phd research methodology sample

Sample PhD Research Methodology Chapter

1.0 introduction.

This chapter designs a comprehensive research methodology tailored to the aim and objectives of the study. Specifically, the research methodology chapter is responsible for explaining the philosophical underpinnings, as well as explaining their role in examining the selected research phenomenon. Second, the researcher’s philosophical orientation is explained and the rationale for the chosen methods is provided. Finally, this chapter defends the selected data collection instruments and analysis procedures, paying close attention to their advantages and limitations. Ethical issues and methodological limitations are covered in the end.

2.0 Ontological Perspective

The process of designing a research methodology starts with the branch of ontology, which is primarily concerned with what exists in the human world and what knowledge could be acquired about this world (Anfara & Mertz, 2014). With the help of ontology, researchers can recognise the extent to which the objects they are researching are ‘real’ and what ‘truth claims’ can be made about these objects (Chawla & Sodhi, 2011). The spectrum of ontological stances ranges from naïve realism to relativism; the former assumes that there is a single reality, which could be understood using appropriate research methods, whereas the latter implies that realities exist as multiple mental constructions that change depending on the subject and context in which they exist (Crotty, 2020).

This study follows a bounded relativist position, according to which there is one shared reality within a bounded group (Easterby-Smith et al., 2012). This choice has been made because organisational culture is a context-specific phenomenon, meaning each organisation has its own culture that is manifested in its values, artefacts, stories, and symbols (Kumar, 2014). The existing literature shows that organisational culture is something that is shared by all employees, suggesting there is a single reality within this group (Crotty, 2020). Although there may be outliers, organisational culture is often viewed as the glue that connects the employees of the same organisation and communicates the reality in which they exist.

3.0 Epistemological Perspective

Having identified how ‘things’ are, it is relevant to identify how the researcher creates knowledge by selecting and justifying an epistemological stance. The methodology literature distinguishes between three major epistemological positions, depending on the relationship between the object and the subject (Anfara & Mertz, 2014). Objectivism implies that meaning exists within an object and there is an objective reality that exists independently of the subject (Dew & Foreman, 2020). On the other end of the spectrum lies subjectivism, according to which it is the subject that creates meaning and imposes it on an object (Saunders & Lewis, 2014). Finally, constructionism is a more balanced epistemological position, where the interplay between the subject and the object creates meaning but the reality of the object is still constructed by the subject (Kumar, 2008).

Since the phenomenon of organisational culture exists independently from the researcher, this study is in keeping with the constructionist stance, which allows for generating a contextual understanding of organisational culture and its role in employees’ well-being perception in the selected company. On the one hand, each employee has their views, values, and beliefs, which inevitably affect their perception of well-being (Pruzan, 2016). This fact implies that multiple realities are likely to exist, which are shaped and formed by the company’s employees, as well as the meanings they attach to the world in general and their employer’s organisational culture, in particular (Dew & Foreman, 2020). Still, since the researcher attempts to establish the relationship between organisational culture and employees’ perceptions of well-being and achieve an adequate level of generalisation, the adoption of subjectivist epistemology seems counterproductive. In turn, objectivism fully excludes the possibility of the existence of multiple sources of meaning, which explains why this epistemology has not been adopted either (Chawla & Sodhi, 2011).

4.0 Theoretical Perspective

The next step in designing a comprehensive research methodology would be to have a look at the researcher’s philosophical orientation that guides their action (Creswell & Creswell, 2017). Per Singh and Nath (2010), all theoretical perspectives could be broadly divided into two groups, namely those that are applied to predict the phenomenon in question and those that help in getting a better understanding of this phenomenon. Since this study is focused on the establishment of the relationship between organisational culture and employees’ well-being perceptions, using more than one method to identify the valid and logical truth seems reasonable. Therefore, the theoretical perspective of post-positivism, according to which a valid belief could be properly identified only using multiple methods because all methods are imperfect, has been selected (Anfara & Mertz, 2014). The research design of this study, including its ontological and epistemological standpoints, is presented as follows.

Figure 1: Research Design

Source: Constructed for this study

Post-positivism can be viewed as a natural evolution of the positivist theoretical stance, primarily driven by the growing complexity of the social sciences, which requires examining social phenomena from multiple vantage points to make relevant and accurate predictions (Khan, 2011). As previously noted, employees’ values, attitudes, and beliefs, which form their realities and affect their perceptions of well-being in the organisational context, make this social phenomenon multifaceted and complex (Bryman & Bell, 2015). That is why using several methods is expected to help the researcher decipher this relationship and possibly extrapolate the produced findings to different contexts (Daniel & Sam, 2011). While post-positivism is less rigorous and ‘scientific’ as compared to positivism, some scholars argue that the social world is too complex to be explained by applying natural science methods. Interpretations of reality cannot be isolated from the cultural and social context, in which the research phenomenon is taking place (Easterby-Smith et al., 2012). Still, the application of natural science methods enables the researcher to identify certain patterns, allowing for examining the relationship between organisational culture and employees’ well-being perceptions in more detail.

5.0 Methodology

Based on the above explanation and justification, this doctoral project follows a mixed-method approach, which implies the collection and analysis of qualitative and quantitative data within the same study (Singh & Nath, 2010). A mixed-method research design enables the researcher to obtain data using multiple data collection instruments, adding to the breadth of this investigation and allowing for assessing the relationship between organisational culture and employees’ perceptions of well-being more comprehensively (Cohen et al., 2017). With that being stated, following a mono method can ensure a much higher level of detalisation as compared to a mixed-method approach (Howell, 2012). Still, within the organisational context, different truths are likely to exist among different stakeholder groups, which substantiates the need to examine and compare these truths in order to identify how they are similar or different (Daniel & Sam, 2011).

This project follows the research strategy of a case study, which enables the researcher to develop an in-depth description of the target organisation and its culture. One of the main advantages of this strategy is that it implies using multiple data collection techniques, which goes in keeping with the selected research design (Bryman & Bell, 2015). Although the case study strategy does not represent the world, it focuses on a single case in relation to the selected research problem and illustrates the contextualised case of the relationship between corporate culture and employee perceptions (Singh & Nath, 2010). Although some scholars believe that case studies have limited generalisation because they are primarily focused on a single object or case, it is still possible to compare the case study findings to existing theory and, hence, provide broader implications to similar contexts outside the selected case (Yin, 2014). Since this project not only draws on existing theory but also attempts to come up with a new theory linking organisational culture to employee well-being in the workplace, it incorporates certain aspects of both inductive and deductive approaches (Pruzan, 2016).

6.0 Data and Methods of Collection

Two primary data collection methods, namely self-administered questionnaires and semi-structured interviews have been selected for this study. The former method implies collecting primary quantitative data from individuals who self-report their perceptions of and attitudes towards the research phenomenon (Singh, 2010). In turn, in semi-structured interviews, a researcher asks questions within a predetermined thematic framework but the questions are not set in order or phrasing (Billups, 2019). Self-administered questionnaires have been selected because they not only enable the researcher to obtain a large amount of primary data in a short period. They also generate primary data that could easily be quantified and processed graphically and statistically, which goes in keeping with the post-positivist nature of this study (Saunders & Lewis, 2014). In turn, semi-structured interviews allow for generating highly detailed data that answers the ‘why’ and ‘how’ questions, leading to more comprehensive and detailed research findings (Cohen et al., 2017).

The decision to incorporate both self-administered questionnaires and semi-structured interviews in the research design allows for offsetting the drawbacks of each of these data collection instruments. For example, in questionnaire surveys, respondents’ answers are usually limited to a set of predefined, concise response options, which may not necessarily reflect how they feel about certain things (Novikov & Novikov, 2013). Concurrently, during interviews, interviewees can provide whatever responses they want, regardless of their length or content. At the same time, interviews are commonly considered a time-consuming data collection procedure (Carson et al., 2001). Due to this reason, as well as potential access issues, it is problematic to engage a relatively large number of individuals in interviews. Alternatively, questionnaires can be distributed among a sizeable population of social actors in a fraction of the time needed to conduct an interview (Daniel & Sam, 2011). Therefore, by utilising both self-administered questionnaires and semi-structured interviews, it is possible to make the most of these instruments while overcoming their drawbacks.

7.0 Sampling

For this study, two samples were drawn to approach the relationship between corporate culture and employees’ perceptions of well-being from different vantage points. First, judgemental sampling, a non-probability sampling technique where units to be sampled are selected by the researcher based on their professional judgement, was chosen to approach the most knowledgeable and experienced managers of the target company (Creswell & Creswell, 2017). While the researcher was not intended to cap the number of interviewees included in the sample, a total of 14 top managers agreed to participate. The selection of this sampling strategy is explained by its ability to ensure a deep focus on the researched phenomenon because the interviewees exist in the same context and share similar opinions and values. In turn, managers were selected because they are expected to have a more profound knowledge of the selected company’s organisational culture and its nuances than their subordinates. Interviews were conducted online using Zoom.

Second, the researcher followed the strategy of convenience sampling, which allows for collecting research data from the most easily accessed respondents, to draw a questionnaire survey sample (Gray, 2017). At this point, around 760 employees of the target multinational company were contacted via social media platforms and asked to participate. According to the existing methodology literature, the sample size plays a crucial role when it comes to data validity and reliability, which explains why the researcher intended to include as many employees in the sample as possible (Easterby-Smith et al., 2012). 549 questionnaires were returned to the researcher, out of which 32 were excluded due to missing values. Therefore, the questionnaire survey sample consisted of 517 participants. This sample size should be enough to ensure an adequate level of validity and reliability and make sure the produced findings are generalisable to a certain extent (Dew & Foreman, 2020). All questionnaires were distributed online using Google Forms.

8.0 Analysis Strategy

As previously noted, post-positivist studies tend to use multiple methods, which translates into employing several analysis instruments within the same research project (Novikov & Novikov, 2013). This study is not the exception to this rule. The primary data obtained using self-administered questionnaires was processed both graphically and statistically. This data was quantified by assigning a code (e.g., ‘1’, ‘2’, ‘3’, ‘4’, etc.) to each response and inserting the constructed set of raw data in Microsoft Excel, which was also used to design charts and graphs. The quantified data was then inserted into an SPSS spreadsheet to make it suitable for statistical analysis. Descriptive statistics and linear regression were used to analyse the primary quantitative data and establish the relationship between organisational culture and employees’ perceptions of well-being and test the Null Hypothesis and Hypothesis 1 presented in Chapter 2. In addition, the analysis of variance (ANOVA) was conducted to test Hypothesis 2 and identify whether those employees who belonged to an older generation perceived the role of organisational culture in their well-being differently than their young colleagues. The key methodological choices, including analysis methods, are presented as follows.

Figure 2: Methodological Choices

The researcher also used a content analysis technique to process the primary data generated by the interviewees. Unlike graphical or statistical analyses, the content analysis does not demonstrate cause-and-effect links between variables but rather enables the researcher to get a deeper understanding of interviewees’ perceptions, feelings, and lived experiences (Carson et al., 2001). The recorded interviews were first converted into text transcripts using word processing software. Afterwards, the transcribed data was processed by NVivo to create codes and nodes according to the main themes of this project, such as ‘organisational culture’, ‘employee well-being’, ‘work-life balance’, ‘employee recognition’, and ‘job satisfaction’. This software was also used to identify the frequency of the aforementioned themes and other tendencies in the interviewees’ responses.

9.0 Ethical Issues

Before obtaining primary data from the participants, they were provided with an information sheet that covered all the important aspects of this project, including its aim and objectives, anticipated outcomes, and research procedure (Saunders & Lewis, 2014). Informed consent was obtained from each potential interviewee and survey participant to make sure their participation was voluntary. They were explicitly communicated both verbally and in writing that they were able to withdraw from the data collection process at will. Moreover, this process was made fully anonymous to prevent the potential leakage of personal data and contribute to the participants’ intention to take part (Singh, 2010).

10.0 Methodological Limitations

Although the designed research methodology is characterised by a relatively high level of replicability, it could be argued that the adoption of bounded relativism does not add to the generalisability of the produced empirical outcomes (Daniel & Sam, 2011). The point is that the mental constructions of reality held by those managers and employees who participated in this project may not necessarily match one another, leading to multiple interpretations of how their well-being is affected by the employer’s organisational culture (Saunders & Lewis, 2014). Another limitation is that self-administered questionnaires significantly limit the researcher’s ability to decipher hidden meaning that exists in organisational practices and events, which makes this study biased towards making predictions rather than deepening our understanding of the research phenomenon (Novikov & Novikov, 2013).

11.0 Chapter Summary

Ontologically, this study is framed within bounded relativism, which implies that reality constructions exist within a boundary of a peculiar group. In turn, from an epistemological viewpoint, this project adopts constructionism and the post-positivist theoretical perspective. Since this study follows a mixed-method approach, the researcher obtained primary qualitative and quantitative data from 14 top managers and 517 employees of the target business entity by means of semi-structured interviews and self-administered questionnaires, respectively. The collected data was processed, thematically, graphically, and statistically using NVivo, Microsoft Excel, and SPSS.

Anfara, V., & Mertz, N. (2014). Theoretical Frameworks in Qualitative Research . SAGE.

Billups, F. (2019). Qualitative Data Collection Tools: Design, Development, and Applications . SAGE.

Bryman, A., & Bell, E. (2015). Business research methods . OUP.

Carson, D., Gilmore, A., Perry, C., & Gronhaug, K. (2001). Qualitative Marketing Research . SAGE.

Chawla, D., & Sodhi, N. (2011). Research Methodology: Concepts and Cases . Vicas Publishing House.

Cohen, L., Manion, L., & Morrison, K. (2017). Research Methods in Education . Routledge.

Creswell, J., & Creswell, D. (2017). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches . SAGE.

Crotty, M. (2020). The foundations of social research: Meaning and perspective in the research process . Routledge.

Daniel, S., & Sam, A. (2011). Research methodology . Gyan Publishing House.

Dew, J., & Foreman, M. (2020). How Do We Know?: An Introduction to Epistemology . InterVarsity Press.

Easterby-Smith, M., Thorpe, R., & Jackson, P. (2012). Management Research . SAGE.

Gray, D. (2017). Doing Research in the Real World . SAGE.

Howell, K. (2012). An introduction to the philosophy of methodology . SAGE.

Khan, J. (2011). Research methodology . APH Publishing.

Kumar, R. (2008). Research methodology . APH Publishing.

Kumar, R. (2014). Research Methodology: A Step-by-Step Guide for Beginners . SAGE.

Novikov, A., & Novikov, D. (2013). Research methodology: From philosophy of science to research design . CRC Press.

Pruzan, P. (2016). Research Methodology: The Aims, Practices and Ethics of Science . Springer.

Saunders, M., & Lewis, P. (2014). Doing Research in Business and Management: An Essential Guide to Planning Your Project . Pearson Education.

Singh, Y. (2010). Research methodology . APH Publishing.

Singh, Y., & Nath, R. (2010). Research methodology . APH Publishing.

Yin, R. (2014). Case study research: Design and methods . SAGE.

PhD Assistance

15 kinds of research methodologies for phd. pupils, basic research.

Pure research or fundamental research or basic research zooms on enhancing scientific knowledge for the exhaustive understanding of a topic or certain natural phenomena, essentially in natural sciences; knowledge that is obtained for the purpose of knowledge it is called fundamental research.

1.Applied research

Research that covers real life applications of the natural sciences; aimed at offering an answer to particular practical issues and develops novel technologies

2.Fixed research versus flexible research

In fixed research, the design of the study is fixed prior to the main phase of data gathering; moreover, fixed designs are essentially theoretical. Variables that need to be controlled and measured need to be known in advance and they are measured quantitatively.

3.Quantitative research and qualitative research

Quantitative research denotes gauging phenomena in various grades; on the other hand, qualitative research sometimes deems Boolean measurements alone; solution can be studied qualitatively for its appropriateness. However, comparison between candidate solutions requires quantitative observation.

4.Experimental research and non-experimental research

In an experimental design , operationalize the variables to be measured; moreover, operationalize in the best manner. Consider the study expectations, outcome measurement, variable measurement, and the methods to answer research questions.

Think of the practical limitations such as the availability of data-sets and experimental set-ups that represent actual scenarios.

5.Exploratory research and confirmatory research

Confirmatory research tests a priori hypotheses—outcome predictions done prior to the measurement stage. Such a priori hypotheses are usually derived from a theory or the results of previous studies.

Exploratory research generates a posteriori hypotheses by investigating a data-set and ascertaining potential connection between variables.

6.Explanatory research or casual research

Causal research is also called explanatory research ; conducted to ascertain the extent and type of cause-effect relationships. Causal research are conducted to evaluate effects of specific changes on existing norms, various processes etc.

7.Descriptive research

Descriptive research is the available statement of affairs; researcher has no control over variable. Descriptive studies are characterised as simply an effort to ascertain, define or recognize.  Not “why it is that way” nor “how it came to be,” which is the objective of analytical research.

8.Historical research

Historical research explores and explains the meanings, phases and traits of a phenomena or process at a certain phase of time in the past; historical research is a research strategy from the research of history.

9.Casual comparative research

Also called as “ex-post facto” research (In Latin, implies “after the fact”); researchers determine the causes or consequences of differences that already exist between or among groups of individuals.

An effort to ascertain a causative relationship between an independent variable and a dependent variable; relationship between the independent variable and dependent variable are usually a suggested relationship (not proved yet) because you do not have complete control over the independent variable

10.Correlational research

Correlational research is a form of non-experimental research technique wherein a researcher measures 2 variables and assesses the statistical connection between them with no influence from any external variable.

The correlation between two variables is given through correlation coefficient, which is a statistical measure that calculates the strength of the relationship between two variables that is a value measured between -1 and +1.

11.Evaluation research method

Evaluation research technique is known as program evaluation and refers to a research purpose instead of a particular technique; objective is to assess the effect of social involvements such as new treatment techniques, innovations in services, etc.

A form of applied research to have some real-world effect. Methods such as surveys and experiments are used in evaluation research.

12.Formative and summative evaluation

While learning is in progress, formative assessment offers feedback and information; measures participant’s progress and also assess researcher’s own progress as well.

For example, when implementing a new program, you can determine whether or not the activity should be used again (or modified) with the help of observation and/or surveying.

Summative assessment happens after the learning has ended and offers info and feedback to sum up the process; essentially, no formal learning is happening at this phase other than incidental learning which might take place through the completion of program.

13.Diagnostic research

Descriptive research studies define the characteristics of a particular individual, or of a group.

Studies showing whether certain variables are linked are examples of diagnostic research.

Researcher defines what he or she wants to measure and finds adequate methods for measuring it along with a clear description of ‘population’.

Aim is to obtain complete and accurate information. And the researcher plans the procedure carefully.

14.Prognostic research

Prognostic research (specifically in clinical research) examines chosen predictive variables and risk factors; prognostic research assesses influence on the outcome of a disease. Clinicians have a better understanding of the history of the ailment.

This understanding facilitates clinical decision-making via providing apt treatment alternatives and helps to predict accurate disease outcomes.

Assessing prognostic studies involves ascertaining the internal validity of the study design and assessing the effects of bias or systemic errors.

15.Action research

A systematic inquiry for improving and/or honing researchers’ actions. Researchers find it an empowering experience.

Action research has positive result for various reasons; most important is that action research is pertinent to the research participants.

Relevance is assured because the aim of each research project is ascertained by the researchers, who are also the main beneficiaries of the research observations.

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15 Research Methodology Examples

Research methodologies can roughly be categorized into three group: quantitative, qualitative, and mixed-methods.

• Qualitative Research : This methodology is based on obtaining deep, contextualized, non-numerical data. It can occur, for example, through open-ended questioning of research particiapnts in order to understand human behavior. It’s all about describing and analyzing subjective phenomena such as emotions or experiences.
• Quantitative Research: This methodology is rationally-based and relies heavily on numerical analysis of empirical data . With quantitative research, you aim for objectivity by creating hypotheses and testing them through experiments or surveys, which allow for statistical analyses.
• Mixed-Methods Research: Mixed-methods research combines both previous types into one project. We have more flexibility when designing our research study with mixed methods since we can use multiple approaches depending on our needs at each time. Using mixed methods can help us validate our results and offer greater predictability than just either type of methodology alone could provide.

Below are research methodologies that fit into each category.

Qualitative Research Methodologies

1. case study.

Conducts an in-depth examination of a specific case, individual, or event to understand a phenomenon.

Instead of examining a whole population for numerical trend data, case study researchers seek in-depth explanations of one event.

The benefit of case study research is its ability to elucidate overlooked details of interesting cases of a phenomenon (Busetto, Wick & Gumbinger, 2020). It offers deep insights for empathetic, reflective, and thoughtful understandings of that phenomenon.

However, case study findings aren’t transferrable to new contexts or for population-wide predictions. Instead, they inform practitioner understandings for nuanced, deep approaches to future instances (Liamputtong, 2020).

2. Grounded Theory

Grounded theory involves generating hypotheses and theories through the collection and interpretation of data (Faggiolani, n.d.). Its distinguishing features is that it doesn’t test a hypothesis generated prior to analysis, but rather generates a hypothesis or ‘theory’ that emerges from the data.

It also involves the application of inductive reasoning and is often contrasted with the hypothetico-deductive model of scientific research. This research methodology was developed by Barney Glaser and Anselm Strauss in the 1960s (Glaser & Strauss, 2009). 

The basic difference between traditional scientific approaches to research and grounded theory is that the latter begins with a question, then collects data, and the theoretical framework is said to emerge later from this data.

By contrast, scientists usually begin with an existing theoretical framework , develop hypotheses, and only then start collecting data to verify or falsify the hypotheses.

3. Ethnography

In ethnographic research , the researcher immerses themselves within the group they are studying, often for long periods of time.

This type of research aims to understand the shared beliefs, practices, and values of a particular community by immersing the researcher within the cultural group.

Although ethnographic research cannot predict or identify trends in an entire population, it can create detailed explanations of cultural practices and comparisons between social and cultural groups.

When a person conducts an ethnographic study of themselves or their own culture, it can be considered autoethnography .

Its strength lies in producing comprehensive accounts of groups of people and their interactions.

Common methods researchers use during an ethnographic study include participant observation , thick description, unstructured interviews, and field notes vignettes. These methods can provide detailed and contextualized descriptions of their subjects.

Example Study

Liquidated: An Ethnography of Wall Street by Karen Ho involves an anthropologist who embeds herself with Wall Street firms to study the culture of Wall Street bankers and how this culture affects the broader economy and world.

4. Phenomenology

Phenomenology to understand and describe individuals’ lived experiences concerning a specific phenomenon.

As a research methodology typically used in the social sciences , phenomenology involves the study of social reality as a product of intersubjectivity (the intersection of people’s cognitive perspectives) (Zahavi & Overgaard, n.d.).

This philosophical approach was first developed by Edmund Husserl.

5. Narrative Research

Narrative research explores personal stories and experiences to understand their meanings and interpretations.

It is also known as narrative inquiry and narrative analysis(Riessman, 1993).

This approach to research uses qualitative material like journals, field notes, letters, interviews, texts, photos, etc., as its data.

It is aimed at understanding the way people create meaning through narratives (Clandinin & Connelly, 2004).

6. Discourse Analysis

A discourse analysis examines the structure, patterns, and functions of language in context to understand how the text produces social constructs.

This methodology is common in critical theory , poststructuralism , and postmodernism. Its aim is to understand how language constructs discourses (roughly interpreted as “ways of thinking and constructing knowledge”).

As a qualitative methodology , its focus is on developing themes through close textual analysis rather than using numerical methods. Common methods for extracting data include semiotics and linguistic analysis.

7. Action Research

Action research involves researchers working collaboratively with stakeholders to address problems, develop interventions, and evaluate effectiveness.

Action research is a methodology and philosophy of research that is common in the social sciences.

The term was first coined in 1944 by Kurt Lewin, a German-American psychologist who also introduced applied research and group communication (Altrichter & Gstettner, 1993).

Lewin originally defined action research as involving two primary processes: taking action and doing research (Lewin, 1946).

Action research involves planning, action, and information-seeking about the result of the action.

Since Lewin’s original formulation, many different theoretical approaches to action research have been developed. These include action science, participatory action research, cooperative inquiry, and living educational theory among others.

Using Digital Sandbox Gaming to Improve Creativity Within Boys’ Writing (Ellison & Drew, 2019) is a study conducted by a school teacher who used video games to help teach his students English. It involved action research, where he interviewed his students to see if the use of games as stimuli for storytelling helped draw them into the learning experience, and iterated on his teaching style based on their feedback (disclaimer: I am the second author of this study).

See More: Examples of Qualitative Research

Quantitative Research Methodologies

8. experimental design.

As the name suggests, this type of research is based on testing hypotheses in experimental settings by manipulating variables and observing their effects on other variables.

The main benefit lies in its ability to manipulate specific variables to determine their effect on outcomes which is a great method for those looking for causational links in their research.

This is common, for example, in high-school science labs, where students are asked to introduce a variable into a setting in order to examine its effect.

9. Non-Experimental Design

Non-experimental design observes and measures associations between variables without manipulating them.

It can take, for example, the form of a ‘fly on the wall’ observation of a phenomenon, allowing researchers to examine authentic settings and changes that occur naturally in the environment.

10. Cross-Sectional Design

Cross-sectional design involves analyzing variables pertaining to a specific time period and at that exact moment.

This approach allows for an extensive examination and comparison of distinct and independent subjects, thereby offering advantages over qualitative methodologies such as case studies or surveys.

While cross-sectional design can be extremely useful in taking a ‘snapshot in time’, as a standalone method, it is not useful for examining changes in subjects after an intervention. The next methodology addresses this issue.

The prime example of this type of study is a census. A population census is mailed out to every house in the country, and each household must complete the census on the same evening. This allows the government to gather a snapshot of the nation’s demographics, beliefs, religion, and so on.

11. Longitudinal Design

Longitudinal research gathers data from the same subjects over an extended period to analyze changes and development.

In contrast to cross-sectional tactics, longitudinal designs examine variables more than once, over a pre-determined time span, allowing for multiple data points to be taken at different times.

A cross-sectional design is also useful for examining cohort effects , by comparing differences or changes in multiple different generations’ beliefs over time.

With multiple data points collected over extended periods ,it’s possible to examine continuous changes within things like population dynamics or consumer behavior. This makes detailed analysis of change possible.

12. Quasi-Experimental Design

Quasi-experimental design involves manipulating variables for analysis, but uses pre-existing groups of subjects rather than random groups.

Because the groups of research participants already exist, they cannot be randomly assigned to a cohort as with a true experimental design study. This makes inferring a causal relationship more difficult, but is nonetheless often more feasible in real-life settings.

Quasi-experimental designs are generally considered inferior to true experimental designs.

13. Correlational Research

Correlational research examines the relationships between two or more variables, determining the strength and direction of their association.

Similar to quasi-experimental methods, this type of research focuses on relationship differences between variables.

This approach provides a fast and easy way to make initial hypotheses based on either positive or negative correlation trends that can be observed within dataset.

Methods used for data analysis may include statistic correlations such as Pearson’s or Spearman’s.

Mixed-Methods Research Methodologies

14. sequential explanatory design (quan→qual).

This methodology involves conducting quantitative analysis first, then supplementing it with a qualitative study.

It begins by collecting quantitative data that is then analyzed to determine any significant patterns or trends.

Secondly, qualitative methods are employed. Their intent is to help interpret and expand the quantitative results.

This offers greater depth into understanding both large and smaller aspects of research questions being addressed.

The rationale behind this approach is to ensure that your data collection generates richer context for gaining insight into the particular issue across different levels, integrating in one study, qualitative exploration as well as statistical procedures.

15. Sequential Exploratory Design (QUAL→QUAN)

This methodology goes in the other direction, starting with qualitative analysis and ending with quantitative analysis.

It starts with qualitative research that delves deeps into complex areas and gathers rich information through interviewing or observing participants.

After this stage of exploration comes to an end, quantitative techniques are used to analyze the collected data through inferential statistics.

The idea is that a qualitative study can arm the researchers with a strong hypothesis testing framework, which they can then apply to a larger sample size using qualitative methods.

When I first took research classes, I had a lot of trouble distinguishing between methodologies and methods.

The key is to remember that the methodology sets the direction, while the methods are the specific tools to be used. A good analogy is transport: first you need to choose a mode (public transport, private transport, motorized transit, non-motorized transit), then you can choose a tool (bus, car, bike, on foot).

While research methodologies can be split into three types, each type has many different nuanced methodologies that can be chosen, before you then choose the methods – or tools – to use in the study. Each has its own strengths and weaknesses, so choose wisely!

Altrichter, H., & Gstettner, P. (1993). Action Research: A closed chapter in the history of German social science? Educational Action Research , 1 (3), 329–360. https://doi.org/10.1080/0965079930010302

Audi, R. (1999). The Cambridge dictionary of philosophy . Cambridge ; New York : Cambridge University Press. http://archive.org/details/cambridgediction00audi

Clandinin, D. J., & Connelly, F. M. (2004). Narrative Inquiry: Experience and Story in Qualitative Research . John Wiley & Sons.

Creswell, J. W. (2008). Educational Research: Planning, Conducting, and Evaluating Quantitative and Qualitative Research . Pearson/Merrill Prentice Hall.

Faggiolani, C. (n.d.). Perceived Identity: Applying Grounded Theory in Libraries . https://doi.org/10.4403/jlis.it-4592

Gauch, H. G. (2002). Scientific Method in Practice . Cambridge University Press.

Glaser, B. G., & Strauss, A. L. (2009). The Discovery of Grounded Theory: Strategies for Qualitative Research . Transaction Publishers.

Kothari, C. R. (2004). Research Methodology: Methods and Techniques . New Age International.

Kuada, J. (2012). Research Methodology: A Project Guide for University Students . Samfundslitteratur.

Lewin, K. (1946). Action research and minority problems. Journal of Social Issues , 2,  4 , 34–46. https://doi.org/10.1111/j.1540-4560.1946.tb02295.x

Mills, J., Bonner, A., & Francis, K. (2006). The Development of Constructivist Grounded Theory. International Journal of Qualitative Methods , 5 (1), 25–35. https://doi.org/10.1177/160940690600500103

Mingers, J., & Willcocks, L. (2017). An integrative semiotic methodology for IS research. Information and Organization , 27 (1), 17–36. https://doi.org/10.1016/j.infoandorg.2016.12.001

OECD. (2015). Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development . Organisation for Economic Co-operation and Development. https://www.oecd-ilibrary.org/science-and-technology/frascati-manual-2015_9789264239012-en

Peirce, C. S. (1992). The Essential Peirce, Volume 1: Selected Philosophical Writings (1867–1893) . Indiana University Press.

Reese, W. L. (1980). Dictionary of Philosophy and Religion: Eastern and Western Thought . Humanities Press.

Riessman, C. K. (1993). Narrative analysis . Sage Publications, Inc.

Saussure, F. de, & Riedlinger, A. (1959). Course in General Linguistics . Philosophical Library.

Thomas, C. G. (2021). Research Methodology and Scientific Writing . Springer Nature.

Zahavi, D., & Overgaard, S. (n.d.). Phenomenological Sociology—The Subjectivity of Everyday Life .

Tio Gabunia (B.Arch, M.Arch)

Tio Gabunia is an academic writer and architect based in Tbilisi. He has studied architecture, design, and urban planning at the Georgian Technical University and the University of Lisbon. He has worked in these fields in Georgia, Portugal, and France. Most of Tio’s writings concern philosophy. Other writings include architecture, sociology, urban planning, and economics.

• Tio Gabunia (B.Arch, M.Arch) #molongui-disabled-link 6 Types of Societies (With 21 Examples)
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Chris Drew (PhD)

Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]

• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 5 Top Tips for Succeeding at University
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 50 Durable Goods Examples
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What Is a Dissertation Methodology?

How to choose your methodology, final thoughts, how to write your dissertation methodology.

Updated September 30, 2021

Due to the complexities of the different research methods, writing your dissertation methodology can often be the most challenging and time-consuming part of your postgraduate dissertation .

This article focuses on the importance of writing a good PhD or master's dissertation methodology – and how to achieve this.

A postgraduate dissertation (or thesis) is usually formed of several detailed sections, including:

Abstract – A summary of your research topic.

Introduction – Provides background information on your topic, putting it into context. You will also confirm the main focus of your study, explain why it will add value to your area of interest and specify your key objectives.

Literature Review – A critical review of literature that relates to your chosen research topic. You will also need to identify which gap in the literature your study aims to address.

Methodology – Focuses on the research methods used within your research.

Results – Used to report on your main findings and how these relate to your research question.

Conclusion – Used to confirm the answer to your main research question, reflect on the research process and offer recommendations on future research.

The dissertation methodology forms the skeleton of any research project. It provides the reader with a clear outline of the methods you decided to use when carrying out your research.

By studying your dissertation methodology, the reader will be able to assess your research in terms of its validity and reliability.

In line with the outline given above, the methodology chapter usually appears after the literature review . Your methodology should be closely linked to the research that you conducted as part of this review, as well as the questions you aim to answer through your research and analysis.

Taking the time to find out about the different types of research available to you will allow you to identify any potential drawbacks to the method you have chosen to use. You should then be able to make allowances or adjustments to address these when it comes to carrying out your research.

Choosing your methodology will largely depend on the discipline of the qualification you are studying for and the question your dissertation will seek to answer. In most cases, you will use quantitative or qualitative research methods, although some projects will benefit from using a combination of both.

Quantitative research methods are used to gather numerical information. This research method is particularly useful if you are seeking to count, categorise, measure or identify patterns in data. To collect quantitative data, you might choose to conduct experiments, tests or surveys.

Qualitative research methods are used to gather non-statistical data. Instead of using numbers to create charts or graphs, you will need to categorise the information according to identifiers. This research method is most useful if you are seeking to develop a hypothesis. To collect qualitative data, you might choose to conduct focus groups, interviews or observations.

What to Include in Your Dissertation Methodology

Below is a dissertation methodology example to show you what information to include:

You will need to reiterate your research topic or question and give an overview of how you plan to investigate this. If there were any ethical or philosophical considerations to be made, give details.

For example, you may have sought informed consent from the people taking part in interviews or surveys.

Outline of the Methods Chosen

Confirm whether you have chosen to use quantitative research, qualitative research or a combination of both.

When choosing between qualitative and quantitative research methods, you will need to carry out initial literature and textbook research to establish the standard research methods that are normally used within your chosen area of research.

If you are not sure where to start, you could visit the library at your college or university and ask one of the librarians to help you to identify the most relevant texts.

Explanation of the Methods Chosen

Explain your rationale for selecting your chosen research methods. You should also give an overview of why these were more appropriate than using another research method.

Think about where and when the research took place and who was involved. For example, this might include information on the venue used for interviews or focus groups, dates and timescales, and whether participants were part of a particular demographic group.

Here are some examples of the type of information you may wish to include:

Qualitative Research Methods

Personal observations – Where and when did you conduct the observations? Who did you observe? Were they part of a particular community or group? How long did each observation take? How did you record your findings – did you collect audio recordings, video footage or written observations?

Focus groups – Where and when did the focus group take place? Who was involved? How were they selected? How many people took part? Were the questions asked structured, unstructured or semi-structured? Remember to include a copy of the questions that were used as an appendix.

Interviews – Where and when did the interviews take place? Who took part? How did you select the participants? What type of questions did you ask? How did you record your findings? Remember to include a copy of the questions that were used as an appendix.

The researcher’s objective was to find out customer perceptions on improving the product range currently offered by Company Y. Semi-structured interviews were held with 15 returning customers from the key target demographic for Company Y (18- to 35-year-olds). For research purposes, a returning customer was defined as somebody who purchased products from Company Y at least two times per week during the past three months. The interviews were held in an office in the staff area of the retail premises. Each interview lasted approximately 25 minutes. Responses were recorded through note-taking as none of the respondents wished to give their consent to be filmed.

Quantitative Research Methods

Existing information or data – What were the sources of the material used? How did you select material? Did you only use data published within a particular time frame?

Experiments – What tools or equipment did you use? What techniques were required? Note that when conducting experiments, it is particularly important to provide enough information to allow another researcher to conduct the experiment and obtain the same results.

Surveys – Were respondents asked to answer multiple-choice questions or complete free-text fields? How many questions were used? How long were people given to answer all of the questions? What were the demographics of the participants? Remember to include a copy of the survey in the appendices.

The survey was made up of 10 multiple-choice questions and 5 questions to be rated using a 5-point Lickert scale. The objective was to have 250 customers of Company Z complete the survey at the Company Z HQ between 1st and 5th February 2019, between the hours of 12 p.m. and 5 p.m. For research purposes, a customer was defined as any person who had purchased a product from Company Z during 2018. Customers completing the survey were allowed a maximum of 10 minutes to answer all of the questions. 200 customers responded, however not all of the surveys were completed in full, so only 150 survey results were able to be used in the data analysis.

How Was the Data Analysed?

If you have chosen to use quantitative research methods, you will need to prepare the data before analysing it – for example, you will need to check for variables, missing data and outliers. If you have used computer software to aid with analysis, information on this should also be included.

For qualitative data, you will need to categorise and code the ideas and themes that are identified from the raw data. You may also need to use techniques such as narrative analysis or discourse analysis to interpret the meaning behind responses given.

What Materials and Equipment Were Used During the Research?

This could include anything from laboratory equipment used in a scientific experiment to computer software used to analyse the results.

Were There Any Hurdles or Difficulties Faced During the Research?

If so, what were they and how did you manage to overcome them? This could be anything from difficulties in finding participants, problems obtaining consent or a shortage of the required resources needed to conduct a scientific experiment.

This paragraph should be used to evaluate the research you have conducted and justify your reasons for choosing this approach.

You do not need to go into great detail, as you will present and discuss your results in-depth within your dissertation’s ‘Results’ section.

You will need to briefly explain whether your results were conclusive, whether there were any variables and whether your choice of methodology was effective in practice.

Tips for Writing Your Dissertation Methodology

The objective for the methodology is not only to describe the methods that you used for your research. You will also need to demonstrate why you chose to use them and how you applied them.

The key point is to show that your research was conducted meticulously.

Try to keep your writing style concise and clear; this will ensure that it is easy for the reader to understand and digest.

Here are five top tips to consider when writing your dissertation methodology:

1. Look at Other Methodology Sections

Ask your supervisor to provide you with a few different examples of previously written dissertations. Reading through methodologies that have been written by past students will give you a good idea of what your finished methodology section should look like.

2. Plan Your Structure

Whichever research methods you have chosen to use, your dissertation methodology should be a clearly structured, well written section that gives a strong and justified argument for your chosen research methods.

You may wish to use headings such as:

• Research methods
• Explanation of research methods chosen
• Data analysis and references

Once you have drafted an outline, ask your supervisor for advice on whether there is anything you have missed and whether your structure looks logical.

3. Consider Your Audience

When writing your methodology, have regard for the people who are likely to be reading it. For example, if you have chosen to use research methods that are commonly chosen within your area of research or discipline, there is no need to give a great deal of justification or background information.

If you decide to use a less popular approach, it is advisable to give much more detailed information on how and why you chose to use this method.

4. Remain Focused on Your Aims and Research Questions

Your dissertation methodology should give a clear indication as to why the research methods you have chosen are suitable for the aims of your research.

When writing your dissertation methodology, ensure that you link your research choices back to the overall aims and objectives of your dissertation. To help you to remain focused, it can be helpful to include a clear definition of the question you are aiming to answer at the start of your methodology section.

5. Refer to Any Obstacles or Difficulties That You Dealt With

If you faced any problems during the data collection or analysis phases, use the methodology section to talk about what you did to address these issues and minimise the impact.

Whether you are completing a PhD or master's degree, writing your thesis or dissertation methodology is often considered to be the most difficult and time-consuming part of completing your major research project.

The key to success when writing a methodology section is to have a clear structure. Remember, the purpose of the methodology section of your research project is to ensure that the reader has a full understanding of the methods you have chosen.

You should use your methodology section to provide clear justification as to why you have chosen a particular research method instead of other potential methods. Avoid referring to your personal opinions, thoughts or interests within your methodology; keep the information that you include factual and ensure that everything is backed up by appropriate academic references.

You might also be interested in these other Wikijob articles:

Or explore the Postgraduate / PHD sections.

What is Research Methodology? Definition, Types, and Examples

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

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

What is research methodology ?

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

Why is research methodology important?

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

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

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Types of research methodology.

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

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

What are the types of sampling designs in research methodology?

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

• Probability sampling

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

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

What are data collection methods?

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

Qualitative research 5

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

Quantitative research 6

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

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What are data analysis methods.

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

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

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

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

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

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

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

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

How to choose a research methodology?

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

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

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How to write a research methodology .

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

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

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

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

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• Check and verify text : Make sure the generated text showcases your methods correctly, has all the right citations, and is original and authentic. .   

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Frequently Asked Questions

Q1. What are the key components of research methodology?

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

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

Q2. Why is ethical consideration important in research methodology?

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

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

Q3. What is the difference between methodology and method?

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

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

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

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Sister Cells Reveal Cancer’s Fate

A new method traces treatment resistant cells and predicts drugs that can make them more susceptible to cancer therapy..

Aparna is a freelance science writer with a PhD in bioinformatics and genomics at Harvard University. Her writing has also appeared in The Philadelphia Inquirer, Popular Science, PBS NOVA, and more.

View full profile.

Learn about our editorial policies.

ABOVE: Sister cells have similar molecular profiles, which researchers leveraged to measure different cellular traits in parallel. © iStock, Rost-9D

C ancer is notoriously hard to treat. In part, this is because the cells making up a tumor are heterogeneous, expressing different genes and molecules that determine their response to treatment. Even if a treatment kills most cancer cells, one survivor is enough for the cancer to persist.

As scientists struggled to find these treatment-resistant cells, they turned to an unexpected tool: sister cells. While human sisters may share clothes or toys, sister cells share their gene expression profiles, which could hint at whether the cells are treatment resistant.

In a study published in Nature Communications , researchers at the University of Helsinki presented a new method called ReSisTrace that utilizes sister cells to identify the molecular states driving treatment resistance in cancer cell lines. 1 Guided by these resistance signatures, the researchers devised a method to predict drugs that would sensitize the cells to treatment. 

“We can have data [on] both drug sensitivity and transcriptomics at the single-cell level,” said Jing Tang , a bioinformatician at the University of Helsinki and coauthor of the study. “This is unique and novel, and not available by using other techniques.”

Tang and Anna Vähärautio , a cancer biologist at the University of Helsinki and coauthor of the study, wanted to develop a method that combined lineage tracing—the process of tracking cell fate and offspring—with the ability to profile gene expression in individual cells. However, measuring gene expression in a cell typically destroys it, so scientists cannot trace its lineage at the same time. Enter sister cells: a way to achieve both goals in parallel.

Vähärautio's team devised a method to insert unique DNA barcodes into an ovarian cancer cell line using lentiviral transduction. Then, they allowed the cells to undergo a single division to each produce two sister cells, which they found had similar gene expression profiles. The researchers split the pool of cells in half: in one half, they measured gene expression by single cell RNA-sequencing (scRNA-seq) to construct a picture of each cell’s state, and in the other half, they tested whether the cells responded to certain common cancer treatments.

Using the treatment-resistant cells’ barcodes, the researchers matched them with their sister cells in the pre-treatment pool and analyzed their gene expression profiles. This comparison helped them identify genes that might have caused the cell to evade being killed. 

At first, the researchers tried to focus on individual genes, but they soon realized this approach might not be enough. “We don't know if [the genes] are really driving the resistance or if they are secondary effects,” Vähärautio said. This inspired the team to search the whole transcriptome for broader gene expression signatures of treatment sensitivity or resistance. Vähärautio and Tang suspected that these signatures could even help predict additional drugs that could sensitize the cells to a subsequent treatment.

Using published gene expression data collected from cell lines treated with a variety of compounds, Tang’s team identified potential drugs that could push treatment-resistant cells’ gene expression toward that of treatment-responsive cells. 2 By doing so, the added drug could prime the cells to respond to cancer treatment. Using computational models, the researchers predicted that administering pevonedistat—a drug that inhibits an enzyme involved in protein degradation—before carboplatin chemotherapy would make the cancer cell line that they were studying easier to kill. They tested their predictions and found that pevonedistat pretreatment, and many other predicted compounds, worked synergistically with common cancer therapies to kill the cancer cells. 

These findings came as a pleasant surprise to Vähärautio, and they convinced Tang that this could be a new approach for developing more effective cancer treatments to overcome drug resistance. 

Amy Brock , a bioengineer at the University of Texas at Austin who was not involved in this study, noted that the authors defined gene expression signatures by comparing all resistant cells to all sensitive cells, but that there might be even more patterns hidden in individual resistant cells. “It would be interesting to further examine whether sister cells become resistant via common or distinct mechanisms,” Brock said.

Brock hopes that, with a slew of similar methods to track cell lineages and single-cell gene expression , researchers will now focus on applying these tools to better understand how cells evade specific treatments. 3,4 Vähärautio and Tang are now applying their method to more sample types, including cancer organoids and acute myeloid leukemia cell lines. But Vähärautio thinks this method could even be useful for studying how cells’ states influence their fates in other contexts, such as development or responses to chemicals. With the computational models for drug prediction, ReSisTrace could even identify ways to change these fates.

“I think the method is really widely applicable and can be used to study many different cell state and fate connections,” Vähärautio said.

• Dai J, et al. Tracing back primed resistance in cancer via sister cells . Nat Commun . 2024;15(1):1158.
• Subramanian A, et al. A next generation connectivity map: L1000 platform and the first 1,000,000 profiles . Cell . 2017;171(6):1437-1452.
• Oren Y, et al. Cycling cancer persister cells arise from lineages with distinct programs . Nature . 2021;596(7873):576-582.
• Gutierrez C, et al. Multifunctional barcoding with ClonMapper enables high-resolution study of clonal dynamics during tumor evolution and treatment . Nat Cancer . 2021;2(7):758-772.

Related cancer genetics Research Resources

New Research Avenues Provide Hope for Metastatic Disease

The Six-Base Genome Reveals Multimodal Data from a Single DNA Sample

Tapping into Immunotherapy’s Potential to Help More People

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Methodology

• What Is a Research Design | Types, Guide & Examples

What Is a Research Design | Types, Guide & Examples

Published on June 7, 2021 by Shona McCombes . Revised on November 20, 2023 by Pritha Bhandari.

A research design is a strategy for answering your   research question  using empirical data. Creating a research design means making decisions about:

• Your overall research objectives and approach
• Whether you’ll rely on primary research or secondary research
• Your sampling methods or criteria for selecting subjects
• Your data collection methods
• The procedures you’ll follow to collect data
• Your data analysis methods

A well-planned research design helps ensure that your methods match your research objectives and that you use the right kind of analysis for your data.

Table of contents

Step 1: consider your aims and approach, step 2: choose a type of research design, step 3: identify your population and sampling method, step 4: choose your data collection methods, step 5: plan your data collection procedures, step 6: decide on your data analysis strategies, other interesting articles, frequently asked questions about research design.

• Introduction

Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.

There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities—start by thinking carefully about what you want to achieve.

The first choice you need to make is whether you’ll take a qualitative or quantitative approach.

Qualitative research designs tend to be more flexible and inductive , allowing you to adjust your approach based on what you find throughout the research process.

Quantitative research designs tend to be more fixed and deductive , with variables and hypotheses clearly defined in advance of data collection.

It’s also possible to use a mixed-methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.

Practical and ethical considerations when designing research

As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics .

• How much time do you have to collect data and write up the research?
• Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?
• Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?
• Will you need ethical approval ?

At each stage of the research design process, make sure that your choices are practically feasible.

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Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.

Types of quantitative research designs

Quantitative designs can be split into four main types.

• Experimental and   quasi-experimental designs allow you to test cause-and-effect relationships
• Descriptive and correlational designs allow you to measure variables and describe relationships between them.

With descriptive and correlational designs, you can get a clear picture of characteristics, trends and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation ).

Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.

Types of qualitative research designs

Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.

The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analyzing the data.

Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.

In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.

Defining the population

A population can be made up of anything you want to study—plants, animals, organizations, texts, countries, etc. In the social sciences, it most often refers to a group of people.

For example, will you focus on people from a specific demographic, region or background? Are you interested in people with a certain job or medical condition, or users of a particular product?

The more precisely you define your population, the easier it will be to gather a representative sample.

• Sampling methods

Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.

To select a sample, there are two main approaches: probability sampling and non-probability sampling . The sampling method you use affects how confidently you can generalize your results to the population as a whole.

Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.

For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.

Case selection in qualitative research

In some types of qualitative designs, sampling may not be relevant.

For example, in an ethnography or a case study , your aim is to deeply understand a specific context, not to generalize to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.

In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question .

For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.

Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.

You can choose just one data collection method, or use several methods in the same study.

Survey methods

Surveys allow you to collect data about opinions, behaviors, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews .

Observation methods

Observational studies allow you to collect data unobtrusively, observing characteristics, behaviors or social interactions without relying on self-reporting.

Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.

Other methods of data collection

There are many other ways you might collect data depending on your field and topic.

If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what kinds of data collection methods they used.

Secondary data

If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected—for example, datasets from government surveys or previous studies on your topic.

With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.

Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.

However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.

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As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.

Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are high in reliability and validity.

Operationalization

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalization means turning these fuzzy ideas into measurable indicators.

If you’re using observations , which events or actions will you count?

If you’re using surveys , which questions will you ask and what range of responses will be offered?

You may also choose to use or adapt existing materials designed to measure the concept you’re interested in—for example, questionnaires or inventories whose reliability and validity has already been established.

Reliability and validity

Reliability means your results can be consistently reproduced, while validity means that you’re actually measuring the concept you’re interested in.

For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.

If you’re developing a new questionnaire or other instrument to measure a specific concept, running a pilot study allows you to check its validity and reliability in advance.

Sampling procedures

As well as choosing an appropriate sampling method , you need a concrete plan for how you’ll actually contact and recruit your selected sample.

That means making decisions about things like:

• How many participants do you need for an adequate sample size?
• What inclusion and exclusion criteria will you use to identify eligible participants?
• How will you contact your sample—by mail, online, by phone, or in person?

If you’re using a probability sampling method , it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?

If you’re using a non-probability method , how will you avoid research bias and ensure a representative sample?

Data management

It’s also important to create a data management plan for organizing and storing your data.

Will you need to transcribe interviews or perform data entry for observations? You should anonymize and safeguard any sensitive data, and make sure it’s backed up regularly.

Keeping your data well-organized will save time when it comes to analyzing it. It can also help other researchers validate and add to your findings (high replicability ).

On its own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyze the data.

Quantitative data analysis

In quantitative research, you’ll most likely use some form of statistical analysis . With statistics, you can summarize your sample data, make estimates, and test hypotheses.

Using descriptive statistics , you can summarize your sample data in terms of:

• The distribution of the data (e.g., the frequency of each score on a test)
• The central tendency of the data (e.g., the mean to describe the average score)
• The variability of the data (e.g., the standard deviation to describe how spread out the scores are)

The specific calculations you can do depend on the level of measurement of your variables.

Using inferential statistics , you can:

• Make estimates about the population based on your sample data.
• Test hypotheses about a relationship between variables.

Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs ) look for differences in the outcomes of different groups.

Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.

Qualitative data analysis

In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.

Two of the most common approaches to doing this are thematic analysis and discourse analysis .

There are many other ways of analyzing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.

If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

A research design is a strategy for answering your   research question . It defines your overall approach and determines how you will collect and analyze data.

A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.

Quantitative research designs can be divided into two main categories:

• Correlational and descriptive designs are used to investigate characteristics, averages, trends, and associations between variables.
• Experimental and quasi-experimental designs are used to test causal relationships .

Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs.

The priorities of a research design can vary depending on the field, but you usually have to specify:

• Your research questions and/or hypotheses
• Your overall approach (e.g., qualitative or quantitative )
• The type of design you’re using (e.g., a survey , experiment , or case study )
• Your data collection methods (e.g., questionnaires , observations)
• Your data collection procedures (e.g., operationalization , timing and data management)
• Your data analysis methods (e.g., statistical tests  or thematic analysis )

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.

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Research Proposal Example/Sample

Detailed Walkthrough + Free Proposal Template

If you’re getting started crafting your research proposal and are looking for a few examples of research proposals , you’ve come to the right place.

In this video, we walk you through two successful (approved) research proposals , one for a Master’s-level project, and one for a PhD-level dissertation. We also start off by unpacking our free research proposal template and discussing the four core sections of a research proposal, so that you have a clear understanding of the basics before diving into the actual proposals.

• Research proposal example/sample – Master’s-level (PDF/Word)
• Research proposal example/sample – PhD-level (PDF/Word)
• Proposal template (Fully editable) 

If you’re working on a research proposal for a dissertation or thesis, you may also find the following useful:

• Research Proposal Bootcamp : Learn how to write a research proposal as efficiently and effectively as possible
• 1:1 Proposal Coaching : Get hands-on help with your research proposal

FAQ: Research Proposal Example

Research proposal example: frequently asked questions, are the sample proposals real.

Yes. The proposals are real and were approved by the respective universities.

Can I copy one of these proposals for my own research?

As we discuss in the video, every research proposal will be slightly different, depending on the university’s unique requirements, as well as the nature of the research itself. Therefore, you’ll need to tailor your research proposal to suit your specific context.

You can learn more about the basics of writing a research proposal here .

How do I get the research proposal template?

You can access our free proposal template here .

Is the proposal template really free?

Yes. There is no cost for the proposal template and you are free to use it as a foundation for your research proposal.

Where can I learn more about proposal writing?

For self-directed learners, our Research Proposal Bootcamp is a great starting point.

For students that want hands-on guidance, our private coaching service is recommended.

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This post is an extract from our bestselling Udemy Course, Research Proposal Bootcamp . If you want to work smart, you don't want to miss this .

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