sample dissertation using secondary data

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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15 Secondary Research Examples

Dave Cornell (PhD)

Dr. Cornell has worked in education for more than 20 years. His work has involved designing teacher certification for Trinity College in London and in-service training for state governments in the United States. He has trained kindergarten teachers in 8 countries and helped businessmen and women open baby centers and kindergartens in 3 countries.

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Chris Drew (PhD)

This article was peer-reviewed and edited by Chris Drew (PhD). The review process on Helpful Professor involves having a PhD level expert fact check, edit, and contribute to articles. Reviewers ensure all content reflects expert academic consensus and is backed up with reference to academic studies. Dr. Drew has published over 20 academic articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education and holds a PhD in Education from ACU.

Secondary research is the analysis, summary or synthesis of already existing published research. Instead of collecting original data, as in primary research , secondary research involves data or the results of data analyses already collected.

It is generally published in books, handbooks, textbooks, articles, encyclopedias, websites, magazines, literature reviews and meta-analyses. These are usually referred to as secondary sources .

Secondary research is a good place to start when wanting to acquire a broad view of a research area. It is usually easier to understand and may not require advanced training in research design and statistics.

Secondary Research Examples

1. literature review.

A literature review summarizes, reviews, and critiques the existing published literature on a topic.

Literature reviews are considered secondary research because it is a collection and analysis of the existing literature rather than generating new data for the study.

They hold value for academic studies because they enable us to take stock of the existing knowledge in a field, evaluate it, and identify flaws or gaps in the existing literature. As a result, they’re almost universally used by academics prior to conducting primary research.

Example 1: Workplace stress in nursing: a literature review

Citation: McVicar, A. (2003). Workplace stress in nursing: a literature review.  Journal of advanced nursing ,  44 (6), 633-642. Source: https://doi.org/10.1046/j.0309-2402.2003.02853.x

Summary: This study conducted a systematic analysis of literature on the causes of stress for nurses in the workplace. The study explored the literature published between 2000 and 2014. The authors found that the literature identifies several main causes of stress for nurses: professional relationships with doctors and staff, communication difficulties with patients and their families, the stress of emergency cases, overwork, lack of staff, and lack of support from the institutions. They conclude that understanding these stress factors can help improve the healthcare system and make it better for both nurses and patients.

Example 2: The impact of shiftwork on health: a literature review

Citation: Matheson, A., O’Brien, L., & Reid, J. A. (2014). The impact of shiftwork on health: a literature review.  Journal of Clinical Nursing ,  23 (23-24), 3309-3320. Source: https://doi.org/10.1111/jocn.12524

In this literature review, 118 studies were analyzed to examine the impact of shift work on nurses’ health. The findings were organized into three main themes: physical health, psychosocial health, and sleep. The majority of shift work research has primarily focused on these themes, but there is a lack of studies that explore the personal experiences of shift workers and how they navigate the effects of shift work on their daily lives. Consequently, it remains challenging to determine how individuals manage their shift work schedules. They found that, while shift work is an inevitable aspect of the nursing profession, there is limited research specifically targeting nurses and the implications for their self-care.

Example 3: Social media and entrepreneurship research: A literature review

Citation: Olanrewaju, A. S. T., Hossain, M. A., Whiteside, N., & Mercieca, P. (2020). Social media and entrepreneurship research: A literature review.  International Journal of Information Management ,  50 , 90-110. Source: https://doi.org/10.1016/j.ijinfomgt.2019.05.011

In this literature review, 118 studies were analyzed to examine the impact of shift work on nurses’ health. The findings were organized into three main themes: physical health, social health , and sleep. The majority of shift work research has primarily focused on these themes, but there is a lack of studies that explore the personal experiences of shift workers and how they navigate the effects of shift work on their daily lives. Consequently, it remains challenging to determine how individuals manage their shift work schedules. They found that, while shift work is an inevitable aspect of the nursing profession, there is limited research specifically targeting nurses and the implications for their self-care.

Example 4: Adoption of electric vehicle: A literature review and prospects for sustainability

Citation: Kumar, R. R., & Alok, K. (2020). Adoption of electric vehicle: A literature review and prospects for sustainability.  Journal of Cleaner Production ,  253 , 119911. Source: https://doi.org/10.1016/j.jclepro.2019.119911

This study is a literature review that aims to synthesize and integrate findings from existing research on electric vehicles. By reviewing 239 articles from top journals, the study identifies key factors that influence electric vehicle adoption. Themes identified included: availability of charging infrastructure and total cost of ownership. The authors propose that this analysis can provide valuable insights for future improvements in electric mobility.

Example 5: Towards an understanding of social media use in the classroom: a literature review

Citation: Van Den Beemt, A., Thurlings, M., & Willems, M. (2020). Towards an understanding of social media use in the classroom: a literature review.  Technology, Pedagogy and Education ,  29 (1), 35-55. Source: https://doi.org/10.1080/1475939X.2019.1695657

This study examines how social media can be used in education and the challenges teachers face in balancing its potential benefits with potential distractions. The review analyzes 271 research papers. They find that ambiguous results and poor study quality plague the literature. However, they identify several factors affecting the success of social media in the classroom, including: school culture, attitudes towards social media, and learning goals. The study’s value is that it organizes findings from a large corpus of existing research to help understand the topic more comprehensively.

2. Meta-Analyses

Meta-analyses are similar to literature reviews, but are at a larger scale and tend to involve the quantitative synthesis of data from multiple studies to identify trends and derive estimates of overall effect sizes.

For example, while a literature review might be a qualitative assessment of trends in the literature, a meta analysis would be a quantitative assessment, using statistical methods, of studies that meet specific inclusion criteria that can be directly compared and contrasted.

Often, meta-analysis aim to identify whether the existing data can provide an authoritative account for a hypothesis and whether it’s confirmed across the body of literature.

Example 6: Cholesterol and Alzheimer’s Disease Risk: A Meta-Meta-Analysis

Citation: Sáiz-Vazquez, O., Puente-Martínez, A., Ubillos-Landa, S., Pacheco-Bonrostro, J., & Santabárbara, J. (2020). Cholesterol and Alzheimer’s disease risk: a meta-meta-analysis.  Brain sciences ,  10 (6), 386. Source: https://doi.org/10.3390/brainsci10060386

This study examines the relationship between cholesterol and Alzheimer’s disease (AD). Researchers conducted a systematic search of meta-analyses and reviewed several databases, collecting 100 primary studies and five meta-analyses to analyze the connection between cholesterol and Alzheimer’s disease. They find that the literature compellingly demonstrates that low-density lipoprotein cholesterol (LDL-C) levels significantly influence the development of Alzheimer’s disease, but high-density lipoprotein cholesterol (HDL-C), total cholesterol (TC), and triglycerides (TG) levels do not show significant effects. This is an example of secondary research because it compiles and analyzes data from multiple existing studies and meta-analyses rather than collecting new, original data.

Example 7: The power of feedback revisited: A meta-analysis of educational feedback research

Citation: Wisniewski, B., Zierer, K., & Hattie, J. (2020). The power of feedback revisited: A meta-analysis of educational feedback research.  Frontiers in Psychology ,  10 , 3087. Source: https://doi.org/10.3389/fpsyg.2019.03087

This meta-analysis examines 435 empirical studies research on the effects of feedback on student learning. They use a random-effects model to ascertain whether there is a clear effect size across the literature. The authors find that feedback tends to impact cognitive and motor skill outcomes but has less of an effect on motivational and behavioral outcomes. A key (albeit somewhat obvious) finding was that the manner in which the feedback is provided is a key factor in whether the feedback is effective.

Example 8: How Much Does Education Improve Intelligence? A Meta-Analysis

Citation: Ritchie, S. J., & Tucker-Drob, E. M. (2018). How much does education improve intelligence? A meta-analysis.  Psychological science ,  29 (8), 1358-1369. Source: https://doi.org/10.1177/0956797618774253

This study investigates the relationship between years of education and intelligence test scores. The researchers analyzed three types of quasiexperimental studies involving over 600,000 participants to understand if longer education increases intelligence or if more intelligent students simply complete more education. They found that an additional year of education consistently increased cognitive abilities by 1 to 5 IQ points across all broad categories of cognitive ability. The effects persisted throughout the participants’ lives, suggesting that education is an effective way to raise intelligence. This study is an example of secondary research because it compiles and analyzes data from multiple existing studies rather than gathering new, original data.

Example 9: A meta-analysis of factors related to recycling

Citation: Geiger, J. L., Steg, L., Van Der Werff, E., & Ünal, A. B. (2019). A meta-analysis of factors related to recycling.  Journal of environmental psychology ,  64 , 78-97. Source: https://doi.org/10.1016/j.jenvp.2019.05.004

This study aims to identify key factors influencing recycling behavior across different studies. The researchers conducted a random-effects meta-analysis on 91 studies focusing on individual and household recycling. They found that both individual factors (such as recycling self-identity and personal norms) and contextual factors (like having a bin at home and owning a house) impacted recycling behavior. The analysis also revealed that individual and contextual factors better predicted the intention to recycle rather than the actual recycling behavior. The study offers theoretical and practical implications and suggests that future research should examine the effects of contextual factors and the interplay between individual and contextual factors.

Example 10: Stress management interventions for police officers and recruits

Citation: Patterson, G. T., Chung, I. W., & Swan, P. W. (2014). Stress management interventions for police officers and recruits: A meta-analysis.  Journal of experimental criminology ,  10 , 487-513. Source: https://doi.org/10.1007/s11292-014-9214-7

The meta-analysis systematically reviews randomized controlled trials and quasi-experimental studies that explore the effects of stress management interventions on outcomes among police officers. It looked at 12 primary studies published between 1984 and 2008. Across the studies, there were a total of 906 participants. Interestingly, it found that the interventions were not effective. Here, we can see how secondary research is valuable sometimes for showing there is no clear trend or consensus in existing literature. The conclusions suggest a need for further research to develop and implement more effective interventions addressing specific stressors and using randomized controlled trials.

3. Textbooks

Academic textbooks tend not to present new research. Rather, they present key academic information in ways that are accessible to university students and academics.

As a result, we can consider textbooks to be secondary rather than primary research. They’re collections of information and research produced by other people, then re-packaged for a specific audience.

Textbooks tend to be written by experts in a topic. However, unlike literature reviews and meta-analyses, they are not necessarily systematic in nature and are not designed to progress current knowledge through identifying gaps, weaknesses, and strengths in the existing literature.

Example 11: Psychology for the Third Millennium: Integrating Cultural and Neuroscience Perspectives

This textbook aims to bridge the gap between two distinct domains in psychology: Qualitative and Cultural Psychology , which focuses on managing meaning and norms, and Neuropsychology and Neuroscience, which studies brain processes. The authors believe that by combining these areas, a more comprehensive general psychology can be achieved, which unites the biological and cultural aspects of human life. This textbook is considered a secondary source because it synthesizes and integrates information from various primary research studies, theories, and perspectives in the field of psychology.

Example 12: Cultural Sociology: An Introduction

Citation: Bennett, A., Back, L., Edles, L. D., Gibson, M., Inglis, D., Jacobs, R., & Woodward, I. (2012).  Cultural sociology: an introduction . New York: John Wiley & Sons.

This student textbook introduces cultural sociology and proposes that it is a valid model for sociological thinking and research. It gathers together existing knowledge within the field to prevent an overview of major sociological themes and empirical approaches utilized within cultural sociological research. It does not present new research, but rather packages existing knowledge in sociology and makes it understandable for undergraduate students.

Example 13: A Textbook of Community Nursing

Citation: Chilton, S., & Bain, H. (Eds.). (2017).  A textbook of community nursing . New York: Routledge.

This textbook presents an evidence-based introduction to professional topics in nursing. In other words, it gathers evidence from other research and presents it to students. It covers areas such as care approaches, public health, eHealth, therapeutic relationships, and mental health. Like many textbooks, it brings together its own secondary research with user-friendly elements like exercises, activities, and hypothetical case studies in each chapter.

4. White Papers

White papers are typically produced within businesses and government departments rather than academic research environments.

Generally, a white paper will focus on a specific topic of concern to the institution in order to present a state of the current situation as well as opportunities that could be pursued for change, improvement, or profit generation in the future.

Unlike a literature review, a white paper generally doesn’t follow standards of academic rigor and may be presented with a bias toward, or focus on, a company or institution’s mission and values.

Example 14: Future of Mobility White Paper

Citation: Shaheen, S., Totte, H., & Stocker, A. (2018). Future of Mobility White Paper.  UC Berkeley: Institute of Transportation Studies at UC Berkeley Source: https://doi.org/10.7922/G2WH2N5D

This white paper explores the how transportation is changing due to concerns over climate change, equity of access to transit, and rapid technological advances (such as shared mobility and automation). The authors aggregate current information and research on key trends, emerging technologies/services, impacts on California’s transportation ecosystem, and future growth projections by reviewing state agency publications, peer-reviewed articles, and forecast reports from various sources. This white paper is an example of secondary research because it synthesizes and integrates information from multiple primary research sources, expert interviews, and input from an advisory committee of local and state transportation agencies.

Example 15: White Paper Concerning Philosophy of Education and Environment

Citation: Humphreys, C., Blenkinsop, S. White Paper Concerning Philosophy of Education and Environment.  Stud Philos Educ   36 (1): 243–264. Source: https://doi.org/10.1007/s11217-017-9567-2

This white paper acknowledges the increasing significance of climate change, environmental degradation, and our relationship with nature, and the need for philosophers of education and global citizens to respond. The paper examines five key journals in the philosophy of education to identify the scope and content of current environmental discussions. By organizing and summarizing the located articles, it assesses the possibilities and limitations of these discussions within the philosophy of education community. This white paper is an example of secondary research because it synthesizes and integrates information from multiple primary research sources, specifically articles from the key journals in the field, to analyze the current state of environmental discussions.

5. Academic Essays

Students’ academic essays tend to present secondary rather than primary research. The student is expected to study current literature on a topic and use it to present a thesis statement.

Academic essays tend to require rigorous standards of analysis, critique, and evaluation, but do not require systematic investigation of a topic like you would expect in a literature review.

In an essay, a student may identify the most relevant or important data from a field of research in order to demonstrate their knowledge of a field of study. They may also, after demonstrating sufficient knowledge and understanding, present a thesis statement about the issue.

Secondary research involves data that has already been collected. The published research might be reviewed, included in a meta-analysis, or subjected to a re-analysis.

These findings might be published in a peer-reviewed journal or handbook, become the foundation of a book for public consumption, or presented in a more narrative form for a popular website or magazine.

Sources for secondary research can range from scientific journals to government databases and archived data accumulated by research institutes.

University students might engage in secondary research to become familiar with an area of research. That might help spark an intriguing hypothesis for a research project of master’s thesis.

Secondary research can yield new insights into human behavior , or confirm existing conceptualizations of psychological constructs.

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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.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Methodology

• What is Secondary Research? | Definition, Types, & Examples

What is Secondary Research? | Definition, Types, & Examples

Published on January 20, 2023 by Tegan George . Revised on January 12, 2024.

Secondary research is a research method that uses data that was collected by someone else. In other words, whenever you conduct research using data that already exists, you are conducting secondary research. On the other hand, any type of research that you undertake yourself is called primary research .

Secondary research can be qualitative or quantitative in nature. It often uses data gathered from published peer-reviewed papers, meta-analyses, or government or private sector databases and datasets.

Table of contents

When to use secondary research, types of secondary research, examples of secondary research, advantages and disadvantages of secondary research, other interesting articles, frequently asked questions.

Secondary research is a very common research method, used in lieu of collecting your own primary data. It is often used in research designs or as a way to start your research process if you plan to conduct primary research later on.

Since it is often inexpensive or free to access, secondary research is a low-stakes way to determine if further primary research is needed, as gaps in secondary research are a strong indication that primary research is necessary. For this reason, while secondary research can theoretically be exploratory or explanatory in nature, it is usually explanatory: aiming to explain the causes and consequences of a well-defined problem.

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Secondary research can take many forms, but the most common types are:

Statistical analysis

Literature reviews, case studies, content analysis.

There is ample data available online from a variety of sources, often in the form of datasets. These datasets are often open-source or downloadable at a low cost, and are ideal for conducting statistical analyses such as hypothesis testing or regression analysis .

Credible sources for existing data include:

• The government
• Government agencies
• Non-governmental organizations
• Educational institutions
• Businesses or consultancies
• Libraries or archives
• Newspapers, academic journals, or magazines

A literature review is a survey of preexisting scholarly sources on your topic. It provides an overview of current knowledge, allowing you to identify relevant themes, debates, and gaps in the research you analyze. You can later apply these to your own work, or use them as a jumping-off point to conduct primary research of your own.

Structured much like a regular academic paper (with a clear introduction, body, and conclusion), a literature review is a great way to evaluate the current state of research and demonstrate your knowledge of the scholarly debates around your topic.

A case study is a detailed study of a specific subject. It is usually qualitative in nature and can focus on  a person, group, place, event, organization, or phenomenon. A case study is a great way to utilize existing research to gain concrete, contextual, and in-depth knowledge about your real-world subject.

You can choose to focus on just one complex case, exploring a single subject in great detail, or examine multiple cases if you’d prefer to compare different aspects of your topic. Preexisting interviews , observational studies , or other sources of primary data make for great case studies.

Content analysis is a research method that studies patterns in recorded communication by utilizing existing texts. It can be either quantitative or qualitative in nature, depending on whether you choose to analyze countable or measurable patterns, or more interpretive ones. Content analysis is popular in communication studies, but it is also widely used in historical analysis, anthropology, and psychology to make more semantic qualitative inferences.

Secondary research is a broad research approach that can be pursued any way you’d like. Here are a few examples of different ways you can use secondary research to explore your research topic .

Secondary research is a very common research approach, but has distinct advantages and disadvantages.

Advantages of secondary research

Advantages include:

• Secondary data is very easy to source and readily available .
• It is also often free or accessible through your educational institution’s library or network, making it much cheaper to conduct than primary research .
• As you are relying on research that already exists, conducting secondary research is much less time consuming than primary research. Since your timeline is so much shorter, your research can be ready to publish sooner.
• Using data from others allows you to show reproducibility and replicability , bolstering prior research and situating your own work within your field.

Disadvantages of secondary research

Disadvantages include:

• Ease of access does not signify credibility . It’s important to be aware that secondary research is not always reliable , and can often be out of date. It’s critical to analyze any data you’re thinking of using prior to getting started, using a method like the CRAAP test .
• Secondary research often relies on primary research already conducted. If this original research is biased in any way, those research biases could creep into the secondary results.

Many researchers using the same secondary research to form similar conclusions can also take away from the uniqueness and reliability of your research. Many datasets become “kitchen-sink” models, where too many variables are added in an attempt to draw increasingly niche conclusions from overused data . Data cleansing may be necessary to test the quality of the research.

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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
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Inclusion and exclusion criteria

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

A systematic review is secondary research because it uses existing research. You don’t collect new data yourself.

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

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.

Sources in this article

We strongly encourage students to use sources in their work. You can cite our article (APA Style) or take a deep dive into the articles below.

George, T. (2024, January 12). What is Secondary Research? | Definition, Types, & Examples. Scribbr. Retrieved September 16, 2024, from https://www.scribbr.com/methodology/secondary-research/

Largan, C., & Morris, T. M. (2019). Qualitative Secondary Research: A Step-By-Step Guide (1st ed.). SAGE Publications Ltd.

Peloquin, D., DiMaio, M., Bierer, B., & Barnes, M. (2020). Disruptive and avoidable: GDPR challenges to secondary research uses of data. European Journal of Human Genetics , 28 (6), 697–705. https://doi.org/10.1038/s41431-020-0596-x

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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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• Next: Ethics >>
• Last Updated: Sep 14, 2022 12:58 PM
• URL: https://libguides.westminster.ac.uk/methodology-for-dissertations

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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?

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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).

TABLE 1 outlines the differences between primary and secondary 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.

TABLE 2 provides a full summary of advantages and disadvantages of secondary research:

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.

TABLE 5 summarises the two sources of secondary data and associated examples:

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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A complete guide to dissertation primary research

How to write a dissertation proposal, navigating tutorials with your dissertation supervisor, planning a dissertation: the dos and don’ts, dissertation research: how to find dissertation resources.

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• dissertation primary research
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Review our examples before placing an order, learn how to draft academic papers, significance and benefits of a secondary research dissertation.

What is Qualitative Research: Review Examples, Strategies and Benefits

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In your dissertation journey, secondary research is a secret weapon. It's all about tapping into the wisdom of others – from journals, newspapers, and books – to gather valuable information. Secondary research can be used to supplement primary research, or it can be used as the sole source of data in a dissertation.

Find Appealing Research Topics Here

In a secondary research dissertation, picking the right data sources is key. They should offer accurate information, enhance topic understanding, and broaden perspectives.

To better understand the dissertation writing pattern, review the following complete secondary research dissertation examples;

• The Abuse of Corporate Veil: A Comparative Analysis of Corporate Veil Lifting Approaches
• The Importance of Procurement Strategy & Impact on Construction Projects
• An Investigation of Cyberbullying and Its Impact on Adolescents’ Mental Health

This article covers secondary research's dissertation significance, its role in building strong arguments, and selecting suitable data sources.

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What is secondary research .

Secondary research uses existing sources to grasp a topic. It involves studying studies, articles, newspapers, and books. It's quicker than primary research, skipping data collection to focus on existing sources. 

Secondary sources , often by expert researchers, offer thorough coverage, benefiting from their primary research and field expertise.

Why is Secondary Research Important?

Secondary research is essential for any dissertation because it gives students the opportunity to draw on existing knowledge and build on the work of other scholars without having to conduct their original experiments or surveys.

By using secondary research instead of conducting original experiments or surveys, students can save time while still being able to access reliable information on their chosen topics.

Secondary sources broaden students' scope beyond just primary materials, enhancing argument strength. This integrates current literature with past expert findings for more robust dissertations.

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Data Analysis for Secondary Research Dissertation

1. understand your topic.

The first step in choosing the right data sources is clearly understanding your topic and what kind of information you need. Probe your objectives and required data. This trims down pertinent resources for your project.

2. Choose Relevant Sources

Once you have identified the type of information you need, it’s time to find relevant sources. Seek books, journals, sites, government documents with detailed information. Ensure they are recent and credible for up-to-date subject insight.

3. Types of Data Sources 

When it comes to researching secondary data, there are two main types of sources: 

•   Quantitative data refers to numerical values collected from surveys, experiments, or censuses. This data type is typically presented in numerical form (for example, percentages or averages).
• Qualitative data is non-numerical information gathered through interviews or focus groups. This type of data is more subjective than quantitative because it relies on individual opinions and experiences rather than objective measurements. 

4. Analyze Your Sources

For found sources, assess before using in your dissertation. Check author's expertise and possible biases. Pick suitable sources for your research paper after careful analysis.

Optimizing Secondary Research in Your Dissertation

A. understand your research question and objectives .

Before you begin searching for secondary sources, take some time to think about what kind of information you need. Consider the scope of your research question and the objectives of your dissertation project. It will help narrow down your search parameters.

b. Synthesize Information from Multiple Sources 

Blend sources into a coherent narrative backing your argument. Use pertinent details, not replacing primary research. Craft a logical, well-rounded argument with clear readability.

c. Citing Sources Properly  

When doing secondary research for your dissertation project, ensure you cite your sources properly according to the style guidelines set by your school or department (e.g., APA style). Respecting contributors and ensuring accuracy for cross-checking and inquiries about source material.

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The Advantages of Secondary Research 

The main benefit of utilizing secondary research in dissertations is its time and cost efficiency. Unlike primary research, which can be both time-consuming and expensive, secondary research relies on existing data that is readily available. This eliminates the need for data collection and expensive surveys.

Secondary research offers greater consistency than primary research, as the initial researcher addresses potential biases. This reduces variables, facilitating accurate conclusions. Moreover, secondary research can unveil unnoticed patterns, enhancing comprehension of the subject.

Finally, secondary research grants access to expert insights beyond your primary study group, enriching your dissertation and enhancing its credibility among academic peers.

Crafting an effective dissertation can be complex, but the right data sources could make all the difference. To help select quality information for your research needs, consider taking these key steps to ensure accuracy and thoroughness in writing. To better understand the use of secondary research and secondary data, study the Secondary Research Dissertation examples. 

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How to do your PhD Thesis Using Secondary Data Collection in 4 Steps

• Secondary research is far simpler. So simple that PhD assistance has been able to explain how to do it entirely in just four steps for PhD Research Methodology Secondary Data Collection .
• If nothing else, secondary research dodges the all-so-tiring exertions usually intricate with Primary Data Collection Methods.
• Like employing your participants, selecting and preparing your measures, and spending days are collecting your data.

Introduction:

Secondary research is a research method that contains using already existing information. Existing information is summarized and organized to increase the overall efficiency of research. Secondary data collection includes research material published in study reports and similar documents. These PhD Secondary Data Collection Resources can be made available by libraries, websites, information obtained from already filled in reviews etc. Some government and non-government interventions also store data, that can be used for study purposes and recovered. Unlike primary research where data is composed first hand by governments or businesses, they can employ a third party to gather data on their behalf in the Methodology of Secondary Data Collection .

Secondary data collection in 4 steps

1. frame your research question.

Secondary research starts exactly like any research: by building up your research question(s). For the Research Proposal , you are frequently given a particular research question by your guide. Yet, for most different sorts of examination, and mainly if you are doing your alumni proposition, you need to show up at a research question yourself. The initial step here is to determine the overall research territory where your examination will fall. Whenever you have distinguished your overall theme, your following stage comprises of perusing existing documents to see whether there is a break in the writing that your research can fill.

2.Recognize a Secondary Data Set

In the wake of looking into the writing and indicating your Research Methodology Secondary Data addresses, you may choose to depend on secondary data. You will do this if you find that past data would be entirely reusable in your research, accordingly assisting you with responding to your examination question all the more altogether. In any case, how would you find if some past data could be valuable for your research? You do this through inspecting the writing on your subject of interest. You will recognize different scientists, associations, organizations, or examination focuses on investigating your research theme during this interaction. Someplace there, you may find a helpful secondary data index. At that point, you need to contact the first creators and request consent to utilize their data. (Note, in any case, that this happens just if you depend on outside wellsprings of secondary research. If you are doing your examination inside (i.e., inside a specific association), you don’t have to look through the writing for a secondary data index – you can reuse some previous data gathered inside the actual association.) For any situation, you need to guarantee that a secondary data index is a solid match for your research question. Whenever you have set up that, you need to determine why you have chosen to depend on PhD Secondary Data collection services .

3. Estimate a Secondary Data Set

• What was the Point of the First Investigation?

While assessing secondary data, you first need to recognize the point of the first investigation. It is significant because the first creators’ objectives will have affected a few significant parts of their examination, including their populace of decision, test, utilized estimation devices, and the research’s general setting. During this progression, you additionally need to give close consideration to any distinctions in PhD Research Methodology Secondary Data inquiries between the first examination and your examination for quantitative secondary data collection methods. As we have discussed already, you will frequently find that the first investigation had an alternate examination question as a top priority. It is significant for you to indicate this distinction in Secondary Data Collection Methods.

• Who has gathered the data?

A further advance in assessing a secondary data index is to ask yourself who has gathered the data. To what organization were the creators partnered? Were the first creators sufficiently proficient at confiding in their research? For the most part, you need to acquire this data through short online pursuits.

• Which measures were utilized?

On the off chance that the investigation on which you are basing your examination was directed expertly, you can hope to approach all the fundamental data concerning this research. Unique creators ought to have archived all their example qualities, measures, methods, and conventions. This data can be acquired either in their last examination report or through reaching the creators straightforwardly. It is significant for you to understand what sort of data was gathered, which measures were utilized, and whether such actions were reliable and legitimate. You also need to remove the kind of data concluded, particularly the data pertinent for your research.

• When was the data gathered?

While assessing secondary data, you ought to likewise note when the data was gathered. The purpose behind this is straightforward: if the data was serene quite a while past, you might presume that it is obsolete. Furthermore, on the off chance that the information is outdated, at that point, why reuse it? In a perfect world, you need your secondary data gathered inside the most recent five years.

• What procedure was utilized to gather the data?

While assessing a secondary data collection’s nature, the utilized approach’s assessment might be the critical advance. We have just noticed that you need to evaluate the dependability and legitimacy of used measures. Moreover, you need to assess how the example, regardless of whether the standard was adequately enormous. Suppose the example was illustrative of the populace, if there were any missing reactions on utilized measures, whether confounders were slow for, and whether the utilized factual investigations were suitable. Any disadvantages in the first technique may restrict your examination too.

• Making the last assessment

Having considered all the things illustrated in the means above, what would you be able to finish up concerning the nature of your secondary data collection? Once more, how about we think about our three models. We would reason that the secondary data from our first examination model has a high calibre. As of late gathered by experts, the utilized measures were both dependable and substantial.

4. Make and Evaluate Secondary data

During the secondary data assessment measure, you will acquaint yourself with the first research. Your subsequent stage is to set up a secondary data index. Your last advance comprises of dissecting the data. You will consistently have to settle on the most appropriate investigation strategy for your secondary data index for Qualitative Secondary Data in Research Methodology.

Conclusion:

The process of preparing and analyzing a secondary data set is slightly different if your secondary data is qualitative.  So simple that PhD assistance has explained how to do it entirely in just four steps and provides Secondary Quantitative Data Collection .

References:

• Johnston, M. P. (2017). Secondary data analysis: A method of which the time has come.  Qualitative and quantitative methods in libraries ,  3 (3), 619-626.
• Smith, E., & Smith Jr, J. (2008).  Using secondary data in educational and social research . McGraw-Hill Education (UK).
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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.

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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.

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A Four-Step Guide to Conducting Secondary Research For Your Dissertation

Secondary research is a useful strategy to obtain data and support your ideas when conducting research for your dissertation. It will always be challenging to write a large project like a dissertation all by yourself (professional essay service, 2019). In dissertation secondary research, a specific study subject or topic is investigated utilizing published data sources, such as books, journals, and internet databases. Although conducting secondary research may seem simple, it necessitates a systematic approach to guarantee that you locate and utilize the most relevant and trustworthy sources.

What Secondary Research Is Used For?

One of the main purposes of secondary research is to thoroughly grasp the body of material that already exists on a certain topic. It frequently serves as a framework for the study and helps contextualize a research topic or hypothesis. Researchers can also find gaps in the literature and areas that need more inquiry by using secondary sources.

Another purpose of secondary research is, secondary research can provide useful insights into the research methodology and analytical techniques employed by other researchers, which can inform the design and implementation of the current study.

Types Of Secondary Research

Dissertation secondary research can be split into two major categories: qualitative and quantitative. While quantitative research is used to gather and analyze numerical data, qualitative research examines individuals’ or groups’ subjective experiences and viewpoints. Other sorts of secondary research fall under these broad categories, including:

literature review: A literature review critically evaluates the body of writing already written about a certain subject. It entails locating, analysing, and synthesizing the pertinent literature to present a thorough overview of the subject field.

Meta-analysis: A meta-analysis is a statistical procedure that combines the findings of several studies to produce a more accurate assessment of the magnitude of an intervention’s or variable’s impact.

Systematic review: Reviewing the literature on a certain topic systematically is a disciplined and organized process in PhD dissertation . It entails formulating an inclusion and exclusion criterion, creating a research topic, then looking up and synthesizing the pertinent literature.

Content analysis: It is a technique for examining written or visual content to spot themes, patterns, and trends.

Historical analysis: Historical analysis is the process of looking at old records or artifacts to understand historical occurrences or social phenomena.

Recognizing these types of overconfidence bias can help individuals become more self-aware and take steps to reduce their impact on decision-making.

Secondary Research Benefits

Compared to primary research methods, there are numerous advantages of secondary research. First off, you can easily find dissertation help in UK . Second, because secondary research includes analyzing existing data rather than gathering new data, it frequently takes less time and costs less money than primary research. Second, by using a variety of sources and disciplines, secondary research can provide a topic with a broader perspective. Thirdly, by conducting secondary research, scholars can steer clear of duplicating prior findings or committing the same errors. Finally, by highlighting areas that need additional research, secondary research can serve as a foundation for subsequent studies.

Difficulties With Secondary Research

Secondary research has several drawbacks despite its benefits. First off, because it depends on the veracity and validity of the material that is already available, secondary research is frequently prone to bias. Second, the calibre and amount of the available types of secondary data may be a constraint for secondary research.

Guide To Conducting A Secondary Research

Here is the secondary research process in 4 steps describes briefly.

Step 2: Select Pertinent Sources

You must choose pertinent dissertation secondary sources after defining your study question. You can conduct secondary research using a range of sources, such as books, journals, online databases, and government papers. Finding answers to open-ended issues as a way of learning and/or developing new knowledge is a part of the research, in addition to simply acquiring information (Goddard, Melville, 2001).

There are many different search techniques you can employ to find relevant dissertation secondary sources. Using keywords related to your research subject to search internet databases and library catalogs is a typical strategy. You can also search for books and articles that are mentioned in the materials you’ve already found.

Step 3: Consider The Sources

After you have found probable sources, you must assess them to see if they are pertinent, trustworthy, and reliable. You can assess sources using a variety of factors, such as the author’s qualifications, the source’s publication date, and its reputation.

It’s crucial to remember that not all sources are created equal while examining them during creating dissertation using only secondary data. Other sources could be out-of-date or irrelevant to your research issue, while some could be prejudiced or unreliable. A source’s context should also be taken into account because it may have an impact on its credibility and applicability.

Step 4: Review And Combine Sources

Analysis and synthesis of the sources you have chosen are the last steps in secondary research. This entails carefully reading each source and making notes on the most important conclusions, points, and supporting details.

You should search for trends and connections among your sources as you study them when you are creating dissertation using only secondary data. Additionally, you should evaluate how the sources you have chosen support or refute your research question and thesis.

You can start combining your sources into a coherent argument after you have evaluated them. This entails determining the key ideas and points that are supported by your sources and utilizing them to strengthen your own argument.

A crucial step in writing a dissertation is conducting secondary research. You can create a compelling argument for your position and prove your subject-matter expertise by using a methodical strategy to locate, assess, and analyze information. You can do efficient secondary research that will aid in the creation of your dissertation by establishing your research question, discovering pertinent sources, assessing these sources, and analyzing and synthesizing your findings.

What Does Dissertation Secondary Research Entail?

In a dissertation, secondary research entails acquiring data from already published sources, including scholarly works, governmental papers, statistical data, and other publically accessible sources or getting secondary data collection help . This research technique entails going through and examining the information that has previously been gathered by others. Secondary research can shed light on a subject and serve to put primary research findings into context. It can also support the development of research questions by highlighting knowledge gaps

How Does Secondary Research For A Dissertation Get Done?

• Determine your research. This will assist you in locating the pertinent sources and information you require to respond to your research inquiry.
• Select pertinent sources. Academic journals, publications, official reports, statistical databases, and other publicly accessible sources may be among them.
• Assess the reliability and applicability of the sources. Look for reliable, current, and pertinent sources that address your research question.
• Examine and assess the information. Consider how important themes and trends are related to your research question after identifying them.
• Summarize the results. Think about how the results relate to the body of prior research on the subject.
• Clearly cite all of your sources. Ensure that you offer comprehensive and correct information for each source, and adhere to the citation style that is advised by your school.

Which Four Kinds Of Secondary Research Are There?

• Literature Review: In this sort of study, the current academic literature on a particular subject is reviewed
• Data Analysis: To address a research topic, data analysis entails examining already-existing quantitative data, such as statistical data, survey results, or market research data.
• Historical Research: To comprehend the historical background of a research subject, historical research entails examining current documents, archives, and other primary materials.
• Case Study Research: To shed light on a research subject, case study research involves examining past case studies and frequently in-depth investigations of a certain company or circumstance.

Which Of The Secondary Research Examples Is The Best?

The precise research issue, the information and resources at hand, and the appropriate secondary research methodology dissertation example will all be determined by these factors. However, completing a literature review is a typical instance of secondary research. A researcher might do a literature review to find studies that have looked into the usefulness of cognitive behavioural therapy (CBT) for treating depression, for instance. The literature study may involve reading books on the subject, examining government reports and other publically accessible sources of information, and searching academic databases for pertinent publications.

Disclaimer: Kindly note that the work we provide is not the final version, it is research based work which you have to incorporate and edit according to your university requirements.

How to... Use secondary data & archival material

Find out what secondary data is – as opposed to primary data – and how to go about collecting and using it.

On this page

What is secondary data & archival material, using published data sets, using archival data, secondary data as part of the research design, gaining access to, and using, archives, primary & secondary data.

All research will involve the collection of data. Much of this data will be collected directly through some form of interaction between the researcher and the people or organisation concerned, using such methods as interviews, focus groups, surveys and participant observation. Such methods involve the collection of primary data, and herein lies the opportunity for the researcher to develop and demonstrate the greatest skill.

However sometimes the researcher will use data which has already been collected for other purposes – in other words, he or she is going to an existing source rather than directly interacting with people. The data may have been:

• Deliberately collected and analysed, for example for some official survey such as the  UK Labour Market Trends  (now published as  Economic & Labour Market Review (ELMR) ) or  General Household Survey .
• Created in a more informal sense as a record of people's activities, for example, letters or other personal items, household bills, company records, etc. At some point, they may have been deliberately collected and organised into an archive.

Either way, such material is termed secondary data.

Rather confusingly, the latter form of secondary data is also referred to as primary source material.

"Primary resources are sources that are usually created at the time of an event. Primary resources are the direct evidence or first hand accounts of historical events without secondary analysis or interpretation." (York University Libraries Archival Research Tutorial)

This distinguishes them from secondary sources which describe, analyse and refer to the primary sources.

The above definitions and distinctions can be described diagrammatically as follows:

Types of secondary data

Secondary data is found in print or electronic form, if the latter, on CD-ROM, as an online computer database, or on the Internet. Furthermore, it can be in the form of statistics collected by governments, trade associations, organisations that exist to collect and sell statistical data, or just as plain documents in archives or company records.

A crucial distinction is whether or not the data has been interpreted, or whether it exists in raw form.

• Raw data, also referred to as documentary or archival data, will exist in the form in which it was originally intended, for example meeting minutes, staff records, reports on new markets, accounts of sales of goods/services etc.
• Interpreted data, which may also be referred to as survey data, will have been collected for a particular purpose, for example, to analyse spending patterns.

Because interpreted data will have been collected deliberately, the plan behind its collection and interpretation will also have been deliberate – that is, it will have been subjected to a particular research design. 

By contrast, raw data will not have been processed, and will exist in its original form. (See " Using archival data " section in this guide.)

When and why to use secondary data

There are various reasons for using secondary data:

• A particularly good collection of data already exists.
• You are doing a historical study – that is, your study begins and ends at a particular point in time.
• You are covering an extended period, and analysing development over that period – a longitudinal study.
• The unit that you are studying may be difficult, or simply too large, to study directly.
• You are doing a case study of a particular organisation/industry/area, and it is important to look at the relevant documents.

You should pay particular attention to the place of secondary documents within your research design. How prominent a role you give to this method may depend on your subject: for example, if you are researching in the area of accounting, finance or business history, secondary documentary sources are likely to play an important part. Otherwise, use of secondary data is likely to play a complementary part in your research design. For example, if you are studying a particular organisation, you would probably want to supplement observation/interviews with a look at particular documents produced by that organisation.

In " Learning lessons? The registration of lobbyists at the Scottish parliament " ( Journal of Communication Management , Vol. 10 No. 1), the author uses archival research at the Scottish parliament as a supplementary research method (along with the media and focus groups), his main method being interviews and participant observation of meetings.

This point is further developed in the " Secondary data as part of the research design " section of this guide. Reasons for using the different types of secondary data are further developed in the individual sections.

NB  If you are doing a research project/dissertation/thesis, check your organisation's view of secondary data. Some organisations may require you to use primary data as your principle research method.

Advantages and disadvantages of secondary data collection

The advantages of using secondary data are:

• The fact that much information exists in documented form – whether deliberately processed or not – means that such information cannot be ignored by the researcher, and generally saves time and effort collecting data which would otherwise have to be collected directly. In particular:
• Many existing data sets are enormous, and far greater than the researcher would be able to collect him or herself, with a far larger sample.
• The data may be particularly good quality, which can apply both to archival data (e.g. a complete collection of records on a particular topic) and to published data sets, particularly those which come from a government source, or from one of the leading commercial providers of data and statistics.
• You can access information which you may otherwise have had to secure in a more obtrusive manner.
• Existence of a large amount of data can facilitate different types of analysis, such as:
• longitudinal or international analysis of information which would have otherwise been difficult to collect due to scale.
• manipulation of data within the particular data set, including the comparison of particular subsets.
• Unforseen discoveries can be made – for example, the link between smoking and lung cancer was made by analysing medical records.

The disadvantages of secondary data collection are:

• There may be a cost to acquiring the data set.
• You will need to familiarise yourself with the data, and if you are dealing with a large and complex data set, it will be hard to manage.
• The data may not match the research question: there may be too much data, or there may be gaps, or the data may have been collected for a completely different purpose.
• The measures, for example between countries/states/historical periods, may not be directly comparable. (See the " Secondary data as part of the research design " section of this guide for a further development of this topic.)
• The researcher has no control over the quality of the data, which may not be seen as rigorous and reliable as data which are specifically collected by the researcher, who has adopted a specific research design for the question.
• Collecting primary data builds up more research skills than collecting secondary data.
• Company data particularly may be seen as commercially sensitive, and it may be difficult to gain access to company archives, which may be stored in different departments or on the company intranet, to which access may be difficult.  

What are they?

As discussed in the previous section, these are sources of data which have already been collected and worked on by someone else, according to a particular research design. Other points to note are:

• Mostly they will have been collect by means of a survey, which may be:
• a census, which is an "official count", normally carried out by the government, with obligatory participation, for example the UK population censuses carried out every ten years
• a repeated survey, which involves collecting information at regular intervals, for example government surveys about household expenditure
• an ad hoc survey, done just once for a particular purpose, such as for example a market research survey.
• Interpreted data as referring to a particular social unit is termed a data set.
• A database is a structured data set, produced as a matrix with each social unit having a row, and each variable a column.
• Sometimes, different data sets are combined to produce multiple source secondary data: for example, the publication  Business Statistics of the United States: Patterns of Economic Change  contains data on virtually all aspects of the US economy from 1929 onwards. Such multiple source data sets may have been compiled on:
• a time series basis, that is they are based on repeated surveys (see above) or on comparable variables from different surveys to provide longitudinal data
• a geographical basis, providing information on different areas.

Key considerations

There are a number of points to consider when using data sets, some practical and others associated with the research design (yours and theirs).

Practical considerations relate to cost and use:

• Whilst much data is freely available, there may be a charge. For example,  Business Statistics of the United States: Patterns of Economic Change  is priced US$147. So, when deciding what data to use it's a good idea to check what's already in your library.
• Is the data available in computerised form, or will you have to enter it manually? If it is available in computerised form, is it in a form suitable to your research design (see below) or will you have to tabulate the data in a different form?

Research considerations include:

• Is the data set so important to your research that you cannot ignore it? For example, if you were doing a project which involved top corporations, you could not afford to ignore the publications which provided data and statistics, such as  Europe's 15,000 Largest Companies 2006 .
• Does the data generally cover the research question?
• Is the coverage relevant, or does it leave out areas (e.g. only Asia as opposed to Australasia) or time periods (e.g. only starting in 1942 when you wanted data from 1928)?
• Are the variables relevant, for example if you are interested in household expenditure does it break down the households in ways relevant to your project?
• Are the measures used the same, for example, is growth in sales expressed as an amount or a percentage?
• In the case of data from different countries, has the data been collected in the same way? For example, workers affected by strikes may include those directly affected in one country, and those indirectly affected in another.
• Is the data reliable, and current? Note that data from government, and reputable commercial sources, is likely to be trustworthy but you should be wary of information on the Internet unless you know its source. Data from trustworthy sources is likely to have been collected by a team of experts, with good quality research design and instruments.
• The advantage of survey data in particular is that you have access to a far larger sample than you would otherwise have been able to collect yourself.
• There is an obvious advantage to using a large data source, however you need to allow for the time needed to extract what you want, and to re-tabulate the data in a form suitable for your research.
• How has the data been collected, for example it it longitudinal or geographical? This will affect the type of research question it can help with, for example, if you were comparing France and Germany, you would obviously want geographical data.
• How intrinsic to your research design will the use of secondary data be? Beware of relying on it entirely, but it may be a useful way of triangulating other research, for example if you have done a survey of shopping habits, you can assess how generalisable your findings are by looking at a census.
• While use of secondary data sets may not be seen as rigorous as collecting data yourself, the big advantage is that they are in a permanently available form and can be checked by others, which is an important point for validity.

And finally...

• Will the benefits you gain from using secondary data sets as a research methods outweigh the costs of acquiring the data, and the time spent sorting out what is relevant?

Producers of published secondary data include:

• Governments and intergovermental organisations, who produce a wide variety of data. For example, from the US Government come such titles as  Budget of the United States Government ,  Business Statistics of the United States: Patterns of Economic Change ,  County and City Extra  (source of data for every state), and  Handbook of U.S. Labor Statistics .
• Trade associations and organisations representing particular interests, such as for example the American Marketing Association. These may have data and information relevant to their particular interest group.
• company information: for example AMADEUS provides pan European information on companies that includes balance sheets, profit and loss, ratios, descriptive etc., while FAME does a similar job for companies in the UK and Ireland.
• market research: for example, Mintel specialises in consumer, media and market research and published reports into particular market sectors, whilst Key Note "boasts one of the most comprehensive databases available to corporations in the UK", having published almost 1,000 reports spanning 30 industry sectors.

Where to find such information? The key is to have a very clear idea of what it is you are trying to find: what particular aspects of the research question are you attempting to answer?

You may well find sources listed in your literature review, or your tutor may point you in certain directions, but at some point you will need to consult the tertiary literature, which will point you in the direction of archives, indexes, catalogues and gateways. Your library will probably have Subject Guides covering your areas of interest. The following is a very basic list:

• UK Economic and Social Data Services (ESDS) . Contains links to: UK Data Archive (University of Essex); Institute for Social and Economic Research (University of Essex); Manchester Information and Associated Services (University of Manchester); and Cathie Marsh Centre for Census and Survey Research (University of Manchester). These contain access to a wide range of national and international data sets.
• http://epp.eurostat.ec.europa.eu . Statistics of the European Union.
• University of Michigan . Gateway to statistical resources on the Web.
• D&B Hoovers . Company information on US and international companies.  

Archival, or documentary secondary data, are documentary records left by people as a by product of their eveyday activity. They may be formally deposited in an archive or they may just exist as company records.

Historians make considerable use of archival material as a key research technique, using a wide range of personal documents such as letters, diaries, household bills, which are often stored in some sort of formal "archive".

Business researchers talk about "archival research" because they use many of the same techniques for recording and analysing information. Companies, by their very nature, tend to create records, both officially in the form of annual reports, declarations of share value etc., and unofficially in the e-mails, letters, meeting minutes and agendas, sales data, employee records etc. which are the by-product of their daily activities.

If you are studying a business and management related subject, you may make use of archival material for a number of reasons:

• Your research takes a historical perspective, and you want to gain insight into management decisions outside the memories of those whom you interview.
• Archival research is an important tool in your particular discipline – for example, finance and accounting.
• You wish to undertake archival research as part of qualitative research in order to triangulate with interviews, focus groups etc., or perhaps as exploratory research prior to the main research.
• You may be undertaking a case study, or basing your research project on your own organisation; in either case, you should look at company documents as part of this research.

In " Financial reporting and local government reform – a (mis)match? " ( Qualitative Research in Accounting & Management , Vol. 2 No. 2), Robyn Pilcher uses archival research – "Data was obtained from annual reports provided electronically to the DLG and checked against hard copies of these reports and supporting notes" – and interviews as exploratory research to investigate use of flawed financial figures by political parties, before carrying out a detailed examination of a few councils.

" Coalport Bridge Tollhouse, 1793-1995 " ( Structural Survey , Vol. 14 No. 4) is a historical study of this building drawing on such documents as maps, plans, photos, account books, meeting minutes, legal opinions and census records.

As distinct from published data sets, you will have to record and process the data yourself, in order to create your own data set.

Sometimes this archival material will be stored in "official" archives, such as the UK Public Record Office. Mostly however, it will be company specific, stored in official company archives or perhaps in smaller collections in individual departments or business units. Records can exist in physical or electronic form – the latter commonly on the company intranet.

Whatever the company's archiving policy, there is no doubt that businesses provide a rich source of data. Here is a (non exhaustive) list of the forms that data can take:

• Organisational records – for example HR, accounts, pay roll data etc.
• Data referring to the sales of goods or services
• Project files
• Organisation charts                
• Meeting minutes and agendas
• Sales literature: catalogues, copies of adverts, brochures etc.
• Annual reports
• Reports to shareholders
• Transcripts of speeches
• Non textual material: maps and plans, videos, tapes, photographs.

Management Information Systems can hold a considerable amount of data. For example, the following HR records may be held:

• data on recruitment, e.g. details of vacancies, dates, job details and criteria
• staff employment details, for example job analysis and evaluation, salary grades, terms and conditions of employment, job objectives, job competencies, performance appraisals
• data relevant to succession and career planning, e.g. the effects of not filling jobs
• management training and development, e.g. training records showing types of training.

Source:  Peter Kingsbury (1997),  IT Answers to HR Questions , CIPD.

The media (newspapers, magazines, advertisements, television and radio programmes, books, the Internet) can also throw valuable light on events, and media sources should not be ignored.

There are a number of points to consider when using archival material:

• You will need to gain access to the company, and this may prove difficult (see the " Gaining access to, and using, archives " section in this guide). On the other hand, if you are doing a report/project on your own organisation, access may be a lot easier, although even here you should gain agreement to access and use of material.
• Even if you are successful in gaining access to the company, it may be difficult and time-consuming to locate all the information you need, especially if the company does not have a clear archiving policy, and you may need to go through a vast range of documents.
• The data may be incomplete, and may not answer your research question – for example, there may be a gap in records, correspondence may be one-sided and not include responses.
• The data may be biased, in other words it will be written by people who have a particular view. For example, meeting minutes are the "official" version and often things go on in meetings which are not recorded; profitability in annual reports may be reported in such a way as to show a positive rather than a true picture.
• Informal and verbal interactions cannot be captured.
• Archival research is time-consuming, both in locating and in recording documents, so for that reason may not be feasible for smaller projects.
• You will also need to decide how to record data: historians are used to laboriously copying out documents considered too frail to photocopy, and business researchers may need to resort to this if (as is likely) company documents are considered confidential, although in such cases, note-taking may also be out. You will also need to find a suitable way of coding and referring to particular documents.
• Finally, you will need to construct your own data set, for which you will need to have a particular research method.

In " Participatory group observation – a tool to analyse strategic decision-making " ( Qualitative Market Research , Vol. 5 No. 1), Christine Vallaster and Oliver Koll highlight the benefit of multiple methods for studying complex issues, it being thus possible to supplement the weaknesses of one method with the strengths of another and study a phenomenon from a diversity of views, and achieve a high degree of validity. In the case in question, archival research was used to analyse documents (organisation charts, company reports, memos, meeting minutes), and whilst the limitations in terms of incompleteness, selectivity, and not being authored by interviewees were acknowledged, so was their supporting value to interviews, and the same textual analysis method was used for both methods.  

We have already mentioned, as part of our discussion of the two main types of secondary data, some considerations in respect to how they are used as part of the research. In this section, we shall look more generally at how secondary data can fit in to the overall research design.

Theoretical framework

Researchers take different views of the facts they are researching. For some, facts exist as independent reality; others admit the possibility of interpretation by the actors concerned. The two views, and their implication for the documents and data concerned, can be summed up as follows:

• Positivists  see facts as existing independently of interpretation, so documents are an objective reflection of reality.
• Interpretivists , and even more so realists, see reality as influenced by the social environment, open to manipulation by those who are part of it. A document must be seen in its social context, and an attempt to make sense of that context.

Some examples would be:

• minutes of a sales meeting the purpose of which was to monitor sales, with sales being affected by external influences
• brochure or flyer which was created for a particular item, and designed to appeal to current fashions
• training records of people doing National Vocational Qualifications (used in the UK to acknowledge the value of existing skills).

Reliability and validity

Reliability and validity is important to any research design, and an important consideration with secondary data is the extent to which it relates to the research question, in other words how reliably it can answer it. You need to consider the fit very carefully before deciding to proceed. Some questions which may help here are:

How reliable is the data?

In the case of published data, you will be able to make a judgement by looking at its provenance: does it come from the government, or from a reputable commercial source? The same applies to the Internet – what is the source? Look for publisher information and copyright statements. How up to date is the material?

You also need to make intrinsic judgements, however: what is the methodology behind the survey, and how robust is it? How large was the sample and what was the response rate?

There are fewer obvious external measures you can use to check unpublished, archival material: that from businesses can be notoriously inconsistent and inaccurate. Records can be incomplete with some documents missing; sometimes, whole archives can disappear when companies are taken over. In addition, some documents such as letters, reports, e-mails, meeting minutes etc. have a subjective element, reflecting the view of the author, or the perceived wishes of the recipient. For example, meeting minutes may not reflect a controversial discussion that took place but only the agreed action points; a report on sales may be intended to put a positive spin on a situation and disguise its real seriousness. It helps when assessing reliability to consider who the intended audience is.

If you are using media reports, be aware that these may only include what they consider to be the most pertinent points.

Measurement validity

One of the biggest problems with secondary data is to do with the measurements involved. These may just not be the same as the ones you want (e.g. sales given in revenue rather than quantity), they may deliberately be distorted (e.g. non recording of minor accidents, sick leave etc.), or they may be different for different countries. If the measures are inexact, you need to take a view as to how serious the problem is and how you can address it.

Does the data cover the time frame, geographical area, and variable in which you are interested? For example, if you are studying a particular period in a company, do you have meeting minutes to cover that period, or do they stop/start at a time within the boundaries of that period? Do you have the sales figures for all the countries your are interested in, and all the product types?

You can greatly increase the validity and reliability of your use of secondary data if you triangulate with another research method. For example if you are seeking insights into a period of change within a company, you can use documentary records to compare with interviews with key informants.

" Leading beyond tragedy: the balance of personal identity and adaptability " ( Leadership & Organization Development Journal , Vol. 26 No. 6) is a case study of the Norwegian company Wilhelmson's Lines loss of key employees in a plane crash, and uses archival research along with on-site interviews and participant observation as the tools of case study analysis.

" The human resource management practice of retail branding: an ethnography within Oxfam Trading Division " ( International Journal of Retail & Distribution Management , Vol. 33 No. 7) uses an ethnographic approach and includes scanning the company intranet along with participant observation and interviews.

Quantitative or qualitative?

Documentary data can be used as part of a qualitative or quantitative research design.

Much data, whether from company archives or from published data sets, is statistical, and can therefore be used as part of a quantitative design, for example how many sales were made of a particular item, what were reasons for absenteeism, company profitability etc.

One way of using secondary data in quantitative research is to compare it with data you have collected yourself, probably by a survey. For example, you can compare your own survey data with that from a census or other published survey, which will inevitably have a much larger sample, thereby helping you generalise, and/or triangulate, your findings.

Textual data can also be used qualitatively, for example marketing literature can be used to as backup information on marketing campaigns, and e-mails, letters, meeting minutes etc. can throw additional light on management decisions.

Content analysis is often quoted as a method of analysis: this involves analysing occurrence of key concepts and ideas and either draw statistical inferences or carry out a qualitative assessment, looking at the main themes that emerge.

Archives may be found in national collections, such as the UK's Public Record Office, or as smaller collections associated with national, local or federal government organisations, academic libraries, professional or trade associations, or charities; they may also be found in companies. The latter are generally closely controlled; the former are most likely to be publically available. This page gives a brief overview of how to gain access to archival collections, and what you can expect when you get there.

Preparation

An archival collection, even an open one, is not like a library where you can just turn up. You need to establish opening hours, and then make arrangements to visit.

It is best to write ahead explaining:

• Your project
• Precisely what it is you are looking for.

In order to be clear about point 2, you will need to know not only the precise scope of your research but also how this particular collection can help you. You will therefore need to spend time researching (perhaps more than one) collection, so make sure that this is allowed for in your research plan.

You also need to understand the key difference between libraries and archives:

• Archives  are collections of unpublished material, housed in closed stacks, organised according to the principles of the original collector. You can only access the material in situ, and you will need to handle the collection with special care.
• Libraries  contain published material, in open stacks, classified according to a particular system, and you may be able to take the material out on loan.

Locating sources

Bibliographic databases are good sources for finding archival collections: you can search by subject, keyword, personal or geographical name. Whilst not containing records of each item, catalogue records of archival collections are generally lengthier than for published materials and may include a summary of materials contained in the collection.

More detailed information about the collection, usually at the level of the box or folder, is found in  Finding Aids .

You can find suitable databases through your library's Subject Guides.

Gaining access to commercial collections

As indicated above, commercial archival or document collections are more tightly controlled than public ones, access to which will depend upon a clearly stated request and proof of identity.

Commercial sources, by contrast, may require more negotiation, and more convincing, because of the perceived sensitivity of their material and the fact that they exist for their customers and shareholders, and not as an archival collection. Companies understandably count the opportunity cost of time spent "helping a researcher with their enquiries", not to mentioning opening up possibly sensitive documents to the prying eyes of an outsider.

This can cause problems to the researcher because if the research project is based on one or a few companies, if access is denied then the overall validity of the research will be prejudiced. Given the likelihood that other research methods, such as interview, survey etc. are also being used, it is best to approach access in the widest sense, and stress the benefits to the organisation, the credibility of the researcher, and assurance of confidentiality.

How To Do Secondary Research or a Literature Review

What is secondary research, why is secondary research important.

• Guide License
• Literature Review
• What If I'm Unfamiliar With My Topic?
• Evaluating Sources
• Develop Your Search Strategy
• Document Your Search and Organize Your Results
• Systematic Literature Review Tips
• Ethics & Integrity
• More Information

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Secondary research, also known as a literature review , preliminary research , historical research , background research , desk research , or library research , is research that analyzes or describes prior research. Rather than generating and analyzing new data, secondary research analyzes existing research results to establish the boundaries of knowledge on a topic, to identify trends or new practices, to test mathematical models or train machine learning systems, or to verify facts and figures. Secondary research is also used to justify the need for primary research as well as to justify and support other activities. For example, secondary research may be used to support a proposal to modernize a manufacturing plant, to justify the use of newly a developed treatment for cancer, to strengthen a business proposal, or to validate points made in a speech.

Because secondary research is used for so many purposes in so many settings, all professionals will be required to perform it at some point in their careers. For managers and entrepreneurs, regardless of the industry or profession, secondary research is a regular part of worklife, although parts of the research, such as finding the supporting documents, are often delegated to juniors in the organization. For all these reasons, it is essential to learn how to conduct secondary research, even if you are unlikely to ever conduct primary research.

Secondary research is also essential if your main goal is primary research. Research funding is obtained only by using secondary research to show the need for the primary research you want to conduct. In fact, primary research depends on secondary research to prove that it is indeed new and original research and not just a rehash or replication of somebody else’s work.

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Secondary data

Using secondary data can be a good alternative to collecting data directly from participants (primary data), removing the need for face-to-face contact. 

Secondary data relating to living human subjects often requires ethical approval depending on the source and nature of the data. The extent to which the ethical review application form must be completed also depends on the source and nature of the data. 

This guidance covers some of the ethical issues relating to use of secondary data and how this impacts the ethical application process. 

Secondary data and ethical review

Ethical approval is required for projects where secondary data includes personal data - data that relates to identifiable living persons.

Data relating to the deceased

When data relates to deceased human subjects, ethical approval is required if the data includes either:

• sensitive personal data about living human subjects, or
• data relating to health or census information from the last 100 years.

And where this data identifies, or could identify, either the deceased individual or others.

Among the reasons ethical review is required is because:

• sensitive personal data can have implications for living relatives
• some data may be covered by Data Protection legislation.

Anonymised data

Data which are completely and robustly anonymised do not contain personal data and so ethical review and approval is usually not required.

For the avoidance of doubt, this means data that are already anonymised rather than data received in identifiable or pseudonymised form and then anonymised by the researcher. 

However, there are scenarios involving anonymised data where ethical approval may be required (discuss with your School ethics committee if you are unsure):

Data from a source which requires assurances or additional approvals

If the data source requires assurances that the project has undergone ethical review or evidence that use of the data is legitimate, an ethical review application can be submitted.

If the data source requires a:

• Data Management Plan - contact Research Data Management . 
• Data Protection Impact Assessment (DPIA) - contact Data Protection ( [email protected] ).

If the data involves or originates from the NHS or health and social care, see the Research involving the NHS page.

Data which risk re-identification of individuals

If the data could be used to re-identify individuals, then an ethical review application may be needed - consider the items of data you will be working with and whether this is a risk.

For example:

Combined data - combining data can lead to re-identification of individuals, particularly if data is linked at an individual level by matching unique reference numbers or data points.

Rare, unusual, or low number data – rare or unique data, such as that relating to unusual characteristics or rare health conditions, are difficult to truly anonymise as there often few individuals with those characteristics or conditions.

Reasonable means – GDPR suggests that the risk of identification, researchers should consider ‘means reasonably likely to be used’, accounting for factors such as costs and time involved and available technology.

Data with additional ethical considerations

If there are additional ethical considerations, an ethical review application can be submitted. For example, if data raises concerns around:

• the original participants’ consent for future use of the data
• the provenance of the data
• access to sensitive data not already in the public domain
• social profiling
• the research, data, or outcomes adversely impacting a particular group or community

See the section below on ethical considerations.

Secondary data types

Secondary data – internal datasets

Secondary datasets may sometimes be sourced from the within the University i.e. data collected as part of previous projects within a School. It is important to consider whether re-use of this data is in line with the original ethical approval and the consent given by participants. An ethical amendment may be required for both the original ethical approval to allow the data to be shared AND a new ethical review application for the new research project (if sufficiently different).

Internally sourced data should still be acknowledged and appropriately referenced, and the same considerations given as to other secondary data sources such as around access and permissions, data management and confidentiality. Researchers should also consider whether using this type of secondary data is appropriate for their needs (i.e. whether it meets the requirements for an academic research project). 

Secondary data - large quantitative datasets

A commonly used source of secondary data are large quantitative data sets such as census data, health data, household surveys and market research.

There are several sources that can give access to these types of data and what is required to access them varies by source and by the nature of the data, for example:

• ‘open’ datasets where the data is freely available to download
• ‘closed’ datasets where users must register with the data source but that require minimal additional work
• datasets that contain more sensitive information and where users may have to complete paperwork such as a data management plan.

Sometimes more sensitive datasets can only be accessed via a secure web portal and no local copies retained.

Secondary data - qualitative and mixed-methods data

Secondary qualitative data is less common, largely due to the difficulty in anonymising qualitative data. However, there are sources of secondary qualitative data including the  UK Data Service and library data such as oral histories, diaries and biographies.

Secondary data - biological data

There are several resources for access to biological data including human-related data. Use of biological data and bioinformatics is a wide are with several ethical concerns around confidentiality, implications of research into DNA and genomics, bias and profiling, the sensitivity of identifying risk levels related to disease. Researchers planning research involving biological data or bioinformatics should consult with disciplinary guidelines and organisations and colleagues with specific expertise. If using secondary data of this type, researchers must ensure they do so in accordance with the requirements of the data sources. Researchers should also ensure that they check if any NHS ethical approval, governance or R&D approvals are required .

Access, permissions and consent

Access to secondary data must always be used in accordance with the requirements of the data source, GDPR and the common law duty of confidentiality. Secondary data must always be appropriately referenced and acknowledged. Researchers should always act in accordance with the Principles of Good Research Conduct , even when working with secondary data.

Researchers should check whether their use is in line with the consent originally obtained from participants and seek assurances on this from the data source.

Where data is obtained in anonymous form, researchers should be conscious of the risk of de-anonymising data through triangulation of several data points or sets.

While there are open access datasets that are freely available, it is common that there are conditions and requirements put in place by the data source or controller around who can access the data and how it is used. For example, this might include:

• that researchers sign terms of use 
• that researchers have a comprehensive data management plan
• that researchers can provide assurances around the security of the data once in their possession
• verification that the person accessing the data has a legitimate reason i.e. evidence that you are a researcher at a recognised institution
• that the data be accessed via a secure portal
• that no local copies are retained
• that any copies of the data be destroyed within a certain timescale (may require a destruction certificate)
• that the raw data be processed by the data source into an anonymised form before it is released

In the latter examples, where there is more complex requirements and the data source is providing a service such as preparing and moderating access, this may incur costs that would need to be factored into researchers plans and budgets.

Ethical considerations

Ethical issues to consider.

The ethical application form includes an early filter question on use of secondary sources. This means that if researchers are using secondary data with no additional ethical issues they can skip to the end of the form – the declarations section. If, however, there are ethical issues, researchers should describe these and how they will be mitigated in the ‘Ethical Considerations’ free text field later in the form.

If data are particularly sensitive, or it is required by the data source, researchers may wish to complete the Data Management section of the ethical review application form (Word) or a separate data management plan .

When making an application for ethical approval of research using secondary data, researchers should consider:

• Is the proposed research in line with the participants original consent? Can the data source provide assurances on participants original consent?
• How will the data be managed? If there is identifiable, personal or sensitive data how will confidentiality be maintained and data kept secure?
• Will the proposed research and use, management and storage of the data meet with the data sources requirements? Have all the appropriate documents been completed and permissions granted?
• Will the data source be acknowledged and referenced?
• Are there any copyright issues around the data?
• By pulling together several data sources is there any risk of de-anonymising participants?
• Will using this data or combining it with other data risk bias or ‘profiling’ of a particular group?
• How will you present the data or analysis? Will this ensure the confidentiality and anonymity of participants?
• Will the data identify individuals as being at risk of a condition or disease where they may have otherwise been unaware?

You may find parts of the UK Government's Data Ethics Framework useful for exploring some of the potential issues.

Resources - data sources

Data sources

The UK Data Service – this is one of the core UK sources of secondary data, including government data such as the Household Survey, plus an increasing amount of qualitative data and data collected as part of research funded by UK research councils   https://www.ukdataservice.ac.uk/

The Office of National Statistics – this is the UK’s recognised national statistics institute and conducts the census in England and Wales amongst other large national and regional surveys   https://www.ons.gov.uk/

The Scottish Governments statistics publications – this includes often aggregated statistics reporting regional level (rather than individual level) data, though some more detailed datasets are available for older data   https://www.gov.scot/publications/?publicationTypes=statistics&page=1

NHS Digital data and statistics publications – this includes details about clinical indicators, health and social data, though again this is often aggregated and at a regional level rather than individual level data   https://digital.nhs.uk/data-and-information/data-collections-and-data-sets/data-sets

Information Services Division (ISD) Scotland – this includes Scottish health and social dare data, often aggregated and at a regional level  https://www.isdscotland.org/

Data.gov.uk – a new resource for ‘open’ UK government data   https://data.gov.uk/

British Library – the British Library hold a number of collections including oral histories, biographies and newspaper articles.  https://www.bl.uk/collection-guides/oral-history#

Qualitative Data Repository – a qualitative data repository hosted by Syracuse University  https://qdr.syr.edu/ European Molecular Biology Laboratory – European Bioinformatics Institute (EMBL-EBI)  https://www.ebi.ac.uk/

Health Informatics Centre (HIC) – local health informatics service linking health data  https://www.dundee.ac.uk/hic/

Open access data directories

OpenAire.eu – A searchable directory of open access datasets such as those accompanying publications   https://explore.openaire.eu/

JISC Directory of Open Access Repositories (OpenDOAR) – a searchable directory of open access repositories    http://v2.sherpa.ac.uk/opendoar/

Resources - ethics

• Association of internet researchers – ethics guidance
• The European Commission (2018) – Use of previously collected data (‘secondary use’). Ethics and Data Protection , VII, 12-14
• Irwin, S. (2013). Qualitative secondary data analysis: Ethics, epistemology and context . Progress in development studies, 13(4), 295-306.
• Morrow, Virginia and Boddy, Janet and Lamb, Rowena (2014) The ethics of secondary data analysis . NCRM Working Paper. NOVELLA.
• Rodriquez, L. (2018) Secondary data analysis with young people. Some ethical and methodological considerations from practice. Children’s Research Digest Volume 4, Issue 3. The Childrens Research Network.
• Salerno, J., Knoppers, B. M., Lee, L. M., Hlaing, W. M., & Goodman, K. W. (2017). Ethics, big data and computing in epidemiology and public health . Annals of epidemiology, 27(5), 297-301.
• UK Data Service guidance on secondary analysis