how to write methodology for secondary data

How To Write The Methodology Chapter

The what, why & how explained simply (with examples).

By: Jenna Crossley (PhD) | Reviewed By: Dr. Eunice Rautenbach | September 2021 (Updated April 2023)

So, you’ve pinned down your research topic and undertaken a review of the literature – now it’s time to write up the methodology section of your dissertation, thesis or research paper . But what exactly is the methodology chapter all about – and how do you go about writing one? In this post, we’ll unpack the topic, step by step .

Overview: The Methodology Chapter

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

What (exactly) is the methodology chapter?

The methodology chapter is where you outline the philosophical underpinnings of your research and outline the specific methodological choices you’ve made. The point of the methodology chapter is to tell the reader exactly how you designed your study and, just as importantly, why you did it this way.

Importantly, this chapter should comprehensively describe and justify all the methodological choices you made in your study. For example, the approach you took to your research (i.e., qualitative, quantitative or mixed), who  you collected data from (i.e., your sampling strategy), how you collected your data and, of course, how you analysed it. If that sounds a little intimidating, don’t worry – we’ll explain all these methodological choices in this post .

Why is the methodology chapter important?

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

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

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

The methodology chapter is also important in that it allows you to identify and discuss any methodological issues or problems you encountered (i.e., research limitations ), and to explain how you mitigated the impacts of these. Every research project has its limitations , so it’s important to acknowledge these openly and highlight your study’s value despite its limitations . Doing so demonstrates your understanding of research design, which will earn you marks. We’ll discuss limitations in a bit more detail later in this post, so stay tuned!

Need a helping hand?

How to write up the methodology chapter

First off, it’s worth noting that the exact structure and contents of the methodology chapter will vary depending on the field of research (e.g., humanities, chemistry or engineering) as well as the university . So, be sure to always check the guidelines provided by your institution for clarity and, if possible, review past dissertations from your university. Here we’re going to discuss a generic structure for a methodology chapter typically found in the sciences.

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

Section 1 – Introduction

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

In this section, you can also briefly mention how you’ll structure the chapter. This will help orient the reader and provide a bit of a roadmap so that they know what to expect. You don’t need a lot of detail here – just a brief outline will do.

Section 2 – The Methodology

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

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

Methodological Choice #1 – Research Philosophy

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

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

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

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

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

Methodological Choice #2 – Research Type

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

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

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

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

Methodological Choice #3 – Research Strategy

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

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

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

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

As you can see, the right research strategy will depend largely on your research aims and research questions – in other words, what you’re trying to figure out. Therefore, as with every other methodological choice, it’s essential to justify why you chose the research strategy you did.

Methodological Choice #4 – Time Horizon

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

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

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

Methodological Choice #5 – Sampling Strategy

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

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

The right sampling approach depends largely on what you’re trying to achieve in your study. Specifically, whether you trying to develop findings that are generalisable to a population or not. Practicalities and resource constraints also play a large role here, as it can oftentimes be challenging to gain access to a truly random sample. In the video below, we explore some of the most common sampling strategies.

Methodological Choice #6 – Data Collection Method

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

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

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

Methodological Choice #7 – Data Analysis Methods/Techniques

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

What exactly you discuss here will depend largely on the type of study you’re conducting (i.e., qualitative, quantitative, or mixed methods). For qualitative studies, common analysis methods include content analysis , thematic analysis and discourse analysis . In the video below, we explain each of these in plain language.

For quantitative studies, you’ll almost always make use of descriptive statistics , and in many cases, you’ll also use inferential statistical techniques (e.g., correlation and regression analysis). In the video below, we unpack some of the core concepts involved in descriptive and inferential statistics.

In this section of your methodology chapter, it’s also important to discuss how you prepared your data for analysis, and what software you used (if any). For example, quantitative data will often require some initial preparation such as removing duplicates or incomplete responses . Similarly, qualitative data will often require transcription and perhaps even translation. As always, remember to state both what you did and why you did it.

Section 3 – The Methodological Limitations

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

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

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

Section 4 – Concluding Summary

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

Importantly, this section needs to be brief – a paragraph or two maximum (it’s a summary, after all). Also, make sure that when you write up your concluding summary, you include only what you’ve already discussed in your chapter; don’t add any new information.

Methodology Chapter Example

In the video below, we walk you through an example of a high-quality research methodology chapter from a dissertation. We also unpack our free methodology chapter template so that you can see how best to structure your chapter.

Wrapping Up

And there you have it – the methodology chapter in a nutshell. As we’ve mentioned, the exact contents and structure of this chapter can vary between universities , so be sure to check in with your institution before you start writing. If possible, try to find dissertations or theses from former students of your specific degree program – this will give you a strong indication of the expectations and norms when it comes to the methodology chapter (and all the other chapters!).

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

If you need a helping hand with your research methodology (or any other component of your research), be sure to check out our private coaching service , where we hold your hand through every step of the research journey. Until next time, good luck!

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

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A Guide To Secondary Data Analysis

What is secondary data analysis? How do you carry it out? Find out in this post.  

Historically, the only way data analysts could obtain data was to collect it themselves. This type of data is often referred to as primary data and is still a vital resource for data analysts.   

However, technological advances over the last few decades mean that much past data is now readily available online for data analysts and researchers to access and utilize. This type of data—known as secondary data—is driving a revolution in data analytics and data science.

Primary and secondary data share many characteristics. However, there are some fundamental differences in how you prepare and analyze secondary data. This post explores the unique aspects of secondary data analysis. We’ll briefly review what secondary data is before outlining how to source, collect and validate them. We’ll cover:

• What is secondary data analysis?
• How to carry out secondary data analysis (5 steps)
• Summary and further reading

Ready for a crash course in secondary data analysis? Let’s go!

1. What is secondary data analysis?

Secondary data analysis uses data collected by somebody else. This contrasts with primary data analysis, which involves a researcher collecting predefined data to answer a specific question. Secondary data analysis has numerous benefits, not least that it is a time and cost-effective way of obtaining data without doing the research yourself.

It’s worth noting here that secondary data may be primary data for the original researcher. It only becomes secondary data when it’s repurposed for a new task. As a result, a dataset can simultaneously be a primary data source for one researcher and a secondary data source for another. So don’t panic if you get confused! We explain exactly what secondary data is in this guide . 

In reality, the statistical techniques used to carry out secondary data analysis are no different from those used to analyze other kinds of data. The main differences lie in collection and preparation. Once the data have been reviewed and prepared, the analytics process continues more or less as it usually does. For a recap on what the data analysis process involves, read this post . 

In the following sections, we’ll focus specifically on the preparation of secondary data for analysis. Where appropriate, we’ll refer to primary data analysis for comparison. 

2. How to carry out secondary data analysis

Step 1: define a research topic.

The first step in any data analytics project is defining your goal. This is true regardless of the data you’re working with, or the type of analysis you want to carry out. In data analytics lingo, this typically involves defining:

• A statement of purpose
• Research design

Defining a statement of purpose and a research approach are both fundamental building blocks for any project. However, for secondary data analysis, the process of defining these differs slightly. Let’s find out how.

Step 2: Establish your statement of purpose

Before beginning any data analytics project, you should always have a clearly defined intent. This is called a ‘statement of purpose.’ A healthcare analyst’s statement of purpose, for example, might be: ‘Reduce admissions for mental health issues relating to Covid-19′. The more specific the statement of purpose, the easier it is to determine which data to collect, analyze, and draw insights from.

A statement of purpose is helpful for both primary and secondary data analysis. It’s especially relevant for secondary data analysis, though. This is because there are vast amounts of secondary data available. Having a clear direction will keep you focused on the task at hand, saving you from becoming overwhelmed. Being selective with your data sources is key.

Step 3: Design your research process

After defining your statement of purpose, the next step is to design the research process. For primary data, this involves determining the types of data you want to collect (e.g. quantitative, qualitative, or both ) and a methodology for gathering them.

For secondary data analysis, however, your research process will more likely be a step-by-step guide outlining the types of data you require and a list of potential sources for gathering them. It may also include (realistic) expectations of the output of the final analysis. This should be based on a preliminary review of the data sources and their quality.

Once you have both your statement of purpose and research design, you’re in a far better position to narrow down potential sources of secondary data. You can then start with the next step of the process: data collection.

Step 4: Locate and collect your secondary data

Collecting primary data involves devising and executing a complex strategy that can be very time-consuming to manage. The data you collect, though, will be highly relevant to your research problem.

Secondary data collection, meanwhile, avoids the complexity of defining a research methodology. However, it comes with additional challenges. One of these is identifying where to find the data. This is no small task because there are a great many repositories of secondary data available. Your job, then, is to narrow down potential sources. As already mentioned, it’s necessary to be selective, or else you risk becoming overloaded.  

Some popular sources of secondary data include:  

• Government statistics , e.g. demographic data, censuses, or surveys, collected by government agencies/departments (like the US Bureau of Labor Statistics).
• Technical reports summarizing completed or ongoing research from educational or public institutions (colleges or government).
• Scientific journals that outline research methodologies and data analysis by experts in fields like the sciences, medicine, etc.
• Literature reviews of research articles, books, and reports, for a given area of study (once again, carried out by experts in the field).
• Trade/industry publications , e.g. articles and data shared in trade publications, covering topics relating to specific industry sectors, such as tech or manufacturing.
• Online resources: Repositories, databases, and other reference libraries with public or paid access to secondary data sources.

Once you’ve identified appropriate sources, you can go about collecting the necessary data. This may involve contacting other researchers, paying a fee to an organization in exchange for a dataset, or simply downloading a dataset for free online .

Step 5: Evaluate your secondary data

Secondary data is usually well-structured, so you might assume that once you have your hands on a dataset, you’re ready to dive in with a detailed analysis. Unfortunately, that’s not the case! 

First, you must carry out a careful review of the data. Why? To ensure that they’re appropriate for your needs. This involves two main tasks:

Evaluating the secondary dataset’s relevance

• Assessing its broader credibility

Both these tasks require critical thinking skills. However, they aren’t heavily technical. This means anybody can learn to carry them out.

Let’s now take a look at each in a bit more detail.  

The main point of evaluating a secondary dataset is to see if it is suitable for your needs. This involves asking some probing questions about the data, including:

What was the data’s original purpose?

Understanding why the data were originally collected will tell you a lot about their suitability for your current project. For instance, was the project carried out by a government agency or a private company for marketing purposes? The answer may provide useful information about the population sample, the data demographics, and even the wording of specific survey questions. All this can help you determine if the data are right for you, or if they are biased in any way.

When and where were the data collected?

Over time, populations and demographics change. Identifying when the data were first collected can provide invaluable insights. For instance, a dataset that initially seems suited to your needs may be out of date.

On the flip side, you might want past data so you can draw a comparison with a present dataset. In this case, you’ll need to ensure the data were collected during the appropriate time frame. It’s worth mentioning that secondary data are the sole source of past data. You cannot collect historical data using primary data collection techniques.

Similarly, you should ask where the data were collected. Do they represent the geographical region you require? Does geography even have an impact on the problem you are trying to solve?

What data were collected and how?

A final report for past data analytics is great for summarizing key characteristics or findings. However, if you’re planning to use those data for a new project, you’ll need the original documentation. At the very least, this should include access to the raw data and an outline of the methodology used to gather them. This can be helpful for many reasons. For instance, you may find raw data that wasn’t relevant to the original analysis, but which might benefit your current task.

What questions were participants asked?

We’ve already touched on this, but the wording of survey questions—especially for qualitative datasets—is significant. Questions may deliberately be phrased to preclude certain answers. A question’s context may also impact the findings in a way that’s not immediately obvious. Understanding these issues will shape how you perceive the data.  

What is the form/shape/structure of the data?

Finally, to practical issues. Is the structure of the data suitable for your needs? Is it compatible with other sources or with your preferred analytics approach? This is purely a structural issue. For instance, if a dataset of people’s ages is saved as numerical rather than continuous variables, this could potentially impact your analysis. In general, reviewing a dataset’s structure helps better understand how they are categorized, allowing you to account for any discrepancies. You may also need to tidy the data to ensure they are consistent with any other sources you’re using.  

This is just a sample of the types of questions you need to consider when reviewing a secondary data source. The answers will have a clear impact on whether the dataset—no matter how well presented or structured it seems—is suitable for your needs.

Assessing secondary data’s credibility

After identifying a potentially suitable dataset, you must double-check the credibility of the data. Namely, are the data accurate and unbiased? To figure this out, here are some key questions you might want to include:

What are the credentials of those who carried out the original research?

Do you have access to the details of the original researchers? What are their credentials? Where did they study? Are they an expert in the field or a newcomer? Data collection by an undergraduate student, for example, may not be as rigorous as that of a seasoned professor.  

And did the original researcher work for a reputable organization? What other affiliations do they have? For instance, if a researcher who works for a tobacco company gathers data on the effects of vaping, this represents an obvious conflict of interest! Questions like this help determine how thorough or qualified the researchers are and if they have any potential biases.

Do you have access to the full methodology?

Does the dataset include a clear methodology, explaining in detail how the data were collected? This should be more than a simple overview; it must be a clear breakdown of the process, including justifications for the approach taken. This allows you to determine if the methodology was sound. If you find flaws (or no methodology at all) it throws the quality of the data into question.  

How consistent are the data with other sources?

Do the secondary data match with any similar findings? If not, that doesn’t necessarily mean the data are wrong, but it does warrant closer inspection. Perhaps the collection methodology differed between sources, or maybe the data were analyzed using different statistical techniques. Or perhaps unaccounted-for outliers are skewing the analysis. Identifying all these potential problems is essential. A flawed or biased dataset can still be useful but only if you know where its shortcomings lie.

Have the data been published in any credible research journals?

Finally, have the data been used in well-known studies or published in any journals? If so, how reputable are the journals? In general, you can judge a dataset’s quality based on where it has been published. If in doubt, check out the publication in question on the Directory of Open Access Journals . The directory has a rigorous vetting process, only permitting journals of the highest quality. Meanwhile, if you found the data via a blurry image on social media without cited sources, then you can justifiably question its quality!  

Again, these are just a few of the questions you might ask when determining the quality of a secondary dataset. Consider them as scaffolding for cultivating a critical thinking mindset; a necessary trait for any data analyst!

Presuming your secondary data holds up to scrutiny, you should be ready to carry out your detailed statistical analysis. As we explained at the beginning of this post, the analytical techniques used for secondary data analysis are no different than those for any other kind of data. Rather than go into detail here, check out the different types of data analysis in this post.

3. Secondary data analysis: Key takeaways

In this post, we’ve looked at the nuances of secondary data analysis, including how to source, collect and review secondary data. As discussed, much of the process is the same as it is for primary data analysis. The main difference lies in how secondary data are prepared.

Carrying out a meaningful secondary data analysis involves spending time and effort exploring, collecting, and reviewing the original data. This will help you determine whether the data are suitable for your needs and if they are of good quality.

Why not get to know more about what data analytics involves with this free, five-day introductory data analytics short course ? And, for more data insights, check out these posts:

• Discrete vs continuous data variables: What’s the difference?
• What are the four levels of measurement? Nominal, ordinal, interval, and ratio data explained
• What are the best tools for data mining?

Library Guides

Dissertations 4: methodology: methods.

• Introduction & Philosophy
• Methodology

Primary & Secondary Sources, Primary & Secondary Data

When describing your research methods, you can start by stating what kind of secondary and, if applicable, primary sources you used in your research. Explain why you chose such sources, how well they served your research, and identify possible issues encountered using these sources.  

Definitions  

There is some confusion on the use of the terms primary and secondary sources, and primary and secondary data. The confusion is also due to disciplinary differences (Lombard 2010). Whilst you are advised to consult the research methods literature in your field, we can generalise as follows:  

Secondary sources 

Secondary sources normally include the literature (books and articles) with the experts' findings, analysis and discussions on a certain topic (Cottrell, 2014, p123). Secondary sources often interpret primary sources.  

Primary sources 

Primary sources are "first-hand" information such as raw data, statistics, interviews, surveys, law statutes and law cases. Even literary texts, pictures and films can be primary sources if they are the object of research (rather than, for example, documentaries reporting on something else, in which case they would be secondary sources). The distinction between primary and secondary sources sometimes lies on the use you make of them (Cottrell, 2014, p123). 

Primary data 

Primary data are data (primary sources) you directly obtained through your empirical work (Saunders, Lewis and Thornhill 2015, p316). 

Secondary data 

Secondary data are data (primary sources) that were originally collected by someone else (Saunders, Lewis and Thornhill 2015, p316).   

Comparison between primary and secondary data   

Use  

Virtually all research will use secondary sources, at least as background information. 

Often, especially at the postgraduate level, it will also use primary sources - secondary and/or primary data. The engagement with primary sources is generally appreciated, as less reliant on others' interpretations, and closer to 'facts'. 

The use of primary data, as opposed to secondary data, demonstrates the researcher's effort to do empirical work and find evidence to answer her specific research question and fulfill her specific research objectives. Thus, primary data contribute to the originality of the research.    

Ultimately, you should state in this section of the methodology: 

What sources and data you are using and why (how are they going to help you answer the research question and/or test the hypothesis. 

If using primary data, why you employed certain strategies to collect them. 

What the advantages and disadvantages of your strategies to collect the data (also refer to the research in you field and research methods literature). 

Quantitative, Qualitative & Mixed Methods

The methodology chapter should reference your use of quantitative research, qualitative research and/or mixed methods. The following is a description of each along with their advantages and disadvantages. 

Quantitative research 

Quantitative research uses numerical data (quantities) deriving, for example, from experiments, closed questions in surveys, questionnaires, structured interviews or published data sets (Cottrell, 2014, p93). It normally processes and analyses this data using quantitative analysis techniques like tables, graphs and statistics to explore, present and examine relationships and trends within the data (Saunders, Lewis and Thornhill, 2015, p496). 

Qualitative research  

Qualitative research is generally undertaken to study human behaviour and psyche. It uses methods like in-depth case studies, open-ended survey questions, unstructured interviews, focus groups, or unstructured observations (Cottrell, 2014, p93). The nature of the data is subjective, and also the analysis of the researcher involves a degree of subjective interpretation. Subjectivity can be controlled for in the research design, or has to be acknowledged as a feature of the research. Subject-specific books on (qualitative) research methods offer guidance on such research designs.  

Mixed methods 

Mixed-method approaches combine both qualitative and quantitative methods, and therefore combine the strengths of both types of research. Mixed methods have gained popularity in recent years.  

When undertaking mixed-methods research you can collect the qualitative and quantitative data either concurrently or sequentially. If sequentially, you can for example, start with a few semi-structured interviews, providing qualitative insights, and then design a questionnaire to obtain quantitative evidence that your qualitative findings can also apply to a wider population (Specht, 2019, p138). 

Ultimately, your methodology chapter should state: 

Whether you used quantitative research, qualitative research or mixed methods. 

Why you chose such methods (and refer to research method sources). 

Why you rejected other methods. 

How well the method served your research. 

The problems or limitations you encountered. 

Doug Specht, Senior Lecturer at the Westminster School of Media and Communication, explains mixed methods research in the following video:

LinkedIn Learning Video on Academic Research Foundations: Quantitative

The video covers the characteristics of quantitative research, and explains how to approach different parts of the research process, such as creating a solid research question and developing a literature review. He goes over the elements of a study, explains how to collect and analyze data, and shows how to present your data in written and numeric form.

Link to quantitative research video

Some Types of Methods

There are several methods you can use to get primary data. To reiterate, the choice of the methods should depend on your research question/hypothesis. 

Whatever methods you will use, you will need to consider: 

why did you choose one technique over another? What were the advantages and disadvantages of the technique you chose? 

what was the size of your sample? Who made up your sample? How did you select your sample population? Why did you choose that particular sampling strategy?) 

ethical considerations (see also tab...)  

safety considerations  

validity  

feasibility  

recording  

procedure of the research (see box procedural method...).  

Check Stella Cottrell's book  Dissertations and Project Reports: A Step by Step Guide  for some succinct yet comprehensive information on most methods (the following account draws mostly on her work). Check a research methods book in your discipline for more specific guidance.  

Experiments 

Experiments are useful to investigate cause and effect, when the variables can be tightly controlled. They can test a theory or hypothesis in controlled conditions. Experiments do not prove or disprove an hypothesis, instead they support or not support an hypothesis. When using the empirical and inductive method it is not possible to achieve conclusive results. The results may only be valid until falsified by other experiments and observations. 

For more information on Scientific Method, click here . 

Observations 

Observational methods are useful for in-depth analyses of behaviours in people, animals, organisations, events or phenomena. They can test a theory or products in real life or simulated settings. They generally a qualitative research method.  

Questionnaires and surveys 

Questionnaires and surveys are useful to gain opinions, attitudes, preferences, understandings on certain matters. They can provide quantitative data that can be collated systematically; qualitative data, if they include opportunities for open-ended responses; or both qualitative and quantitative elements. 

Interviews  

Interviews are useful to gain rich, qualitative information about individuals' experiences, attitudes or perspectives. With interviews you can follow up immediately on responses for clarification or further details. There are three main types of interviews: structured (following a strict pattern of questions, which expect short answers), semi-structured (following a list of questions, with the opportunity to follow up the answers with improvised questions), and unstructured (following a short list of broad questions, where the respondent can lead more the conversation) (Specht, 2019, p142). 

This short video on qualitative interviews discusses best practices and covers qualitative interview design, preparation and data collection methods. 

Focus groups   

In this case, a group of people (normally, 4-12) is gathered for an interview where the interviewer asks questions to such group of participants. Group interactions and discussions can be highly productive, but the researcher has to beware of the group effect, whereby certain participants and views dominate the interview (Saunders, Lewis and Thornhill 2015, p419). The researcher can try to minimise this by encouraging involvement of all participants and promoting a multiplicity of views. 

This video focuses on strategies for conducting research using focus groups.  

Check out the guidance on online focus groups by Aliaksandr Herasimenka, which is attached at the bottom of this text box. 

Case study 

Case studies are often a convenient way to narrow the focus of your research by studying how a theory or literature fares with regard to a specific person, group, organisation, event or other type of entity or phenomenon you identify. Case studies can be researched using other methods, including those described in this section. Case studies give in-depth insights on the particular reality that has been examined, but may not be representative of what happens in general, they may not be generalisable, and may not be relevant to other contexts. These limitations have to be acknowledged by the researcher.     

Content analysis 

Content analysis consists in the study of words or images within a text. In its broad definition, texts include books, articles, essays, historical documents, speeches, conversations, advertising, interviews, social media posts, films, theatre, paintings or other visuals. Content analysis can be quantitative (e.g. word frequency) or qualitative (e.g. analysing intention and implications of the communication). It can detect propaganda, identify intentions of writers, and can see differences in types of communication (Specht, 2019, p146). Check this page on collecting, cleaning and visualising Twitter data.

Extra links and resources:  

Research Methods  

A clear and comprehensive overview of research methods by Emerald Publishing. It includes: crowdsourcing as a research tool; mixed methods research; case study; discourse analysis; ground theory; repertory grid; ethnographic method and participant observation; interviews; focus group; action research; analysis of qualitative data; survey design; questionnaires; statistics; experiments; empirical research; literature review; secondary data and archival materials; data collection. 

Doing your dissertation during the COVID-19 pandemic  

Resources providing guidance on doing dissertation research during the pandemic: Online research methods; Secondary data sources; Webinars, conferences and podcasts; 

• Virtual Focus Groups Guidance on managing virtual focus groups

5 Minute Methods Videos

The following are a series of useful videos that introduce research methods in five minutes. These resources have been produced by lecturers and students with the University of Westminster's School of Media and Communication. 

Case Study Research

Research Ethics

Quantitative Content Analysis 

Sequential Analysis 

Qualitative Content Analysis 

Thematic Analysis 

Social Media Research 

Mixed Method Research 

Procedural Method

In this part, provide an accurate, detailed account of the methods and procedures that were used in the study or the experiment (if applicable!). 

Include specifics about participants, sample, materials, design and methods. 

If the research involves human subjects, then include a detailed description of who and how many participated along with how the participants were selected.  

Describe all materials used for the study, including equipment, written materials and testing instruments. 

Identify the study's design and any variables or controls employed. 

Write out the steps in the order that they were completed. 

Indicate what participants were asked to do, how measurements were taken and any calculations made to raw data collected. 

Specify statistical techniques applied to the data to reach your conclusions. 

Provide evidence that you incorporated rigor into your research. This is the quality of being thorough and accurate and considers the logic behind your research design. 

Highlight any drawbacks that may have limited your ability to conduct your research thoroughly. 

You have to provide details to allow others to replicate the experiment and/or verify the data, to test the validity of the research. 

Bibliography

Cottrell, S. (2014). Dissertations and project reports: a step by step guide. Hampshire, England: Palgrave Macmillan.

Lombard, E. (2010). Primary and secondary sources.  The Journal of Academic Librarianship , 36(3), 250-253

Saunders, M.N.K., Lewis, P. and Thornhill, A. (2015).  Research Methods for Business Students.  New York: Pearson Education. 

Specht, D. (2019).  The Media And Communications Study Skills Student Guide . London: University of Westminster Press.  

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Secondary research: definition, methods, & examples.

19 min read This ultimate guide to secondary research helps you understand changes in market trends, customers buying patterns and your competition using existing data sources.

In situations where you’re not involved in the data gathering process ( primary research ), you have to rely on existing information and data to arrive at specific research conclusions or outcomes. This approach is known as secondary research.

In this article, we’re going to explain what secondary research is, how it works, and share some examples of it in practice.

Free eBook: The ultimate guide to conducting market research

What is secondary research?

Secondary research, also known as desk research, is a research method that involves compiling existing data sourced from a variety of channels . This includes internal sources (e.g.in-house research) or, more commonly, external sources (such as government statistics, organizational bodies, and the internet).

Secondary research comes in several formats, such as published datasets, reports, and survey responses , and can also be sourced from websites, libraries, and museums.

The information is usually free — or available at a limited access cost — and gathered using surveys , telephone interviews, observation, face-to-face interviews, and more.

When using secondary research, researchers collect, verify, analyze and incorporate it to help them confirm research goals for the research period.

As well as the above, it can be used to review previous research into an area of interest. Researchers can look for patterns across data spanning several years and identify trends — or use it to verify early hypothesis statements and establish whether it’s worth continuing research into a prospective area.

How to conduct secondary research

There are five key steps to conducting secondary research effectively and efficiently:

1.    Identify and define the research topic

First, understand what you will be researching and define the topic by thinking about the research questions you want to be answered.

Ask yourself: What is the point of conducting this research? Then, ask: What do we want to achieve?

This may indicate an exploratory reason (why something happened) or confirm a hypothesis. The answers may indicate ideas that need primary or secondary research (or a combination) to investigate them.

2.    Find research and existing data sources

If secondary research is needed, think about where you might find the information. This helps you narrow down your secondary sources to those that help you answer your questions. What keywords do you need to use?

Which organizations are closely working on this topic already? Are there any competitors that you need to be aware of?

Create a list of the data sources, information, and people that could help you with your work.

3.    Begin searching and collecting the existing data

Now that you have the list of data sources, start accessing the data and collect the information into an organized system. This may mean you start setting up research journal accounts or making telephone calls to book meetings with third-party research teams to verify the details around data results.

As you search and access information, remember to check the data’s date, the credibility of the source, the relevance of the material to your research topic, and the methodology used by the third-party researchers. Start small and as you gain results, investigate further in the areas that help your research’s aims.

4.    Combine the data and compare the results

When you have your data in one place, you need to understand, filter, order, and combine it intelligently. Data may come in different formats where some data could be unusable, while other information may need to be deleted.

After this, you can start to look at different data sets to see what they tell you. You may find that you need to compare the same datasets over different periods for changes over time or compare different datasets to notice overlaps or trends. Ask yourself: What does this data mean to my research? Does it help or hinder my research?

5.    Analyze your data and explore further

In this last stage of the process, look at the information you have and ask yourself if this answers your original questions for your research. Are there any gaps? Do you understand the information you’ve found? If you feel there is more to cover, repeat the steps and delve deeper into the topic so that you can get all the information you need.

If secondary research can’t provide these answers, consider supplementing your results with data gained from primary research. As you explore further, add to your knowledge and update your findings. This will help you present clear, credible information.

Primary vs secondary research

Unlike secondary research, primary research involves creating data first-hand by directly working with interviewees, target users, or a target market. Primary research focuses on the method for carrying out research, asking questions, and collecting data using approaches such as:

• Interviews (panel, face-to-face or over the phone)
• Questionnaires or surveys
• Focus groups

Using these methods, researchers can get in-depth, targeted responses to questions, making results more accurate and specific to their research goals. However, it does take time to do and administer.

Unlike primary research, secondary research uses existing data, which also includes published results from primary research. Researchers summarize the existing research and use the results to support their research goals.

Both primary and secondary research have their places. Primary research can support the findings found through secondary research (and fill knowledge gaps), while secondary research can be a starting point for further primary research. Because of this, these research methods are often combined for optimal research results that are accurate at both the micro and macro level.

Sources of Secondary Research

There are two types of secondary research sources: internal and external. Internal data refers to in-house data that can be gathered from the researcher’s organization. External data refers to data published outside of and not owned by the researcher’s organization.

Internal data

Internal data is a good first port of call for insights and knowledge, as you may already have relevant information stored in your systems. Because you own this information — and it won’t be available to other researchers — it can give you a competitive edge . Examples of internal data include:

• Database information on sales history and business goal conversions
• Information from website applications and mobile site data
• Customer-generated data on product and service efficiency and use
• Previous research results or supplemental research areas
• Previous campaign results

External data

External data is useful when you: 1) need information on a new topic, 2) want to fill in gaps in your knowledge, or 3) want data that breaks down a population or market for trend and pattern analysis. Examples of external data include:

• Government, non-government agencies, and trade body statistics
• Company reports and research
• Competitor research
• Public library collections
• Textbooks and research journals
• Media stories in newspapers
• Online journals and research sites

Three examples of secondary research methods in action

How and why might you conduct secondary research? Let’s look at a few examples:

1.    Collecting factual information from the internet on a specific topic or market

There are plenty of sites that hold data for people to view and use in their research. For example, Google Scholar, ResearchGate, or Wiley Online Library all provide previous research on a particular topic. Researchers can create free accounts and use the search facilities to look into a topic by keyword, before following the instructions to download or export results for further analysis.

This can be useful for exploring a new market that your organization wants to consider entering. For instance, by viewing the U.S Census Bureau demographic data for that area, you can see what the demographics of your target audience are , and create compelling marketing campaigns accordingly.

2.    Finding out the views of your target audience on a particular topic

If you’re interested in seeing the historical views on a particular topic, for example, attitudes to women’s rights in the US, you can turn to secondary sources.

Textbooks, news articles, reviews, and journal entries can all provide qualitative reports and interviews covering how people discussed women’s rights. There may be multimedia elements like video or documented posters of propaganda showing biased language usage.

By gathering this information, synthesizing it, and evaluating the language, who created it and when it was shared, you can create a timeline of how a topic was discussed over time.

3.    When you want to know the latest thinking on a topic

Educational institutions, such as schools and colleges, create a lot of research-based reports on younger audiences or their academic specialisms. Dissertations from students also can be submitted to research journals, making these places useful places to see the latest insights from a new generation of academics.

Information can be requested — and sometimes academic institutions may want to collaborate and conduct research on your behalf. This can provide key primary data in areas that you want to research, as well as secondary data sources for your research.

Advantages of secondary research

There are several benefits of using secondary research, which we’ve outlined below:

• Easily and readily available data – There is an abundance of readily accessible data sources that have been pre-collected for use, in person at local libraries and online using the internet. This data is usually sorted by filters or can be exported into spreadsheet format, meaning that little technical expertise is needed to access and use the data.
• Faster research speeds – Since the data is already published and in the public arena, you don’t need to collect this information through primary research. This can make the research easier to do and faster, as you can get started with the data quickly.
• Low financial and time costs – Most secondary data sources can be accessed for free or at a small cost to the researcher, so the overall research costs are kept low. In addition, by saving on preliminary research, the time costs for the researcher are kept down as well.
• Secondary data can drive additional research actions – The insights gained can support future research activities (like conducting a follow-up survey or specifying future detailed research topics) or help add value to these activities.
• Secondary data can be useful pre-research insights – Secondary source data can provide pre-research insights and information on effects that can help resolve whether research should be conducted. It can also help highlight knowledge gaps, so subsequent research can consider this.
• Ability to scale up results – Secondary sources can include large datasets (like Census data results across several states) so research results can be scaled up quickly using large secondary data sources.

Disadvantages of secondary research

The disadvantages of secondary research are worth considering in advance of conducting research :

• Secondary research data can be out of date – Secondary sources can be updated regularly, but if you’re exploring the data between two updates, the data can be out of date. Researchers will need to consider whether the data available provides the right research coverage dates, so that insights are accurate and timely, or if the data needs to be updated. Also, fast-moving markets may find secondary data expires very quickly.
• Secondary research needs to be verified and interpreted – Where there’s a lot of data from one source, a researcher needs to review and analyze it. The data may need to be verified against other data sets or your hypotheses for accuracy and to ensure you’re using the right data for your research.
• The researcher has had no control over the secondary research – As the researcher has not been involved in the secondary research, invalid data can affect the results. It’s therefore vital that the methodology and controls are closely reviewed so that the data is collected in a systematic and error-free way.
• Secondary research data is not exclusive – As data sets are commonly available, there is no exclusivity and many researchers can use the same data. This can be problematic where researchers want to have exclusive rights over the research results and risk duplication of research in the future.

When do we conduct secondary research?

Now that you know the basics of secondary research, when do researchers normally conduct secondary research?

It’s often used at the beginning of research, when the researcher is trying to understand the current landscape . In addition, if the research area is new to the researcher, it can form crucial background context to help them understand what information exists already. This can plug knowledge gaps, supplement the researcher’s own learning or add to the research.

Secondary research can also be used in conjunction with primary research. Secondary research can become the formative research that helps pinpoint where further primary research is needed to find out specific information. It can also support or verify the findings from primary research.

You can use secondary research where high levels of control aren’t needed by the researcher, but a lot of knowledge on a topic is required from different angles.

Secondary research should not be used in place of primary research as both are very different and are used for various circumstances.

Questions to ask before conducting secondary research

Before you start your secondary research, ask yourself these questions:

• Is there similar internal data that we have created for a similar area in the past?

If your organization has past research, it’s best to review this work before starting a new project. The older work may provide you with the answers, and give you a starting dataset and context of how your organization approached the research before. However, be mindful that the work is probably out of date and view it with that note in mind. Read through and look for where this helps your research goals or where more work is needed.

• What am I trying to achieve with this research?

When you have clear goals, and understand what you need to achieve, you can look for the perfect type of secondary or primary research to support the aims. Different secondary research data will provide you with different information – for example, looking at news stories to tell you a breakdown of your market’s buying patterns won’t be as useful as internal or external data e-commerce and sales data sources.

• How credible will my research be?

If you are looking for credibility, you want to consider how accurate the research results will need to be, and if you can sacrifice credibility for speed by using secondary sources to get you started. Bear in mind which sources you choose — low-credibility data sites, like political party websites that are highly biased to favor their own party, would skew your results.

• What is the date of the secondary research?

When you’re looking to conduct research, you want the results to be as useful as possible , so using data that is 10 years old won’t be as accurate as using data that was created a year ago. Since a lot can change in a few years, note the date of your research and look for earlier data sets that can tell you a more recent picture of results. One caveat to this is using data collected over a long-term period for comparisons with earlier periods, which can tell you about the rate and direction of change.

• Can the data sources be verified? Does the information you have check out?

If you can’t verify the data by looking at the research methodology, speaking to the original team or cross-checking the facts with other research, it could be hard to be sure that the data is accurate. Think about whether you can use another source, or if it’s worth doing some supplementary primary research to replicate and verify results to help with this issue.

We created a front-to-back guide on conducting market research, The ultimate guide to conducting market research , so you can understand the research journey with confidence.

In it, you’ll learn more about:

• What effective market research looks like
• The use cases for market research
• The most important steps to conducting market research
• And how to take action on your research findings

Download the free guide for a clearer view on secondary research and other key research types for your business.

Related resources

Market intelligence 10 min read, marketing insights 11 min read, ethnographic research 11 min read, qualitative vs quantitative research 13 min read, qualitative research questions 11 min read, qualitative research design 12 min read, primary vs secondary research 14 min read, request demo.

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• What Is a Research Methodology? | Steps & Tips

What Is a Research Methodology? | Steps & Tips

Published on August 25, 2022 by Shona McCombes and Tegan George. Revised on November 20, 2023.

Your research methodology discusses and explains the data collection and analysis methods you used in your research. A key part of your thesis, dissertation , or research paper , the methodology chapter explains what you did and how you did it, allowing readers to evaluate the reliability and validity of your research and your dissertation topic .

It should include:

• The type of research you conducted
• How you collected and analyzed your data
• Any tools or materials you used in the research
• How you mitigated or avoided research biases
• Why you chose these methods
• Your methodology section should generally be written in the past tense .
• Academic style guides in your field may provide detailed guidelines on what to include for different types of studies.
• Your citation style might provide guidelines for your methodology section (e.g., an APA Style methods section ).

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Table of contents

How to write a research methodology, why is a methods section important, step 1: explain your methodological approach, step 2: describe your data collection methods, step 3: describe your analysis method, step 4: evaluate and justify the methodological choices you made, tips for writing a strong methodology chapter, other interesting articles, frequently asked questions about methodology.

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Your methods section is your opportunity to share how you conducted your research and why you chose the methods you chose. It’s also the place to show that your research was rigorously conducted and can be replicated .

It gives your research legitimacy and situates it within your field, and also gives your readers a place to refer to if they have any questions or critiques in other sections.

You can start by introducing your overall approach to your research. You have two options here.

Option 1: Start with your “what”

What research problem or question did you investigate?

• Aim to describe the characteristics of something?
• Explore an under-researched topic?
• Establish a causal relationship?

And what type of data did you need to achieve this aim?

• Quantitative data , qualitative data , or a mix of both?
• Primary data collected yourself, or secondary data collected by someone else?
• Experimental data gathered by controlling and manipulating variables, or descriptive data gathered via observations?

Option 2: Start with your “why”

Depending on your discipline, you can also start with a discussion of the rationale and assumptions underpinning your methodology. In other words, why did you choose these methods for your study?

• Why is this the best way to answer your research question?
• Is this a standard methodology in your field, or does it require justification?
• Were there any ethical considerations involved in your choices?
• What are the criteria for validity and reliability in this type of research ? How did you prevent bias from affecting your data?

Once you have introduced your reader to your methodological approach, you should share full details about your data collection methods .

Quantitative methods

In order to be considered generalizable, you should describe quantitative research methods in enough detail for another researcher to replicate your study.

Here, explain how you operationalized your concepts and measured your variables. Discuss your sampling method or inclusion and exclusion criteria , as well as any tools, procedures, and materials you used to gather your data.

Surveys Describe where, when, and how the survey was conducted.

• How did you design the questionnaire?
• What form did your questions take (e.g., multiple choice, Likert scale )?
• Were your surveys conducted in-person or virtually?
• What sampling method did you use to select participants?
• What was your sample size and response rate?

Experiments Share full details of the tools, techniques, and procedures you used to conduct your experiment.

• How did you design the experiment ?
• How did you recruit participants?
• How did you manipulate and measure the variables ?
• What tools did you use?

Existing data Explain how you gathered and selected the material (such as datasets or archival data) that you used in your analysis.

• Where did you source the material?
• How was the data originally produced?
• What criteria did you use to select material (e.g., date range)?

The survey consisted of 5 multiple-choice questions and 10 questions measured on a 7-point Likert scale.

The goal was to collect survey responses from 350 customers visiting the fitness apparel company’s brick-and-mortar location in Boston on July 4–8, 2022, between 11:00 and 15:00.

Here, a customer was defined as a person who had purchased a product from the company on the day they took the survey. Participants were given 5 minutes to fill in the survey anonymously. In total, 408 customers responded, but not all surveys were fully completed. Due to this, 371 survey results were included in the analysis.

• Information bias
• Omitted variable bias
• Regression to the mean
• Survivorship bias
• Undercoverage bias
• Sampling bias

Qualitative methods

In qualitative research , methods are often more flexible and subjective. For this reason, it’s crucial to robustly explain the methodology choices you made.

Be sure to discuss the criteria you used to select your data, the context in which your research was conducted, and the role you played in collecting your data (e.g., were you an active participant, or a passive observer?)

Interviews or focus groups Describe where, when, and how the interviews were conducted.

• How did you find and select participants?
• How many participants took part?
• What form did the interviews take ( structured , semi-structured , or unstructured )?
• How long were the interviews?
• How were they recorded?

Participant observation Describe where, when, and how you conducted the observation or ethnography .

• What group or community did you observe? How long did you spend there?
• How did you gain access to this group? What role did you play in the community?
• How long did you spend conducting the research? Where was it located?
• How did you record your data (e.g., audiovisual recordings, note-taking)?

Existing data Explain how you selected case study materials for your analysis.

• What type of materials did you analyze?
• How did you select them?

In order to gain better insight into possibilities for future improvement of the fitness store’s product range, semi-structured interviews were conducted with 8 returning customers.

Here, a returning customer was defined as someone who usually bought products at least twice a week from the store.

Surveys were used to select participants. Interviews were conducted in a small office next to the cash register and lasted approximately 20 minutes each. Answers were recorded by note-taking, and seven interviews were also filmed with consent. One interviewee preferred not to be filmed.

• The Hawthorne effect
• Observer bias
• The placebo effect
• Response bias and Nonresponse bias
• The Pygmalion effect
• Recall bias
• Social desirability bias
• Self-selection bias

Mixed methods

Mixed methods research combines quantitative and qualitative approaches. If a standalone quantitative or qualitative study is insufficient to answer your research question, mixed methods may be a good fit for you.

Mixed methods are less common than standalone analyses, largely because they require a great deal of effort to pull off successfully. If you choose to pursue mixed methods, it’s especially important to robustly justify your methods.

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Next, you should indicate how you processed and analyzed your data. Avoid going into too much detail: you should not start introducing or discussing any of your results at this stage.

In quantitative research , your analysis will be based on numbers. In your methods section, you can include:

• How you prepared the data before analyzing it (e.g., checking for missing data , removing outliers , transforming variables)
• Which software you used (e.g., SPSS, Stata or R)
• Which statistical tests you used (e.g., two-tailed t test , simple linear regression )

In qualitative research, your analysis will be based on language, images, and observations (often involving some form of textual analysis ).

Specific methods might include:

• Content analysis : Categorizing and discussing the meaning of words, phrases and sentences
• Thematic analysis : Coding and closely examining the data to identify broad themes and patterns
• Discourse analysis : Studying communication and meaning in relation to their social context

Mixed methods combine the above two research methods, integrating both qualitative and quantitative approaches into one coherent analytical process.

Above all, your methodology section should clearly make the case for why you chose the methods you did. This is especially true if you did not take the most standard approach to your topic. In this case, discuss why other methods were not suitable for your objectives, and show how this approach contributes new knowledge or understanding.

In any case, it should be overwhelmingly clear to your reader that you set yourself up for success in terms of your methodology’s design. Show how your methods should lead to results that are valid and reliable, while leaving the analysis of the meaning, importance, and relevance of your results for your discussion section .

• Quantitative: Lab-based experiments cannot always accurately simulate real-life situations and behaviors, but they are effective for testing causal relationships between variables .
• Qualitative: Unstructured interviews usually produce results that cannot be generalized beyond the sample group , but they provide a more in-depth understanding of participants’ perceptions, motivations, and emotions.
• Mixed methods: Despite issues systematically comparing differing types of data, a solely quantitative study would not sufficiently incorporate the lived experience of each participant, while a solely qualitative study would be insufficiently generalizable.

Remember that your aim is not just to describe your methods, but to show how and why you applied them. Again, it’s critical to demonstrate that your research was rigorously conducted and can be replicated.

1. Focus on your objectives and research questions

The methodology section should clearly show why your methods suit your objectives and convince the reader that you chose the best possible approach to answering your problem statement and research questions .

2. Cite relevant sources

Your methodology can be strengthened by referencing existing research in your field. This can help you to:

• Show that you followed established practice for your type of research
• Discuss how you decided on your approach by evaluating existing research
• Present a novel methodological approach to address a gap in the literature

3. Write for your audience

Consider how much information you need to give, and avoid getting too lengthy. If you are using methods that are standard for your discipline, you probably don’t need to give a lot of background or justification.

Regardless, your methodology should be a clear, well-structured text that makes an argument for your approach, not just a list of technical details and procedures.

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

• Normal distribution
• Measures of central tendency
• Chi square tests
• Confidence interval
• Quartiles & Quantiles

Methodology

• Cluster sampling
• Stratified sampling
• Thematic analysis
• Cohort study
• Peer review
• Ethnography

Research bias

• Implicit bias
• Cognitive bias
• Conformity bias
• Hawthorne effect
• Availability heuristic
• Attrition bias

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

In a scientific paper, the methodology always comes after the introduction and before the results , discussion and conclusion . The same basic structure also applies to a thesis, dissertation , or research proposal .

Depending on the length and type of document, you might also include a literature review or theoretical framework before the methodology.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

Reliability and validity are both about how well a method measures something:

• Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
• Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

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.

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How to do your dissertation secondary research in 4 steps

(Last updated: 12 May 2021)

Since 2006, Oxbridge Essays has been the UK’s leading paid essay-writing and dissertation service

We have helped 10,000s of undergraduate, Masters and PhD students to maximise their grades in essays, dissertations, model-exam answers, applications and other materials. If you would like a free chat about your project with one of our UK staff, then please just reach out on one of the methods below.

If you are reading this guide, it's very likely you may be doing secondary research for your dissertation, rather than primary. If this is indeed you, then here's the good news: secondary research is the easiest type of research! Congratulations!

In a nutshell, secondary research is far more simple. So simple, in fact, that we have been able to explain how to do it completely in just 4 steps (see below). If nothing else, secondary research avoids the all-so-tiring efforts usually involved with primary research. Like recruiting your participants, choosing and preparing your measures, and spending days (or months) collecting your data.

That said, you do still need to know how to do secondary research. Which is what you're here for. So, go make a decent-sized mug of your favourite hot beverage (consider a glass of water , too) then come back and get comfy.

Here's what we'll cover in this guide:

The basics: What's secondary research all about?

Understanding secondary research, advantages of secondary research, disadvantages of secondary research, methods and purposes of secondary research, types of secondary data, sources of secondary data, secondary research process in 4 steps, step 1: develop your research question(s), step 2: identify a secondary data set, step 3: evaluate a secondary data set, step 4: prepare and analyse secondary data.

To answer this question, let’s first recall what we mean by primary research . As you probably already know, primary research is when the researcher collects the data himself or herself. The researcher uses so-called “real-time” data, which means that the data is collected during the course of a specific research project and is under the researcher’s direct control.

In contrast, secondary research involves data that has been collected by somebody else previously. This type of data is called “past data” and is usually accessible via past researchers, government records, and various online and offline resources.

So to recap, secondary research involves re-analysing, interpreting, or reviewing past data. The role of the researcher is always to specify how this past data informs his or her current research.

In contrast to primary research, secondary research is easier, particularly because the researcher is less involved with the actual process of collecting the data. Furthermore, secondary research requires less time and less money (i.e., you don’t need to provide your participants with compensation for participating or pay for any other costs of the research).

One of the most obvious advantages is that, compared to primary research, secondary research is inexpensive . Primary research usually requires spending a lot of money. For instance, members of the research team should be paid salaries. There are often travel and transportation costs. You may need to pay for office space and equipment, and compensate your participants for taking part. There may be other overhead costs too.

These costs do not exist when doing secondary research. Although researchers may need to purchase secondary data sets, this is always less costly than if the research were to be conducted from scratch.

As an undergraduate or graduate student, your dissertation project won't need to be an expensive endeavour. Thus, it is useful to know that you can further reduce costs, by using freely available secondary data sets.

But this is far from the only consideration.

Most students value another important advantage of secondary research, which is that secondary research saves you time . Primary research usually requires months spent recruiting participants, providing them with questionnaires, interviews, or other measures, cleaning the data set, and analysing the results. With secondary research, you can skip most of these daunting tasks; instead, you merely need to select, prepare, and analyse an existing data set.

Moreover, you probably won’t need a lot of time to obtain your secondary data set, because secondary data is usually easily accessible . In the past, students needed to go to libraries and spend hours trying to find a suitable data set. New technologies make this process much less time-consuming. In most cases, you can find your secondary data through online search engines or by contacting previous researchers via email.

A third important advantage of secondary research is that you can base your project on a large scope of data . If you wanted to obtain a large data set yourself, you would need to dedicate an immense amount of effort. What's more, if you were doing primary research, you would never be able to use longitudinal data in your graduate or undergraduate project, since it would take you years to complete. This is because longitudinal data involves assessing and re-assessing a group of participants over long periods of time.

When using secondary data, however, you have an opportunity to work with immensely large data sets that somebody else has already collected. Thus, you can also deal with longitudinal data, which may allow you to explore trends and changes of phenomena over time.

With secondary research, you are relying not only on a large scope of data, but also on professionally collected data . This is yet another advantage of secondary research. For instance, data that you will use for your secondary research project has been collected by researchers who are likely to have had years of experience in recruiting representative participant samples, designing studies, and using specific measurement tools.

If you had collected this data yourself, your own data set would probably have more flaws, simply because of your lower level of expertise when compared to these professional researchers.

The first such disadvantage is that your secondary data may be, to a greater or lesser extent, inappropriate for your own research purposes. This is simply because you have not collected the data yourself.

When you collect your data personally, you do so with a specific research question in mind. This makes it easy to obtain the relevant information. However, secondary data was always collected for the purposes of fulfilling other researchers’ goals and objectives.

Thus, although secondary data may provide you with a large scope of professionally collected data, this data is unlikely to be fully appropriate to your own research question. There are several reasons for this. For instance, you may be interested in the data of a particular population, in a specific geographic region, and collected during a specific time frame. However, your secondary data may have focused on a slightly different population, may have been collected in a different geographical region, or may have been collected a long time ago.

Apart from being potentially inappropriate for your own research purposes, secondary data could have a different format than you require. For instance, you might have preferred participants’ age to be in the form of a continuous variable (i.e., you want your participants to have indicated their specific age). But the secondary data set may contain a categorical age variable; for example, participants might have indicated an age group they belong to (e.g., 20-29, 30-39, 40-49, etc.). Or another example: A secondary data set may contain too few ethnic categories (e.g., “White” and “Other”), while you would ideally want a wider range of racial categories (e.g., “White”, “Black or African American”, “American Indian”, and “Asian”). Differences such as these mean that secondary data may not be perfectly appropriate for your research.

The above two disadvantages may lead to yet another one: the existing data set may not answer your own research question(s) in an ideal way. As noted above, secondary data was collected with a different research question in mind, and this may limit its application to your own research purpose.

Unfortunately, the list of disadvantages does not end here. An additional weakness of secondary data is that you have a lack of control over the quality of data. All researchers need to establish that their data is reliable and valid. But if the original researchers did not establish the reliability and validity of their data, this may limit its reliability and validity for your research as well. To establish reliability and validity, you are usually advised to critically evaluate how the data was gathered, analysed, and presented.

But here lies the final disadvantage of doing secondary research: original researchers may fail to provide sufficient information on how their research was conducted. You might be faced with a lack of information on recruitment procedures, sample representativeness, data collection methods, employed measurement tools and statistical analyses, and the like. This may require you to take extra steps to obtain such information, if that is possible at all.

At this point, we should ask: “What are the methods of secondary research?” and “When do we use each of these methods?” Here, we can differentiate between three methods of secondary research: using a secondary data set in isolation , combining two secondary data sets, and combining secondary and primary data sets. Let’s outline each of these separately, and also explain when to use each of these methods.

Initially, you can use a secondary data set in isolation – that is, without combining it with other data sets. You dig and find a data set that is useful for your research purposes and then base your entire research on that set of data. You do this when you want to re-assess a data set with a different research question in mind.

Let’s illustrate this with a simple example. Suppose that, in your research, you want to investigate whether pregnant women of different nationalities experience different levels of anxiety during different pregnancy stages. Based on the literature, you have formed an idea that nationality may matter in this relationship between pregnancy and anxiety.

If you wanted to test this relationship by collecting the data yourself, you would need to recruit many pregnant women of different nationalities and assess their anxiety levels throughout their pregnancy. It would take you at least a year to complete this research project.

Instead of undertaking this long endeavour, you thus decide to find a secondary data set – one that investigated (for instance) a range of difficulties experienced by pregnant women in a nationwide sample. The original research question that guided this research could have been: “to what extent do pregnant women experience a range of mental health difficulties, including stress, anxiety, mood disorders, and paranoid thoughts?” The original researchers might have outlined women’s nationality, but weren’t particularly interested in investigating the link between women’s nationality and anxiety at different pregnancy stages. You are, therefore, re-assessing their data set with your own research question in mind.

Your research may, however, require you to combine two secondary data sets . You will use this kind of methodology when you want to investigate the relationship between certain variables in two data sets or when you want to compare findings from two past studies.

To take an example: One of your secondary data sets may focus on a target population’s tendency to smoke cigarettes, while the other data set focuses on the same population’s tendency to drink alcohol. In your own research, you may thus be looking at whether there is a correlation between smoking and drinking among this population.

Here is a second example: Your two secondary data sets may focus on the same outcome variable, such as the degree to which people go to Greece for a summer vacation. However, one data set could have been collected in Britain and the other in Germany. By comparing these two data sets, you can investigate which nation tends to visit Greece more.

Finally, your research project may involve combining primary and secondary data . You may decide to do this when you want to obtain existing information that would inform your primary research.

Let’s use another simple example and say that your research project focuses on American versus British people’s attitudes towards racial discrimination. Let’s say that you were able to find a recent study that investigated Americans’ attitudes of these kind, which were assessed with a certain set of measures. However, your search finds no recent studies on Britons’ attitudes. Let’s also say that you live in London and that it would be difficult for you to assess Americans’ attitudes on the topic, but clearly much more straightforward to conduct primary research on British attitudes.

In this case, you can simply reuse the data from the American study and adopt exactly the same measures with your British participants. Your secondary data is being combined with your primary data. Alternatively, you may combine these types of data when the role of your secondary data is to outline descriptive information that supports your research. For instance, if your project is focusing on attitudes towards McDonald’s food, you may want to support your primary research with secondary data that outlines how many people eat McDonald’s in your country of choice.

TABLE 3 summarises particular methods and purposes of secondary research:

We have already provided above several examples of using quantitative secondary data. This type of data is used when the original study has investigated a population’s tendency to smoke or drink alcohol, the degree to which people from different nationalities go to Greece for their summer vacation, or the degree to which pregnant women experience anxiety.

In all these examples, outcome variables were assessed by questionnaires, and thus the obtained data was numerical.

Quantitative secondary research is much more common than qualitative secondary research. However, this is not to say that you cannot use qualitative secondary data in your research project. This type of secondary data is used when you want the previously-collected information to inform your current research. More specifically, it is used when you want to test the information obtained through qualitative research by implementing a quantitative methodology.

For instance, a past qualitative study might have focused on the reasons why people choose to live on boats. This study might have interviewed some 30 participants and noted the four most important reasons people live on boats: (1) they can lead a transient lifestyle, (2) they have an increased sense of freedom, (3) they feel that they are “world citizens”, and (4) they can more easily visit their family members who live in different locations. In your own research, you can therefore reuse this qualitative data to form a questionnaire, which you then give to a larger population of people who live on boats. This will help you to generalise the previously-obtained qualitative results to a broader population.

Importantly, you can also re-assess a qualitative data set in your research, rather than using it as a basis for your quantitative research. Let’s say that your research focuses on the kind of language that people who live on boats use when describing their transient lifestyles. The original research did not focus on this research question per se – however, you can reuse the information from interviews to “extract” the types of descriptions of a transient lifestyle that were given by participants.

TABLE 4 highlights the two main types of secondary data and their associated purposes:

Internal sources of data are those that are internal to the organisation in question. For instance, if you are doing a research project for an organisation (or research institution) where you are an intern, and you want to reuse some of their past data, you would be using internal data sources.

The benefit of using these sources is that they are easily accessible and there is no associated financial cost of obtaining them.

External sources of data, on the other hand, are those that are external to an organisation or a research institution. This type of data has been collected by “somebody else”, in the literal sense of the term. The benefit of external sources of data is that they provide comprehensive data – however, you may sometimes need more effort (or money) to obtain it.

Let’s now focus on different types of internal and external secondary data sources.

There are several types of internal sources. For instance, if your research focuses on an organisation’s profitability, you might use their sales data . Each organisation keeps a track of its sales records, and thus your data may provide information on sales by geographical area, types of customer, product prices, types of product packaging, time of the year, and the like.

Alternatively, you may use an organisation’s financial data . The purpose of using this data could be to conduct a cost-benefit analysis and understand the economic opportunities or outcomes of hiring more people, buying more vehicles, investing in new products, and so on.

Another type of internal data is transport data . Here, you may focus on outlining the safest and most effective transportation routes or vehicles used by an organisation.

Alternatively, you may rely on marketing data , where your goal would be to assess the benefits and outcomes of different marketing operations and strategies.

Some other ideas would be to use customer data to ascertain the ideal type of customer, or to use safety data to explore the degree to which employees comply with an organisation’s safety regulations.

The list of the types of internal sources of secondary data can be extensive; the most important thing to remember is that this data comes from a particular organisation itself, in which you do your research in an internal manner.

The list of external secondary data sources can be just as extensive. One example is the data obtained through government sources . These can include social surveys, health data, agricultural statistics, energy expenditure statistics, population censuses, import/export data, production statistics, and the like. Government agencies tend to conduct a lot of research, therefore covering almost any kind of topic you can think of.

Another external source of secondary data are national and international institutions , including banks, trade unions, universities, health organisations, etc. As with government, such institutions dedicate a lot of effort to conducting up-to-date research, so you simply need to find an organisation that has collected the data on your own topic of interest.

Alternatively, you may obtain your secondary data from trade, business, and professional associations . These usually have data sets on business-related topics and are likely to be willing to provide you with secondary data if they understand the importance of your research. If your research is built on past academic studies, you may also rely on scientific journals as an external data source.

Once you have specified what kind of secondary data you need, you can contact the authors of the original study.

As a final example of a secondary data source, you can rely on data from commercial research organisations. These usually focus their research on media statistics and consumer information, which may be relevant if, for example, your research is within media studies or you are investigating consumer behaviour.

At this point, you should have a clearer understanding of secondary research in general terms.

Now it may be useful to focus on the actual process of doing secondary research. This next section is organised to introduce you to each step of this process, so that you can rely on this guide while planning your study. At the end of this blog post, in Table 6 , you will find a summary of all the steps of doing secondary research.

For an undergraduate thesis, you are often provided with a specific research question by your supervisor. But for most other types of research, and especially if you are doing your graduate thesis, you need to arrive at a research question yourself.

The first step here is to specify the general research area in which your research will fall. For example, you may be interested in the topic of anxiety during pregnancy, or tourism in Greece, or transient lifestyles. Since we have used these examples previously, it may be useful to rely on them again to illustrate our discussion.

Once you have identified your general topic, your next step consists of reading through existing papers to see whether there is a gap in the literature that your research can fill. At this point, you may discover that previous research has not investigated national differences in the experiences of anxiety during pregnancy, or national differences in a tendency to go to Greece for a summer vacation, or that there is no literature generalising the findings on people’s choice to live on boats.

Having found your topic of interest and identified a gap in the literature, you need to specify your research question. In our three examples, research questions would be specified in the following manner: (1) “Do women of different nationalities experience different levels of anxiety during different stages of pregnancy?”, (2) “Are there any differences in an interest in Greek tourism between Germans and Britons?”, and (3) “Why do people choose to live on boats?”.

It is at this point, after reviewing the literature and specifying your research questions, that you may decide to rely on secondary data. You will do this if you discover that there is past data that would be perfectly reusable in your own research, therefore helping you to answer your research question more thoroughly (and easily).

But how do you discover if there is past data that could be useful for your research? You do this through reviewing the literature on your topic of interest. During this process, you will identify other researchers, organisations, agencies, or research centres that have explored your research topic.

Somewhere there, you may discover a useful secondary data set. You then need to contact the original authors and ask for a permission to use their data. (Note, however, that this happens only if you are relying on external sources of secondary data. If you are doing your research internally (i.e., within a particular organisation), you don’t need to search through the literature for a secondary data set – you can just reuse some past data that was collected within the organisation itself.)

In any case, you need to ensure that a secondary data set is a good fit for your own research question. Once you have established that it is, you need to specify the reasons why you have decided to rely on secondary data.

For instance, your choice to rely on secondary data in the above examples might be as follows: (1) A recent study has focused on a range of mental difficulties experienced by women in a multinational sample and this data can be reused; (2) There is existing data on Germans’ and Britons’ interest in Greek tourism and these data sets can be compared; and (3) There is existing qualitative research on the reasons for choosing to live on boats, and this data can be relied upon to conduct a further quantitative investigation.

Because such disadvantages of secondary data can limit the effectiveness of your research, it is crucial that you evaluate a secondary data set. To ease this process, we outline here a reflective approach that will allow you to evaluate secondary data in a stepwise fashion.

Step 3(a): What was the aim of the original study?

During this step, you also need to pay close attention to any differences in research purposes and research questions between the original study and your own investigation. As we have discussed previously, you will often discover that the original study had a different research question in mind, and it is important for you to specify this difference.

Let’s put this step of identifying the aim of the original study in practice, by referring to our three research examples. The aim of the first research example was to investigate mental difficulties (e.g., stress, anxiety, mood disorders, and paranoid thoughts) in a multinational sample of pregnant women.

How does this aim differ from your research aim? Well, you are seeking to reuse this data set to investigate national differences in anxiety experienced by women during different pregnancy stages. When it comes to the second research example, you are basing your research on two secondary data sets – one that aimed to investigate Germans’ interest in Greek tourism and the other that aimed to investigate Britons’ interest in Greek tourism.

While these two studies focused on particular national populations, the aim of your research is to compare Germans’ and Britons’ tendency to visit Greece for summer vacation. Finally, in our third example, the original research was a qualitative investigation into the reasons for living on boats. Your research question is different, because, although you are seeking to do the same investigation, you wish to do so by using a quantitative methodology.

Importantly, in all three examples, you conclude that secondary data may in fact answer your research question. If you conclude otherwise, it may be wise to find a different secondary data set or to opt for primary research.

Step 3(b): Who has collected the data?

Let’s say that, in our example of research on pregnancy, data was collected by the UK government; that in our example of research on Greek tourism, the data was collected by a travel agency; and that in our example of research on the reasons for choosing to live on boats, the data was collected by researchers from a UK university.

Let’s also say that you have checked the background of these organisations and researchers, and that you have concluded that they all have a sufficiently professional background, except for the travel agency. Given that this agency’s research did not lead to a publication (for instance), and given that not much can be found about the authors of the research, you conclude that the professionalism of this data source remains unclear.

Step 3(c): Which measures were employed?

Original authors should have documented all their sample characteristics, measures, procedures, and protocols. This information can be obtained either in their final research report or through contacting the authors directly.

It is important for you to know what type of data was collected, which measures were used, and whether such measures were reliable and valid (if they were quantitative measures). You also need to make a clear outline of the type of data collected – and especially the data relevant for your research.

Let’s say that, in our first example, researchers have (among other assessed variables) used a demographic measure to note women’s nationalities and have used the State Anxiety Inventory to assess women’s anxiety levels during different pregnancy stages, both of which you conclude are valid and reliable tools. In our second example, the authors might have crafted their own measure to assess interest in Greek tourism, but there may be no established validity and reliability for this measure. And in our third example, the authors have employed semi-structured interviews, which cover the most important reasons for wanting to live on boats.

Step 3(d): When was the data collected?

Ideally, you want your secondary data to have been collected within the last five years. For the sake of our examples, let’s say that all three original studies were conducted within this time-range.

Step 3(e): What methodology was used to collect the data?

We have already noted that you need to evaluate the reliability and validity of employed measures. In addition to this, you need to evaluate how the sample was obtained, whether the sample was large enough, if the sample was representative of the population, if there were any missing responses on employed measures, whether confounders were controlled for, and whether the employed statistical analyses were appropriate. Any drawbacks in the original methodology may limit your own research as well.

For the sake of our examples, let’s say that the study on mental difficulties in pregnant women recruited a representative sample of pregnant women (i.e., they had different nationalities, different economic backgrounds, different education levels, etc.) in maternity wards of seven hospitals; that the sample was large enough (N = 945); that the number of missing values was low; that many confounders were controlled for (e.g., education level, age, presence of partnership, etc.); and that statistical analyses were appropriate (e.g., regression analyses were used).

Let’s further say that our second research example had slightly less sufficient methodology. Although the number of participants in the two samples was high enough (N1 = 453; N2 = 488), the number of missing values was low, and statistical analyses were appropriate (descriptive statistics), the authors failed to report how they recruited their participants and whether they controlled for any confounders.

Let’s say that these authors also failed to provide you with more information via email. Finally, let’s assume that our third research example also had sufficient methodology, with a sufficiently large sample size for a qualitative investigation (N = 30), high sample representativeness (participants with different backgrounds, coming from different boat communities), and sufficient analyses (thematic analysis).

Note that, since this was a qualitative investigation, there is no need to evaluate the number of missing values and the use of confounders.

Step 3(f): Making a final evaluation

We would conclude that the secondary data from our first research example has a high quality. Data was recently collected by professionals, the employed measures were both reliable and valid, and the methodology was more than sufficient. We can be confident that our new research question can be sufficiently answered with the existing data. Thus, the data set for our first example is ideal.

The two secondary data sets from our second research example seem, however, less than ideal. Although we can answer our research questions on the basis of these recent data sets, the data was collected by an unprofessional source, the reliability and validity of the employed measure is uncertain, and the employed methodology has a few notable drawbacks.

Finally, the data from our third example seems sufficient both for answering our research question and in terms of the specific evaluations (data was collected recently by a professional source, semi-structured interviews were well made, and the employed methodology was sufficient).

The final question to ask is: “what can be done if our evaluation reveals the lack of appropriateness of secondary data?”. The answer, unfortunately, is “nothing”. In this instance, you can only note the drawbacks of the original data set, present its limitations, and conclude that your own research may not be sufficiently well grounded.

Your first sub-step here (if you are doing quantitative research) is to outline all variables of interest that you will use in your study. In our first example, you could have at least five variables of interest: (1) women’s nationality, (2) anxiety levels at the beginning of pregnancy, (3) anxiety levels at three months of pregnancy, (4) anxiety levels at six months of pregnancy, and (5) anxiety levels at nine months of pregnancy. In our second example, you will have two variables of interest: (1) participants’ nationality, and (2) the degree of interest in going to Greece for a summer vacation. Once your variables of interest are identified, you need to transfer this data into a new SPSS or Excel file. Remember simply to copy this data into the new file – it is vital that you do not alter it!

Once this is done, you should address missing data (identify and label them) and recode variables if necessary (e.g., giving a value of 1 to German participants and a value of 2 to British participants). You may also need to reverse-score some items, so that higher scores on all items indicate a higher degree of what is being assessed.

Most of the time, you will also need to create new variables – that is, to compute final scores. For instance, in our example of research on anxiety during pregnancy, your data will consist of scores on each item of the State Anxiety Inventory, completed at various times during pregnancy. You will need to calculate final anxiety scores for each time the measure was completed.

Your final step consists of analysing the data. You will always need to decide on the most suitable analysis technique for your secondary data set. In our first research example, you would rely on MANOVA (to see if women of different nationalities experience different stress levels at the beginning, at three months, at six months, and at nine months of pregnancy); and in our second example, you would use an independent samples t-test (to see if interest in Greek tourism differs between Germans and Britons).

The process of preparing and analysing a secondary data set is slightly different if your secondary data is qualitative. In our example on the reasons for living on boats, you would first need to outline all reasons for living on boats, as recognised by the original qualitative research. Then you would need to craft a questionnaire that assesses these reasons in a broader population.

Finally, you would need to analyse the data by employing statistical analyses.

Note that this example combines qualitative and quantitative data. But what if you are reusing qualitative data, as in our previous example of re-coding the interviews from our study to discover the language used when describing transient lifestyles? Here, you would simply need to recode the interviews and conduct a thematic analysis.

In summary…

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Home » Secondary Data – Types, Methods and Examples

Secondary Data – Types, Methods and Examples

Table of Contents

Secondary Data

Definition:

Secondary data refers to information that has been collected, processed, and published by someone else, rather than the researcher gathering the data firsthand. This can include data from sources such as government publications, academic journals, market research reports, and other existing datasets.

Secondary Data Types

Types of secondary data are as follows:

• Published data: Published data refers to data that has been published in books, magazines, newspapers, and other print media. Examples include statistical reports, market research reports, and scholarly articles.
• Government data: Government data refers to data collected by government agencies and departments. This can include data on demographics, economic trends, crime rates, and health statistics.
• Commercial data: Commercial data is data collected by businesses for their own purposes. This can include sales data, customer feedback, and market research data.
• Academic data: Academic data refers to data collected by researchers for academic purposes. This can include data from experiments, surveys, and observational studies.
• Online data: Online data refers to data that is available on the internet. This can include social media posts, website analytics, and online customer reviews.
• Organizational data: Organizational data is data collected by businesses or organizations for their own purposes. This can include data on employee performance, financial records, and customer satisfaction.
• Historical data : Historical data refers to data that was collected in the past and is still available for research purposes. This can include census data, historical documents, and archival records.
• International data: International data refers to data collected from other countries for research purposes. This can include data on international trade, health statistics, and demographic trends.
• Public data : Public data refers to data that is available to the general public. This can include data from government agencies, non-profit organizations, and other sources.
• Private data: Private data refers to data that is not available to the general public. This can include confidential business data, personal medical records, and financial data.
• Big data: Big data refers to large, complex datasets that are difficult to manage and analyze using traditional data processing methods. This can include social media data, sensor data, and other types of data generated by digital devices.

Secondary Data Collection Methods

Secondary Data Collection Methods are as follows:

• Published sources: Researchers can gather secondary data from published sources such as books, journals, reports, and newspapers. These sources often provide comprehensive information on a variety of topics.
• Online sources: With the growth of the internet, researchers can now access a vast amount of secondary data online. This includes websites, databases, and online archives.
• Government sources : Government agencies often collect and publish a wide range of secondary data on topics such as demographics, crime rates, and health statistics. Researchers can obtain this data through government websites, publications, or data portals.
• Commercial sources: Businesses often collect and analyze data for marketing research or customer profiling. Researchers can obtain this data through commercial data providers or by purchasing market research reports.
• Academic sources: Researchers can also obtain secondary data from academic sources such as published research studies, academic journals, and dissertations.
• Personal contacts: Researchers can also obtain secondary data from personal contacts, such as experts in a particular field or individuals with specialized knowledge.

Secondary Data Formats

Secondary data can come in various formats depending on the source from which it is obtained. Here are some common formats of secondary data:

• Numeric Data: Numeric data is often in the form of statistics and numerical figures that have been compiled and reported by organizations such as government agencies, research institutions, and commercial enterprises. This can include data such as population figures, GDP, sales figures, and market share.
• Textual Data: Textual data is often in the form of written documents, such as reports, articles, and books. This can include qualitative data such as descriptions, opinions, and narratives.
• Audiovisual Data : Audiovisual data is often in the form of recordings, videos, and photographs. This can include data such as interviews, focus group discussions, and other types of qualitative data.
• Geospatial Data: Geospatial data is often in the form of maps, satellite images, and geographic information systems (GIS) data. This can include data such as demographic information, land use patterns, and transportation networks.
• Transactional Data : Transactional data is often in the form of digital records of financial and business transactions. This can include data such as purchase histories, customer behavior, and financial transactions.
• Social Media Data: Social media data is often in the form of user-generated content from social media platforms such as Facebook, Twitter, and Instagram. This can include data such as user demographics, content trends, and sentiment analysis.

Secondary Data Analysis Methods

Secondary data analysis involves the use of pre-existing data for research purposes. Here are some common methods of secondary data analysis:

• Descriptive Analysis: This method involves describing the characteristics of a dataset, such as the mean, standard deviation, and range of the data. Descriptive analysis can be used to summarize data and provide an overview of trends.
• Inferential Analysis: This method involves making inferences and drawing conclusions about a population based on a sample of data. Inferential analysis can be used to test hypotheses and determine the statistical significance of relationships between variables.
• Content Analysis: This method involves analyzing textual or visual data to identify patterns and themes. Content analysis can be used to study the content of documents, media coverage, and social media posts.
• Time-Series Analysis : This method involves analyzing data over time to identify trends and patterns. Time-series analysis can be used to study economic trends, climate change, and other phenomena that change over time.
• Spatial Analysis : This method involves analyzing data in relation to geographic location. Spatial analysis can be used to study patterns of disease spread, land use patterns, and the effects of environmental factors on health outcomes.
• Meta-Analysis: This method involves combining data from multiple studies to draw conclusions about a particular phenomenon. Meta-analysis can be used to synthesize the results of previous research and provide a more comprehensive understanding of a particular topic.

Secondary Data Gathering Guide

Here are some steps to follow when gathering secondary data:

• Define your research question: Start by defining your research question and identifying the specific information you need to answer it. This will help you identify the type of secondary data you need and where to find it.
• Identify relevant sources: Identify potential sources of secondary data, including published sources, online databases, government sources, and commercial data providers. Consider the reliability and validity of each source.
• Evaluate the quality of the data: Evaluate the quality and reliability of the data you plan to use. Consider the data collection methods, sample size, and potential biases. Make sure the data is relevant to your research question and is suitable for the type of analysis you plan to conduct.
• Collect the data: Collect the relevant data from the identified sources. Use a consistent method to record and organize the data to make analysis easier.
• Validate the data: Validate the data to ensure that it is accurate and reliable. Check for inconsistencies, missing data, and errors. Address any issues before analyzing the data.
• Analyze the data: Analyze the data using appropriate statistical and analytical methods. Use descriptive and inferential statistics to summarize and draw conclusions from the data.
• Interpret the results: Interpret the results of your analysis and draw conclusions based on the data. Make sure your conclusions are supported by the data and are relevant to your research question.
• Communicate the findings : Communicate your findings clearly and concisely. Use appropriate visual aids such as graphs and charts to help explain your results.

Examples of Secondary Data

Here are some examples of secondary data from different fields:

• Healthcare : Hospital records, medical journals, clinical trial data, and disease registries are examples of secondary data sources in healthcare. These sources can provide researchers with information on patient demographics, disease prevalence, and treatment outcomes.
• Marketing : Market research reports, customer surveys, and sales data are examples of secondary data sources in marketing. These sources can provide marketers with information on consumer preferences, market trends, and competitor activity.
• Education : Student test scores, graduation rates, and enrollment statistics are examples of secondary data sources in education. These sources can provide researchers with information on student achievement, teacher effectiveness, and educational disparities.
• Finance : Stock market data, financial statements, and credit reports are examples of secondary data sources in finance. These sources can provide investors with information on market trends, company performance, and creditworthiness.
• Social Science : Government statistics, census data, and survey data are examples of secondary data sources in social science. These sources can provide researchers with information on population demographics, social trends, and political attitudes.
• Environmental Science : Climate data, remote sensing data, and ecological monitoring data are examples of secondary data sources in environmental science. These sources can provide researchers with information on weather patterns, land use, and biodiversity.

Purpose of Secondary Data

The purpose of secondary data is to provide researchers with information that has already been collected by others for other purposes. Secondary data can be used to support research questions, test hypotheses, and answer research objectives. Some of the key purposes of secondary data are:

• To gain a better understanding of the research topic : Secondary data can be used to provide context and background information on a research topic. This can help researchers understand the historical and social context of their research and gain insights into relevant variables and relationships.
• To save time and resources: Collecting new primary data can be time-consuming and expensive. Using existing secondary data sources can save researchers time and resources by providing access to pre-existing data that has already been collected and organized.
• To provide comparative data : Secondary data can be used to compare and contrast findings across different studies or datasets. This can help researchers identify trends, patterns, and relationships that may not have been apparent from individual studies.
• To support triangulation: Triangulation is the process of using multiple sources of data to confirm or refute research findings. Secondary data can be used to support triangulation by providing additional sources of data to support or refute primary research findings.
• To supplement primary data : Secondary data can be used to supplement primary data by providing additional information or insights that were not captured by the primary research. This can help researchers gain a more complete understanding of the research topic and draw more robust conclusions.

When to use Secondary Data

Secondary data can be useful in a variety of research contexts, and there are several situations in which it may be appropriate to use secondary data. Some common situations in which secondary data may be used include:

• When primary data collection is not feasible : Collecting primary data can be time-consuming and expensive, and in some cases, it may not be feasible to collect primary data. In these situations, secondary data can provide valuable insights and information.
• When exploring a new research area : Secondary data can be a useful starting point for researchers who are exploring a new research area. Secondary data can provide context and background information on a research topic, and can help researchers identify key variables and relationships to explore further.
• When comparing and contrasting research findings: Secondary data can be used to compare and contrast findings across different studies or datasets. This can help researchers identify trends, patterns, and relationships that may not have been apparent from individual studies.
• When triangulating research findings: Triangulation is the process of using multiple sources of data to confirm or refute research findings. Secondary data can be used to support triangulation by providing additional sources of data to support or refute primary research findings.
• When validating research findings : Secondary data can be used to validate primary research findings by providing additional sources of data that support or refute the primary findings.

Characteristics of Secondary Data

Secondary data have several characteristics that distinguish them from primary data. Here are some of the key characteristics of secondary data:

• Non-reactive: Secondary data are non-reactive, meaning that they are not collected for the specific purpose of the research study. This means that the researcher has no control over the data collection process, and cannot influence how the data were collected.
• Time-saving: Secondary data are pre-existing, meaning that they have already been collected and organized by someone else. This can save the researcher time and resources, as they do not need to collect the data themselves.
• Wide-ranging : Secondary data sources can provide a wide range of information on a variety of topics. This can be useful for researchers who are exploring a new research area or seeking to compare and contrast research findings.
• Less expensive: Secondary data are generally less expensive than primary data, as they do not require the researcher to incur the costs associated with data collection.
• Potential for bias : Secondary data may be subject to biases that were present in the original data collection process. For example, data may have been collected using a biased sampling method or the data may be incomplete or inaccurate.
• Lack of control: The researcher has no control over the data collection process and cannot ensure that the data were collected using appropriate methods or measures.
• Requires careful evaluation : Secondary data sources must be evaluated carefully to ensure that they are appropriate for the research question and analysis. This includes assessing the quality, reliability, and validity of the data sources.

Advantages of Secondary Data

There are several advantages to using secondary data in research, including:

• Time-saving : Collecting primary data can be time-consuming and expensive. Secondary data can be accessed quickly and easily, which can save researchers time and resources.
• Cost-effective: Secondary data are generally less expensive than primary data, as they do not require the researcher to incur the costs associated with data collection.
• Large sample size : Secondary data sources often have larger sample sizes than primary data sources, which can increase the statistical power of the research.
• Access to historical data : Secondary data sources can provide access to historical data, which can be useful for researchers who are studying trends over time.
• No ethical concerns: Secondary data are already in existence, so there are no ethical concerns related to collecting data from human subjects.
• May be more objective : Secondary data may be more objective than primary data, as the data were not collected for the specific purpose of the research study.

Limitations of Secondary Data

While there are many advantages to using secondary data in research, there are also some limitations that should be considered. Some of the main limitations of secondary data include:

• Lack of control over data quality : Researchers do not have control over the data collection process, which means they cannot ensure the accuracy or completeness of the data.
• Limited availability: Secondary data may not be available for the specific research question or study design.
• Lack of information on sampling and data collection methods: Researchers may not have access to information on the sampling and data collection methods used to gather the secondary data. This can make it difficult to evaluate the quality of the data.
• Data may not be up-to-date: Secondary data may not be up-to-date or relevant to the current research question.
• Data may be incomplete or inaccurate : Secondary data may be incomplete or inaccurate due to missing or incorrect data points, data entry errors, or other factors.
• Biases in data collection: The data may have been collected using biased sampling or data collection methods, which can limit the validity of the data.
• Lack of control over variables: Researchers have limited control over the variables that were measured in the original data collection process, which can limit the ability to draw conclusions about causality.

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How to Write a Research Methodology for Your Academic Article

This article is part of an ongoing series on academic writing help of scholarly articles. Previous parts explored how to write an introduction for a research paper and a literature review outline and format .

The Methodology section portrays the reasoning for the application of certain techniques and methods in the context of the study.

For your academic article, when you describe and explain your chosen methods it is very important to correlate them to your research questions and/or hypotheses. The description of the methods used should include enough details so that the study can be replicated by other Researchers, or at least repeated in a similar situation or framework.

Every stage of your research needs to be explained and justified with clear information on why you chose those particular methods, and how they help you answer your research question or purpose.

As the Authors, in this section you get to explain the rationale of your article for other Researchers. You should focus on answering the following questions:

• How did you collect the data or how did you generate the data?
• Which research methods did you use?
• Why did you choose these methods and techniques?
• How did you use these methods for analyzing the research question or problem?

The responses to these questions should be clear and precise, and the answers should be written in past tense.

First off, let’s establish the differences between research methods and research methodology.

Research Methods and Research Methodology

As an Academic and Author of valuable research papers, it’s important not to confuse these two terms.

Research Methodology Definition

Research Methodology refers the discussion regarding the specific methods chosen and used in a research paper. This discussion also encompasses the theoretical concepts that further provide information about the methods selection and application.

In other words, you should highlight how these theoretical concepts are connected with these methods in a larger knowledge framework and explain their relevance in examining the purpose, problem and questions of your study. Thus, the discussion that forms your academic article’s research methodology also incorporates an extensive literature review about similar methods, used by other Authors to examine a certain research subject.

Research Method Definition

A Research Method represents the technical steps involved in conducting the research. Details about the methods focus on characterizing and defining them, but also explaining your chosen techniques, and providing a full account on the procedures used for selecting, collecting and analyzing the data.

Important Tips for a Good Methodology Section

The methodology section is very important for the credibility of your article and for a professional academic writing style.

Data Collection or Generation for Your Academic Article

Readers, academics and other researchers need to know how the information used in your academic article was collected. The research methods used for collecting or generating data will influence the discoveries and, by extension, how you will interpret them and explain their contribution to general knowledge.

The most basic methods for data collection are:

Secondary data

Secondary data are data that have been previously collected or gathered for other purposes than the aim of the academic article’s study. This type of data is already available, in different forms, from a variety of sources.

Secondary data collection could lead to Internal or External secondary data research.

Primary data

Primary data represent data originated for the specific purpose of the study, with its research questions. The methods vary on how Authors and Researchers conduct an experiment, survey or study, but, in general, it uses a particular scientific method.

Primary data collection could lead to Quantitative and Qualitative research.

Readers need to understand how the information was gathered or generated in a way that is consistent with research practices in a field of study . For instance, if you are using a multiple choice survey, the readers need to know which questionnaire items you have examined in your primary quantitative research. Similarly, if your academic article involves secondary data from FED or Eurostat it is important to mention the variables used in your study, their values, and their time-frame.

For primary research, that involve surveys, experiments or observations, for a valuable academic article, Authors should provide information about:

• Study participants or group participants,
• Inclusion or exclusion criteria

Selecting and Applying Research Methods

Establishing the main premises of methodology is pivotal for any research because a method or technique that is not reliable for a certain study context will lead to unreliable results, and the outcomes’ interpretation (and overall academic article) will not be valuable.

In most cases, there is a wide variety of methods and procedures that you can use to explore a research topic in your academic article. The methods section should fully explain the reasons for choosing a specific methodology or technique .

Also, it’s essential that you describe the specific research methods of data collection you are going to use , whether they are primary or secondary data collection.

For primary research methods, describe the surveys, interviews, observation methods, etc.

For secondary research methods, describe how the data was originally created, gathered and which institution created and published it.

Reasons for Choosing Specific Research Methods

For this aspect that characterizes a good research methodology, indicate how the research approach fits with the general study , considering the literature review outline and format , and the following sections.

The methods you choose should have a clear connection with the overall research approach and you need to explain the reasons for choosing the research techniques in your study, and how they help you towards understanding your study’s purpose.

Data Analysis Methods

This section should also focus on information on how you intend to analyze your results .

Describe how you plan and intend to achieve an accurate assessment of the hypotheses, relationships, patterns, trends, distributions associated with your data and research purpose.

The data type, how it was measured, and which statistical tests were conducted and performed, should be detailed and reported in an accurate manner.

For explaining the data analysis methods, you should aim to answer questions, such as:

• Will your research be based on statistical analysis?
• Will you use theoretical frameworks to help you (and your Readers) analyze a set of hypotheses or relationships?
• Which data analysis methods will you choose?
• Which other Authors or studies have used the same methods and should be cited in your academic article?

Issues to Avoid

There are certain aspects that you need to pay extra attention in relation to your research methodology section. The most common issues to avoid are:

• Irrelevant details and complicated background information that provides too information and does not provide accurate understanding for Readers
• Unnecessary description and explanations of basic or well-known procedures, for an academic audience who is already has a basin understanding of the study
• For unconventional research approaches, it is important to provide accurate details and explain why your innovative method contributes to general knowledge (save more details for your Discussion/ Conclusion section in which you can highlight your contributions)
• Research limitations and obstacles should be described in a separate section (Research Limitations)
• The methodology should include sources and references that support your choice of methods and procedures, compared to the literature review that provides a general outlook and framework for your study.

Which aspects are you generally focusing on when writing your academic article’s research methodology section?

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Dissertation Methodology Writing Guide

Introduction.

The methodology section will be the chapter that you write following on from your literature review . After you have researched and discovered the gap in the available literature, it is possible for you to create ideas for your proposed research.

In your research proposal , you will have had a suggested methodology where you would have given ideas about how to approach the research: this would have been either through a primary data approach or through collecting secondary data .

Primary data

Primary data is any form of evidence that you collect yourself through your own research in the form of surveys, interviews, questionnaires, focus groups, observations, experiments. Primary data collection methods does not involve the collection of data from other researchers’ work and their studies.

Secondary data

Collecting secondary data is the collection of evidence from previous researchers’ work. An example could be focusing on another researchers’ experiment and using their findings as a basis for your dissertation. An example could be collecting the findings from two different experiments and comparing the findings of these studies in relation to the question posed.

Once you have decided what type of data you will be collecting, you will then need to determine whether the data being collected is qualitative or quantitative as this will have an impact on the analysis of your research.

Quantitative

Quantitative research only produces results on the specific issue that is being investigated and uses statistical, mathematical and computational programmes.

A closed-ended questionnaire would be analysed using quantitative research if the researcher merely computed the results and produced a series of comments as to the percentages of respondents who gave specific answers. A common programme by which to analyse quantitative research is SPSS.

Qualitative

Qualitative research tends to be used more in the social sciences and arts and is when a research seeks to ask ‘why’ and ‘how’ something has happened and explains the reasons with recourse to empirical mathematical models.

Within primary research that uses qualitative research, small focus groups can often be employed.

An open-ended questionnaire that collates and assesses a range of verbal responses would be analysed using qualitative techniques as the answers given do not lend themselves to being processed in the manner described above relating to closed questionnaires.

Mixed Methodology

Another option is through a mixed methods approach, which would be the collection of both primary and secondary data.

In a dissertation where one is assessing, for instance, the effects of flooding in the Wirral peninsula, it is likely that all the research techniques mentioned above would be used.

Secondary data would be used through a literature review. Closed-ended questionnaires could be analysed using a statistical panel and interviews with experts would be commented upon with reference to existing literature.

Accordingly, both primary and secondary research techniques would be utilised as well as qualitative and quantitative mechanisms.

Writing Your Methodology

You should begin your methodology with a brief introduction to the chapter, this should also include relaying the aims of the study. Following on from this, it is best to start by defining and choosing the research paradigm for the dissertation.

Research paradigms – there are 4 main approaches to research. These are positivism, interpretivism (also known as constructivism), post-positivism and critical theory.

• Positivism: philosophical viewpoint that the validity of research comes from objective experimental testing
• Interpretivism (Constructivism): usually associated with qualitative research, interpretivism research is subjective. This means that results from research are down to interpretation by the researcher i.e answers to questions in an interview
• Post-positivism: as opposed to positivism, post positivism accepts subjectivity in research and tests qualitative data alongside quantitative data

Once you have defined your research philosophy, the next step would be to identify your research approach and instrument.

Research approach – This can be separated by two types:

• Deductive research
• Inductive research

Deductive research is the approach you would take if you had hypotheses that were being tested, then you would be using a deductive research approach.

Inductive research is when there is a set of observations and a theory is developed to explain those observations or any patterns that are amongst those observations.

Following on from this, you would then be expected to discuss your chosen data collection method along with stating if the research is either quantitative or qualitative. When writing about key terms i.e. primary data; it is always best to define, explain and justify why.

In so doing, you should also note (briefly) what is inappropriate about the other approaches as well as the ways in which you have overcome any negatives that are associated with your approach.

If your chosen methodology is the collection of primary data, the next step would be the describe and explain the sampling and participant selection.

Here you would need to describe and explain the chosen sampling method along with the number of participants selected. It is always good to include how you contacted the participants and recruited them for the study.

If you are using primary data, it is always crucial to include a sub-chapter of the work that discusses any ethical concerns and considerations that arose due to your chosen methodology.

For both primary and secondary data, it is necessary to include a sub-section on the data analysis that will be used to collate and analyse the data gathered in the research.

Here you will discuss how you intend to analyse the data and why you have chosen this analytical technique.

Justification

Whichever approach you use it is important that you justify your decision and that you do so via reference to existing academic works – and writing only in the third person.

As with the background section of your dissertation, your methodology section needs to be grounded in existing academic opinion.

The following books provide not only an overview of methodological approaches (and the strengths and weaknesses associated with each) but are also the sorts of books that your lecturers may expect to see referenced within your methodology section, depending on the type of course you are doing.

Bell, J. (1993). Doing your research project . Maidenhead: Open University Press.

Bryman, A. (2012). Social research methods (4th edn). Oxford: Oxford University Press.

Denscombe, M. (2007). The good research guide (3rd edn). Maidenhead: Open University Press.

Flick, U. (2011). Introducing research methodology . London: SAGE.

Grinyer, A. (2002). ‘The anonymity of research participants: Assumptions, ethics and practicalities’. Social Research Update , Vol. 36, University of Surrey.

Morgan, G. and Smircich, L. (1980). ‘The case for qualitative research’, The Academy of Management Review . Vol. 5 (4), pp. 491-500.

Ritchie, J. and Lewis, L. (2003). Qualitative research practice: A guide for social science students and researchers . London: SAGE.

Robson, C. (2002). Real world research (2nd edn). Oxford: Blackwell Publishing.

Silverman, D. (2010). Doing qualitative research: A practical handbook (3rd edn). London: SAGE.

You do not need to read them all, but you should show (using appropriate and limited direct quotation for extra marks) at least some knowledge of the arguments contained within these books. For an undergraduate dissertation it would be good practice to include at least five of these books (or their equivalent – depending upon what is available within your library) in your bibliography.

We can help

If you require assistance to write the methodology section of your dissertation, you may want to consider our helpful service which is a great way to get a head start on your work.

Checklist: Writing a Methodology

• Have I selected a research method which is within my abilities and matches my aims?
• Have I considered other research methods which may be appropriate?
• Can I critically explain why I ruled these methods out?
• Have I acknowledged the strengths and weaknesses of my chosen method?

Congratulations!

Well done on completing this checklist! You're doing great.

Dissertation Methodology FAQ's

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The Essential Guide to Doing Your Research Project

Student resources, steps in secondary data analysis, stepping your way through effective secondary data analysis.

Determine your research question  – As indicated above, knowing exactly what you are looking for

Locating data – Knowing what is out there and whether you can gain access to it. A quick Internet search, possibly with the help of a librarian, will reveal a wealth of options.

Evaluating relevance of the data  – Considering things like the data’s original purpose, when it was collected, population, sampling strategy/sample, data collection protocols, operationalization of concepts, questions asked, and form/shape of the data.

Assessing credibility of the data  – Establishing the credentials of the original researchers, searching for full explication of methods including any problems encountered, determining how consistent the data is with data from other sources, and discovering whether the data has been used in any credible published research.

Analysis –  This will generally involve a range of statistical processes as discussed in Chapter 13.

Write Your Dissertation Using Only Secondary Research

Writing a dissertation is already difficult to begin with but it can appear to be a daunting challenge when you only have other people’s research as a guide for proving a brand new hypothesis! You might not be familiar with the research or even confident in how to use it but if secondary research is what you’re working with then you’re in luck. It’s actually one of the easiest methods to write about!

Secondary research is research that has already been carried out and collected by someone else. It means you’re using data that’s already out there rather than conducting your own research – this is called primary research. Thankfully secondary will save you time in the long run! Primary research often means spending time finding people and then relying on them for results, something you could do without, especially if you’re in a rush. Read more about the advantages and disadvantages of primary research .

So, where do you find secondary data?

Secondary research is available in many different places and it’s important to explore all areas so you can be sure you’re looking at research you can trust. If you’re just starting your dissertation you might be feeling a little overwhelmed with where to begin but once you’ve got your subject clarified, it’s time to get researching! Some good places to search include:

• Libraries (your own university or others – books and journals are the most popular resources!)
• Government records
• Online databases
• Credible Surveys (this means they need to be from a reputable source)
• Search engines (google scholar for example).

The internet has everything you’ll need but you’ve got to make sure it’s legitimate and published information. It’s also important to check out your student library because it’s likely you’ll have access to a great range of materials right at your fingertips. There’s a strong chance someone before you has looked for the same topic so it’s a great place to start.

What are the two different types of secondary data?

It’s important to know before you start looking that they are actually two different types of secondary research in terms of data, Qualitative and quantitative. You might be looking for one more specifically than the other, or you could use a mix of both. Whichever it is, it’s important to know the difference between them.

• Qualitative data – This is usually descriptive data and can often be received from interviews, questionnaires or observations. This kind of data is usually used to capture the meaning behind something.
• Quantitative data – This relates to quantities meaning numbers. It consists of information that can be measured in numerical data sets.

The type of data you want to be captured in your dissertation will depend on your overarching question – so keep it in mind throughout your search!

Getting started

When you’re getting ready to write your dissertation it’s a good idea to plan out exactly what you’re looking to answer. We recommend splitting this into chapters with subheadings and ensuring that each point you want to discuss has a reliable source to back it up. This is always a good way to find out if you’ve collected enough secondary data to suit your workload. If there’s a part of your plan that’s looking a bit empty, it might be a good idea to do some more research and fill the gap. It’s never a bad thing to have too much research, just as long as you know what to do with it and you’re willing to disregard the less important parts. Just make sure you prioritise the research that backs up your overall point so each section has clarity.

Then it’s time to write your introduction. In your intro, you will want to emphasise what your dissertation aims to cover within your writing and outline your research objectives. You can then follow up with the context around this question and identify why your research is meaningful to a wider audience.

The body of your dissertation

Before you get started on the main chapters of your dissertation, you need to find out what theories relate to your chosen subject and the research that has already been carried out around it.

Literature Reviews

Your literature review will be a summary of any previous research carried out on the topic and should have an intro and conclusion like any other body of the academic text. When writing about this research you want to make sure you are describing, summarising, evaluating and analysing each piece. You shouldn’t just rephrase what the researcher has found but make your own interpretations. This is one crucial way to score some marks. You also want to identify any themes between each piece of research to emphasise their relevancy. This will show that you understand your topic in the context of others, a great way to prove you’ve really done your reading!

Theoretical Frameworks

The theoretical framework in your dissertation will be explaining what you’ve found. It will form your main chapters after your lit review. The most important part is that you use it wisely. Of course, depending on your topic there might be a lot of different theories and you can’t include them all so make sure to select the ones most relevant to your dissertation. When starting on the framework it’s important to detail the key parts to your hypothesis and explain them. This creates a good foundation for what you’re going to discuss and helps readers understand the topic.

To finish off the theoretical framework you want to start suggesting where your research will fit in with those texts in your literature review. You might want to challenge a theory by critiquing it with another or explain how two theories can be combined to make a new outcome. Either way, you must make a clear link between their theories and your own interpretations – remember, this is not opinion based so don’t make a conclusion unless you can link it back to the facts!

Concluding your dissertation

Your conclusion will highlight the outcome of the research you’ve undertaken. You want to make this clear and concise without repeating information you’ve already mentioned in your main body paragraphs. A great way to avoid repetition is to highlight any overarching themes your conclusions have shown

When writing your conclusion it’s important to include the following elements:

• Summary – A summary of what you’ve found overall from your research and the conclusions you have come to as a result.
• Recommendations – Recommendations on what you think the next steps should be. Is there something you would change about this research to improve it or further develop it?
• Show your contribution – It’s important to show how you’ve contributed to the current knowledge on the topic and not just repeated what other researchers have found.

Hopefully, this helps you with your secondary data research for your dissertation! It’s definitely not as hard as it seems, the hardest part will be gathering all of the information in the first place. It may take a while but once you’ve found your flow – it’ll get easier, promise! You may also want to read about the advantages and disadvantages of secondary research .

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• What is Secondary Data? + [Examples, Sources, & Analysis]

• Data Collection

Aside from consulting the primary origin or source, data can also be collected through a third party, a process common with secondary data. It takes advantage of the data collected from previous research and uses it to carry out new research.

Secondary data is one of the two main types of data, where the second type is the primary data. These 2 data types are very useful in research and statistics, but for the sake of this article, we will be restricting our scope to secondary data.

We will study secondary data, its examples, sources, and methods of analysis.

What is Secondary Data?  

Secondary data is the data that has already been collected through primary sources and made readily available for researchers to use for their own research. It is a type of data that has already been collected in the past.

A researcher may have collected the data for a particular project, then made it available to be used by another researcher. The data may also have been collected for general use with no specific research purpose like in the case of the national census.

Data classified as secondary for particular research may be said to be primary for another research. This is the case when data is being reused, making it primary data for the first research and secondary data for the second research it is being used for.

Sources of Secondary Data

Sources of secondary data include books, personal sources, journals, newspapers, websitess, government records etc. Secondary data are known to be readily available compared to that of primary data. It requires very little research and needs for manpower to use these sources.

With the advent of electronic media and the internet, secondary data sources have become more easily accessible. Some of these sources are highlighted below.

Books are one of the most traditional ways of collecting data. Today, there are books available for all topics you can think of.  When carrying out research, all you have to do is look for a book on the topic being researched, then select from the available repository of books in that area. Books, when carefully chosen are an authentic source of authentic data and can be useful in preparing a literature review.

• Published Sources

There are a variety of published sources available for different research topics. The authenticity of the data generated from these sources depends majorly on the writer and publishing company. 

Published sources may be printed or electronic as the case may be. They may be paid or free depending on the writer and publishing company’s decision.

• Unpublished Personal Sources

This may not be readily available and easily accessible compared to the published sources. They only become accessible if the researcher shares with another researcher who is not allowed to share it with a third party.

For example, the product management team of an organization may need data on customer feedback to assess what customers think about their product and improvement suggestions. They will need to collect the data from the customer service department, which primarily collected the data to improve customer service.

Journals are gradually becoming more important than books these days when data collection is concerned. This is because journals are updated regularly with new publications on a periodic basis, therefore giving to date information.

Also, journals are usually more specific when it comes to research. For example, we can have a journal on, “Secondary data collection for quantitative data ” while a book will simply be titled, “Secondary data collection”.

In most cases, the information passed through a newspaper is usually very reliable. Hence, making it one of the most authentic sources of collecting secondary data.

The kind of data commonly shared in newspapers is usually more political, economic, and educational than scientific. Therefore, newspapers may not be the best source for scientific data collection.

The information shared on websites is mostly not regulated and as such may not be trusted compared to other sources. However, there are some regulated websites that only share authentic data and can be trusted by researchers.

Most of these websites are usually government websites or private organizations that are paid, data collectors.

Blogs are one of the most common online sources for data and may even be less authentic than websites. These days, practically everyone owns a blog, and a lot of people use these blogs to drive traffic to their website or make money through paid ads.

Therefore, they cannot always be trusted. For example, a blogger may write good things about a product because he or she was paid to do so by the manufacturer even though these things are not true.

They are personal records and as such rarely used for data collection by researchers. Also, diaries are usually personal, except for these days when people now share public diaries containing specific events in their life.

A common example of this is Anne Frank’s diary which contained an accurate record of the Nazi wars.

• Government Records

Government records are a very important and authentic source of secondary data. They contain information useful in marketing, management, humanities, and social science research.

Some of these records include; census data, health records, education institute records, etc. They are usually collected to aid proper planning, allocation of funds, and prioritizing of projects.

Podcasts are gradually becoming very common these days, and a lot of people listen to them as an alternative to radio. They are more or less like online radio stations and are generating increasing popularity.

Information is usually shared during podcasts, and listeners can use it as a source of data collection. 

Some other sources of data collection include:

• Radio stations
• Public sector records.

What are the Secondary Data Collection Tools?

Popular tools used to collect secondary data include; bots, devices, libraries, etc. In order to ease the data collection process from the sources of secondary data highlighted above, researchers use these important tools which are explained below.

There are a lot of data online and it may be difficult for researchers to browse through all these data and find what they are actually looking for. In order to ease this process of data collection, programmers have created bots to do an automatic web scraping for relevant data.

These bots are “ software robots ” programmed to perform some task for the researcher. It is common for businesses to use bots to pull data from forums and social media for sentiment and competitive analysis.

• Internet-Enabled Devices

This could be a mobile phone, PC, or tablet that has access to an internet connection. They are used to access journals, books, blogs, etc. to collect secondary data.

This is a traditional secondary data collection tool for researchers. The library contains relevant materials for virtually all the research areas you can think of, and it is accessible to everyone.

A researcher might decide to sit in the library for some time to collect secondary data or borrow the materials for some time and return when done collecting the required data.

Radio stations are one of the secondary sources of data collection, and one needs radio to access them. The advent of technology has even made it possible to listen to the radio on mobile phones, deeming it unnecessary to get a radio.

Secondary Data Analysis  

Secondary data analysis is the process of analyzing data collected from another researcher who primarily collected this data for another purpose. Researchers leverage secondary data to save time and resources that would have been spent on primary data collection.

The secondary data analysis process can be carried out quantitatively or qualitatively depending on the kind of data the researcher is dealing with. The quantitative method of secondary data analysis is used on numerical data and is analyzed mathematically, while the qualitative method uses words to provide in-depth information about data.

How to Analyse Secondary Data

There are different stages of secondary data analysis, which involve events before, during, and after data collection. These stages include;

• Statement of Purpose

Before collecting secondary data for analysis, you need to know your statement of purpose. That is, a clear understanding of why you are collecting the data—the ultimate aim of the research work and how this data will help achieve it.

This will help direct your path towards collecting the right data, and choosing the best data source and method of analysis.

• Research Design

This is a written-down plan on how the research activities will be carried out. It describes the kind of data to be collected, the sources of data collection, method of data collection, tools, and even method of analysis.

A research design may also contain a timestamp of when each of these activities will be carried out. Therefore, serving as a guide for the secondary data analysis.

After identifying the purpose of the research, the researcher should design a research process that will guide the data analysis process.

• Developing the Research Questions

It is not enough to just know the research purpose, you need to develop research questions that will help in better identifying Secondary data. This is because they are usually a pool of data to choose from, and asking the right questions will assist in collecting authentic data.

For example, a researcher trying to collect data about the best fish feeds to enable fast growth in fishes will have to ask questions like, What kind of fish is considered? Is the data meant to be quantitative or qualitative? What is the content of the fish feed? The growth rate in fishes after feeding on it, and so on.

• Identifying Secondary Data

After developing the research questions, researchers use them as a guide to identifying relevant data from the data repository. For example, if the kind of data to be collected is qualitative, a researcher can filter out qualitative data.

The suitable secondary data will be the one that correctly answers the questions highlighted above. When looking for the solutions to a linear programming problem, for instance, the solutions will be numbers that satisfy both the objective and the constraints.

Any answer that doesn’t satisfy both, is not a solution.

• Evaluating Secondary Data

This stage is what many classify as the real data analysis stage because it is the point where analysis is actually performed. However, the stages highlighted above are a part of the data analysis process, because they influence how the analysis is performed.

Once a dataset that appears viable in addressing the initial requirements discussed above is located, the next step in the process is the evaluation of the dataset to ensure the appropriateness for the research topic. The data is evaluated to ensure that it really addresses the statement of the problem and answers the research questions.

After which it will now be analyzed either using the quantitative method or the qualitative method depending on the type of data it is.

Advantages of Secondary Data

• Ease of Access

Most of the sources of secondary data are easily accessible to researchers. Most of these sources can be accessed online through a mobile device.  People who do not have access to the internet can also access them through print.

They are usually available in libraries, book stores, and can even be borrowed from other people.

• Inexpensive

Secondary data mostly require little to no cost for people to acquire them. Many books, journals, and magazines can be downloaded for free online.  Books can also be borrowed for free from public libraries by people who do not have access to the internet.

Researchers do not have to spend money on investigations, and very little is spent on acquiring books if any.

• Time-Saving

The time spent on collecting secondary data is usually very little compared to that of primary data. The only investigation necessary for secondary data collection is the process of sourcing for necessary data sources.

Therefore, cutting the time that would normally be spent on the investigation. This will save a significant amount of time for the researcher 

• Longitudinal and Comparative Studies

Secondary data makes it easy to carry out longitudinal studies without having to wait for a couple of years to draw conclusions. For example, you may want to compare the country’s population according to census 5 years ago, and now.

Rather than waiting for 5 years, the comparison can easily be made by collecting the census 5 years ago and now.

• Generating new insights

When re-evaluating data, especially through another person’s lens or point of view, new things are uncovered. There might be a thing that wasn’t discovered in the past by the primary data collector, that secondary data collection may reveal.

For example, when customers complain about difficulty using an app to the customer service team, they may decide to create a user guide teaching customers how to use it. However, when a product developer has access to this data, it may be uncovered that the issue came from and UI/UX design that needs to be worked on.

Disadvantages of Secondary Data  

• Data Quality:

The data collected through secondary sources may not be as authentic as when collected directly from the source. This is a very common disadvantage with online sources due to a lack of regulatory bodies to monitor the kind of content that is being shared.

Therefore, working with this kind of data may have negative effects on the research being carried out.

• Irrelevant Data:

Researchers spend so much time surfing through a pool of irrelevant data before finally getting the one they need. This is because the data was not collected mainly for the researcher.

In some cases, a researcher may not even find the exact data he or she needs, but have to settle for the next best alternative. 

• Exaggerated Data

Some data sources are known to exaggerate the information that is being shared. This bias may be some to maintain a good public image or due to a paid advert.

This is very common with many online blogs that even go a bead to share false information just to gain web traffic. For example, a FinTech startup may exaggerate the amount of money it has processed just to attract more customers.

A researcher gathering this data to investigate the total amount of money processed by FinTech startups in the US for the quarter may have to use this exaggerated data.

• Outdated Information

Some of the data sources are outdated and there are no new available data to replace the old ones. For example, the national census is not usually updated yearly.

Therefore, there have been changes in the country’s population since the last census. However, someone working with the country’s population will have to settle for the previously recorded figure even though it is outdated.

Secondary data has various uses in research, business, and statistics. Researchers choose secondary data for different reasons, with some of it being due to price, availability, or even needs of the research.

Although old, secondary data may be the only source of data in some cases. This may be due to the huge cost of performing research or due to its delegation to a particular body (e.g. national census). 

In short, secondary data has its shortcomings, which may affect the outcome of the research negatively and also some advantages over primary data. It all depends on the situation, the researcher in question, and the kind of research being carried out.

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Secondary Data Collection Methods

Data is physical or digital information; information is knowledge and knowledge is power! But to leverage that powerful data and…

Data is physical or digital information; information is knowledge and knowledge is power! But to leverage that powerful data and execute a successful strategy, businesses need to first gather the data—simply known as data collection.

Collecting data is more than just searching on Google. Although our society is heavily dependent on data, the importance of collecting it still eludes many. Accurately collecting data is crucial for ensuring quality assurance, keeping research integrity and making informed business decisions. There are methods, goals, time and money involved. Researchers have to have a data-driven approach and achieve the desired end results. Only after having a clear picture of the objective can a researcher decide whether to use primary or secondary data and where the primary or s econdary data can be collected from.

But before we learn about the sources of secondary data in research methodology , we must first understand the meaning of data collection. 

What Is Data Collection?

What is secondary data collection, various methods of collecting secondary data, how to use sources of secondary data in research methodology, advantages of secondary data collection methods, disadvantages of secondary data collection methods, secondary data collection examples.

Data collection is a crucial element of statistical research. The process involves collecting information from available sources to come up with solutions for a problem. The process evaluates the outcome and predicts trends and possibilities of the future. Researchers start by collecting the most basic data related to the problem and then progress with the volume and type of data to be collected.

There are two methods of data collection—primary data collection methods and secondary data collection methods. Data collection involves identifying data types, their sources and the methods being used. There are different collection methods that are used across commercial, governmental and research fields, and various sources are accessed where primary and secondary data can be collected from . Whether it’s for academic research or promoting a new product, data collection helps us make better choices and get better results. 

In this article, we’ll discuss secondary data collection, the various methods of collecting secondary data , its advantages, disadvantages, secondary data collection examples and sources of secondary data in research methodology .

Secondary data collection refers to gathering information that’s already available. The data was previously collected, has undergone necessary statistical analysis and isn’t owned by the researcher. This data is usually one that was collected from primary sources and later made available for everyone to access. In other words, secondary data is second-hand information that’s collected by third parties. A researcher may ask others to collect data or obtain it from other sources. Existing data is typically collated and summarized to boost the overall effectiveness of a research.

There are two t ypes of secondary data collection —qualitative secondary data collection and quantitative secondary data collection. Qualitative data deals with the intangibles and covers factors such as quality, color, preference or appearance. Quantitative data deals with numbers, statistics and percentages. Although the end goal determines which of the two types of secondary data collection a researcher chooses, secondary data collection is mostly concerned with quantitative data.

Let’s look at the common secondary data collection methods :

Collecting Information Available On The Internet 

One of the most popular methods of collecting secondary data is by using the internet. Readily available data can be accessed with the click of a button, which makes the internet one of the best places where secondary data can be collected from . It’s practically free of cost, although some websites may charge money—usually low prices. However, organizations and individuals must look out for inauthentic and untrustworthy sources of information.

Collecting Data Available In Government And Non-Government Agencies 

Government and non-government agencies such as Census bureaus, government printing offices and business development centers store relevant data and valuable information that both individuals and organizations can access.

Accessing Public Libraries 

Public libraries house copies of research, public documents and statistical information. Although services may vary, libraries usually have a vast collection of publications highlighting market statistics, business directories and newsletters. 

Using Data From Educational Institutions

Educational institutions are often overlooked when deciding a method of collection. Educational institutions conduct more research than any other sector. Universities have a plethora of primary data that can act as vital information for secondary research.

Using Sources Of Commercial Information 

Secondary data collection methods are cost-effective and hence quite popular among businesses and individuals. Small businesses that can’t afford expensive research have to resort to a cheaper method of data collection. They can request and obtain data from anywhere it’s available to identify prospective clients and have a wider reach when promoting products and services.

Here are the steps to conduct research using sources of secondary data collection :

• Identify the topic of research, make a list of research attributes and define the purpose of research. 
• Information sources have to be narrowed down and identified to access the most relevant data applicable to the research. 
• Once the secondary data sources are narrowed down, check and collect all existing data related to the research from similar sources. 
• After collecting the data, check for duplication before assembling it into a usable format. 
• Analyze the collected data and check if it answers all questions crucial to meet the objective. 

The most important aspect of secondary research is looking out for any inauthentic source or incorrect data that may hamper the research.

These are the advantages of secondary data collection: Most of the data and information is readily available and there are plenty of sources of secondary data collection .  

• The process is less expensive compared to the primary method. There’s minimum expenditure associated with obtaining data from authentic sources. 
• Data collected for secondary research can give a fair idea about how effective the primary research was. Businesses can hypothesize and evaluate the cost of primary research. 
• Re-evaluating data from another person’s point of view can uncover things that may have been overlooked. This may lead to discovering new features or fixing a bug in an app. 
• Secondary data collection is less time-consuming as the data doesn’t need to be collected from the root. Hence, data collection time is significantly lower than primary methods. 
• Longitudinal and comparative studies are easier to conduct with secondary data as we don’t have to wait to draw conclusions. For example, to compare the population difference in a country across five years, we can simply compare the present census with that of five years back. 

Researchers can look to collect data from both internal and external sources, which prevents relying on any special or specific data collection method. 

Let’s discuss the disadvantages of secondary data collection:

• Data may be readily available but the credibility of sources is under constant scrutiny. Research can break down due to a lack of credible and authentic information
• Most secondary data sources don’t offer the latest statistics, studies or reports. Accurate data doesn’t necessarily mean updated data
• As a researcher has no control over the primary source or quality of information, the success of secondary research heavily depends on the quality of the primary research that was conducted 

Primary data collection may often be expensive but the credibility, accuracy and quality of information is seldom questionable. 

Here are some secondary data collection examples :

• Journals and blogs are popular examples of secondary sources of data collection today. They’re both regularly updated but blogs run the risk of being less authentic than journals as the latter is backed by periodically updated information with new publications.
• Newspapers have been at the top of the most reliable and authentic sources of secondary data collection for centuries. Although they mostly cover economic, educational and political information, there is specialized content available with newspapers dedicated to covering topics such as science, environment and sports. 
• Podcasts are the new-age alternative to radio and are widely becoming a common source of secondary information. Presenters talk to the audience about specific topics or conduct interviews on the show. With the digital media boom, interactive podcasts have become wildly common and popular.

Some other examples of secondary data collection are letters, books, government records and columns.

Secondary data finds use across the fields of business, research and statistics. Researchers may choose secondary data due to finance issues, availability, research needs or time. Due to various factors, secondary data may sometimes be the only data available. In such cases, collecting authentic and relevant data and coming up with solutions to meet the objective may come down to a manager’s ability of CRITICAL THINKING . 

Using secondary data has its drawbacks and data collection is concerned with finding solutions. Managers need to go behind the scenes to fully understand the process of problem-solving. Learn to make research foolproof and analyze scenarios error-free with Harappa’s Create New Solutions pathway. Continuously seek, absorb and interpret new information. Lay down insightful questions, look for relevant data and use smart analyses to create working solutions. Strive to get all available information first and then make the best possible decision. Make well-reasoned and clearly articulated arguments that are backed by logic and evidence. 

Methodology

Ai generator.

Methodology refers to the systematic study of methods used in research. It includes Research Methodology , which is the framework for conducting investigations, and Survey Methodology , which involves techniques for collecting and analyzing survey data. A key part of any methodology is the Research Question, guiding the study’s focus and direction.

What is Methodology?

Methodology refers to the systematic study of methods used in research, encompassing principles and procedures that guide scientific investigations. It includes Research Methodology, which outlines the framework for conducting studies, and Survey Methodology, which involves techniques for collecting and analyzing survey data.

Examples of Methodology

• Surveys : Distributing questionnaires to gather quantitative data from a large sample.
• Interviews : Conducting one-on-one conversations to collect detailed qualitative data.
• Focus Groups : Facilitating group discussions to explore participants’ perceptions and opinions.
• Case Studies : Performing in-depth analysis of a single subject or group to understand complex issues.
• Experiments : Implementing controlled tests to determine causal relationships between variables.
• Participant Observation : Observing and engaging with participants in their natural environment.
• Longitudinal Studies : Tracking the same individuals over an extended period to observe changes.
• Cross-Sectional Studies : Analyzing data from different groups at a single point in time.
• Content Analysis : Systematically analyzing text or media to identify patterns and themes.
• Secondary Data Analysis : Using existing data collected by others to conduct new analyses.
• Meta-Analysis : Combining results from multiple studies to draw a broader conclusion.
• Delphi Technique : Gathering expert opinions through multiple rounds of questionnaires to achieve consensus.
• Ethnography : Immersing in a community to understand its culture and practices.
• Grounded Theory : Developing theories based on data collected during the research.
• Action Research : Collaborating with participants to address a problem and implement solutions.
• Comparative Method : Comparing different groups or cases to identify similarities and differences.
• Historical Analysis : Examining historical records to understand past events and trends.
• Systematic Review : Summarizing and evaluating existing research on a specific topic.
• Descriptive Research : Describing characteristics of a population or phenomenon.
• Narrative Inquiry : Studying personal stories and experiences to gain insights.
• Visual Analysis : Analyzing visual materials such as photographs and videos.
• Experimental Design : Using control and experimental groups to test hypotheses.
• Phenomenology : Exploring individuals’ lived experiences to understand their perceptions.
• Biographical Research : Studying an individual’s life history and experiences.
• Field Experiments : Conducting experiments in natural settings.
• Survey Design : Creating and administering surveys to collect data.
• Program Evaluation : Assessing the effectiveness of a program or intervention.
• Network Analysis : Examining relationships and interactions within a network.
• Discourse Analysis : Studying language use in texts and conversations.
• Quasi-Experimental Design : Implementing studies with non-randomized control and treatment groups.

Examples of Methodology in a Sentences

• The interview methodology involved conducting in-depth, one-on-one interviews with participants.
• A survey methodology was used to gather data from a large population using structured questionnaires.
• The case study methodology focused on an in-depth analysis of a single organization.
• Ethnographic methodology involved immersing researchers in the community to observe daily activities.
• A mixed-methods approach was utilized, combining quantitative surveys and qualitative interviews.
• Experimental methodology included a control group and a treatment group to test the hypothesis.
• Participant observation was employed to understand the behaviors and interactions within the group.
• The longitudinal study methodology tracked participants over several years to observe changes.
• Content analysis was used to analyze the themes and patterns in social media posts.
• The focus group methodology gathered diverse opinions on the new product concept.
• A cross-sectional study was conducted to compare different population groups at a single point in time.
• Action research methodology involved the participants in the research process to improve practices.
• The phenomenological methodology aimed to understand individuals’ lived experiences.
• Grounded theory methodology was used to develop a theory based on data collected from participants.
• The narrative research methodology focused on the stories and personal accounts of the participants.
• Secondary data analysis involved analyzing data previously collected by other researchers.
• Delphi methodology gathered expert opinions through multiple rounds of questionnaires.
• Comparative methodology analyzed differences and similarities between two distinct groups.
• The meta-analysis methodology combined results from multiple studies to draw a comprehensive conclusion.
• Historical research methodology examined past events to understand their impact on the present.
• The survey methodology included both closed-ended and open-ended questions to capture detailed responses.
• Field experiments were conducted to test the intervention in a natural setting.
• Discourse analysis examined the language and communication patterns within the texts.
• The biographical research methodology studied individuals’ life histories and personal experiences.
• Quantitative content analysis was used to count and analyze the frequency of specific words or themes.
• Case-control study methodology compared individuals with a specific condition to those without it.
• Systematic review methodology evaluated and synthesized findings from existing research studies.
• Experimental design methodology manipulated variables to observe their effect on the outcome.
• Visual ethnography involved analyzing visual materials such as photographs and videos.
• Clinical trial methodology tested the efficacy and safety of new medical treatments through controlled experiments.

Methodology Examples in Project Proposal

1. Survey Methodology : We will distribute online surveys to 500 participants to gather quantitative data on customer satisfaction levels.

2. Interview Methodology : Conduct semi-structured interviews with 20 key stakeholders to gain insights into project requirements and expectations.

3. Focus Group Methodology : Facilitate focus groups with selected users to discuss and refine the design of the new software interface.

4 . Case Study Methodology : Analyze three case studies of similar projects to identify best practices and potential pitfalls.

5. Experimental Methodology : Implement a controlled experiment to test the impact of the new training program on employee productivity.

6. Ethnographic Methodology : Engage in participant observation within the target community for three months to understand user behavior and cultural influences.

7. Mixed Methods Approach : Combine quantitative data from surveys with qualitative insights from interviews to provide a comprehensive analysis of project outcomes.

8. Action Research Methodology : Collaborate with project team members to iteratively implement and assess improvements, ensuring continuous feedback and adaptation.

9. Content Analysis : Review and analyze project-related documents and communications to identify common themes and areas for improvement.

10. Delphi Methodology : Use the Delphi technique to gather and refine expert opinions through multiple rounds of questionnaires to achieve a consensus on project goals and strategies.

Methodology Examples in Report

Example 1: survey methodology.

In this study, we employed a survey methodology to collect data from participants. The survey was designed to gather information on consumer preferences and behaviors. The key steps in our survey methodology were as follows:

• Population : All residents of City X aged 18 and above.
• Sample Size : 500 participants selected through random sampling.
• Questionnaire : A structured questionnaire with 25 closed-ended questions.
• Pilot Testing : Conducted with 50 participants to ensure clarity and reliability of the questions.
• Mode : Online survey distributed via email.
• Duration : Data collection spanned over two weeks from January 10 to January 24, 2024.
• Software : SPSS version 26.
• Techniques : Descriptive statistics, cross-tabulations, and chi-square tests.

Example 2: Experimental Methodology

This experiment aimed to evaluate the effectiveness of a new teaching method on students’ performance. The experimental methodology comprised the following steps:

• Selection : 100 high school students from School Y.
• Grouping : Randomly assigned to control (n=50) and experimental (n=50) groups.
• Pre-test : Administered to both groups to assess initial knowledge levels.
• Intervention : The experimental group received the new teaching method, while the control group continued with the traditional method for six weeks.
• Post-test : Conducted to measure knowledge acquisition and retention.
• Teaching Aids : Interactive multimedia tools for the experimental group.
• Traditional Tools : Textbooks and lectures for the control group.
• Software : R programming language.
• Techniques : T-tests to compare pre-test and post-test scores between groups.

Example 3: Qualitative Methodology

For this research, we utilized a qualitative methodology to explore the experiences of healthcare workers during the pandemic. The methodology included:

• Selection : 30 healthcare workers from various hospitals.
• Sampling Technique : Purposive sampling to ensure diverse perspectives.
• Interviews : Semi-structured interviews conducted in-person and via Zoom.
• Duration : Each interview lasted approximately 45-60 minutes.
• Recording : With participants’ consent, interviews were a-recorded and transcribed verbatim.
• Approach : Thematic analysis.
• Software : NVivo for coding and organizing themes.
• Validation : Member checking and peer debriefing to ensure credibility.

Example 4: Case Study Methodology

In this case study, we investigated the implementation of a new software system in Company Z. The methodology involved:

• Criteria : Companies that recently implemented the software within the past year.
• Company Profile : Medium-sized company with 200 employees.
• Interviews : Conducted with key stakeholders including IT staff, managers, and end-users.
• Documents : Analysis of company reports, project plans, and user feedback forms.
• Observations : On-site visits to observe the software in use.
• Techniques : Triangulation to corroborate findings from multiple sources.
• Framework : SWOT analysis to identify strengths, weaknesses, opportunities, and threats related to the software implementation.

Example 5: Mixed-Methods Methodology

This mixed-methods study examined the impact of remote work on employee productivity and well-being. The methodology comprised both quantitative and qualitative components:

• Survey : Online survey with Likert-scale questions administered to 300 employees.
• Analysis : Regression analysis to identify factors affecting productivity.
• Focus Groups : Three focus groups with 8-10 participants each to discuss remote work experiences.
• Thematic Analysis : Coding and theme development using Atlas.ti.
• Data Triangulation : Combined findings from both quantitative and qualitative data to provide a comprehensive understanding of the impact of remote work.

Quantitative Methodology Examples

• Survey Research : Conducting a large-scale survey to collect numerical data on consumer preferences.
• Experimental Design : Implementing a controlled experiment to test the effects of a new drug on patient recovery rates.
• Cross-Sectional Study : Analyzing data from different population groups at a single point in time to identify correlations.
• Longitudinal Study : Tracking the same group of individuals over several years to observe changes in health outcomes.
• Secondary Data Analysis : Using existing datasets from government databases to analyze employment trends.
• Quasi-Experimental Design : Comparing outcomes between a group receiving an intervention and a non-randomized control group.
• Descriptive Statistics : Summarizing and describing the main features of a dataset using measures such as mean, median, and mode.
• Regression Analysis : Investigating the relationship between independent variables and a dependent variable to predict outcomes.
• Correlation Study : Measuring the strength and direction of the relationship between two variables, such as income and education level.
• Time Series Analysis : Analyzing data points collected or recorded at specific time intervals to identify trends over time.

Types of Methodology

1. qualitative methodology.

This involves collecting non-numerical data to understand concepts, opinions, or experiences. Methods include:

• Interviews : Conducting one-on-one conversations to gather detailed insights.
• Focus Groups : Facilitating group discussions to explore a specific topic.
• Observations : Watching and recording behaviors in a natural setting.

2. Quantitative Methodology

This focuses on numerical data and statistical analysis. Methods include:

• Surveys : Using questionnaires to collect data from a large number of respondents.
• Experiments : Conducting controlled tests to determine cause-and-effect relationships.
• Secondary Data Analysis : Analyzing existing data collected by other researchers.

3. Mixed Methods

This combines both qualitative and quantitative approaches. It provides a comprehensive understanding by integrating diverse data sources.

• Sequential Explanatory Design : Collecting and analyzing quantitative data first, followed by qualitative data to explain the quantitative results.
• Concurrent Triangulation : Collecting both types of data simultaneously to cross-verify findings.

4. Case Study Methodology

This involves an in-depth study of a particular case within a real-world context. Methods include:

• Document Analysis : Reviewing existing documents related to the case.
• Interviews : Gathering detailed information from individuals involved in the case.
• Observations : Observing the case in its natural setting to gather contextual data.

5. Ethnographic Methodology

This focuses on studying cultures and communities. Methods include:

• Participant Observation : Engaging with the community while observing their behaviors and interactions.
• Field Notes : Recording detailed notes of observations and experiences in the field.
• Interviews : Conducting interviews with community members to gain deeper insights.

Each of these methodologies provides a different approach to research, helping researchers to choose the most appropriate method for their specific study objectives.

Importance of Methodology in Research

1. ensures research validity and reliability.

• Validity : Methodology ensures that the research measures what it is intended to measure. It guarantees that the results accurately represent the phenomenon being studied.
• Reliability : It ensures consistency in the research results. Reliable methodologies produce stable and consistent results over repeated trials.

2. Provides a Clear Research Framework

• Structured Process : Methodology provides a detailed plan outlining the steps involved in the research process. This structure helps researchers stay organized and focused.
• Replicability : A well-defined methodology allows other researchers to replicate the study, verifying results and contributing to the body of knowledge.

3. Enhances Credibility and Objectivity

• Transparency : Clearly documenting the research methodology enhances the transparency of the study, allowing others to understand how data was collected and analyzed.
• Objectivity : By following a systematic approach, methodology minimizes biases and ensures objective analysis and interpretation of data.

4. Facilitates Data Collection and Analysis

• Appropriate Tools and Techniques : Methodology helps in selecting the most suitable tools and techniques for data collection and analysis, ensuring accurate and relevant data is gathered.
• Efficient Analysis : With a clear methodological framework, data analysis becomes more efficient, leading to valid conclusions and insights.

5. Supports Theory Development and Hypothesis Testing

• Theory Development : Methodologies, particularly in qualitative research, help in developing new theories based on observed patterns and themes.
• Hypothesis Testing : In quantitative research, methodologies are crucial for testing hypotheses, allowing researchers to confirm or refute their assumptions.

Synonyms of Methodology

How to write a Methodology

1. introduction.

Begin with a brief overview of the research problem and objectives. Explain why the chosen methodology is appropriate for addressing the research question.

2. Research Design

Describe the overall approach of your study:

• Qualitative , Quantitative , or Mixed Methods .
• Provide a rationale for your choice.

3. Data Collection Methods

Detail the specific methods you will use to collect data:

• Surveys : Include information about the type of survey, sample size, and how respondents are selected.
• Interviews : Describe the format (structured, semi-structured, or unstructured), and the selection process for participants.
• Observations : Explain what will be observed, the context, and how observations will be recorded.
• Experiments : Outline the experimental design, control variables, and the procedure.

4. Data Analysis Methods

Explain how you will analyze the collected data:

• Quantitative Analysis : Statistical tests, software used, and how you will ensure reliability and validity.
• Qualitative Analysis : Coding processes, thematic analysis, or other methods used to interpret data.

5. Sampling

Describe your sampling strategy:

• Population : Define the population from which your sample will be drawn.
• Sample Size : Justify the size of your sample.
• Sampling Technique : Explain whether you will use random sampling, stratified sampling, convenience sampling, etc.

6. Ethical Considerations

Detail how you will address ethical issues:

• Informed Consent : How you will obtain and document consent from participants.
• Confidentiality : Measures to protect the privacy of participants.
• Approval : Mention any institutional review board (IRB) or ethics committee approvals.

7. Limitations

Acknowledge potential limitations of your methodology:

• Discuss possible weaknesses and how they may impact your results.
• Explain steps you will take to mitigate these limitations.

8. Conclusion

Summarize the key points of your methodology. Reinforce why your chosen methods are the best fit for your research objectives.

FAQ’s

What are qualitative methods.

Qualitative methods involve non-numerical data collection, like interviews and observations, to understand concepts, opinions, or experiences.

What are quantitative methods?

Quantitative methods involve numerical data collection and statistical analysis to identify patterns, relationships, or trends.

What is a mixed-methods approach?

A mixed-methods approach combines qualitative and quantitative methods to provide a comprehensive analysis.

How do you choose a methodology?

Choosing a methodology depends on the research question, objectives, and the type of data needed.

What is a research design?

Research design is the framework that guides the collection and analysis of data, ensuring the research question is effectively addressed.

What is the difference between methodology and methods?

Methodology refers to the overall approach and rationale, while methods are specific techniques used for data collection and analysis.

What is a case study?

A case study is an in-depth examination of a particular instance, event, or individual to explore or illustrate broader principles.

What is an experiment in research?

An experiment involves manipulating variables to determine their effect on other variables, establishing cause-and-effect relationships.

What is a survey?

A survey is a data collection method using questionnaires or interviews to gather information from a large group.

What is sampling in research?

Sampling is selecting a subset of a population to represent the whole, ensuring the study’s findings are generalizable.

Text prompt

• Instructive
• Professional

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