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Chapter 3: Methodology (Quantitative

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Research Methodology

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• Vaneet Kaur 3  

Part of the book series: Innovation, Technology, and Knowledge Management ((ITKM))

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The chapter presents methodology employed for examining framework developed, during the literature review, for the purpose of present study. In light of the research objectives, the chapter works upon the ontology, epistemology as well as the methodology adopted for the present study. The research is based on positivist philosophy which postulates that phenomena of interest in the social world, can be studied as concrete cause and effect relationships, following a quantitative research design and a deductive approach. Consequently, the present study has used the existing body of literature to deduce relationships between constructs and develops a strategy to test the proposed theory with the ultimate objective of confirming and building upon the existing knowledge in the field. Further, the chapter presents a roadmap for the study which showcases the journey towards achieving research objectives in a series of well-defined logical steps. The process followed for building survey instrument as well as sampling design has been laid down in a similar manner. While the survey design enumerates various methods adopted along with justifications, the sampling design sets forth target population, sampling frame, sampling units, sampling method and suitable sample size for the study. The chapter also spells out the operational definitions of the key variables before exhibiting the three-stage research process followed in the present study. In the first stage, questionnaire has been developed based upon key constructs from various theories/researchers in the field. Thereafter, the draft questionnaire has been refined with the help of a pilot study and its reliability and validity has been tested. Finally, in light of the results of the pilot study, the questionnaire has been finalized and final data has been collected. In doing so, the step-by-step process of gathering data from various sources has been presented. Towards end, the chapter throws spotlight on various statistical methods employed for analysis of data, along with the presentation of rationale for the selection of specific techniques used for the purpose of presentation of outcomes of the present research.

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Chapter 3 – Dissertation Methodology (example)

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Introduction

The current chapter presents developing the research methods needed to complete the experimentation portion of the current study. The chapter will discuss in detail the various stages of developing the methodology of the current study. This includes a detailed discussion of the philosophical background of the research method chosen. In addition to this, the chapter describes the data collection strategy, including the selection of research instrumentation and sampling. The chapter closes with a discussion on the analysis tools used to analyse the data collected.

Selecting an Appropriate Research Approach

Creswall (2013) stated that research approaches are plans and procedures that range from steps, including making broad assumptions to detailed methods of data collection, analysis, and interpretation. The several decisions involved in the process are used to decide which approach should be used in a specific study that is informed using philosophical assumptions brought to the study (Creswall 2013). Included in this are procedures of inquiry or research designs and specific research methods used for data collection, its analysis, and finally, its interpretation. However, Guetterman (2015); Lewis (2015); and Creswall (2013) argue that the selection of the specific research approach is based on the nature of the research problem, or the issue that is being addressed by any study, personal experiences of the researchers’, and even the audience for which the study is being developed for.

There are many ways to customise research approaches to develop an approach most suited for a particular study. However, the main three categories with which research approaches are organised include; qualitative, quantitative, and mixed research methods. Creswall (2013) comments that all three approaches are not considered so discrete or distinct from one another. Creswall (2013) states, “qualitative and quantitative approaches should not be viewed as rigid, distinct categories, polar opposite, or dichotomies” (p.32). Newmand and Benz (1998) pointed out that quantitative and qualitative approaches instead represent different ends on a continuum since a study “tends” to be more quantitative than qualitative or vice versa. Lastly, mixed methods research resides in the middle of the continuum as it can incorporate elements and characteristics of both quantitative and qualitative approaches. Lewis (2015) points out that the main distinction that is often cited between quantitative and qualitative research is that it is framed in terms of using numbers rather than words; or using closed-ended questions for quantitative hypotheses over open-ended questions for qualitative interview questions. Guetterman (2015) points out that a clearer way of viewing gradations of differences between the approaches is to examine the basic philosophical assumptions brought to the study, the kinds of research strategies used, and the particular methods implemented in conducting the strategies.

Underlying Philosophical Assumptions

An important component of defining the research approach involves philosophical assumptions that contribute to the broad research approach of planning or proposing to conduct research. It involves the intersection of philosophy, research designs and specific methods as illustrated in Fig. 1 below.

Figure 3.2-1- Research Onion (Source; Saunders and Tosey 2013)

Slife and Williams (1995) have argued that philosophical ideas have remained hidden within the research. However, they still play an influential role in the research practice, and it is for this reason that it is most identified. Various philosophical assumptions are used to construct or develop a study. Saunders et al. (2009) define research philosophy as a belief about how data about a phenomenon should be gathered, analysed and used. Saunders et al. (2009) identify common research philosophies such as positivism, realism, interpretivism, subjectivism, and pragmatism. Dumke (2002) believes that two views, positivism and phenomenology, mainly characterise research philosophy.

Positivism reflects acceptance in adopting the philosophical stance of natural scientists (Saunders, 2003). According to Remenyi et al. (1998), there is a greater preference in working with an “observable social reality” and that the outcome of such research can be “law-like” generalisations that are the same as those which are produced by physical and natural scientists. Gill and Johnson (1997) add that it will also emphasise a high structure methodology to allow for replication for other studies. Dumke (2002) agrees and explains

that a positivist philosophical assumption produces highly structured methodologies and allows for generalisation and quantification of objectives that can be evaluated by statistical methods. For this philosophical approach, the researcher is considered an objective observer who should not be impacted by or impact the subject of research.

On the other hand, more phenomenological approaches agree that the social world of business and management is too complex to develop theories and laws similar to natural sciences. Saunders et al. (2000) argue that this is the reason why reducing observations in the real world to simple laws and generalisations produces a sense of reality which is a bit superficial and doesn’t present the complexity of it.

The current study chooses positivistic assumptions due to the literature review’s discussion of the importance of Big Data in industrial domains and the need for measuring its success in the operations of the business. The current study aims to examine the impact that Big Data has on automobile companies’ operations. To identify a positive relationship between Big Data usage and beneficial business outcomes, the theory needs to be used to generate hypotheses that can later be tested of the relationship which would allow for explanations of laws that can later be assessed (Bryman and Bell, 2015).

Selecting Interpretive Research Approach

Interpretive research approaches are derived from the research philosophy that is adopted. According to Dumke (2002), the two main research approaches are deductive and inductive. The inductive approach is commonly referred to when theory is derived from observations. Thus, the research begins with specific observations and measures. It is then from detecting some pattern that a hypothesis is developed. Dumke (2002) argues that researchers who use an inductive approach usually work with qualitative data and apply various methods to gather specific information that places different views. From the philosophical assumptions discussed in the previous section, it is reasonable to use the deductive approach for the current study. It is also considered the most commonly used theory to establish a relationship between theory and research. The figure below illustrates the steps used for the process of deduction.

Data Collection

• confirmed or rejected
• Revision of theory

Based on what is known about a specific domain, the theoretical considerations encompassing it a hypothesis or hypotheses are deduced that will later be subjected to empirical enquiry (Daum, 2013). Through these hypotheses, concepts of the subject of interest will be translated into entities that are rational for a study. Researchers are then able to deduce their hypotheses and convert them into operational terms.

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Justifying the Use of Quantitative Research Method

Saunders (2003) notes that almost all research will involve some numerical data or even contain data quantified to help a researcher answer their research questions and meet the study’s objectives. However, quantitative data refers to all data that can be a product of all research strategies (Bryman and Bell, 2015; Guetterman, 2015; Lewis, 2015; Saunders, 2003). Based on the philosophical assumptions and interpretive research approach, a quantitative research method is the best suited for the current study. Haq (2014) explains that quantitative research is about collecting numerical data and then analysing it through statistical methods to explain a specific phenomenon. Mujis (2010) defends the use of quantitative research because, unlike qualitative research, which argues that there is no pre-existing reality, quantitative assumes that there is only a single reality about social conditions that researchers cannot influence in any way. Also, qualitative research is commonly used when there is little to no knowledge of a phenomenon, whereas quantitative research is used to find the cause and effect relationship between variables to either verify or nullify some theory or hypothesis (Creswall 2002; Feilzer 2010; Teddlie and Tashakkori 2012).

Selecting an Appropriate Research Strategy

There are many strategies available to implement in a study, as evidenced from Fig. 1. There are many mono-quantitative methods, such as telephone interviews, web-based surveys, postal surveys, and structured questionnaires (Haq 2014). Each instrument has its own pros and cons in terms of quality, time, and data cost. Brymand (2006); Driscoll et al. (2007); Edwards et al. (2002); and Newby et al. (2003) note that most researchers use structured questionnaires for data collection they are unable to control or influence respondents, which leads to low response rates but more accurate data obtained. Saunders and Tosey (2015) have argued that quantitative data is simpler to obtain and more concise to present. Therefore, the current study uses a survey-based questionnaire (See Appendix A).

Justifying the use of Survey Based Questionnaire

Surveys are considered the most traditional forms of research and are used in non-experimental descriptive designs that describe some reality. Survey-based questionnaires are often restricted to a representative sample of a potential group of the study’s interest. In this case, it is the executives currently working for automobile companies in the UK. The survey instrument is then chosen for its effectiveness at being practical and inexpensive (Kelley et al., 2003). Due to the philosophical assumptions, interpretive approach, and methodological approach, the survey design for the current study is considered the best instrument in line with these premises, besides being cost-effective.

Empirical Research Methodology

Research design.

This section describes how research is designed to use the techniques used for data collection, sampling strategy, and data analysis for a quantitative method. Before going into the strategies of data collection and analysis, a set of hypotheses were developed.

Hypotheses Development

The current study uses a quantitative research approach, making it essential to develop a set of hypotheses that will be used as a test standard for the mono-method quantitative design. The following are a set of hypotheses that have been developed from the examination of the literature review.

H1- The greater the company’s budget for Big Data initiatives (More than 1 million GBP), the greater its ability to monetise and generate new revenues.

H2- The greater the company’s budget for Big Data initiatives (More than 1 million GBP) the more decrease in expenses in found.

H3- The greatest impact of Big Data on a company is changing the way business is done.

H4- Big Data integrating with a company has resulted in competitive significance.

H5- The analytical abilities of a company allows for achieved measurable results.

H6- Investing in Big Data will lead to highly successful business results.

H7- A business’s operations function is fuelling Big Data initiatives and effecting change in operations.

H8- The implementation of Big Data in the company has positive impacts on business.

This section includes the sampling method used to collect the number of respondents needed to provide information, then analysed after collection.

Sampling Method

Collis (2009) explains that there are many kinds of sampling methods that can be used for creating a specific target sample from a population. This current study uses simple random sampling to acquire respondents with which the survey will be conducted. Simple random sampling is considered the most basic form of probability sampling. Under the method, elements are taken from the population at random, with all elements having an equal chance of being selected. According to () as of 2014, there are about thirty-five active British car manufacturers in the UK, each having an employee population of 150 or more. This is why the total population of employees in car manufacturers is estimated to be 5,250 employees. The sample, therefore, developed used the following equation;

2  ×   (1 −   )

+(   2 × (1−  ) )  2

Where; N is the population size,  e  is the margin of error (as a decimal),  z  is confidence level (as a z-score), and  p  is percentage value (as a decimal). Thus, the sample size is with a normal distribution of 50%. With the above equation, a population of 5,250; with a 95% confidence level and 5% margin of error, the total sample size needed for the current equals 300. Therefore, N=300, which is the sample size of the current study.

The survey develops (see Appendix A) has a total of three sections, A, B, and C, with a total of 39 questions. Each section has its own set of questions to accomplish. The survey is a mix of closed-end questions that look to comprehend the respondents’ demographic makeup, the Big Data initiatives of the company, and the impact that Big Data was having on their company. The survey is designed to take no longer than twenty minutes. The survey was constructed on Survey Monkey.com, and an online survey provided website. The survey was left on the website for a duration of 40 days to ensure that the maximum number of respondents were able to answer the survey. The only way that the survey was allowed for a respondent is if they passed a security question as if they were working for an automobile company in the UK when taking the survey. Gupta et al. (2004) believe that web surveys are visual stimuli, and the respondent has complete control about whether or how each question is read and understood. That is why Dillman (2000) argued that web questionnaires are expected to resemble those taken through the mail/postal services closely.

Data Analysis

The collected data is then analysed through the Statistical Package for Social Science (SPSS) version 24 for descriptive analysis. The demographic section of the survey will be analysed using descriptive statistics. Further analysis of the data includes regression analysis. Simple regression analysis includes only one independent variable and one dependent variable. Farrar and Glauber (1967) assert that the purpose of regression analysis is to estimate the parameters of dependency, and it should not be used to determine the interdependency of a relationship.

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Conclusions.

The chapter provides a descriptive and in-depth discussion of the methods involved in the current study’s research. The current study is looking towards a quantitative approach that considers positivism as its philosophical undertaking, using deductive reasoning for its interpretive approach, is a mono-quantitative method that involves the use of a survey instrument for data collection. The methodology chapter also provided the data analysis technique, which is descriptive statistics through frequency analysis and regression analysis.

Examples of results;

Question 8- Of these staff, are mostly working in or for your consumer-facing (B2C) businesses, your commercial or wholesale (B2B) businesses, or both?

Based on the illustration, nineteen (19) respondents indicated that 501-1000 employees are dedicated to analytics for both B2B and B2C. The category of using Big Data analytics for both B2B and B2C comprises the most agreement of respondents with 72 of 132 indicated.

The figure above represents the respondents’ answers to their automobile company’s plan for measuring Big Data’s success. Of the 132 participants, 44.70 per cent responded that the company is planning on using quantitative metrics associated with business performance to analyse if Big Data is actually successful. Another, 30.30 per cent indicated that their company was planning on using qualitative metrics tied to business performance. Using business performance to analyse the success of Big Data is coherent to the results of the literature review that indicated previous studies of doing such. As an automobile company, they need to know the results of using Big Data analytics, and that is only by using business performance indicators regardless of being qualitative or quantitative.

Fig. 4.3-6 portrays the response of participants in regards to actually achieving measurable results from Big Data. According to 68.18 per cent of respondents, the company that they worked for did indeed show measurable results from their investments in Big Data. However, 31.82 per cent indicated that there was indeed no measurable result in investing in Big Data.

Bryman, A., Bell, E., 2015. Business Research Methods. Oxford University Press.

Daum, P., 2013. International Synergy Management: A Strategic Approach for Raising Efficiencies in the Cross-border Interaction Process. Anchor Academic Publishing (aap_verlag).

Dümke, R., 2002. Corporate Reputation and its Importance for Business Success: A European

Perspective and its Implication for Public Relations Consultancies. diplom.de.

Guetterman, T.C., 2015. Descriptions of Sampling Practices Within Five Approaches to Qualitative Research in Education and the Health Sciences. Forum Qualitative Sozialforschung /

Forum: Qualitative Social Research 16.

Haq, M., 2014. A Comparative Analysis of Qualitative and Quantitative Research Methods and a Justification for Adopting Mixed Methods in Social Research (PDF Download Available).

ResearchGate 1–22. doi:http://dx.doi.org/10.13140/RG.2.1.1945.8640

Kelley, K., Clark, B., Brown, V., Sitzia, J., 2003. Good practice in the conduct and reporting of survey research. Int J Qual Health Care 15, 261–266. doi:10.1093/intqhc/mzg031

Lewis, S., 2015. Qualitative Inquiry and Research Design: Choosing Among Five Approaches.

Health Promotion Practice 16, 473–475. doi:10.1177/1524839915580941

Saunders, M., 2003. Research Methods for Business Students. Pearson Education India.

Saunders, M.N.K., Tosey, P., 2015. Handbook of Research Methods on Human Resource

Development. Edward Elgar Publishing.

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Methods for Quantitative Research in Psychology

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This seven-hour course provides a comprehensive exploration of research methodologies, beginning with the foundational steps of the scientific method. Students will learn about hypotheses, experimental design, data collection, and the analysis of results. Emphasis is placed on defining variables accurately, distinguishing between independent, dependent, and controlled variables, and understanding their roles in research.

The course delves into major research designs, including experimental, correlational, and observational studies. Students will compare and contrast these designs, evaluating their strengths and weaknesses in various contexts. This comparison extends to the types of research questions scientists pose, highlighting how different designs are suited to different inquiries.

A critical component of the course is developing the ability to judge the quality of sources for literature reviews. Students will learn criteria for evaluating the credibility, relevance, and reliability of sources, ensuring that their understanding of the research literature is built on a solid foundation.

Reliability and validity are key concepts addressed in the course. Students will explore what it means for an observation to be reliable, focusing on consistency and repeatability. They will also compare and contrast different forms of validity, such as internal, external, construct, and criterion validity, and how these apply to various research designs.

The course concepts are thoroughly couched in examples drawn from the psychological research literature. By the end of the course, students will be equipped with the skills to design robust research studies, critically evaluate sources, and understand the nuances of reliability and validity in scientific research. This knowledge will be essential for conducting high-quality research and contributing to the scientific community.

Learning objectives

• Describe the steps of the scientific method.
• Specify how variables are defined.
• Compare and contrast the major research designs.
• Explain how to judge the quality of a source for a literature review.
• Compare and contrast the kinds of research questions scientists ask.
• Explain what it means for an observation to be reliable.
• Compare and contrast forms of validity as they apply to the major research designs.

This program does not offer CE credit.

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Introduces applying statistical methods effectively in psychology or related fields for undergraduates, high school students, and professionals.

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Introduces the importance of ethical practice in scientific research for undergraduates, high school students, and professionals.

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Journal Article Reporting Standards (JARS)

APA Style Journal Article Reporting Standards offer guidance on what information should be included in all manuscript sections for quantitative, qualitative, and mixed methods research and include how to best discuss race, ethnicity, and culture.

Introducing Journal Article Reporting Standards for Race, Ethnicity, and Culture (JARS–REC)

JARS–REC were created to develop best practices related to the manner in which race, ethnicity, and culture are discussed within scientific manuscripts in psychological science.

Quantitative research

Use JARS–Quant when you collect your study data in numerical form or report them through statistical analyses.

Qualitative research

Use JARS–Qual when you collect your study data in the form of natural language and expression.

Mixed methods research

Use JARS–Mixed when your study combines both quantitative and qualitative methods.

Race, ethnicity, culture

Use JARS–REC for all studies for guidance on how to discuss race, ethnicity, and culture.

What are APA Style JARS?

APA Style Journal Article Reporting Standards (APA Style Jars ) are a set of standards designed for journal authors, reviewers, and editors to enhance scientific rigor in peer-reviewed journal articles. Educators and students can use APA Style JARS as teaching and learning tools for conducting high quality research and determining what information to report in scholarly papers.

The standards include information on what should be included in all manuscript sections for:

• Quantitative research ( Jars –Quant)
• Qualitative research ( Jars –Qual)
• Mixed methods research ( Jars –Mixed)

Additionally, the APA Style Journal Article Reporting Standards for Race, Ethnicity, and Culture ( Jars – Rec ) provide guidance on how to discuss race, ethnicity, and culture in scientific manuscripts. Jars – Rec should be applied to all research, whether it is quantitative, qualitative, or mixed methods.

• Race, Ethnicity, and Culture ( Jars – Rec )

Using these standards will make your research clearer and more accurate as well as more transparent for readers. For quantitative research, using the standards will increase the reproducibility of science. For qualitative research, using the standards will increase the methodological integrity of research.

Jars –Quant should be used in research where findings are reported numerically (quantitative research). Jars –Qual should be used in research where findings are reported using nonnumerical descriptive data (qualitative research). Jars –Mixed should be applied to research that includes both quantitative and qualitative research (mixed methods research). JARS–REC should be applied to all research, whether it is quantitative, qualitative, or mixed methods.

For more information on APA Style JARS:

• Read Editorial: Journal Article Reporting Standards
• View an infographic (PDF, 453KB) to learn about the benefits of JARS and how they are relevant to you
• Listen to a podcast with Drs. Harris Cooper and David Frost discussing JARS and implications for research in psychology

Many aspects of research methodology warrant a close look, and journal editors can promote better methods if we encourage authors to take responsibility to report their work in clear, understandable ways. —Nelson Cowan, Editor, Journal of Experimental Psychology: General

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This video describes and discusses the updated APA Style Journal Article Reporting Standards.

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Reporting Qualitative Research in Psychology

Journal article reporting standards for qualitative research

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Journal article reporting standards for quantitative research

Publication Manual, 7th Edition

The official source for writing papers and creating references in seventh edition APA Style

Jars resources

• History of APA’s journal article reporting standards
• APA Style JARS supplemental glossary
• Supplemental resource on the ethic of transparency in JARS
• Frequently asked questions
• JARS-Quant Decision Flowchart (PDF, 98KB)
• JARS-Quant Participant Flowchart (PDF, 98KB)

Jars articles

• Jars –Quant article
• Jars –Qual / Mixed article
• Jars – rec executive summary

Questions / feedback

Email an APA Style Expert if you have questions, feedback, or suggestions for modules to be included in future JARS updates.

APA resources

• APA Databases and Electronic Resources
• APA Journals
• Journal Author Resource Center
• Education and Career
• Psychological Science
• Open Science at APA
• How to Review a Manuscript

From the APA Style blog

Introducing APA Style Journal Article Reporting Standards for Race, Ethnicity, and Culture

These standards are for all authors, reviewers, and editors seeking to improve manuscript quality by encouraging more racially and ethnically conscious and culturally responsive journal reporting standards for empirical studies in psychological science.

APA Style JARS for high school students

In this post, we provide an overview of APA Style JARS and resources that can be shared with high school students who want to learn more about effective communication in scholarly research.

Happy 2022, APA Stylers!

This blog post is dedicated to our awesome APA Style users. You can use the many resources on our website to help you master APA Style and improve your scholarly writing.

APA Style JARS on the EQUATOR Network

The APA Style Journal Article Reporting Standards (APA Style JARS) have been added to the EQUATOR Network. The network aims to promote accuracy and quality in reporting of research.

APA Style JARS: Resources for instructors and students

APA Style Journal Article Reporting Standards (APA Style JARS) are a set of guidelines for papers reporting quantitative, qualitative, and mixed methods research that can be used by instructors, students, and all others reading and writing research papers.