of Social Media Addiction among Young People in Turkey” looks into the statistics of this problem. For your quantitative research, use this study as a guide in organizing and formatting your quantitative data. 24. Course Grades and Retention Comparing Online and Face-to-face ClassesSize: 274.4 KB Are you taking online classes, or are your classes held in a classroom? Do you believe there is a difference between online and face-to-face courses? There has always been a discussion between which education instructional method is more effective. Although both help students learn, some argue that the way they are taught makes an education gap. This quantitative study of course grades and retention comparing online and face-to-face classes can help answer your questions. It can also serve as a model in making your own quantitative research. Pattern your research design like this one now! 25. Free Nursing Quantitative Research Proposal ExampleSize: 201.7 KB One of a nurse’s primary duties is to assure patients are taken care of and attended to. Their line of work deals with peoples’ lives and health. This also means that they still need to address patients even if they’re close to death. In Ireland, a study called “A Quantitative Study of the Attitude, Knowledge, and Experience of Staff Nurses on Prioritizing Comfort measures in Care of the Dying Patient in an Acute Hospital Setting” was conducted. If you plan on undertaking any medical SWOT analysis , using this study as a guide would be beneficial for you. 26. Quantitative Research Of Consumer’s Attitude Towards Food Products AdvertisingSize: 845.8 KB In the corporate world, you can’t just start selling something without proper research. You first have to make sure that your products and services are relevant and marketable. The first step should be conducting marketing research. Marketing research can use either qualitative or quantitative data collection methods. But if you want to figure out how your clients react to your products and marketing strategy, this quantitative research of consumer’s attitude towards food products advertising could be your guide. You can even use this for your undergraduate research. 27. Free Effective Teacher Leadership ExampleSize: 407.1 KB Research projects have to be conducted with precision and accuracy, especially if it’s quantitative research. You need to make sure you get the right numbers to get valid results. In research called “Effective Teacher Leadership: A Quantitative Study of the Relationship Between School Structures and Effective Teacher Leaders,” quantitative data analysis is conducted to look into the school’s management plans. For your research, this would be a useful guide in doing comprehensive qualitative research. You can outline your investigations and even term papers using this as a sample. 28. Quantitative Studies of Water and Sanitation Utilities ExampleSize: 376 KB Quantitative research is a method that studies numerical values. It follows a strict process of data collection. This type of research is used by different industries and even as undergraduate research. That is why the research design should reflect the nature of your research. It should look professional and comprehensive. But that doesn’t mean that your research project plan has to look dull. This study called “Quantitative Studies of Water and Sanitation Utilities: A Literature Survey” can be used as a sample. It’s research methodology utilizes surveys as a way to collect data needed for research. 29. Free Perceptions of First Year College Students ExampleDo you want kids to be college-ready? Are you looking for a college planner to prepare high school kids for a higher level of education? The first year of college serves as an adjustment period for students. The way they cope and accustom themselves use different methods. That’s why you need a study to help you. If your research looks into college kids, this qualitative study of the perceptions of first-year college students regarding technology and college readiness could be your guide. Us it as an outline for the quantitative research you are conducting. 30. Free Qualitative Research Paper ExampleSize: 698.6 KB Like any research, you must follow a particular format. A poorly organized study might give the impression of having unreliable data and results. You need to make sure your research is detailed and understandable. This applies significantly to quantitative project analysis example . This type of investigation urges researchers to be careful and efficient when gathering and analyzing information and statistics. Getting the wrong value can mess up your whole investigation. For your research, you can make use of this qualitative research paper as an outline. It details all the right parts needed in your research. 31. Quantitative Research For Health Programmes ExampleSize: 2.4 MB If you are creating health newspapers and programs, you need to make sure you have the correct data. Your program will tackle a person’s health so you need to have the correct information as not to cause further complications. That’s also why you need to conduct quantitative research to get precise data. For your research, you can make this quantitative research for health programmes your guide. The World Health Organization uses it so you can be sure it is professionally made. Follow the formats on this document to make sure your research is high-quality. What are the Quantitative research characteristics?
What are the 4 types of quantitative research?1. Descriptive Research: Descriptive research aims to describe and analyze a phenomenon, population, or variable. It provides a detailed account of the characteristics, behaviors, or attributes of a subject without manipulating it. Surveys, observational studies, and content analysis are often used in descriptive research. 2. Correlational Research: Correlational research examines the relationship between two or more variables. It assesses how changes in one variable are associated with changes in another. The strength and direction of the relationship are measured using correlation coefficients. This type of research doesn’t establish causation but helps identify patterns and associations. 3. Experimental Research: Experimental research is conducted to establish cause-and-effect relationships between variables. Researchers manipulate one or more independent variables to observe their impact on a dependent variable while controlling extraneous factors. Randomized controlled trials (RCTs) and laboratory experiments are common experimental research designs. 4. Quasi-Experimental Research: Quasi-experimental research shares similarities with experimental research but lacks the full level of control over variables. In quasi-experiments, researchers often cannot use random assignment due to ethical or practical constraints. However, they still manipulate independent variables and measure their effects on dependent variables. What is Quantitative Research vs Qualitative Research?
Which example demonstrates quantitative research?Example 1: A study that surveys 1,000 consumers to determine the percentage who prefer Product A over Product B for a specific feature. Example 1 demonstrates quantitative research because it involves collecting numerical data (the percentage of consumers) and relies on surveys, which are a common quantitative data collection method. This type of research is suitable for quantifying preferences and making statistical comparisons between products. What are the advantages of quantitative research?
General FAQ’sWhat is quantitative research. Quantitative research is a systematic approach to gathering and analyzing numerical data to understand and draw conclusions about a specific phenomenon or problem, often using statistical techniques. What is the greatest strength of quantitative research?The greatest strength of quantitative research is its ability to provide precise, objective, and statistically reliable data, enabling researchers to identify patterns, relationships, and make generalizable conclusions. What is a common weakness of quantitative research?A common weakness of quantitative research is its potential for oversimplification, as it may not capture the full complexity of human behavior or phenomena and may rely on limited predefined variables. What are the risks of quantitative research?Risks in quantitative research include the potential for data inaccuracies, oversimplification of complex phenomena, and overlooking unmeasurable factors, which can lead to biased or incomplete conclusions. Text prompt
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Home » 500+ Quantitative Research Titles and Topics 500+ Quantitative Research Titles and TopicsTable of Contents Quantitative research involves collecting and analyzing numerical data to identify patterns, trends, and relationships among variables. This method is widely used in social sciences, psychology , economics , and other fields where researchers aim to understand human behavior and phenomena through statistical analysis. If you are looking for a quantitative research topic, there are numerous areas to explore, from analyzing data on a specific population to studying the effects of a particular intervention or treatment. In this post, we will provide some ideas for quantitative research topics that may inspire you and help you narrow down your interests. Quantitative Research TitlesQuantitative Research Titles are as follows: Business and Economics
Medicine and Health Sciences
Social Sciences
Engineering and Technology
Quantitative Research TopicsQuantitative Research Topics are as follows:
About the authorMuhammad HassanResearcher, Academic Writer, Web developer You may also like300+ Communication Research Topics300+ Controversial Research Topics500+ Computer Science Research Topics1000+ Sociology Research Topics500+ Sports Research Topics300+ AP Research Topic IdeasIntegrations What's new? In-Product Prompts Participant Management Interview Studies Prototype Testing Card Sorting Tree Testing Live Website Testing Automated Reports Templates Gallery Choose from our library of pre-built mazes to copy, customize, and share with your own users Browse all templates Financial Services Tech & Software Product Designers Product Managers User Researchers By use case Concept & Idea Validation Wireframe & Usability Test Content & Copy Testing Feedback & Satisfaction Content Hub Educational resources for product, research and design teams Explore all resources Question Bank Research Maturity Model Guides & Reports Help Center Future of User Research Report The Optimal Path Podcast User Research Aug 19, 2024 • 17 minutes read Qualitative research examples: How to unlock, rich, descriptive insightsQualitative research uncovers in-depth user insights, but what does it look like? Here are seven methods and examples to help you get the data you need. Armin Tanovic Behind every what, there’s a why . Qualitative research is how you uncover that why. It enables you to connect with users and understand their thoughts, feelings, wants, needs, and pain points. There’s many methods for conducting qualitative research, and many objectives it can help you pursue—you might want to explore ways to improve NPS scores, combat reduced customer retention, or understand (and recreate) the success behind a well-received product. The common thread? All these metrics impact your business, and qualitative research can help investigate and improve that impact. In this article, we’ll take you through seven methods and examples of qualitative research, including when and how to use them. Qualitative UX research made easyConduct qualitative research with Maze, analyze data instantly, and get rich, descriptive insights that drive decision-making. 7 Qualitative research methods: An overviewThere are various qualitative UX research methods that can help you get in-depth, descriptive insights. Some are suited to specific phases of the design and development process, while others are more task-oriented. Here’s our overview of the most common qualitative research methods. Keep reading for their use cases, and detailed examples of how to conduct them.
1. User interviewsA user interview is a one-on-one conversation between a UX researcher, designer or Product Manager and a target user to understand their thoughts, perspectives, and feelings on a product or service. User interviews are a great way to get non-numerical data on individual experiences with your product, to gain a deeper understanding of user perspectives. Interviews can be structured, semi-structured, or unstructured . Structured interviews follow a strict interview script and can help you get answers to your planned questions, while semi and unstructured interviews are less rigid in their approach and typically lead to more spontaneous, user-centered insights. When to use user interviewsInterviews are ideal when you want to gain an in-depth understanding of your users’ perspectives on your product or service, and why they feel a certain way. Interviews can be used at any stage in the product design and development process, being particularly helpful during:
How to conduct user interviews: The basics
💡 A specialized user interview tool makes interviewing easier. With Maze Interview Studies , you can recruit, host, and analyze interviews all on one platform. User interviews: A qualitative research exampleLet’s say you’ve designed a recruitment platform, called Tech2Talent , that connects employers with tech talent. Before starting the design process, you want to clearly understand the pain points employers experience with existing recruitment tools'. You draft a list of ten questions for a semi-structured interview for 15 different one-on-one interviews. As it’s semi-structured, you don’t expect to ask all the questions—the script serves as more of a guide. One key question in your script is: “Have tech recruitment platforms helped you find the talent you need in the past?” Most respondents answer with a resounding and passionate ‘no’ with one of them expanding: “For our company, it’s been pretty hit or miss honestly. They let just about anyone make a profile and call themselves tech talent. It’s so hard sifting through serious candidates. I can’t see any of their achievements until I invest time setting up an interview.” You begin to notice a pattern in your responses: recruitment tools often lack easily accessible details on talent profiles. You’ve gained contextual feedback on why other recruitment platforms fail to solve user needs. 2. Focus groupsA focus group is a research method that involves gathering a small group of people—around five to ten users—to discuss a specific topic, such as their’ experience with your new product feature. Unlike user interviews, focus groups aim to capture the collective opinion of a wider market segment and encourage discussion among the group. When to use focus groupsYou should use focus groups when you need a deeper understanding of your users’ collective opinions. The dynamic discussion among participants can spark in-depth insights that might not emerge from regular interviews. Focus groups can be used before, during, and after a product launch. They’re ideal:
How to conduct focus group studies: The basics
The number of participants can make it difficult to take notes or do manual transcriptions. We recommend using a transcription or a specialized UX research tool , such as Maze, that can automatically create ready-to-share reports and highlight key user insights. Focus groups: A qualitative research exampleYou’re a UX researcher at FitMe , a fitness app that creates customized daily workouts for gym-goers. Unlike many other apps, FitMe takes into account the previous day’s workout and aims to create one that allows users to effectively rest different muscles. However, FitMe has an issue. Users are generating workouts but not completing them. They’re accessing the app, taking the necessary steps to get a workout for the day, but quitting at the last hurdle. Time to talk to users. You organize a focus group to get to the root of the drop-off issue. You invite five existing users, all of whom have dropped off at the exact point you’re investigating, and ask them questions to uncover why. A dialog develops: Participant 1: “Sometimes I’ll get a workout that I just don’t want to do. Sure, it’s a good workout—but I just don’t want to physically do it. I just do my own thing when that happens.” Participant 2: “Same here, some of them are so boring. I go to the gym because I love it. It’s an escape.” Participant 3: “Right?! I get that the app generates the best one for me on that specific day, but I wish I could get a couple of options.” Participant 4: “I’m the same, there are some exercises I just refuse to do. I’m not coming to the gym to do things I dislike.” Conducting the focus groups and reviewing the transcripts, you realize that users want options. A workout that works for one gym-goer doesn’t necessarily work for the next. A possible solution? Adding the option to generate a new workout (that still considers previous workouts)and the ability to blacklist certain exercises, like burpees. 3. Ethnographic researchEthnographic research is a research method that involves observing and interacting with users in a real-life environment. By studying users in their natural habitat, you can understand how your product fits into their daily lives. Ethnographic research can be active or passive. Active ethnographic research entails engaging with users in their natural environment and then following up with methods like interviews. Passive ethnographic research involves letting the user interact with the product while you note your observations. When to use ethnographic researchEthnographic research is best suited when you want rich insights into the context and environment in which users interact with your product. Keep in mind that you can conduct ethnographic research throughout the entire product design and development process —from problem discovery to post-launch. However, it’s mostly done early in the process:
How to conduct ethnographic research:
While ethnographic studies provide a comprehensive view of what potential users actually do, they are resource-intensive and logistically difficult. A common alternative is diary studies. Like ethnographic research, diary studies examine how users interact with your product in their day-to-day, but the data is self-reported by participants. ⚙️ Recruiting participants proving tough and time-consuming? Maze Panel makes it easy, with 400+ filters to find your ideal participants from a pool of 3 million participants. Ethnographic research: A qualitative research exampleYou're a UX researcher for a project management platform called ProFlow , and you’re conducting an ethnographic study of the project creation process with key users, including a startup’s COO. The first thing you notice is that the COO is rushing while navigating the platform. You also take note of the 46 tabs and Zoom calls opened on their monitor. Their attention is divided, and they let out an exasperated sigh as they repeatedly hit “refresh” on your website’s onboarding interface. You conclude the session with an interview and ask, “How easy or difficult did you find using ProFlow to coordinate a project?” The COO answers: “Look, the whole reason we turn to project platforms is because we need to be quick on our feet. I’m doing a million things so I need the process to be fast and simple. The actual project management is good, but creating projects and setting up tables is way too complicated.” You realize that ProFlow ’s project creation process takes way too much time for professionals working in fast-paced, dynamic environments. To solve the issue, propose a quick-create option that enables them to move ahead with the basics instead of requiring in-depth project details. 4. Qualitative observationQualitative observation is a similar method to ethnographic research, though not as deep. It involves observing your users in a natural or controlled environment and taking notes as they interact with a product. However, be sure not to interrupt them, as this compromises the integrity of the study and turns it into active ethnographic research. When to qualitative observationQualitative observation is best when you want to record how users interact with your product without anyone interfering. Much like ethnographic research, observation is best done during:
How to conduct qualitative observation:
Qualitative observation: An qualitative research exampleYou’re conducting UX research for Stackbuilder , an app that connects businesses with tools ideal for their needs and budgets. To determine if your app is easy to use for industry professionals, you decide to conduct an observation study. Sitting in with the participant, you notice they breeze past the onboarding process, quickly creating an account for their company. Yet, after specifying their company’s budget, they suddenly slow down. They open links to each tool’s individual page, confusingly switching from one tab to another. They let out a sigh as they read through each website. Conducting your observation study, you realize that users find it difficult to extract information from each tool’s website. Based on your field notes, you suggest including a bullet-point summary of each tool directly on your platform. 5. Case study researchCase studies are a UX research method that provides comprehensive and contextual insights into a real-world case over a long period of time. They typically include a range of other qualitative research methods, like interviews, observations, and ethnographic research. A case study allows you to form an in-depth analysis of how people use your product, helping you uncover nuanced differences between your users. When to use case studiesCase studies are best when your product involves complex interactions that need to be tracked over a longer period or through in-depth analysis. You can also use case studies when your product is innovative, and there’s little existing data on how users interact with it. As for specific phases in the product design and development process:
How to conduct case studies:
Case study research: A qualitative research exampleYour team has recently launched Pulse , a platform that analyzes social media posts to identify rising digital marketing trends. Pulse has been on the market for a year, and you want to better understand how it helps small businesses create successful campaigns. To conduct your case study, you begin with a series of interviews to understand user expectations, ethnographic research sessions, and focus groups. After sorting responses and observations into common themes you notice a main recurring pattern. Users have trouble interpreting the data from their dashboards, making it difficult to identify which trends to follow. With your synthesized insights, you create a report with detailed narratives of individual user experiences, common themes and issues, and recommendations for addressing user friction points. Some of your proposed solutions include creating intuitive graphs and summaries for each trend study. This makes it easier for users to understand trends and implement strategic changes in their campaigns. 6. Secondary researchSecondary research is a research method that involves collecting and analyzing documents, records, and reviews that provide you with contextual data on your topic. You’re not connecting with participants directly, but rather accessing pre-existing available data. For example, you can pull out insights from your UX research repository to reexamine how they apply to your new UX research objective. Strictly speaking, it can be both qualitative and quantitative—but today we focus on its qualitative application. When to use secondary researchRecord keeping is particularly useful when you need supplemental insights to complement, validate, or compare current research findings. It helps you analyze shifting trends amongst your users across a specific period. Some other scenarios where you need record keeping include:
Secondary research is especially valuable when your team faces budget constraints, tight deadlines, or limited resources. Through review mining and collecting older findings, you can uncover useful insights that drive decision-making throughout the product design and development process. How to conduct secondary research:
Secondary research: A qualitative research exampleSafeSurf is a cybersecurity platform that offers threat detection, security audits, and real-time reports. After conducting multiple rounds of testing, you need a quick and easy way to identify remaining usability issues. Instead of conducting another resource-intensive method, you opt for social listening and data mining for your secondary research. Browsing through your company’s X, you identify a recurring theme: many users without a background in tech find SafeSurf ’s reports too technical and difficult to read. Users struggle with understanding what to do if their networks are breached. After checking your other social media channels and review sites, the issue pops up again. With your gathered insights, your team settles on introducing a simplified version of reports, including clear summaries, takeaways, and step-by-step protocols for ensuring security. By conducting secondary research, you’ve uncovered a major usability issue—all without spending large amounts of time and resources to connect with your users. 7. Open-ended surveysOpen-ended surveys are a type of unmoderated UX research method that involves asking users to answer a list of qualitative research questions designed to uncover their attitudes, expectations, and needs regarding your service or product. Open-ended surveys allow users to give in-depth, nuanced, and contextual responses. When to use open-ended surveysUser surveys are an effective qualitative research method for reaching a large number of users. You can use them at any stage of the design and product development process, but they’re particularly useful:
How to conduct open-ended surveys:
Open-ended surveys: A qualitative research exampleYou're a UX researcher for RouteReader , a comprehensive logistics platform that allows users to conduct shipment tracking and route planning. Recently, you’ve launched a new predictive analytics feature that allows users to quickly identify and prepare for supply chain disruptions. To better understand if users find the new feature helpful, you create an open-ended, in-app survey. The questions you ask your users:
Most of the responses are positive. Users report using the predictive analytics feature to make last-minute adjustments to their route plans, and some even rely on it regularly. However, a few users find the feature hard to notice, making it difficult to adjust their routes on time. To ensure users have supply chain insights on time, you integrate the new feature into each interface so users can easily spot important information and adjust their routes accordingly. 💡 Surveys are a lot easier with a quality survey tool. Maze’s Feedback Surveys solution has all you need to ensure your surveys get the insights you need—including AI-powered follow-up and automated reports. Qualitative research vs. quantitative research: What’s the difference?Alongside qualitative research approaches, UX teams also use quantitative research methods. Despite the similar names, the two are very different. Here are some of the key differences between qualitative research and quantitative research .
Before selecting either qualitative or quantitative methods, first identify what you want to achieve with your UX research project. As a general rule of thumb, think qualitative data collection for in-depth understanding and quantitative studies for measurement and validation. Conduct qualitative research with MazeYou’ll often find that knowing the what is pointless without understanding the accompanying why . Qualitative research helps you uncover your why. So, what about how —how do you identify your 'what' and your 'why'? The answer is with a user research tool like Maze. Maze is the leading user research platform that lets you organize, conduct, and analyze both qualitative and quantitative research studies—all from one place. Its wide variety of UX research methods and advanced AI capabilities help you get the insights you need to build the right products and experiences faster. Frequently asked questions about qualitative research examplesWhat is qualitative research? Qualitative research is a research method that aims to provide contextual, descriptive, and non-numerical insights on a specific issue. Qualitative research methods like interviews, case studies, and ethnographic studies allow you to uncover the reasoning behind your user’s attitudes and opinions. Can a study be both qualitative and quantitative? Absolutely! You can use mixed methods in your research design, which combines qualitative and quantitative approaches to gain both descriptive and statistical insights. For example, user surveys can have both close-ended and open-ended questions, providing comprehensive data like percentages of user views and descriptive reasoning behind their answers. Is qualitative or quantitative research better? The choice between qualitative and quantitative research depends upon your research goals and objectives. Qualitative research methods are better suited when you want to understand the complexities of your user’s problems and uncover the underlying motives beneath their thoughts, feelings, and behaviors. Quantitative research excels in giving you numerical data, helping you gain a statistical view of your user's attitudes, identifying trends, and making predictions. What are some approaches to qualitative research? There are many approaches to qualitative studies. An approach is the underlying theory behind a method, and a method is a way of implementing the approach. Here are some approaches to qualitative research:
Title Page SetupA title page is required for all APA Style papers. There are both student and professional versions of the title page. Students should use the student version of the title page unless their instructor or institution has requested they use the professional version. APA provides a student title page guide (PDF, 199KB) to assist students in creating their title pages. Student title pageThe student title page includes the paper title, author names (the byline), author affiliation, course number and name for which the paper is being submitted, instructor name, assignment due date, and page number, as shown in this example. Title page setup is covered in the seventh edition APA Style manuals in the Publication Manual Section 2.3 and the Concise Guide Section 1.6 Related handouts
Student papers do not include a running head unless requested by the instructor or institution. Follow the guidelines described next to format each element of the student title page.
Professional title pageThe professional title page includes the paper title, author names (the byline), author affiliation(s), author note, running head, and page number, as shown in the following example. Follow the guidelines described next to format each element of the professional title page.
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10 Research Question Examples to Guide your Research ProjectPublished on October 30, 2022 by Shona McCombes . Revised on October 19, 2023. The research question is one of the most important parts of your research paper , thesis or dissertation . It’s important to spend some time assessing and refining your question before you get started. The exact form of your question will depend on a few things, such as the length of your project, the type of research you’re conducting, the topic , and the research problem . However, all research questions should be focused, specific, and relevant to a timely social or scholarly issue. Once you’ve read our guide on how to write a research question , you can use these examples to craft your own.
Note that the design of your research question can depend on what method you are pursuing. Here are a few options for qualitative, quantitative, and statistical research questions.
Other interesting articlesIf you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples. Methodology
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Cite this Scribbr articleIf you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator. McCombes, S. (2023, October 19). 10 Research Question Examples to Guide your Research Project. Scribbr. Retrieved August 19, 2024, from https://www.scribbr.com/research-process/research-question-examples/ Is this article helpful?Shona McCombesOther students also liked, writing strong research questions | criteria & examples, how to choose a dissertation topic | 8 steps to follow, evaluating sources | methods & examples, get unlimited documents corrected. ✔ Free APA citation check included ✔ Unlimited document corrections ✔ Specialized in correcting academic texts
Qualitative Research Questionnaire – Types & ExamplesPublished by Alvin Nicolas at August 19th, 2024 , Revised On August 20, 2024 Before you start your research, the first thing you need to identify is the research method . Depending on different factors, you will either choose a quantitative or qualitative study. Qualitative research is a great tool that helps understand the depth and richness of human opinions and experiences. Unlike quantitative research, which focuses on numerical data , qualitative research allows exploring and interpreting the experiences of the subject. Questionnaires, although mostly associated with quantitative research, can also be a valuable instrument in qualitative studies. Let’s explore what qualitative research questionnaires are and how you can create one. What Is A Qualitative Research QuestionnaireQualitative research questionnaires are a structured or semi-structured set of questions designed to gather detailed, open-ended participant responses. It allows you to uncover underlying reasons and opinions and provides insights into a particular phenomenon. While quantitative questionnaires often have closed-ended questions and numerical responses, a qualitative questionnaire encourages participants to express themselves freely. Before you design your questionnaire, you should know exactly what you need so you can keep your questions specific enough for the participants to understand. For example:
Types of Qualitative Research Questions With ExamplesNow that you are familiar with what qualitative research questions are, let’s look at the different types of questions you can use in your survey . Descriptive QuestionsThese are used to explore and describe a phenomenon in detail. It helps answer the “what” part of the research, and the questions are mostly foundational. Example: How do students experience online learning? Comparative QuestionsThis type allows you to compare and contrast different groups or situations. You can explore the differences and similarities to highlight the impact of specific variables. Example: How do the study habits of first-year and fourth-year university students differ? Interpretive QuestionsThese questions help you understand the meanings people attach to experiences or phenomena by answering the “how” and “why”. Example: What does “success” mean to entrepreneurs? Evaluative QuestionsYou can use these to assess the quality or value of something. These allow you to understand the outcomes of various situations. Example: How effective is the new customer service training program? Process-Oriented QuestionsTo understand how something happens or develops over time, researchers often use process-oriented questions. Example: How do individuals develop their career goals? Exploratory QuestionsThese allow you to discover new perspectives on a topic. However, you have to be careful that there must be no preconceived notions or research biases to it. Example: What are the emerging trends in the mobile gaming industry? How To Write Qualitative Research Questions?For your study to be successful, it is important to consider designing a questionnaire for qualitative research critically, as it will shape your research and data collection. Here is an easy guide to writing your qualitative research questions perfectly. Tip 1: Understand Your Research GoalsMany students start their research without clear goals, and they have to make substantial changes to their study in the middle of the research. This wastes time and resources. Before you start crafting your questions, it is important to know your research objectives. You should know what you aim to discover through your research, or what specific knowledge gaps you are going to fill. With the help of a well-defined research focus, you can develop relevant and meaningful information. Tip 2: Choose The Structure For Research QuestionsThere are mostly open-ended questionnaires in qualitative research. They begin with words like “how,” “what,” and “why.” However, the structure of your research questions depends on your research design . You have to consider using broad, overarching questions to explore the main research focus, and then add some specific probes to further research the particular aspects of the topic. Tip 3: Use Clear LanguageThe more clear and concise your research questions are, the more effective and free from ambiguity they will be. Do not use complex terminology that might confuse participants. Try using simple and direct language that accurately conveys your intended meaning. Here is a table to explain the wrong and right ways of writing your qualitative research questions.
Tip 4: Check Relevance With Research GoalsOnce you have developed some questions, check if they align with your research objectives. You must ensure that each question contributes to your overall research questions. After this, you can eliminate any questions that do not serve a clear purpose in your study. Tip 5: Concentrate On A Single ThemeWhile it is tempting to cover multiple aspects of a topic in one question, it is best to focus on a single theme per question. This helps to elicit focused responses from participants. Moreover, you have to avoid combining unrelated concepts into a single question. If your main research question is complicated, you can create sub-questions with a “ladder structure”. These allow you to understand the attributes, consequences, and core values of your research. For example, let’s say your main broad research question is:
The intermediate questions may be:
Types Of Survey Questionnaires In Qualitative ResearchIt is important to consider your research objectives, target population, resources and needed depth of research when selecting a survey method. The main types of qualitative surveys are discussed below. Face To Face SurveysFace-to-face surveys involve direct interaction between the researcher and the participant. This method allows observers to capture non-verbal cues, body language, and facial expressions, and helps adapt questions based on participant responses. They also let you clarify any misunderstandings. Moreover, there is a higher response rate because of personal interaction. Example: A researcher conducting a study on consumer experiences with a new product might visit participants’ homes to conduct a detailed interview. Telephone SurveysThese type of qualitative research survey questionnaires provide a less intrusive method for collecting qualitative data. The benefits of telephone surveys include, that it allows you to collect data from a wider population. Moreover, it is generally less expensive than face-to-face interviews and interviews can be conducted efficiently. Example: A market research firm might conduct telephone surveys to understand customer satisfaction with a telecommunication service. Online SurveysOnline survey questionnaires are a convenient and cost-effective way to gather qualitative data. You can reach a wide audience quickly, and participants may feel more comfortable sharing sensitive information because of anonymity. Additionally, there are no travel or printing expenses. Example: A university might use online surveys to explore students’ perceptions of online learning experiences. Strengths & Limitations Of Questionnaires In Qualitative ResearchQuestionnaires are undoubtedly a great data collection tool. However, it comes with its fair share of advantages and disadvantages. Let’s discuss the benefits of questionnaires in qualitative research and their cons as well.
Qualitative Research Questionnaire ExampleHere is a concise qualitative research questionnaire sample for research papers to give you a better idea of its format and how it is presented. Thank you for participating in our survey. We value your feedback on our new mobile app. Your responses will help us improve the applications and better meet your needs. Demographic Information
Are questionnaires quantitative or qualitative research?A survey research questionnaire can have both qualitative and quantitative questions. The qualitative questions are mostly open-ended, and quantitative questions take the form of yes/no, or Likert scale rating. Can we use questionnaires in qualitative research?Yes, survey questionnaires can be used in qualitative research for data collection. However, instead of a Likert scale or rating, you can post open-ended questions to your respondents. The participants can provide detailed responses to the questions asked. Why are questionnaires good for qualitative research?In qualitative research, questionnaires allow you to collect qualitative data. The open-ended and unstructured questions help respondents present their ideas freely and provide insights. You May Also LikeAction research for my dissertation?, A brief overview of action research as a responsive, action-oriented, participative and reflective research technique. Baffled by the concept of reliability and validity? Reliability refers to the consistency of measurement. Validity refers to the accuracy of measurement. You can transcribe an interview by converting a conversation into a written format including question-answer recording sessions between two or more people. USEFUL LINKS LEARNING RESOURCES COMPANY DETAILS
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The answer is written in length in the discussion section of the paper. Thus, the research question gives a preview of the different parts and variables of the study meant to address the problem posed in the research question.1 An excellent research ... Definitions and examples of quantitative research questions. Quantitative research questions;
In the methods section, researchers clearly describe the methods they used to obtain and analyze the data for their research. When relying on data collected specifically for a given paper, researchers will need to discuss the sample and data collection; in most cases, though, quantitative research relies on pre-existing datasets.
Revised on June 22, 2023. Quantitative research is the process of collecting and analyzing numerical data. It can be used to find patterns and averages, make predictions, test causal relationships, and generalize results to wider populations. Quantitative research is the opposite of qualitative research, which involves collecting and analyzing ...
Research papers in the social and natural sciences often follow APA style. This article focuses on reporting quantitative research methods. In your APA methods section, you should report enough information to understand and replicate your study, including detailed information on the sample, measures, and procedures used.
Quantitative methods emphasize objective measurements and the statistical, mathematical, or numerical analysis of data collected through polls, questionnaires, and surveys, or by manipulating pre-existing statistical data using computational techniques.Quantitative research focuses on gathering numerical data and generalizing it across groups of people or to explain a particular phenomenon.
The results chapter (also referred to as the findings or analysis chapter) is one of the most important chapters of your dissertation or thesis because it shows the reader what you've found in terms of the quantitative data you've collected. It presents the data using a clear text narrative, supported by tables, graphs and charts.
Reporting quantitative research results. If you conducted quantitative research, you'll likely be working with the results of some sort of statistical analysis.. Your results section should report the results of any statistical tests you used to compare groups or assess relationships between variables.It should also state whether or not each hypothesis was supported.
Large sample size: Quantitative research often involves collecting data from a large sample of individuals or groups in order to increase the reliability and ... This may involve writing a research report, presenting at a conference, or publishing in a peer-reviewed journal. It is important to clearly communicate the research question, methods ...
Summarizing quantitative data and its effective presentation and discussion can be challenging for students and researchers. This chapter provides a framework for adequately reporting findings from quantitative analysis in a research study for those contemplating to write a research paper. The rationale underpinning the reporting methods to ...
Quantitative research is the process of collecting and analyzing numerical data to describe, predict, or control variables of interest. This type of research helps in testing the causal relationships between variables, making predictions, and generalizing results to wider populations. The purpose of quantitative research is to test a predefined ...
The study utilized a quantitative research design, which involved a survey questionnaire administered to a sample of 200 high school students. The questionnaire was developed based on previous studies and was designed to measure the frequency and duration of social media use, as well as academic performance. ... For example, a research report ...
Surprises at a Local "Family" Restaurant: Example Quantitative Research Paper A quantitative research paper with that title might start with a paragraph like this: Quaintville, located just off the main highway only five miles from the university campus, may normally be a sleepy community, but recent plans to close the only fast-food ...
In quantitative research, a variable is something (an intervention technique, a pharmaceutical, a temperature, etc.) that changes. There are two kinds of variables: independent variables and dependent variables.In the simplest terms, the independent variable is whatever the researchers are using to attempt to make a change in their dependent variable.
Understanding Quantitative Research Questions. Quantitative research involves collecting and analyzing numerical data to answer research questions and test hypotheses. These questions typically seek to understand the relationships between variables, predict outcomes, or compare groups. Let's explore some examples of quantitative research ...
The four examples we just saw were simple hypothetical quantitative research examples. Now, let us see some real-life examples of quantitative research. ... The study involved 384 male school students with a questionnaire and Achenbach's Youth Self-Report (YSR) to assess their behavior problems. The YSR evaluates various issues, such as ...
Quantitative research is a type of research that focuses on collecting and analyzing numerical data to answer research questions. There are two main methods used to conduct quantitative research: 1. Primary Method. There are several methods of primary quantitative research, each with its own strengths and limitations.
Because this domain of research is less susceptible to biases, the findings are generally seen as credible and reliable information. The following are sample templates of where quantitative research can be utilized. 1. Business Research Report Template. Details. File Format. MS Word. Pages. Google Docs.
Qualitative research aims to produce rich and detailed descriptions of the phenomenon being studied, and to uncover new insights and meanings. Quantitative data is information about quantities, and therefore numbers, and qualitative data is descriptive, and regards phenomenon which can be observed but not measured, such as language.
When collecting and analyzing data, quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings. Both are important for gaining different kinds of knowledge. Quantitative research. Quantitative research is expressed in numbers and graphs. It is used to test or confirm theories and assumptions.
Here are three examples of quantitative research in motion. Example 1: Leading food distribution company. We helped a leading food distribution company identify changes in the needs and values of their restaurant clients as a result of COVID-19. This helped inform opportunities to become more valuable partners.
31+ Quantitative Research Examples. Quantitative research demands focus and precision from the researcher. If you need a guide in doing your research, here are 10+ Quantitative research examples you can use. 1. Free Quantitative Research Flowchart Example. Details. File Format. MS Word. Google Docs.
Quantitative Research Topics. Quantitative Research Topics are as follows: The effects of social media on self-esteem among teenagers. A comparative study of academic achievement among students of single-sex and co-educational schools. The impact of gender on leadership styles in the workplace.
Here are seven qualitative research methods and examples to inspire your next UX research project. ... and past UX research study reports. Analyze data for themes and patterns to gain descriptive insights. ... feelings, and behaviors. Quantitative research excels in giving you numerical data, helping you gain a statistical view of your user's ...
Example. Paper title. Place the title three to four lines down from the top of the title page. Center it and type it in bold font. Capitalize major words of the title. Place the main title and any subtitle on separate double-spaced lines if desired. There is no maximum length for titles; however, keep titles focused and include key terms.
The first question asks for a ready-made solution, and is not focused or researchable. The second question is a clearer comparative question, but note that it may not be practically feasible. For a smaller research project or thesis, it could be narrowed down further to focus on the effectiveness of drunk driving laws in just one or two countries.
Qualitative research is a great tool that helps understand the depth and richness of human opinions and experiences. Unlike quantitative research, which focuses on numerical data, qualitative research allows exploring and interpreting the experiences of the subject. Questionnaires, although mostly associated with quantitative research, can also ...