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How To Write Chapter 3 Of A PhD Thesis Proposal (A Detailed Guide)

The format of a PhD thesis proposal varies from one institution to another. In most cases, however, chapter 3 of the PhD thesis is the research methodology chapter.

This article discusses the main sections of the research methodology chapter and provides tips on how best to write it.

Introduction

Research design, population and sampling, random sampling, non-random sampling, data collection methods and tools, questionnaires, focus group discussions, observation, document review, ethical considerations, data analysis, limitations of the study, final thoughts on how to write chapter 3 of a phd thesis proposal.

The first section is a brief introduction to the chapter, which highlights what the chapter is about.

This section discusses the research design that the study will use. The research design should be guided by the research objectives and research questions the student wants to answer. The research design can be: quantitative, qualitative, or mixed-methods design.

In quantitative research, the study will collect, analyse and present numerical data in the form of statistics. The statistics can be descriptive, inferential, or a mix of both.

In qualitative research, the study collects, analyses and presents data that is in the form of words, opinions, or thoughts of the respondents. Its focus is on the lived-in experiences of the respondents with regard to the problem under investigation.

In mixed-methods research, the study uses a combination of quantitative and qualitative research methods. So some of the research questions render themselves to quantitative research, while others to qualitative research.

Each of these research designs has its pros and cons.

Population of study refers to the entire list of your subjects of interest. If the population is so small, the student can opt to include all the subjects in the study. However, if the population is large, it becomes difficult – both time-wise and resource-wise – to include all the subjects in the study.

A sample is a sub-set of the population of study from which data will be collected to enable the student understand the population.

An example of population vs. sample: Suppose a study aims at investigating the effects of COVID-19 pandemic on micro and small enterprises (MSEs) in Kenya. The population of the study will be all the MSEs in Kenya, which are thousands in number, distributed across the country. It will be impossible for the student to collect data from all those MSEs and therefore a sample will be necessary. The sample size the student decides to use will depend on whether the study is quantitative, qualitative or both. For quantitative studies, a large sample size is necessary, while for qualitative study, the sample size need not be large.

Sampling is the process by which a sample is drawn from a population. There are two categories of sampling techniques, namely: random and non-random sampling. The use of either depends on your research design.

In random sampling, the sample is selected randomly and each subject in the population has an equal chance of being selected for the sample.

The advantage of random sampling is that the results from the sample can be generalised to the population, especially if the sample size is sufficiently large.

Random sampling is used primarily for quantitative studies.

In non-random sampling, the sample is selected deliberately rather than randomly. As a result, the subjects do not have an equal chance of being selected for the sample.

It is also referred to as purposive sampling, meaning that the sample being selected have a specific purpose.

Non-random sampling is used primarily for qualitative sampling.

In this section, the student is expected to discuss in detail the type of data he will collect, that is, whether primary or secondary data (or both) and how he will go about collecting the data from the sample. The methods and tools used also depend on the research design. They include:

Questionnaires are mostly used to collect quantitative data.

Questionnaires are structured in nature and include closed-ended questions.

There are four main types of closed-ended questions used in questionnaires:

• for example: how many children do you have?
• for example: does your household have a radio? 1. Yes 2. No
• for example: what is your highest level of education? 1. No education 2. Primary education 3. Secondary education 4. Tertiary level
• for example: please rate your level of satisfaction with the water services board. 1. Very dissatisfied 2. Dissatisfied 3. Neutral 4. Satisfied 5. Very satisfied

There are two forms of questionnaaire delivery: facilitated questionnaires and self-administered questionnaires.

For facilitated questionnaires, the researcher administers the questionnaire while in self-administered questionnaires, the respondent fills in the questionnaire without the presence of the researcher.

Self-administered questionnaires can be delivered by hand, or mailed via the post office or through email. Facilitated questionnaires can be done either face-to-face or through telephone. Each of these options has its pros and cons.

Interviews are oral discussions between the researcher and the respondent.

Unlike questionnaires, interviews are semi-structured. The researcher uses an interview guide to guide the discussion. The interview guide has some questions that the researcher asks the respondent. However, subsequent questions and discussions are determined by the responses given by the respondent to previous questions.

The flow of interviews will therefore vary from one respondent to another depending on their personalities and openness to responding to the questions.

Whereas interviews are held with individuals, focus group discussions (FGDs) are held with a group of respondents who are key to the problem under investigation.

The participants for an FGD should be selected carefully to represent diverse subjects of the population under investigation.

In the example of the study on the effects of COVID-19 pandemic on micro and small enterprises in Kenya, the student can create a focus group that has the following members: a female-owned enterprise, a male-owned enterprise, a youth-owned enterprise, a family-run enterprise, a non-family-run enterprise, customers of the enterprises, and an employee of the Micro and Small Enterprises Authority (MSEA). Such a focus group would have rich discussions of the views of the different players in the industry.

Observation is also a method of data collection that is commonly used. There are two types of observation: participant observation and non-participant observation.

In participant observation, the researcher immerses himself into the environment of study. In the MSEs study, for example, the researcher would choose to work in one of the enterprises for a period of time where he would observe how the business performs on a day-to-day basis.

In non-participant observation, the researcher removes himself from the environment of study and instead observes from a distance. In the MSEs study, for example, the researcher would go somewhere close to an enterprise and observe how the business performs e.g. how many clients visit the business on a day-to-day basis.

Each observation type has its own pros and cons.

During observation, the researcher should use an observation checklist that guides him on what needs to be observed and the frequency of observation.

In this data collection methods, the student obtains relevant documents to his study and reviews them in-depth. For instance, in the MSEs, the student can review the MSEs Policy of Kenya, Strategic Plan of the Micro and Small Enterprises Authority etc. Such documents are useful in informing the researcher the current state of affairs of the problem under investigation.

This section highlights the ethical considerations that would be followed during the data collection process. The ethical considerations vary from study to study and include:

Consent: the researcher should seek informed consent from the respondent before the data collection begins. For instance, when administering the questionnaire or conducting interviews, the researcher should start by informing the respondent what the study is about, how the respondent was selected, and the benefits of the study and then seek permission to continue with the study. The consent can be in written or oral form.

Compensation for participation: while participating in the study should be voluntary, some research have allowance for monetary compensation. The respondents should be informed of any plans to compensate them but after they have participated in the study, not before.

Confidentiality: the researcher should assure the respondents that their responses will be kept confidential.

Dissemination of the study findings with the respondents: there should be a plan for the student to disseminate the results of the study with the participants, for instance, through validation workshops or written publications.

Additionally, most academic institutions require their students to obtain ethical clearance for their research from the relevant authorities. Students should check if this requirement applies to them and follow the necessary procedure.

In this section, the student should discuss how the data collected will be analysed. Data analysis methods and techniques vary depending on whether the data is quantitative or qualitative.

For quantitative research, the interest of data analysis is the numbers which can be obtained through descriptive statistics and inferential statistics.

Descriptive statistics is usually the first step in analysing quantitative data. There are three categories of descriptive statistics:

• Measures of frequency: frequency table or cross-tabulation table.
• Measures of central tendency: mean, median and mode.
• Measures of variability: range, standard deviation and variance.

Inferential analysis goes a step further and looks at whether the results from the sample can be generalised to the wider population. For studies that involve interventions, inferential analysis is used to check if the intervention has any impact on the population in which it was implemented.

Some inferential analysis techniques include:

• Checking for differences between groups: t-test, analysis of variance (ANOVA) and Chi-square test.
• Checking for correlation or causation between variables: linear regression, logistic regression (logit, probit, multinomial logit/probit models etc).

The choice of data analysis technique will depend on the type of data the student has. For instance, a dependent variable that is continuous will use a different analysis technique from a dependent variable that is categorical in nature. Additionally, the choice of the data analysis technique should be guided by the research questions. The results from the analysis should be able to provide answers to the research questions posed.

For qualitative research, data analysis involves analysing the content of the interviews and focus group discussions. The content can be in different forms such as interview recordings and hand-written notes.

The recordings should be transcribed first and the notes should be organised well before analysis can take place.

The analysis of qualitative data involves coding the data, indexing the data and framing the data to identify the themes that emerge from the data.

Besides discussing the data analysis techniques, the student should discuss the softwares that will be used for analysis. There are many softwares in the market that are used for quantitative (such as SPSS and STATA) and qualitative data (such as NVivo).

The last section in the research methodology chapter discusses the potential limitations of the study and how the limitations will be mitigated. An example of study limitation is low response rate of questionnaires, which can be mitigated through triangulation.

The limitations of the study will vary from one study to another and depend on the context within which the study is conducted.

This article provided a detailed guide on how to write the research methodology chapter of a PhD thesis proposal. The research methodology chapter is informed by the research problem and research questions specified in chapter 1 of the thesis proposal. Students should therefore think through carefully their research study from the beginning because what is in the introduction chapter informs the content in the remaining chapters of the proposal and final thesis.

Related posts

How To Write Chapter 1 Of A PhD Thesis Proposal (A Practical Guide)

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Grace Njeri-Otieno

Grace Njeri-Otieno is a Kenyan, a wife, a mom, and currently a PhD student, among many other balls she juggles. She holds a Bachelors' and Masters' degrees in Economics and has more than 7 years' experience with an INGO. She was inspired to start this site so as to share the lessons learned throughout her PhD journey with other PhD students. Her vision for this site is "to become a go-to resource center for PhD students in all their spheres of learning."

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Methods Section: Chapter Three

The methods section , or chapter three, of the dissertation or thesis is often the most challenging for graduate students.  The methodology section, chapter three should reiterate the research questions and hypotheses, present the research design, discuss the participants, the instruments to be used, the procedure, the data analysis plan , and the sample size justification.

Research Questions and Null Hypotheses

Chapter three should begin with a portion that discusses the research questions and null hypotheses.  In the research questions and null hypotheses portion of the methodology chapter, the research questions should be restated in statistical language.  For example, “Is there a difference in GPA by gender?” is a t-test type of question, whereas “Is there a relationship between GPA and income level?” is a correlation type of question.  The important thing to remember is to use the language that foreshadows the data analysis plan .  The null hypotheses are just the research questions stated in the null; for example, “There is no difference in GPA by gender,” or “There is no relationship between GPA and income level.”

Research Design

The next portion of the methods section, chapter three is focused on developing the research design.  The research design has several possibilities. First, you must decide if you are doing quantitative, qualitative, or mixed methods research. In a quantitative study, you are assessing participants’ responses on a measure.  For example, participants can endorse their level of agreement on some scale.  A qualitative design is a typically a semi-structured interview which gets transcribed, and the themes among the participants are derived.  A mixed methods project is a mixture of both a quantitative and qualitative study.

Participants

In the research methodology, the participants are typically a sample of the population you want to study.  You are probably not going to study all school children, but you may sample from the population of school children.  You need to include information about the characteristics of the population in your study (Are you sampling all males? teachers with under five years of experience?).  This represents the participants portion of your methods section, chapter three.

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Instruments

The instruments section is a critical part of the methodology section, chapter three.  The instruments section should include the name of the instruments, the scales or subscales, how the scales are computed, and the reliability and validity of the scales.  The instruments portion should have references to the researchers who created the instruments.

The procedure section of the methods chapter is simply how you are going to administer the instruments that you just described to the participants you are going to select.  You should walk the reader through the procedure in detail so that they can replicate your steps and your study.

Data Analysis Plan

The data analysis plan is just that — how you are going to analyze the data when you get the data from your participants.   It includes the statistical tests you are going to use, the statistical assumptions of these tests, and the justification for the statistical tests.

Sample Size Justification

Another important portion of your methods chapter three, is the sample size justification.  Sample size justification (or power analysis) is selecting how many participants you need to have in your study.  The sample size is based on several criteria:  the power you select (which is typically .80), the alpha level selected (which is typically .05), and the effect size (typically, a large or medium effect size is selected).  Importantly, once these criteria are selected, the sample size is going to be based on the type of statistic: an ANOVA is going to have a different sample size calculation than a multiple regression.

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How To Write The Methodology Chapter

Overview: The Methodology Chapter

• The purpose  of the methodology chapter
• Why you need to craft this chapter (really) well
• How to write and structure the chapter
• Methodology chapter example
• Essential takeaways

What (exactly) is the methodology chapter?

The methodology chapter is where you outline the philosophical foundations of your research and detail the specific methodological choices you’ve made. In other words, the purpose of this chapter is to explain exactly how you designed your study and, just as importantly, why you made those choices.

Your methodology chapter should comprehensively describe and justify all the methodological decisions involved in your study. For instance, the research approach you took (qualitative, quantitative, or mixed methods), your sampling strategy (who you collected data from), how you gathered your data, and how you analysed it. If that sounds a bit daunting, don’t worry – we’ll walk you through all these methodological aspects in this post .

Why is the methodology chapter important?

The methodology chapter plays two important roles in your dissertation or thesis:

Firstly, it demonstrates your understanding of research theory, which is what earns you marks. A flawed research design or methodology would mean flawed results. So, this chapter is vital as it allows you to show the marker that you know what you’re doing and that your results are credible .

Secondly, the methodology chapter is what helps to make your study replicable. In other words, it allows other researchers to undertake your study using the same methodological approach, and compare their findings to yours. This is very important within academic research, as each study builds on previous studies.

The methodology chapter is also important in that it allows you to identify and discuss any methodological issues or problems you encountered (i.e., research limitations ), and to explain how you mitigated the impacts of these.

Now, it’s important to understand that every research project has its limitations , so it’s important to acknowledge these openly and highlight your study’s value despite its limitations . Doing so demonstrates your understanding of research design, which will earn you marks. 

Need a helping hand?

How to write up the methodology chapter

Before you start writing, it’s always a good idea to draw up a rough outline to guide your writing. Don’t just start writing without knowing what you’ll discuss where. If you do, you’ll likely end up with a disjointed, ill-flowing narrative . You’ll then waste a lot of time rewriting in an attempt to try to stitch all the pieces together. Do yourself a favour and start with the end in mind .

Section 1 – Introduction

As with all chapters in your dissertation or thesis, the methodology chapter should have a brief introduction. In this section, you should remind your readers what the focus of your study is, especially the research aims . As we’ve discussed many times on the blog, your methodology needs to align with your research aims, objectives and research questions. Therefore, it’s useful to frontload this component to remind the reader (and yourself!) what you’re trying to achieve.

Section 2 – The Methodology

The next section of your chapter is where you’ll present the actual methodology. In this section, you need to detail and justify the key methodological choices you’ve made in a logical, intuitive fashion. Importantly, this is the heart of your methodology chapter, so you need to get specific – don’t hold back on the details here. This is not one of those “less is more” situations.

Let’s take a look at the most common components you’ll likely need to cover.

Methodological Choice #1 – Research Philosophy

Research philosophy refers to the underlying beliefs (i.e., the worldview) regarding how data about a phenomenon should be gathered , analysed and used . The research philosophy will serve as the core of your study and underpin all of the other research design choices, so it’s critically important that you understand which philosophy you’ll adopt and why you made that choice. If you’re not clear on this, take the time to get clarity before you make any further methodological choices.

While several research philosophies exist, two commonly adopted ones are positivism and interpretivism . These two sit roughly on opposite sides of the research philosophy spectrum.

Positivism states that the researcher can observe reality objectively and that there is only one reality, which exists independently of the observer. As a consequence, it is quite commonly the underlying research philosophy in quantitative studies and is oftentimes the assumed philosophy in the physical sciences.

Contrasted with this, interpretivism , which is often the underlying research philosophy in qualitative studies, assumes that the researcher performs a role in observing the world around them and that reality is unique to each observer . In other words, reality is observed subjectively .

These are just two philosophies (there are many more), but they demonstrate significantly different approaches to research and have a significant impact on all the methodological choices. Therefore, it’s vital that you clearly outline and justify your research philosophy at the beginning of your methodology chapter, as it sets the scene for everything that follows.

The next thing you would typically discuss in your methodology section is the research type. The starting point for this is to indicate whether the research you conducted is inductive or deductive .

Inductive research takes a bottom-up approach , where the researcher begins with specific observations or data and then draws general conclusions or theories from those observations. Therefore these studies tend to be exploratory in terms of approach.

Conversely , d eductive research takes a top-down approach , where the researcher starts with a theory or hypothesis and then tests it using specific observations or data. Therefore these studies tend to be confirmatory in approach.

Related to this, you’ll need to indicate whether your study adopts a qualitative, quantitative or mixed  approach. As we’ve mentioned, there’s a strong link between this choice and your research philosophy, so make sure that your choices are tightly aligned . When you write this section up, remember to clearly justify your choices, as they form the foundation of your study.

Methodological Choice #3 – Research Strategy

Next, you’ll need to discuss your research strategy (also referred to as a research design ). This methodological choice refers to the broader strategy in terms of how you’ll conduct your research, based on the aims of your study.

Several research strategies exist, including experimental , case studies , ethnography , grounded theory, action research , and phenomenology . Let’s take a look at two of these, experimental and ethnographic, to see how they contrast.

Experimental research makes use of the scientific method , where one group is the control group (in which no variables are manipulated ) and another is the experimental group (in which a specific variable is manipulated). This type of research is undertaken under strict conditions in a controlled, artificial environment (e.g., a laboratory). By having firm control over the environment, experimental research typically allows the researcher to establish causation between variables. Therefore, it can be a good choice if you have research aims that involve identifying causal relationships.

Ethnographic research , on the other hand, involves observing and capturing the experiences and perceptions of participants in their natural environment (for example, at home or in the office). In other words, in an uncontrolled environment.  Naturally, this means that this research strategy would be far less suitable if your research aims involve identifying causation, but it would be very valuable if you’re looking to explore and examine a group culture, for example.

The next thing you’ll need to detail in your methodology chapter is the time horizon. There are two options here: cross-sectional and longitudinal . In other words, whether the data for your study were all collected at one point in time (cross-sectional) or at multiple points in time (longitudinal).

The choice you make here depends again on your research aims, objectives and research questions. If, for example, you aim to assess how a specific group of people’s perspectives regarding a topic change over time , you’d likely adopt a longitudinal time horizon.

Another important factor to consider is simply whether you have the time necessary to adopt a longitudinal approach (which could involve collecting data over multiple months or even years). Oftentimes, the time pressures of your degree program will force your hand into adopting a cross-sectional time horizon, so keep this in mind.

Methodological Choice #5 – Sampling Strategy

Next, you’ll need to discuss your sampling strategy . There are two main categories of sampling, probability and non-probability sampling.

Probability sampling involves a random (and therefore representative) selection of participants from a population, whereas non-probability sampling entails selecting participants in a non-random  (and therefore non-representative) manner. For example, selecting participants based on ease of access (this is called a convenience sample).

The right sampling approach depends largely on what you’re trying to achieve in your study. Specifically, whether you trying to develop findings that are generalisable to a population or not. Practicalities and resource constraints also play a large role here, as it can oftentimes be challenging to gain access to a truly random sample. In the video below, we explore some of the most common sampling strategies. https://www.youtube.com/watch?v=fSmedyVv-Us Video can't be loaded because JavaScript is disabled: Sampling Methods 101: Probability & Non-Probability Sampling Explained Simply (https://www.youtube.com/watch?v=fSmedyVv-Us) Methodological Choice #6 – Data Collection Method

Next up, you’ll need to explain how you’ll go about collecting the necessary data for your study. Your data collection method (or methods) will depend on the type of data that you plan to collect – in other words, qualitative or quantitative data.

Typically, quantitative research relies on surveys , data generated by lab equipment, analytics software or existing datasets. Qualitative research, on the other hand, often makes use of collection methods such as interviews , focus groups , participant observations, and ethnography.

So, as you can see, there is a tight link between this section and the design choices you outlined in earlier sections. Strong alignment between these sections, as well as your research aims and questions is therefore very important.

Methodological Choice #7 – Data Analysis Methods/Techniques

The final major methodological choice that you need to address is that of analysis techniques . In other words, how you’ll go about analysing your date once you’ve collected it. Here it’s important to be very specific about your analysis methods and/or techniques – don’t leave any room for interpretation. Also, as with all choices in this chapter, you need to justify each choice you make.

With the key methodological choices outlined and justified, the next step is to discuss the limitations of your design. No research methodology is perfect – there will always be trade-offs between the “ideal” methodology and what’s practical and viable, given your constraints. Therefore, this section of your methodology chapter is where you’ll discuss the trade-offs you had to make, and why these were justified given the context.

Methodological limitations can vary greatly from study to study, ranging from common issues such as time and budget constraints to issues of sample or selection bias . For example, you may find that you didn’t manage to draw in enough respondents to achieve the desired sample size (and therefore, statistically significant results), or your sample may be skewed heavily towards a certain demographic, thereby negatively impacting representativeness .

In this section, it’s important to be critical of the shortcomings of your study. There’s no use trying to hide them (your marker will be aware of them regardless). By being critical, you’ll demonstrate to your marker that you have a strong understanding of research theory, so don’t be shy here. At the same time, don’t beat your study to death . State the limitations, why these were justified, how you mitigated their impacts to the best degree possible, and how your study still provides value despite these limitations .

Section 4 – Concluding Summary

Finally, it’s time to wrap up the methodology chapter with a brief concluding summary. In this section, you’ll want to concisely summarise what you’ve presented in the chapter. Here, it can be a good idea to use a figure to summarise the key decisions, especially if your university recommends using a specific model (for example, Saunders’ Research Onion ).

Methodology Chapter Example

Wrapping up.

Also, remember the golden rule of the methodology chapter – justify every choice ! Make sure that you clearly explain the “why” for every “what”, and reference credible methodology textbooks or academic sources to back up your justifications.

Learn More About Methodology

Triangulation: The Ultimate Credibility Enhancer

Triangulation is one of the best ways to enhance the credibility of your research. Learn about the different options here.

Research Limitations 101: What You Need To Know

Learn everything you need to know about research limitations (AKA limitations of the study). Includes practical examples from real studies.

In Vivo Coding 101: Full Explainer With Examples

Learn about in vivo coding, a popular qualitative coding technique ideal for studies where the nuances of language are central to the aims.

Process Coding 101: Full Explainer With Examples

Learn about process coding, a popular qualitative coding technique ideal for studies exploring processes, actions and changes over time.

Qualitative Coding 101: Inductive, Deductive & Hybrid Coding

Inductive, Deductive & Abductive Coding Qualitative Coding Approaches Explained...

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• > The Research Imagination
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Book contents

• Frontmatter
• 1 RESEARCH PROCESS
• 2 THEORY AND METHOD
• 3 RESEARCH DESIGN
• 4 MEASUREMENT
• 5 ETHICAL AND POLITICAL ISSUES
• 7 SURVEY RESEARCH
• 8 INTENSIVE INTERVIEWING
• 9 OBSERVATIONAL FIELD RESEARCH
• 10 FEMINIST METHODS
• 11 HISTORICAL ANALYSIS
• 12 EXPERIMENTAL RESEARCH
• 13 CONTENT ANALYSIS
• 14 AGGREGATE DATA ANALYSIS
• 15 COMPARATIVE RESEARCH METHODS
• 16 EVALUATION RESEARCH
• 17 INDEXES AND SCALES
• 18 BASIC STATISTICAL ANALYSIS
• 19 MULTIVARIATE ANALYSIS AND STATISTICAL SIGNIFICANCE
• EPILOGUE: THE VALUE AND LIMITS OF SOCIAL SCIENCE KNOWLEDGE
• Appendix A A Precoded Questionnaire
• Appendix B Excerpt from a Codebook
• Author Index
• Subject Index

3 - RESEARCH DESIGN

Published online by Cambridge University Press:  05 June 2012

INTRODUCTION

The first two chapters have explained the elements of the scientific method and examined the connection between theory and the research process. After reading these chapters, you are now ready to look at how research is actually designed, step-by-step. research design is the overall process of using your imagination as well as the strategy and tactics of science to guide the collection and analysis of data . Have you have ever wondered: “What is a proper problem to write about in my term paper?” This issue, selecting a topic for study, is the first and most crucial one to be addressed in social research. The dilemmas of research design are sometimes difficult to resolve. Professional social scientists with years of experience sometimes wonder if investigating a problem that happens to interest them personally will produce a worthy addition to knowledge in their field of inquiry.

A related and equally thorny issue to be explored in this chapter is whether it is legitimate, or desirable, to be wholly descriptive in one's work, rather than explanatory. In other words, should all research make some theoretical contribution? In addition, we will examine the question of how much information is needed to substantiate an argument. The old axiom that “the more we know, the more we realize our own ignorance” certainly applies in social research. Are there guidelines to help us determine when we should stop collecting data and begin to analyze it?

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• RESEARCH DESIGN
• Paul S. Gray , Boston College, Massachusetts , John B. Williamson , Boston College, Massachusetts , David A. Karp , Boston College, Massachusetts , John R. Dalphin
• Book: The Research Imagination
• Online publication: 05 June 2012
• Chapter DOI: https://doi.org/10.1017/CBO9780511819391.004

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Chapter 3 The Research Process

In Chapter 1, we saw that scientific research is the process of acquiring scientific knowledge using the scientific method. But how is such research conducted? This chapter delves into the process of scientific research, and the assumptions and outcomes of the research process.

Paradigms of Social Research

Our design and conduct of research is shaped by our mental models or frames of references that we use to organize our reasoning and observations. These mental models or frames (belief systems) are called paradigms. The word “paradigm” was popularized by

Thomas Kuhn (1962) in his book The Structure of Scientific Revolutions, where he examined the history of the natural sciences to identify patterns of activities that shape the progress of science. Similar ideas are applicable to social sciences as well, where a social reality can be viewed by different people in different ways, which may constrain their thinking and reasoning about the observed phenomenon. For instance, conservatives and liberals tend to have very different perceptions of the role of government in people’s lives, and hence, have different opinions on how to solve social problems. Conservatives may believe that lowering taxes is the best way to stimulate a stagnant economy because it increases people’s disposable income and spending, which in turn expands business output and employment. In contrast, liberals may believe that governments should invest more directly in job creation programs such as public works and infrastructure projects, which will increase employment and people’s ability to consume and drive the economy. Likewise, Western societies place greater emphasis on individual rights, such as one’s right to privacy, right of free speech, and right to bear arms. In contrast, Asian societies tend to balance the rights of individuals against the rights of families, organizations, and the government, and therefore tend to be more communal and less individualistic in their policies. Such differences in perspective often lead Westerners to criticize Asian governments for being autocratic, while Asians criticize Western societies for being greedy, having high crime rates, and creating a “cult of the individual.” Our personal paradigms are like “colored glasses” that govern how we view the world and how we structure our thoughts about what we see in the world.

Paradigms are often hard to recognize, because they are implicit, assumed, and taken for granted. However, recognizing these paradigms is key to making sense of and reconciling differences in people’ perceptions of the same social phenomenon. For instance, why do liberals believe that the best way to improve secondary education is to hire more teachers, but conservatives believe that privatizing education (using such means as school vouchers) are more effective in achieving the same goal? Because conservatives place more faith in competitive markets (i.e., in free competition between schools competing for education dollars), while liberals believe more in labor (i.e., in having more teachers and schools). Likewise, in social science research, if one were to understand why a certain technology was successfully implemented in one organization but failed miserably in another, a researcher looking at the world through a “rational lens” will look for rational explanations of the problem such as inadequate technology or poor fit between technology and the task context where it is being utilized, while another research looking at the same problem through a “social lens” may seek out social deficiencies such as inadequate user training or lack of management support, while those seeing it through a “political lens” will look for instances of organizational politics that may subvert the technology implementation process. Hence, subconscious paradigms often constrain the concepts that researchers attempt to measure, their observations, and their subsequent interpretations of a phenomenon. However, given the complex nature of social phenomenon, it is possible that all of the above paradigms are partially correct, and that a fuller understanding of the problem may require an understanding and application of multiple paradigms.

Two popular paradigms today among social science researchers are positivism and post-positivism. Positivism , based on the works of French philosopher Auguste Comte (1798-1857), was the dominant scientific paradigm until the mid-20 th century. It holds that science or knowledge creation should be restricted to what can be observed and measured. Positivism tends to rely exclusively on theories that can be directly tested. Though positivism was originally an attempt to separate scientific inquiry from religion (where the precepts could not be objectively observed), positivism led to empiricism or a blind faith in observed data and a rejection of any attempt to extend or reason beyond observable facts. Since human thoughts and emotions could not be directly measured, there were not considered to be legitimate topics for scientific research. Frustrations with the strictly empirical nature of positivist philosophy led to the development of post-positivism (or postmodernism) during the mid-late 20 th century. Post-positivism argues that one can make reasonable inferences about a phenomenon by combining empirical observations with logical reasoning. Post-positivists view science as not certain but probabilistic (i.e., based on many contingencies), and often seek to explore these contingencies to understand social reality better. The post -positivist camp has further fragmented into subjectivists , who view the world as a subjective construction of our subjective minds rather than as an objective reality, and critical realists , who believe that there is an external reality that is independent of a person’s thinking but we can never know such reality with any degree of certainty.

Burrell and Morgan (1979), in their seminal book Sociological Paradigms and Organizational Analysis, suggested that the way social science researchers view and study social phenomena is shaped by two fundamental sets of philosophical assumptions: ontology and epistemology. Ontology refers to our assumptions about how we see the world, e.g., does the world consist mostly of social order or constant change. Epistemology refers to our assumptions about the best way to study the world, e.g., should we use an objective or subjective approach to study social reality. Using these two sets of assumptions, we can categorize social science research as belonging to one of four categories (see Figure 3.1).

If researchers view the world as consisting mostly of social order (ontology) and hence seek to study patterns of ordered events or behaviors, and believe that the best way to study such a world is using objective approach (epistemology) that is independent of the person conducting the observation or interpretation, such as by using standardized data collection tools like surveys, then they are adopting a paradigm of functionalism . However, if they believe that the best way to study social order is though the subjective interpretation of participants involved, such as by interviewing different participants and reconciling differences among their responses using their own subjective perspectives, then they are employing an interpretivism paradigm. If researchers believe that the world consists of radical change and seek to understand or enact change using an objectivist approach, then they are employing a radical structuralism paradigm. If they wish to understand social change using the subjective perspectives of the participants involved, then they are following a radical humanism paradigm.

Figure 3.1. Four paradigms of social science research (Source: Burrell and Morgan, 1979)

Figure 3.2. Functionalistic research process

The first phase of research is exploration . This phase includes exploring and selecting research questions for further investigation, examining the published literature in the area of inquiry to understand the current state of knowledge in that area, and identifying theories that may help answer the research questions of interest.

The first step in the exploration phase is identifying one or more research questions dealing with a specific behavior, event, or phenomena of interest. Research questions are specific questions about a behavior, event, or phenomena of interest that you wish to seek answers for in your research. Examples include what factors motivate consumers to purchase goods and services online without knowing the vendors of these goods or services, how can we make high school students more creative, and why do some people commit terrorist acts. Research questions can delve into issues of what, why, how, when, and so forth. More interesting research questions are those that appeal to a broader population (e.g., “how can firms innovate” is a more interesting research question than “how can Chinese firms innovate in the service-sector”), address real and complex problems (in contrast to hypothetical or “toy” problems), and where the answers are not obvious. Narrowly focused research questions (often with a binary yes/no answer) tend to be less useful and less interesting and less suited to capturing the subtle nuances of social phenomena. Uninteresting research questions generally lead to uninteresting and unpublishable research findings.

The next step is to conduct a literature review of the domain of interest. The purpose of a literature review is three-fold: (1) to survey the current state of knowledge in the area of inquiry, (2) to identify key authors, articles, theories, and findings in that area, and (3) to identify gaps in knowledge in that research area. Literature review is commonly done today using computerized keyword searches in online databases. Keywords can be combined using “and” and “or” operations to narrow down or expand the search results. Once a shortlist of relevant articles is generated from the keyword search, the researcher must then manually browse through each article, or at least its abstract section, to determine the suitability of that article for a detailed review. Literature reviews should be reasonably complete, and not restricted to a few journals, a few years, or a specific methodology. Reviewed articles may be summarized in the form of tables, and can be further structured using organizing frameworks such as a concept matrix. A well-conducted literature review should indicate whether the initial research questions have already been addressed in the literature (which would obviate the need to study them again), whether there are newer or more interesting research questions available, and whether the original research questions should be modified or changed in light of findings of the literature review. The review can also provide some intuitions or potential answers to the questions of interest and/or help identify theories that have previously been used to address similar questions.

Since functionalist (deductive) research involves theory-testing, the third step is to identify one or more theories can help address the desired research questions. While the literature review may uncover a wide range of concepts or constructs potentially related to the phenomenon of interest, a theory will help identify which of these constructs is logically relevant to the target phenomenon and how. Forgoing theories may result in measuring a wide range of less relevant, marginally relevant, or irrelevant constructs, while also minimizing the chances of obtaining results that are meaningful and not by pure chance. In functionalist research, theories can be used as the logical basis for postulating hypotheses for empirical testing. Obviously, not all theories are well-suited for studying all social phenomena. Theories must be carefully selected based on their fit with the target problem and the extent to which their assumptions are consistent with that of the target problem. We will examine theories and the process of theorizing in detail in the next chapter.

The next phase in the research process is research design . This process is concerned with creating a blueprint of the activities to take in order to satisfactorily answer the research questions identified in the exploration phase. This includes selecting a research method, operationalizing constructs of interest, and devising an appropriate sampling strategy.

Operationalization is the process of designing precise measures for abstract theoretical constructs. This is a major problem in social science research, given that many of the constructs, such as prejudice, alienation, and liberalism are hard to define, let alone measure accurately. Operationalization starts with specifying an “operational definition” (or “conceptualization”) of the constructs of interest. Next, the researcher can search the literature to see if there are existing prevalidated measures matching their operational definition that can be used directly or modified to measure their constructs of interest. If such measures are not available or if existing measures are poor or reflect a different conceptualization than that intended by the researcher, new instruments may have to be designed for measuring those constructs. This means specifying exactly how exactly the desired construct will be measured (e.g., how many items, what items, and so forth). This can easily be a long and laborious process, with multiple rounds of pretests and modifications before the newly designed instrument can be accepted as “scientifically valid.” We will discuss operationalization of constructs in a future chapter on measurement.

Simultaneously with operationalization, the researcher must also decide what research method they wish to employ for collecting data to address their research questions of interest. Such methods may include quantitative methods such as experiments or survey research or qualitative methods such as case research or action research, or possibly a combination of both. If an experiment is desired, then what is the experimental design? If survey, do you plan a mail survey, telephone survey, web survey, or a combination? For complex, uncertain, and multi-faceted social phenomena, multi-method approaches may be more suitable, which may help leverage the unique strengths of each research method and generate insights that may not be obtained using a single method.

Researchers must also carefully choose the target population from which they wish to collect data, and a sampling strategy to select a sample from that population. For instance, should they survey individuals or firms or workgroups within firms? What types of individuals or firms they wish to target? Sampling strategy is closely related to the unit of analysis in a research problem. While selecting a sample, reasonable care should be taken to avoid a biased sample (e.g., sample based on convenience) that may generate biased observations. Sampling is covered in depth in a later chapter.

At this stage, it is often a good idea to write a research proposal detailing all of the decisions made in the preceding stages of the research process and the rationale behind each decision. This multi-part proposal should address what research questions you wish to study and why, the prior state of knowledge in this area, theories you wish to employ along with hypotheses to be tested, how to measure constructs, what research method to be employed and why, and desired sampling strategy. Funding agencies typically require such a proposal in order to select the best proposals for funding. Even if funding is not sought for a research project, a proposal may serve as a useful vehicle for seeking feedback from other researchers and identifying potential problems with the research project (e.g., whether some important constructs were missing from the study) before starting data collection. This initial feedback is invaluable because it is often too late to correct critical problems after data is collected in a research study.

Having decided who to study (subjects), what to measure (concepts), and how to collect data (research method), the researcher is now ready to proceed to the research execution phase. This includes pilot testing the measurement instruments, data collection, and data analysis.

Pilot testing is an often overlooked but extremely important part of the research process. It helps detect potential problems in your research design and/or instrumentation (e.g., whether the questions asked is intelligible to the targeted sample), and to ensure that the measurement instruments used in the study are reliable and valid measures of the constructs of interest. The pilot sample is usually a small subset of the target population. After a successful pilot testing, the researcher may then proceed with data collection using the sampled population. The data collected may be quantitative or qualitative, depending on the research method employed.

Following data collection, the data is analyzed and interpreted for the purpose of drawing conclusions regarding the research questions of interest. Depending on the type of data collected (quantitative or qualitative), data analysis may be quantitative (e.g., employ statistical techniques such as regression or structural equation modeling) or qualitative (e.g., coding or content analysis).

The final phase of research involves preparing the final research report documenting the entire research process and its findings in the form of a research paper, dissertation, or monograph. This report should outline in detail all the choices made during the research process (e.g., theory used, constructs selected, measures used, research methods, sampling, etc.) and why, as well as the outcomes of each phase of the research process. The research process must be described in sufficient detail so as to allow other researchers to replicate your study, test the findings, or assess whether the inferences derived are scientifically acceptable. Of course, having a ready research proposal will greatly simplify and quicken the process of writing the finished report. Note that research is of no value unless the research process and outcomes are documented for future generations; such documentation is essential for the incremental progress of science.

Common Mistakes in Research

The research process is fraught with problems and pitfalls, and novice researchers often find, after investing substantial amounts of time and effort into a research project, that their research questions were not sufficiently answered, or that the findings were not interesting enough, or that the research was not of “acceptable” scientific quality. Such problems typically result in research papers being rejected by journals. Some of the more frequent mistakes are described below.

Insufficiently motivated research questions. Often times, we choose our “pet” problems that are interesting to us but not to the scientific community at large, i.e., it does not generate new knowledge or insight about the phenomenon being investigated. Because the research process involves a significant investment of time and effort on the researcher’s part, the researcher must be certain (and be able to convince others) that the research questions they seek to answer in fact deal with real problems (and not hypothetical problems) that affect a substantial portion of a population and has not been adequately addressed in prior research.

Pursuing research fads. Another common mistake is pursuing “popular” topics with limited shelf life. A typical example is studying technologies or practices that are popular today. Because research takes several years to complete and publish, it is possible that popular interest in these fads may die down by the time the research is completed and submitted for publication. A better strategy may be to study “timeless” topics that have always persisted through the years.

Unresearchable problems. Some research problems may not be answered adequately based on observed evidence alone, or using currently accepted methods and procedures. Such problems are best avoided. However, some unresearchable, ambiguously defined problems may be modified or fine tuned into well-defined and useful researchable problems.

Favored research methods. Many researchers have a tendency to recast a research problem so that it is amenable to their favorite research method (e.g., survey research). This is an unfortunate trend. Research methods should be chosen to best fit a research problem, and not the other way around.

Blind data mining. Some researchers have the tendency to collect data first (using instruments that are already available), and then figure out what to do with it. Note that data collection is only one step in a long and elaborate process of planning, designing, and executing research. In fact, a series of other activities are needed in a research process prior to data collection. If researchers jump into data collection without such elaborate planning, the data collected will likely be irrelevant, imperfect, or useless, and their data collection efforts may be entirely wasted. An abundance of data cannot make up for deficits in research planning and design, and particularly, for the lack of interesting research questions.

• Social Science Research: Principles, Methods, and Practices. Authored by : Anol Bhattacherjee. Provided by : University of South Florida. Located at : http://scholarcommons.usf.edu/oa_textbooks/3/ . License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike

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CHAPTER 3 - RESEARCH METHODOLOGY: Data collection method and Research tools

As it is indicated in the title, this chapter includes the research methodology of the dissertation. In more details, in this part the author outlines the research strategy, the research method, the research approach, the methods of data collection, the selection of the sample, the research process, the type of data analysis, the ethical considerations and the research limitations of the project. The research held with respect to this dissertation was an applied one, but not new. Rather, numerous pieces of previous academic research exist regarding the role of DMOs in promoting and managing tourist destinations, not only for Athens in specific, but also for other tourist destinations in Greece and other places of the world. As such, the proposed research took the form of a new research but on an existing research subject. In order to satisfy the objectives of the dissertation, a qualitative research was held. The main characteristic of qualitative research is that it is mostly appropriate for small samples, while its outcomes are not measurable and quantifiable (see table 3.1). Its basic advantage, which also constitutes its basic difference with quantitative research, is that it offers a complete description and analysis of a research subject, without limiting the scope of the research and the nature of participant’s responses (Collis & Hussey, 2003).

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Spyros Langkos

DOI: 10.13140/2.1.3231.1683 INDEPENDENT STUDΥ - THESIS " Athens as an international tourism destination: An empirical investigation to the city’s imagery and the role of local DMO’s.” The aim of this project was to identify the role of DMOs in promoting Athens as a tourist destination, as well as to evaluate their effectiveness in terms of marketing and managing the tourist product of Athens, its popularity and imagery. The aim of this thesis is to identify the role of DMOs in promoting Athens as a tourist destination, as well as to evaluate their effectiveness in terms of marketing and managing the tourist product of Athens, its popularity and imagery. For that purposes, 6 personal interviews were conducted with executives who were working in 6 famous local DMOs operating both generally in Greece and specifically in Athens. The result of this study indicated that DMOs are playing a crucial role for the promotion of Athens as a tourist destination. DMOs key responsibilities include: development of sophisticated online marketing strategies, creation of high quality published material, participation in international tourism fairs for developing relationships with key stakeholders and development of network synergies with airline companies, and international tourism organizations. Athens is a destination with great potential for future growth and for that reason DMOs have designed certain plans for the next three years in order to exploit the opportunities which are presented. The future plans of the DMOs give particular emphasis in the opening in new tourist markets and more particularly in the markets of Russia, Turkey China, and USA. Besides, DMOs will focus in five forms of tourism which can be developed successfully in Athens, namely: 1) cultural tourism, 2) health tourism, 3) luxury tourism, 4) city break tourism, and 5) convention tourism On the other hand, the executives of the DMOs underlined several problems which prevent the tourism development of Athens. The majority of these problems are related with the business environment in Greece which has become less competitive due to the crisis. Besides, the city as a destination faces the problems of seasonality as well as missing infrastructures. Finally, the research showed that DMOs have established strong and long term relationships with DMOs in foreign countries. These partnerships allow the Greek DMOs to be updated concerning the trends of the global tourism market as well as enhance the movement of tourists between cooperating countries. Nevertheless, the promotion of Athens as a tourism destination requires a more concerted effort between the public and the private stakeholders which are involved in the tourism industry. The benefits will be multiplied for businesses, the state and the society in general. Keywords & terms: Destination Marketing Organizations, DMO’s, tourism destination, tourist product, popularity & imagery, interviews, online marketing strategies, Athens, Greece, international tourism fairs, stakeholder relationships, network synergies, airline companies, future growth, tourist markets, cultural tourism, health tourism, luxury tourism, city break tourism, convention tourism, tourism development of Athens, business environment in Greece, seasonality, infrastructures

HOTELARIA & TURISMO UNIV ALGARVE, PORTUGAL

Aan Jaelani (SCOPUS ID: 57195963463)

Dear Participant, I am Spyros Langkos and I am collecting data from you which will be used in my dissertation for: Athens as an international tourism destination. An empirical investigation to the city’s imagery and the role of local DMO’s, as part of my MSc in Marketing Management at the University of Derby. The objective of the dissertation research, will be to evaluate the contribution of Athens DMO’s towards the rising popularity of the city of Athens as an international destination within the context of Destination Marketing and the information you will be asked to provide will be used to help to provide insights to achieve this objective. The data you provide will only be used for the dissertation, and will not be disclosed to any third party, except as part of the dissertation findings, or as part of the supervisory or assessment processes of the University of Derby. The data you provide will be kept until the 31st of December 2014, so that it is available for scrutiny by the University of Derby as part of the assessment process. If you feel uncomfortable with any of the questions being asked, you may decline to answer specific questions. You may also withdraw from the study completely, and your answers will not be used. And, if you later decide that you wish to withdraw from the study, please write to me at Spyros Langkos, email: [email protected] no later than the 30th of March 2014 and I will be able to remove your response from my analysis and findings, and destroy your response. The Researcher Spyros Langkos

Gregory T Papanikos

This abstract book includes all the summaries of the papers presented at the 9th Annual International Conference on Tourism 10-13 June 2013, organized by the Sciences and Engineering Research Division of the Athens Institute for Education and Research. In total there were 34 papers and 45 presenters, coming from 19 different countries (Australia, Canada, China, Cyprus, Egypt, Hong Kong, Hungary, Ireland, Israel, Lithuania, Poland, Portugal, South Africa Spain, Taiwan, Turkey, UAE, UK, USA). The conference was organized into IX sessions that included areas of Tourism Marketing Issues, Tourism Destination and Development, Special Tourism Themes Entrepreneurship, Economics and Business in the Tourism Industry and other related fields. As it is the publication policy of the Institute, the papers presented in this conference will be considered for publication in one of the books of ATINER.

The tourism industry in Greece is one of the most important sectors of the country’s economy it terms of value (Hellenic Statistical Authority, 2014). There are several public and private organizations which are involved in the tourism industry in Greece for promoting destinations such as the Destination Management Organizations (DMOs). In this context, the aim of this project is to evaluate the contribution of Athens DMO’s towards the rising popularity of the city of Athens as an international destination within the context of Destination Marketing. More specifically, the project has the following objectives:  To identify the activities which are performed by DMOs for promoting Athens and to evaluate the strategic role of DMO’s.  To identify the importance of destination marketing through its application in the Greek Tourism Industry and the particular case of Athens.  To portrait the opinions and activity planning of Greek DMO’s Executives, who are considered to be experts in the tourism field.  To provide insights and new trends of high informational value about the Tourism Industry in Athens.  To highlight the latest incentives and programming concerning the city’s future developments.  To identify the key problems that Athens faces as a tourist destination and to recommend points for improvement from the DMOs perspective.

The aim of this thesis is to identify the role of DMOs in promoting Athens as a tourist destination, as well as to evaluate their effectiveness in terms of marketing and managing the tourist product of Athens, its popularity and imagery. For that purposes, 6 personal interviews were conducted with executives who were working in 6 famous local DMOs operating both generally in Greece and specifically in Athens.

Annals of Tourism Research

This paper examines the directions and methodological practices of tourism research carried out in Greece over the last three decades, highlights critical issues in the developmental path of this study in the country, and makes certain proposals concerning its future orientation. Although the reviewed published works are informed by disciplinary methods and theories, it is the anthropological approach that guides this paper’s interpretations. Further, a preliminary analysis of tourism representations illuminates the local-global relationships. In addition, this exploration demonstrates how Greece’s tourism identity is shaped by powerful discourses embedded in historical, political, and ideological processes.Les recherches de tourisme au sujet de la Grèce. Cet article examine les directions et les pratiques méthodologiques des recherches de tourisme qui ont été réalisées en Grèce dans les trente dernières années, souligne des questions critiques dans le chemin de développement de cette matière dans ce pays et fait certaines suggestions au sujet de son orientation future. Quoique les ouvrages publiés qui sont examinés sont fondés sur des méthodes et des théories disciplinaires, c’est une approche anthropologique qui guide les interprétations de cet article. De plus, une analyse préliminaire des représentations du tourisme éclaire les relations globales/locales. Et cette exploration démontre comment l’identité du tourisme en Grèce est formulée par des discours puissants qui font partie des processus historiques, politiques et idéologiques.

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Basics of Qualitative Data Collection

• First Online: 10 June 2024

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• Rashina Hoda   ORCID: orcid.org/0000-0001-5147-8096 2  

Qualitative data is the main currency of the trade in a socio-technical grounded theory (STGT) study and in qualitative research in general. The quality of a STGT study depends first and foremost on the quality of the data collected. In this chapter, we will learn about popular sampling techniques such as convenience, snowball, random, representative, and theoretical sampling. Then we will learn some tips for data collection , including tips for effective sampling, recruitment, and engagement. Finally, we will learn about several aspects of dealing with data such as data ethics, data sharing, data quality using data quality evaluation guidelines , data usage, and data quantity including theoretical saturation. Researchers will benefit from reading this chapter in conjunction with the Techniques of Qualitative Data Collection , Qualitative Data Preparation and Filtering , and Socio-technical Grounded Theory for Qualitative Data Analysis chapters. Collectively, they cover socio-technical grounded theory’s basic stage.

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Rashina Hoda

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Hoda, R. (2024). Basics of Qualitative Data Collection. In: Qualitative Research with Socio-Technical Grounded Theory. Springer, Cham. https://doi.org/10.1007/978-3-031-60533-8_7

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DOI : https://doi.org/10.1007/978-3-031-60533-8_7

Published : 10 June 2024

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