secondary data collection dissertation

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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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Home » Secondary Data – Types, Methods and Examples

Secondary Data – Types, Methods and Examples

Table of Contents

Secondary Data

Definition:

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

Secondary Data Types

Types of secondary data are as follows:

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 This can include census data, historical documents, and archival records.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

Secondary Data Collection Methods

Secondary Data Collection Methods are as follows:

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

Secondary Data Formats

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

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

Secondary Data Analysis Methods

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

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

Secondary Data Gathering Guide

Here are some steps to follow when gathering secondary data:

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

Examples of Secondary Data

Here are some examples of secondary data from different fields:

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

Purpose of Secondary Data

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

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

When to use Secondary Data

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

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

Characteristics of Secondary Data

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

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

Advantages of Secondary Data

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

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

Limitations of Secondary Data

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

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

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• Data Collection Methods | Step-by-Step Guide & Examples

Data Collection Methods | Step-by-Step Guide & Examples

Published on 4 May 2022 by Pritha Bhandari .

Data collection is a systematic process of gathering observations or measurements. Whether you are performing research for business, governmental, or academic purposes, data collection allows you to gain first-hand knowledge and original insights into your research problem .

While methods and aims may differ between fields, the overall process of data collection remains largely the same. Before you begin collecting data, you need to consider:

• The  aim of the research
• The type of data that you will collect
• The methods and procedures you will use to collect, store, and process the data

To collect high-quality data that is relevant to your purposes, follow these four steps.

Table of contents

Step 1: define the aim of your research, step 2: choose your data collection method, step 3: plan your data collection procedures, step 4: collect the data, frequently asked questions about data collection.

Before you start the process of data collection, you need to identify exactly what you want to achieve. You can start by writing a problem statement : what is the practical or scientific issue that you want to address, and why does it matter?

Next, formulate one or more research questions that precisely define what you want to find out. Depending on your research questions, you might need to collect quantitative or qualitative data :

• Quantitative data is expressed in numbers and graphs and is analysed through statistical methods .
• Qualitative data is expressed in words and analysed through interpretations and categorisations.

If your aim is to test a hypothesis , measure something precisely, or gain large-scale statistical insights, collect quantitative data. If your aim is to explore ideas, understand experiences, or gain detailed insights into a specific context, collect qualitative data.

If you have several aims, you can use a mixed methods approach that collects both types of data.

• Your first aim is to assess whether there are significant differences in perceptions of managers across different departments and office locations.
• Your second aim is to gather meaningful feedback from employees to explore new ideas for how managers can improve.

Prevent plagiarism, run a free check.

Based on the data you want to collect, decide which method is best suited for your research.

• Experimental research is primarily a quantitative method.
• Interviews , focus groups , and ethnographies are qualitative methods.
• Surveys , observations, archival research, and secondary data collection can be quantitative or qualitative methods.

Carefully consider what method you will use to gather data that helps you directly answer your research questions.

When you know which method(s) you are using, you need to plan exactly how you will implement them. What procedures will you follow to make accurate observations or measurements of the variables you are interested in?

For instance, if you’re conducting surveys or interviews, decide what form the questions will take; if you’re conducting an experiment, make decisions about your experimental design .

Operationalisation

Sometimes your variables can be measured directly: for example, you can collect data on the average age of employees simply by asking for dates of birth. However, often you’ll be interested in collecting data on more abstract concepts or variables that can’t be directly observed.

Operationalisation means turning abstract conceptual ideas into measurable observations. When planning how you will collect data, you need to translate the conceptual definition of what you want to study into the operational definition of what you will actually measure.

• You ask managers to rate their own leadership skills on 5-point scales assessing the ability to delegate, decisiveness, and dependability.
• You ask their direct employees to provide anonymous feedback on the managers regarding the same topics.

You may need to develop a sampling plan to obtain data systematically. This involves defining a population , the group you want to draw conclusions about, and a sample, the group you will actually collect data from.

Your sampling method will determine how you recruit participants or obtain measurements for your study. To decide on a sampling method you will need to consider factors like the required sample size, accessibility of the sample, and time frame of the data collection.

Standardising procedures

If multiple researchers are involved, write a detailed manual to standardise data collection procedures in your study.

This means laying out specific step-by-step instructions so that everyone in your research team collects data in a consistent way – for example, by conducting experiments under the same conditions and using objective criteria to record and categorise observations.

This helps ensure the reliability of your data, and you can also use it to replicate the study in the future.

Creating a data management plan

Before beginning data collection, you should also decide how you will organise and store your data.

• If you are collecting data from people, you will likely need to anonymise and safeguard the data to prevent leaks of sensitive information (e.g. names or identity numbers).
• If you are collecting data via interviews or pencil-and-paper formats, you will need to perform transcriptions or data entry in systematic ways to minimise distortion.
• You can prevent loss of data by having an organisation system that is routinely backed up.

Finally, you can implement your chosen methods to measure or observe the variables you are interested in.

The closed-ended questions ask participants to rate their manager’s leadership skills on scales from 1 to 5. The data produced is numerical and can be statistically analysed for averages and patterns.

To ensure that high-quality data is recorded in a systematic way, here are some best practices:

• Record all relevant information as and when you obtain data. For example, note down whether or how lab equipment is recalibrated during an experimental study.
• Double-check manual data entry for errors.
• If you collect quantitative data, you can assess the reliability and validity to get an indication of your data quality.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organisations.

When conducting research, collecting original data has significant advantages:

• You can tailor data collection to your specific research aims (e.g., understanding the needs of your consumers or user testing your website).
• You can control and standardise the process for high reliability and validity (e.g., choosing appropriate measurements and sampling methods ).

However, there are also some drawbacks: data collection can be time-consuming, labour-intensive, and expensive. In some cases, it’s more efficient to use secondary data that has already been collected by someone else, but the data might be less reliable.

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

Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.

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

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

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

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

Operationalisation means turning abstract conceptual ideas into measurable observations.

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

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

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Qualitative Secondary Analysis: A Case Exemplar

Judith ann tate.

The Ohio State University, College of Nursing

Mary Beth Happ

Qualitative secondary analysis (QSA) is the use of qualitative data collected by someone else or to answer a different research question. Secondary analysis of qualitative data provides an opportunity to maximize data utility particularly with difficult to reach patient populations. However, QSA methods require careful consideration and explicit description to best understand, contextualize, and evaluate the research results. In this paper, we describe methodologic considerations using a case exemplar to illustrate challenges specific to QSA and strategies to overcome them.

Health care research requires significant time and resources. Secondary analysis of existing data provides an efficient alternative to collecting data from new groups or the same subjects. Secondary analysis, defined as the reuse of existing data to investigate a different research question ( Heaton, 2004 ), has a similar purpose whether the data are quantitative or qualitative. Common goals include to (1) perform additional analyses on the original dataset, (2) analyze a subset of the original data, (3) apply a new perspective or focus to the original data, or (4) validate or expand findings from the original analysis ( Hinds, Vogel, & Clarke-Steffen, 1997 ). Synthesis of knowledge from meta-analysis or aggregation may be viewed as an additional purpose of secondary analysis ( Heaton, 2004 ).

Qualitative studies utilize several different data sources, such as interviews, observations, field notes, archival meeting minutes or clinical record notes, to produce rich descriptions of human experiences within a social context. The work typically requires significant resources (e.g., personnel effort/time) for data collection and analysis. When feasible, qualitative secondary analysis (QSA) can be a useful and cost-effective alternative to designing and conducting redundant primary studies. With advances in computerized data storage and analysis programs, sharing qualitative datasets has become easier. However, little guidance is available for conducting, structuring procedures, or evaluating QSA ( Szabo & Strang, 1997 ).

QSA has been described as “an almost invisible enterprise in social research” ( Fielding, 2004 ). Primary data is often re-used; however, descriptions of this practice are embedded within the methods section of qualitative research reports rather than explicitly identified as QSA. Moreover, searching or classifying reports as QSA is difficult because many researchers refrain from identifying their work as secondary analyses ( Hinds et al., 1997 ; Thorne, 1998a ). In this paper, we provide an overview of QSA, the purposes, and modes of data sharing and approaches. A unique, expanded QSA approach is presented as a methodological exemplar to illustrate considerations.

QSA Typology

Heaton (2004) classified QSA studies based on the relationship between the secondary and primary questions and the scope of data analyzed. Types of QSA included studies that (1) investigated questions different from the primary study, (2) applied a unique theoretical perspective, or (3) extended the primary work. Heaton’s literature review (2004) showed that studies varied in the choice of data used, from selected portions to entire or combined datasets.

Modes of Data Sharing

Heaton (2004) identified three modes of data sharing: formal, informal and auto-data. Formal data sharing involves accessing and analyzing deposited or archived qualitative data by an independent group of researchers. Historical research often uses formal data sharing. Informal data sharing refers to requests for direct access to an investigator’s data for use alone or to pool with other data, usually as a result of informal networking. In some instances, the primary researchers may be invited to collaborate. The most common mode of data sharing is auto-data, defined as further exploration of a qualitative data set by the primary research team. Due to the iterative nature of qualitative research, when using auto-data, it may be difficult to determine where the original study questions end and discrete, distinct analysis begins ( Heaton, 1998 ).

An Exemplar QSA

Below we describe a QSA exemplar conducted by the primary author of this paper (JT), a member of the original research team, who used a supplementary approach to examine concepts revealed but not fully investigated in the primary study. First, we describe an overview of the original study on which the QSA was based. Then, the exemplar QSA is presented to illustrate: (1) the use of auto-data when the new research questions are closely related to or extend the original study aims ( Table 1 ), (2) the collection of additional clinical record data to supplement the original dataset and (3) the performance of separate member checking in the form of expert review and opinion. Considerations and recommendations for use of QSA are reviewed with illustrations taken from the exemplar study ( Table 2 ). Finally, discussion of conclusions and implications is included to assist with planning and implementation of QSA studies.

Research question comparison

Application of the Exemplar Qualitative Secondary Analysis (QSA)

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The Primary Study

Briefly, the original study was a micro-level ethnography designed to describe the processes of care and communication with patients weaning from prolonged mechanical ventilation (PMV) in a 28-bed Medical Intensive Care Unit ( Broyles, Colbert, Tate, & Happ, 2008 ; Happ, Swigart, Tate, Arnold, Sereika, & Hoffman, 2007 ; Happ et al, 2007 , 2010 ). Both the primary study and the QSA were approved by the Institutional Review Board at the University of Pittsburgh. Data were collected by two experienced investigators and a PhD student-research project coordinator. Data sources consisted of sustained field observations, interviews with patients, family members and clinicians, and clinical record review, including all narrative clinical documentation recorded by direct caregivers.

During iterative data collection and analysis in the original study, it became apparent that anxiety and agitation had an effect on the duration of ventilator weaning episodes, an observation that helped to formulate the questions for the QSA ( Tate, Dabbs, Hoffman, Milbrandt & Happ, 2012 ). Thus, the secondary topic was closely aligned as an important facet of the primary phenomenon. The close, natural relationship between the primary and QSA research questions is demonstrated in the side-by-side comparison in Table 1 . This QSA focused on new questions which extended the original study to recognition and management of anxiety or agitation, behaviors that often accompany mechanical ventilation and weaning but occur throughout the trajectory of critical illness and recovery.

Considerations when Undertaking QSA ( Table 2 )

Practical advantages.

A key practical advantage of QSA is maximizing use of existing data. Data collection efforts represent a significant percentage of the research budget in terms of cost and labor ( Coyer & Gallo, 2005 ). This is particularly important in view of the competition for research funding. Planning and implementing a qualitative study involves considerable time and expertise not only for data collecting (e.g., interviews, participant observation or focus group), but in establishing access, credibility and relationships ( Thorne, 1994 ) and in conducting the analysis. The cost of QSA is often seen as negligible since the outlay of resources for data collection is assumed by the original study. However, QSA incurs costs related to storage, researcher’s effort for review of existing data, analysis, and any further data collection that may be necessary.

Another advantage of QSA is access to data from an assembled cohort. In conducting original primary research, practical concerns arise when participants are difficult to locate or reluctant to divulge sensitive details to a researcher. In the case of vulnerable critically ill patients, participation in research may seem an unnecessary burden to family members who may be unwilling to provide proxy consent ( Fielding, 2004 ). QSA permits new questions to be asked of data collected previously from these vulnerable groups ( Rew, Koniak-Griffin, Lewis, Miles, & O'Sullivan, 2000 ), or from groups or events that occur with scarcity ( Thorne, 1994 ). Participants’ time and effort in the primary study therefore becomes more worthwhile. In fact, it is recommended that data already collected from existing studies of vulnerable populations or about sensitive topics be analyzed prior to engaging new participants. In this way, QSA becomes a cumulative rather than a repetitive process ( Fielding, 2004 ).

Data Adequacy and Congruency

Secondary researchers must determine that the primary data set meets the needs of the QSA. Data may be insufficient to answer a new question or the focus of the QSA may be so different as to render the pursuit of a QSA impossible ( Heaton, 1998 ). The underlying assumptions, sampling plan, research questions, and conceptual framework selected to answer the original study question may not fit the question posed during QSA ( Coyer & Gallo, 2005 ). The researchers of the primary study may have selectively sampled participants and analyzed the resulting data in a manner that produced a narrow or uneven scope of data ( Hinds et al., 1997 ). Thus, the data needed to fully answer questions posed by the QSA may be inadequately addressed in the primary study. A critical review of the existing dataset is an important first step in determining whether the primary data fits the secondary questions ( Hinds et al., 1997 ).

Passage of Time

The timing of the QSA is another important consideration. If the primary study and secondary study are performed sequentially, findings of the original study may influence the secondary study. On the other hand, studies performed concurrently offer the benefit of access to both the primary research team and participants member checking ( Hinds et al., 1997 ).

The passage of time since the primary study was conducted can also have a distinct effect on the usefulness of the primary dataset. Data may be outdated or contain a historical bias ( Coyer & Gallo, 2005 ). Since context changes over time, characteristics of the phenomena of interest may have changed. Analysis of older datasets may not illuminate the phenomena as they exist today.( Hinds et al., 1997 ) Even if participants could be re-contacted, their perspectives, memories and experiences change. The passage of time also has an affect on the relationship of the primary researchers to the data – so auto-data may be interpreted differently by the same researcher with the passage of time. Data are bound by time and history, therefore, may be a threat to internal validity unless a new investigator is able to account for these effects when interpreting data ( Rew et al., 2000 ).

Researcher stance/Context involvement

Issues related to context are a major source of criticism of QSA ( Gladstone, Volpe, & Boydell, 2007 ). One of the hallmarks of qualitative research is the relationship of the researcher to the participants. It can be argued that removing active contact with participants violates this premise. Tacit understandings developed in the field may be difficult or impossible to reconstruct ( Thorne, 1994 ). Qualitative fieldworkers often react and redirect the data collection based on a growing knowledge of the setting. The setting may change as a result of external or internal factors. Interpretation of researchers as participants in a unique time and social context may be impossible to re-construct even if the secondary researchers were members of the primary team ( Mauthner, Parry, & Milburn, 1998 ). Because the context in which the data were originally produced cannot be recovered, the ability of the researcher to react to the lived experience may be curtailed in QSA ( Gladstone et al., 2007 ). Researchers utilize a number of tactics to filter and prioritize what to include as data that may not be apparent in either the written or spoken records of those events ( Thorne, 1994 ). Reflexivity between the researcher, participants and setting is impossible to recreate when examining pre-existing data.

Relationship of QSA Researcher to Primary Study

The relationship of the QSA researcher to the primary study is an important consideration. When the QSA researcher is not part of the original study team, contractual arrangements detailing access to data, its format, access to the original team, and authorship are required ( Hinds et al., 1997 ). The QSA researcher should assess the condition of the data, documents including transcripts, memos and notes, and clarity and flow of interactions ( Hinds et al., 1997 ). An outline of the original study and data collection procedures should be critically reviewed ( Heaton, 1998 ). If the secondary researcher was not a member of the original study team, access to the original investigative team for the purpose of ongoing clarification is essential ( Hinds et al., 1997 ).

Membership on the original study team may, however, offer the secondary researcher little advantage depending on their role in the primary study. Some research team members may have had responsibility for only one type of data collection or data source. There may be differences in involvement with analysis of the primary data.

Informed Consent of Participants

Thorne (1998) questioned whether data collected for one study purpose can ethically be re-examined to answer another question without participants’ consent. Many institutional review boards permit consent forms to include language about the possibility of future use of existing data. While this mechanism is becoming routine and welcomed by researchers, concerns have been raised that a generic consent cannot possibly address all future secondary questions and may violate the principle of full informed consent ( Gladstone et al., 2007 ). Local variations in study approval practices by institutional review boards may influence the ability of researchers to conduct a QSA.

Rigor of QSA

The primary standards for evaluating rigor of qualitative studies are trustworthiness (logical relationship between the data and the analytic claims), fit (the context within which the findings are applicable), transferability (the overall generalizability of the claims) and auditabilty (the transparency of the procedural steps and the analytic moves processes) ( Lincoln & Guba, 1991 ). Thorne suggests that standard procedures for assuring rigor can be modified for QSA ( Thorne, 1994 ). For instance, the original researchers may be viewed as sources of confirmation while new informants, other related datasets and validation by clinical experts are sources of triangulation that may overcome the lack of access to primary subjects ( Heaton, 2004 ; Thorne, 1994 ).

Our observations, derived from the experience of posing a new question of existing qualitative data serves as a template for researchers considering QSA. Considerations regarding quality, availability and appropriateness of existing data are of primary importance. A realistic plan for collecting additional data to answer questions posed in QSA should consider burden and resources for data collection, analysis, storage and maintenance. Researchers should consider context as a potential limitation to new analyses. Finally, the cost of QSA should be fully evaluated prior to making a decision to pursue QSA.

Acknowledgments

This work was funded by the National Institute of Nursing Research (RO1-NR07973, M Happ PI) and a Clinical Practice Grant from the American Association of Critical Care Nurses (JA Tate, PI).

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Disclosure statement: Drs. Tate and Happ have no potential conflicts of interest to disclose that relate to the content of this manuscript and do not anticipate conflicts in the foreseeable future.

Contributor Information

Judith Ann Tate, The Ohio State University, College of Nursing.

Mary Beth Happ, The Ohio State University, College of Nursing.

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What Is Secondary Data? A Complete Guide

What is secondary data, and why is it important? Find out in this post.

Within data analytics, there are many ways of categorizing data. A common distinction, for instance, is that between qualitative and quantitative data . In addition, you might also distinguish your data based on factors like sensitivity. For example, is it publicly available or is it highly confidential?  

Probably the most fundamental distinction between different types of data is their source. Namely, are they primary, secondary, or third-party data? Each of these vital data sources supports the data analytics process in its own way. In this post, we’ll focus specifically on secondary data. We’ll look at its main characteristics, provide some examples, and highlight the main pros and cons of using secondary data in your analysis.  

We’ll cover the following topics:  

What is secondary data?

• What’s the difference between primary, secondary, and third-party data?
• What are some examples of secondary data?
• How to analyse secondary data
• Advantages of secondary data
• Disadvantages of secondary data
• Wrap-up and further reading

Ready to learn all about secondary data? Then let’s go.

1. What is secondary data?

Secondary data (also known as second-party data) refers to any dataset collected by any person other than the one using it.  

Secondary data sources are extremely useful. They allow researchers and data analysts to build large, high-quality databases that help solve business problems. By expanding their datasets with secondary data, analysts can enhance the quality and accuracy of their insights. Most secondary data comes from external organizations. However, secondary data also refers to that collected within an organization and then repurposed.

Secondary data has various benefits and drawbacks, which we’ll explore in detail in section four. First, though, it’s essential to contextualize secondary data by understanding its relationship to two other sources of data: primary and third-party data. We’ll look at these next.

2. What’s the difference between primary, secondary, and third-party data?

To best understand secondary data, we need to know how it relates to the other main data sources: primary and third-party data.

What is primary data?

‘Primary data’ (also known as first-party data) are those directly collected or obtained by the organization or individual that intends to use them. Primary data are always collected for a specific purpose. This could be to inform a defined goal or objective or to address a particular business problem. 

For example, a real estate organization might want to analyze current housing market trends. This might involve conducting interviews, collecting facts and figures through surveys and focus groups, or capturing data via electronic forms. Focusing only on the data required to complete the task at hand ensures that primary data remain highly relevant. They’re also well-structured and of high quality.

As explained, ‘secondary data’ describes those collected for a purpose other than the task at hand. Secondary data can come from within an organization but more commonly originate from an external source. If it helps to make the distinction, secondary data is essentially just another organization’s primary data. 

Secondary data sources are so numerous that they’ve started playing an increasingly vital role in research and analytics. They are easier to source than primary data and can be repurposed to solve many different problems. While secondary data may be less relevant for a given task than primary data, they are generally still well-structured and highly reliable.

What is third-party data?

‘Third-party data’ (sometimes referred to as tertiary data) refers to data collected and aggregated from numerous discrete sources by third-party organizations. Because third-party data combine data from numerous sources and aren’t collected with a specific goal in mind, the quality can be lower. 

Third-party data also tend to be largely unstructured. This means that they’re often beset by errors, duplicates, and so on, and require more processing to get them into a usable format. Nevertheless, used appropriately, third-party data are still a useful data analytics resource. You can learn more about structured vs unstructured data here . 

OK, now that we’ve placed secondary data in context, let’s explore some common sources and types of secondary data.

3. What are some examples of secondary data?

External secondary data.

Before we get to examples of secondary data, we first need to understand the types of organizations that generally provide them. Frequent sources of secondary data include:  

• Government departments
• Public sector organizations
• Industry associations
• Trade and industry bodies
• Educational institutions
• Private companies
• Market research providers

While all these organizations provide secondary data, government sources are perhaps the most freely accessible. They are legally obliged to keep records when registering people, providing services, and so on. This type of secondary data is known as administrative data. It’s especially useful for creating detailed segment profiles, where analysts hone in on a particular region, trend, market, or other demographic.

Types of secondary data vary. Popular examples of secondary data include:

• Tax records and social security data
• Census data (the U.S. Census Bureau is oft-referenced, as well as our favorite, the U.S. Bureau of Labor Statistics )
• Electoral statistics
• Health records
• Books, journals, or other print media
• Social media monitoring, internet searches, and other online data
• Sales figures or other reports from third-party companies
• Libraries and electronic filing systems
• App data, e.g. location data, GPS data, timestamp data, etc.

Internal secondary data 

As mentioned, secondary data is not limited to that from a different organization. It can also come from within an organization itself.  

Sources of internal secondary data might include:

• Sales reports
• Annual accounts
• Quarterly sales figures
• Customer relationship management systems
• Emails and metadata
• Website cookies

In the right context, we can define practically any type of data as secondary data. The key takeaway is that the term ‘secondary data’ doesn’t refer to any inherent quality of the data themselves, but to how they are used. Any data source (external or internal) used for a task other than that for which it was originally collected can be described as secondary data.

4. How to analyse secondary data

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

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

• Statement of purpose: Before collecting secondary data, you need to know your statement of purpose. This means you should have a clear awareness of the goal of the research work and how this data will help achieve it. This will guide you to collect the right data, then choosing the best data source and method of analysis.
• Research design: This is a plan on how the research activities will be carried out. It describes the kind of data to be collected, the sources of data collection, the method of data collection, tools used, and method of analysis. Once the purpose of the research has been identified, the researcher should design a research process that will guide the data analysis process.
• Developing the research questions: Once you’ve identified the research purpose, an analyst should also prepare research questions to help identify secondary data. For example, if a researcher is looking to learn more about why working adults are increasingly more interested in the “gig economy” as opposed to full-time work, they may ask, “What are the main factors that influence adults decisions to engage in freelance work?” or, “Does education level have an effect on how people engage in freelance work?
• Identifying secondary data: Using the research questions as a guide, researchers will then begin to identify relevant data from the sources provided. If the kind of data to be collected is qualitative, a researcher can filter out qualitative data—for example.
• Evaluating secondary data: Once relevant data has been identified and collates, it will be evaluated to ensure it fulfils the criteria of the research topic. Then, it is analyzed either using the quantitative or qualitative method, depending on the type of data it is.

You can learn more about secondary data analysis in this post .  

5. Advantages of secondary data

Secondary data is suitable for any number of analytics activities. The only limitation is a dataset’s format, structure, and whether or not it relates to the topic or problem at hand. 

When analyzing secondary data, the process has some minor differences, mainly in the preparation phase. Otherwise, it follows much the same path as any traditional data analytics project. 

More broadly, though, what are the advantages and disadvantages of using secondary data? Let’s take a look.

Advantages of using secondary data

It’s an economic use of time and resources: Because secondary data have already been collected, cleaned, and stored, this saves analysts much of the hard work that comes from collecting these data firsthand. For instance, for qualitative data, the complex tasks of deciding on appropriate research questions or how best to record the answers have already been completed. Secondary data saves data analysts and data scientists from having to start from scratch.  

It provides a unique, detailed picture of a population: Certain types of secondary data, especially government administrative data, can provide access to levels of detail that it would otherwise be extremely difficult (or impossible) for organizations to collect on their own. Data from public sources, for instance, can provide organizations and individuals with a far greater level of population detail than they could ever hope to gather in-house. You can also obtain data over larger intervals if you need it., e.g. stock market data which provides decades’-worth of information.  

Secondary data can build useful relationships: Acquiring secondary data usually involves making connections with organizations and analysts in fields that share some common ground with your own. This opens the door to a cross-pollination of disciplinary knowledge. You never know what nuggets of information or additional data resources you might find by building these relationships.

Secondary data tend to be high-quality: Unlike some data sources, e.g. third-party data, secondary data tends to be in excellent shape. In general, secondary datasets have already been validated and therefore require minimal checking. Often, such as in the case of government data, datasets are also gathered and quality-assured by organizations with much more time and resources available. This further benefits the data quality , while benefiting smaller organizations that don’t have endless resources available.

It’s excellent for both data enrichment and informing primary data collection: Another benefit of secondary data is that they can be used to enhance and expand existing datasets. Secondary data can also inform primary data collection strategies. They can provide analysts or researchers with initial insights into the type of data they might want to collect themselves further down the line.

6. Disadvantages of secondary data

They aren’t always free: Sometimes, it’s unavoidable—you may have to pay for access to secondary data. However, while this can be a financial burden, in reality, the cost of purchasing a secondary dataset usually far outweighs the cost of having to plan for and collect the data firsthand.  

The data isn’t always suited to the problem at hand: While secondary data may tick many boxes concerning its relevance to a business problem, this is not always true. For instance, secondary data collection might have been in a geographical location or time period ill-suited to your analysis. Because analysts were not present when the data were initially collected, this may also limit the insights they can extract.

The data may not be in the preferred format: Even when a dataset provides the necessary information, that doesn’t mean it’s appropriately stored. A basic example: numbers might be stored as categorical data rather than numerical data. Another issue is that there may be gaps in the data. Categories that are too vague may limit the information you can glean. For instance, a dataset of people’s hair color that is limited to ‘brown, blonde and other’ will tell you very little about people with auburn, black, white, or gray hair.  

You can’t be sure how the data were collected: A structured, well-ordered secondary dataset may appear to be in good shape. However, it’s not always possible to know what issues might have occurred during data collection that will impact their quality. For instance, poor response rates will provide a limited view. While issues relating to data collection are sometimes made available alongside the datasets (e.g. for government data) this isn’t always the case. You should therefore treat secondary data with a reasonable degree of caution.

Being aware of these disadvantages is the first step towards mitigating them. While you should be aware of the risks associated with using secondary datasets, in general, the benefits far outweigh the drawbacks.

7. Wrap-up and further reading

In this post we’ve explored secondary data in detail. As we’ve seen, it’s not so different from other forms of data. What defines data as secondary data is how it is used rather than an inherent characteristic of the data themselves. 

To learn more about data analytics, check out this free, five-day introductory data analytics short course . You can also check out these articles to learn more about the data analytics process:

• What is data cleaning and why is it important?
• What is data visualization? A complete introductory guide
• 10 Great places to find free datasets for your next project

Primary and Secondary Data Collection to Conduct Researches, Write Thesis and Dissertation Amidst COVID-19 Pandemic: A Guidepost

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Research has always been regarded by many as tedious because of the difficulties and challenges associated with doing research such as having to forego certain habits like social life. Doing research became even more difficult, especially with regard to limitation on collecting applicable primary and secondary data due to the COVID-19 pandemic lockdowns. It is to be noted that substantive, thorough, sophisticated literature review and intensive pertinent primary data availability are ncessary for doing quality research relevant to the status quo. Various novel approaches have been adopted by scholars through their diverse academic spheres in conducting internationally acceptable research amidst the COVID-19 pandemic. This research aims to come up with a guidepost to facilitate researchers and other stakeholders with fundamental knowledge and skills in conducting substantive, thorough, sophisticated researches that are of international standards. A comparative and diagnostic analysis method is used for analyzing existing literature and policies developed by higher education institutions and schools for doing research in the advent of the COVID-19 pandemic. The output allowed authors to develop a guidepost with rules on using limited primary and extensive secondary data in doing research. The guidepost consists of various sections explaining on how to do research and write theses and dissertations. These sections include among others research title, statement of the problem, research objectives, theoretical and conceptual frameworks, review of related literature, research methodology, analysis and interpretation of data, and conclusion and recommendations. The guidepost is very significant in doing researches and aids researchers in conducting internationally accepted researches with limited primary data and extensive secondary data in the advent of the COVID-19 Pandemic. The guidepost is flexible and can easily be used by local and international institutions’ researchers through little modification in context of their research fields.

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About this chapter

Valdez, A.S. et al. (2023). Primary and Secondary Data Collection to Conduct Researches, Write Thesis and Dissertation Amidst COVID-19 Pandemic: A Guidepost. In: Pal, I., Kolathayar, S., Tawhidul Islam, S., Mukhopadhyay, A., Ahmed, I. (eds) Proceedings of the 2nd International Symposium on Disaster Resilience and Sustainable Development. Lecture Notes in Civil Engineering, vol 294. Springer, Singapore. https://doi.org/10.1007/978-981-19-6297-4_20

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PhD Assistance

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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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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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

Primary Research vs Secondary Research: A Comparative Analysis

Understand the differences between primary research vs secondary research. Learn how they can be used to generate valuable insights.

Primary research and secondary research are two fundamental approaches used in research studies to gather information and explore topics of interest. Both primary and secondary research offer unique advantages and have their own set of considerations, making them valuable tools for researchers in different contexts.

Understanding the distinctions between primary and secondary research is crucial for researchers to make informed decisions about the most suitable approach for their study objectives and available resources.

What is Primary Research?

Primary research refers to the collection and analysis of data directly from original sources. It involves gathering information directly to address specific research objectives and generate new insights. This research method conducts surveys, interviews, observations, experiments, or focus groups to obtain data that is relevant to the research question at hand. By engaging directly with subjects or sources, primary research provides firsthand and up-to-date information, allowing researchers to have control over the data collection process and adjust it to their specific needs.

Types of Primary Research

There are several types of primary research methods commonly used in various fields:

Surveys are the systematic collection of data through questionnaires or interviews, aiming to gather information from a large number of participants. Surveys can be conducted in person, over the phone, through mail, or online.

Interviews entail direct one-on-one or group interactions with individuals or key informants to obtain detailed information about their experiences, opinions, or expertise. Interviews can be structured (using predetermined questions) or unstructured (allowing for open-ended discussions).

Observations

Observational research carefully observes and documents behaviors, interactions, or phenomena in real-life settings. It can be done in a participant or non-participant manner, depending on the level of involvement of the researcher.

Data analysis

Examining and interpreting collected data, data analysis uncovers patterns, trends, and insights, providing a deeper understanding of the research topic. It enables drawing meaningful conclusions for decision-making and guides further research.

Focus groups

Focus groups facilitated group discussions with a small number of participants who shared their opinions, attitudes, and experiences on a specific topic. This method allows for interactive and in-depth exploration of a subject.

Benefits of Primary Research

Original and specific data: Primary research provides first hand data directly relevant to the research objectives, ensuring its freshness and specificity to the research context.

Control over data collection: Researchers have control over the design, implementation, and data collection process, allowing them to adapt the research methods and instruments to suit their needs.

Depth of understanding: Primary research methods, such as interviews and focus groups, enable researchers to gain a deep understanding of participants’ perspectives, experiences, and motivations.

Validity and reliability: By directly collecting data from original sources, primary research enhances the validity and reliability of the findings, reducing potential biases associated with using secondary or existing data.

Challenges of Primary Research

Time and Resource-intensive: Primary research requires careful planning, data collection, analysis, and interpretation. It may require recruiting participants, conducting interviews or surveys, and analyzing data, all of which require time and resources.

Sampling limitations: Primary research often relies on sampling techniques to select participants. Ensuring a representative sample that accurately reflects the target population can be challenging, and sampling biases may affect the generalizability of the findings.

Subjectivity: The involvement of researchers in primary research methods, such as interviews or observations, introduces the potential for subjective interpretations or biases that can influence the data collection and analysis process.

Limited generalizability: Findings from primary research may have limited generalizability due to the specific characteristics of the sample or context. It is essential to acknowledge the scope and limitations of the findings and avoid making broad generalizations beyond the studied sample or context.

What is Secondary Research?

It is a method of research that relies on data that is readily available, rather than gathering new data through primary research methods. Secondary research relies on reviewing and analyzing sources such as published studies, reports, articles, books, government databases, and online resources to extract relevant information for a specific research objective.

Sources of Secondary Research

Published studies and academic journals.

Researchers can review published studies and academic journals to gather information, data, and findings related to their research topic. These sources often provide comprehensive and in-depth analyses of specific subjects.

Reports and white papers

Reports and white papers produced by research organizations, government agencies, and industry associations provide valuable data and insights on specific topics or sectors. These documents often contain statistical data, market research, trends, and expert opinions.

Books and reference materials

Books and reference materials written by experts in a particular field can offer comprehensive overviews, theories, and historical perspectives that contribute to secondary research.

Online databases

Online databases, such as academic libraries, research repositories, and specialized platforms, provide access to a vast array of published research articles, theses, dissertations, and conference proceedings.

Benefits of Secondary Research

Time and Cost-effectiveness: Secondary research saves time and resources since the data and information already exist and are readily accessible. Researchers can utilize existing resources instead of conducting time-consuming primary research.

Wide range of data: Secondary research provides access to a wide range of data sources, including large-scale surveys, census data, and comprehensive reports. This allows researchers to explore diverse perspectives and make comparisons across different studies.

Comparative analyses: Researchers can compare findings from different studies or datasets, allowing for cross-referencing and verification of results. This enhances the robustness and validity of research outcomes.

Ethical considerations: Secondary research does not involve direct interaction with participants, which reduces ethical concerns related to privacy, informed consent, and confidentiality.

Challenges of Secondary Research

Data availability and quality: The availability and quality of secondary data can vary. Researchers must critically evaluate the credibility, reliability, and relevance of the sources to ensure the accuracy of the information used in their research.

Limited control over data: Researchers have limited control over the design, collection methods, and variables included in the secondary data. The data may not perfectly align with the research objectives, requiring careful selection and analysis.

Potential bias and outdated information: Secondary data may contain inherent biases or limitations introduced by the original researchers. Additionally, the data may become outdated, and newer information or developments may not be captured.

Lack of customization: Since secondary data is collected for various purposes, it may not perfectly align with the specific research needs. Researchers may encounter limitations in terms of variables, definitions, or granularity of data.

Comparing Primary and Secondary Research

Primary research vs secondary research, examples of primary and secondary research, examples of primary research.

• Conducting a survey to collect data on customer satisfaction and preferences for a new product directly from the target audience.
• Designing and conducting an experiment to test the effectiveness of a new teaching method by comparing the learning outcomes of students in different groups.
• Observing and documenting the behavior of a specific animal species in its natural habitat to gather data for ecological research.
• Organizing a focus group with potential consumers to gather insights and feedback on a new advertising campaign.
• Conducting interviews with healthcare professionals to understand their experiences and perspectives on a specific medical treatment.

Examples of Secondary Research

• Accessing a market research report to gather information on consumer trends, market size, and competitor analysis in the smartphone industry.
• Using existing government data on unemployment rates to analyze the impact of economic policies on employment patterns.
• Examining historical records and letters to understand the political climate and social conditions during a particular historical event.
• Conducting a meta-analysis of published studies on the effectiveness of a specific medication to assess its overall efficacy and safety.

How to Use Primary and Secondary Research Together

Having explored the distinction between primary research vs secondary research, the integration of these two approaches becomes a crucial consideration. By incorporating primary and secondary research, a comprehensive and well-informed research methodology can be achieved. The utilization of secondary research provides researchers with a broader understanding of the subject, allowing them to identify gaps in knowledge and refine their research questions properly.

Primary research methods, such as surveys or interviews, can then be employed to collect new data that directly address these research questions. The findings from primary research can be compared and validated against the existing knowledge obtained through secondary research. By combining the insights from both types of research, researchers can fill knowledge gaps, strengthen the reliability of their findings through triangulation, and draw meaningful conclusions that contribute to the overall understanding of the subject matter.

Ethical Considerations for Primary and Secondary Research

In primary research, researchers must obtain informed consent from participants, ensuring they are fully aware of the study’s purpose, procedures, and any potential risks or benefits involved. Confidentiality and anonymity should be maintained to safeguard participants’ privacy. Researchers should also ensure that the data collection methods and research design are conducted in an ethical manner, adhering to ethical guidelines and standards set by relevant institutional review boards or ethics committees.

In secondary research, ethical considerations primarily revolve around the proper and responsible use of existing data sources. Researchers should respect copyright laws and intellectual property rights when accessing and using secondary data. They should also critically evaluate the credibility and reliability of the sources to ensure the validity of the data used in their research. Proper citation and acknowledgment of the original sources are essential to maintain academic integrity and avoid plagiarism.

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Green Chemistry

Agile synthesis and automated, high-throughput evaluation of diglycolamides for liquid–liquid extraction of rare-earth elements †.

* Corresponding authors

a U.S. DOE Ames National Laboratory, Iowa State University, Ames, Iowa 50011, USA E-mail: [email protected]

b School of Environmental and Ecological Engineering, Purdue University, West Lafayette, IN 47907, USA E-mail: [email protected]

c School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, USA

Liquid–liquid extraction is one of the most scalable processes to produce rare-earth elements (REEs) from natural and recycled resources. Accelerating the research, development, and deployment (RD&D) of sustainable processes to manufacture REEs requires both facile synthesis of extractive ligands at scale and fast evaluation of process conditions. Here, we establish an integrated RD&D methodology comprised of agile ligand synthesis and automated high-throughput extraction studies. Using diglycolamides (DGAs) as an example, we first developed a method for DGA synthesis (scalable to 200 g) by directly coupling diglycolic acid and secondary amines via the solvent-free melt-amidation reaction. A substrate scope of the melt-amidation synthesis was demonstrated for 9 different DGAs with good yields (85–96%) and purities (88–96%) without any post-reaction workup or purification process. Life cycle assessment shows that our synthesis method outperforms the prior-art pathway in each environmental impact category, especially showing a 67% reduction in global warming potential. Furthermore, we investigate the structure–activity relationship of various alkyl-substituted DGAs using an automated, high-throughput workflow for liquid–liquid extraction, achieving over 180 runs in 48 hours. The acquired data enables the development of a promising flowsheet for separating light and heavy REEs. The integrated RD&D method of agile synthesis and automated, high-throughput extraction studies paves the way for future iterative development of sustainable production of REEs and other critical materials to meet the needs for clean energy transformation.

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Agile synthesis and automated, high-throughput evaluation of diglycolamides for liquid–liquid extraction of rare-earth elements

L. An, Y. Yao, T. B. Hall, F. Zhao and L. Qi, Green Chem. , 2024, Advance Article , DOI: 10.1039/D4GC01146E

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