data gathering in thesis

Reference management. Clean and simple.

How to collect data for your thesis

Collecting theoretical data

Search for theses on your topic, use content-sharing platforms, collecting empirical data, qualitative vs. quantitative data, frequently asked questions about gathering data for your thesis, related articles.

After choosing a topic for your thesis , you’ll need to start gathering data. In this article, we focus on how to effectively collect theoretical and empirical data.

Empirical data : unique research that may be quantitative, qualitative, or mixed.

Theoretical data : secondary, scholarly sources like books and journal articles that provide theoretical context for your research.

Thesis : the culminating, multi-chapter project for a bachelor’s, master’s, or doctoral degree.

Qualitative data : info that cannot be measured, like observations and interviews .

Quantitative data : info that can be measured and written with numbers.

At this point in your academic life, you are already acquainted with the ways of finding potential references. Some obvious sources of theoretical material are:

• edited volumes
• conference proceedings
• online databases like Google Scholar , ERIC , or Scopus

You can also take a look at the top list of academic search engines .

Looking at other theses on your topic can help you see what approaches have been taken and what aspects other writers have focused on. Pay close attention to the list of references and follow the bread-crumbs back to the original theories and specialized authors.

Another method for gathering theoretical data is to read through content-sharing platforms. Many people share their papers and writings on these sites. You can either hunt sources, get some inspiration for your own work or even learn new angles of your topic. 

Some popular content sharing sites are:

With these sites, you have to check the credibility of the sources. You can usually rely on the content, but we recommend double-checking just to be sure. Take a look at our guide on what are credible sources?

The more you know, the better. The guide, " How to undertake a literature search and review for dissertations and final year projects ," will give you all the tools needed for finding literature .

In order to successfully collect empirical data, you have to choose first what type of data you want as an outcome. There are essentially two options, qualitative or quantitative data. Many people mistake one term with the other, so it’s important to understand the differences between qualitative and quantitative research .

Boiled down, qualitative data means words and quantitative means numbers. Both types are considered primary sources . Whichever one adapts best to your research will define the type of methodology to carry out, so choose wisely.

In the end, having in mind what type of outcome you intend and how much time you count on will lead you to choose the best type of empirical data for your research. For a detailed description of each methodology type mentioned above, read more about collecting data .

Once you gather enough theoretical and empirical data, you will need to start writing. But before the actual writing part, you have to structure your thesis to avoid getting lost in the sea of information. Take a look at our guide on how to structure your thesis for some tips and tricks.

The key to knowing what type of data you should collect for your thesis is knowing in advance the type of outcome you intend to have, and the amount of time you count with.

Some obvious sources of theoretical material are journals, libraries and online databases like Google Scholar , ERIC or Scopus , or take a look at the top list of academic search engines . You can also search for theses on your topic or read content sharing platforms, like Medium , Issuu , or Slideshare .

To gather empirical data, you have to choose first what type of data you want. There are two options, qualitative or quantitative data. You can gather data through observations, interviews, focus groups, or with surveys, tests, and existing databases.

Qualitative data means words, information that cannot be measured. It may involve multimedia material or non-textual data. This type of data claims to be detailed, nuanced and contextual.

Quantitative data means numbers, information that can be measured and written with numbers. This type of data claims to be credible, scientific and exact.

Step-by-Step Guide: Data Gathering in Research Projects

• by Willie Wilson
• October 22, 2023

Welcome to our ultimate guide on data gathering in research projects! Whether you’re an aspiring researcher or a seasoned professional, this blog post will equip you with the essential steps to effectively gather data. In this ever-evolving digital age, data has become the cornerstone of decision-making and problem-solving in various fields. So, understanding the process of data gathering is crucial to ensure accurate and reliable results.

In this article, we will delve into the ten key steps involved in data gathering. From formulating research questions to selecting the right data collection methods , we’ll cover everything you need to know to conduct a successful research project. So, grab your notebook and get ready to embark on an exciting journey of data exploration!

Let’s dive right in and discover the step-by-step process of data gathering, enabling you to enhance your research skills and deliver impactful results.

10 Steps to Master Data Gathering

Data gathering is a crucial step in any research or analysis process. It provides the foundation for informed decision-making , insightful analysis, and meaningful insights. Whether you’re a data scientist, a market researcher, or just someone curious about a specific topic, understanding the steps involved in data gathering is essential. So, let’s dive into the 10 steps you need to master to become a data gathering wizard!

Step 1: Define Your Objective

First things first, clearly define your objective. Ask yourself what you’re trying to achieve with the data you gather. Are you looking for trends, patterns, or correlations? Do you want to support a hypothesis or disprove it? Having a clear goal in mind will help you stay focused and ensure that your data gathering efforts are purposeful.

Step 2: Determine Your Data Sources

Once you know what you’re after, it’s time to identify your data sources. Will you be collecting primary data through surveys, interviews, or experiments? Or will you rely on secondary sources like databases, research papers, or official reports? Consider the pros and cons of each source and choose the ones that align best with your objective.

Step 3: Create a Data Collection Plan

Planning is key! Before you start gathering data, create a detailed data collection plan. Outline the key variables you want to measure, determine the sampling technique, and devise a timeline. This plan will serve as your roadmap throughout the data gathering process and ensure that you don’t miss any important steps or variables.

Step 4: Design Your Data Collection Tools

Now that your plan is in place, it’s time to design the tools you’ll use to collect the data. This could be a survey questionnaire, an interview script, or an observation checklist . Remember to keep your questions clear, concise, and unbiased to ensure high-quality data.

Step 5: Pretest Your Tools

Before you launch into full-scale data collection, it’s wise to pretest your tools. This involves trying out your survey questionnaire, interview script, or observation checklist on a small sample of respondents. This step allows you to identify any issues or ambiguities in your tools and make necessary revisions.

Step 6: Collect Your Data

Now comes the exciting part—collecting the actual data! Deploy your data collection tools on your chosen sample and gather the information you need. Be organized, diligent, and ethical in your data collection, ensuring that you respect respondents’ privacy and confidentiality.

Step 7: Clean and Validate Your Data

Raw data can be messy. Before you start analyzing it, you need to clean and validate it. Remove any duplicate entries, correct any errors or inconsistencies, and check for data integrity. This step is critical to ensure the accuracy and reliability of your findings.

Step 8: Analyze Your Data

With clean and validated data in hand, it’s time to analyze! Use statistical techniques , visualization tools, or any other relevant methods to uncover patterns, relationships, and insights within your data. This step is where the true magic happens, so put on your analytical hat and dig deep!

Step 9: Interpret Your Findings

Analyzing data is just the first step; interpreting the findings is where the real value lies. Look for meaningful patterns, draw connections, and uncover insights that align with your objective. Remember to consider the limitations of your data and acknowledge any potential biases.

Step 10: Communicate Your Results

Last but not least, share your findings with the world! Prepare visualizations, reports, or presentations that effectively communicate your results. Make sure your audience understands the key takeaways and implications of your findings. Remember, knowledge is power, but only if it’s effectively shared.

And voila! You’ve now familiarized yourself with the 10 steps to master data gathering. Whether you’re a data enthusiast or a professional in the field, following these steps will set you on the path to success. So go forth, embrace the data, and uncover the hidden treasures within!

FAQ: What are the 10 Steps in Data Gathering

In the world of data-driven decision-making, gathering accurate and reliable data is crucial. Whether you’re conducting market research, academic studies, or simply exploring a topic of interest, the process of data gathering involves various steps. In this FAQ-style guide, we’ll explore the 10 steps of data gathering that will help you collect and analyze data effectively.

What are the Steps in Data Gathering

Identify your research objective: Before diving into data gathering, it’s essential to define the purpose of your research. Determine what information you need to collect and how it will contribute to your overall goal.

Create a research plan: Develop a detailed plan outlining the methods and strategies you’ll use to gather data. Consider factors such as time constraints, available resources, and potential obstacles.

Choose your data collection method: There are various methods to collect data, including surveys, interviews, observations, and experiments. Select a method or combination of methods that align with your research objective and provide the most accurate and relevant data.

Design your data collection tool: Once you’ve chosen your data collection method, design the tools you’ll use to gather information. This may include developing survey questionnaires, interview guides, or observation protocols.

Collect your data: Now it’s time to put your plan into action and start gathering data. Ensure proper training for data collectors, maintain accurate records, and adhere to ethical guidelines if applicable.

Clean and organize your data: After collecting the data, it’s essential to clean and organize it to ensure accuracy and ease of analysis. Remove any inconsistencies, irrelevant information, or duplicate entries. Use software tools such as spreadsheets or statistical software to manage your data effectively.

Analyze your data: With the cleaned and organized data, begin analyzing it to uncover patterns, trends, and insights. Utilize statistical techniques and visualizations to make sense of your data and draw meaningful conclusions .

Interpret your findings: Once you’ve analyzed the data, interpret the results in the context of your research objective. Look for connections, relationships, and implications that can inform your decision-making process.

Draw conclusions and make recommendations: Based on your analysis and interpretation, draw conclusions about your research question and provide recommendations for further action or future studies.

Communicate your findings: Finally, present your findings in a clear and concise manner. This could be through a research report, presentation, or infographic. Consider the appropriate format for your audience and ensure your communication is engaging and accessible.

Data gathering may seem like a daunting process, but by following these 10 steps, you can navigate it successfully. Remember to stay focused on your research objective, choose the right methods and tools, and analyze your data thoroughly. With proper planning and execution, you’ll gather valuable insights that can inform decision-making and drive meaningful outcomes.

• data gathering
• data sources
• decision-making
• essential steps
• impactful results
• informed decision-making
• insightful analysis
• observation checklist
• research projects
• right data collection methods

Willie Wilson

Which actor has a lazy eye, is light pink ok to wear to a wedding, you may also like, why does 911 ask for your name.

• by Mr. Gilbert Preston
• October 9, 2023

Can You Eat Donkey in America?

• by Daniel Taylor
• October 30, 2023

Is the 2021 Blue Team Leader Rare?

• by Richard Edwards
• October 18, 2023

Where is the Salvatore House?

• by Brian Thomas

The Ultimate Guide: How to Get a Free Tortured Soul in Terraria

• by Thomas Harrison
• November 4, 2023

How Old Are All the JoJo Characters?

• October 14, 2023

11 Tips For Writing a Dissertation Data Analysis

Since the evolution of the fourth industrial revolution – the Digital World; lots of data have surrounded us. There are terabytes of data around us or in data centers that need to be processed and used. The data needs to be appropriately analyzed to process it, and Dissertation data analysis forms its basis. If data analysis is valid and free from errors, the research outcomes will be reliable and lead to a successful dissertation. 

Considering the complexity of many data analysis projects, it becomes challenging to get precise results if analysts are not familiar with data analysis tools and tests properly. The analysis is a time-taking process that starts with collecting valid and relevant data and ends with the demonstration of error-free results.

So, in today’s topic, we will cover the need to analyze data, dissertation data analysis, and mainly the tips for writing an outstanding data analysis dissertation. If you are a doctoral student and plan to perform dissertation data analysis on your data, make sure that you give this article a thorough read for the best tips!

What is Data Analysis in Dissertation?

Dissertation Data Analysis  is the process of understanding, gathering, compiling, and processing a large amount of data. Then identifying common patterns in responses and critically examining facts and figures to find the rationale behind those outcomes.

Even f you have the data collected and compiled in the form of facts and figures, it is not enough for proving your research outcomes. There is still a need to apply dissertation data analysis on your data; to use it in the dissertation. It provides scientific support to the thesis and conclusion of the research.

Data Analysis Tools

There are plenty of indicative tests used to analyze data and infer relevant results for the discussion part. Following are some tests  used to perform analysis of data leading to a scientific conclusion:

11 Most Useful Tips for Dissertation Data Analysis

Doctoral students need to perform dissertation data analysis and then dissertation to receive their degree. Many Ph.D. students find it hard to do dissertation data analysis because they are not trained in it.

1. Dissertation Data Analysis Services

The first tip applies to those students who can afford to look for help with their dissertation data analysis work. It’s a viable option, and it can help with time management and with building the other elements of the dissertation with much detail.

Dissertation Analysis services are professional services that help doctoral students with all the basics of their dissertation work, from planning, research and clarification, methodology, dissertation data analysis and review, literature review, and final powerpoint presentation.

One great reference for dissertation data analysis professional services is Statistics Solutions , they’ve been around for over 22 years helping students succeed in their dissertation work. You can find the link to their website here .

For a proper dissertation data analysis, the student should have a clear understanding and statistical knowledge. Through this knowledge and experience, a student can perform dissertation analysis on their own. 

Following are some helpful tips for writing a splendid dissertation data analysis:

2. Relevance of Collected Data

If the data is irrelevant and not appropriate, you might get distracted from the point of focus. To show the reader that you can critically solve the problem, make sure that you write a theoretical proposition regarding the selection  and analysis of data.

3. Data Analysis

For analysis, it is crucial to use such methods that fit best with the types of data collected and the research objectives. Elaborate on these methods and the ones that justify your data collection methods thoroughly. Make sure to make the reader believe that you did not choose your method randomly. Instead, you arrived at it after critical analysis and prolonged research.

On the other hand,  quantitative analysis  refers to the analysis and interpretation of facts and figures – to build reasoning behind the advent of primary findings. An assessment of the main results and the literature review plays a pivotal role in qualitative and quantitative analysis.

The overall objective of data analysis is to detect patterns and inclinations in data and then present the outcomes implicitly.  It helps in providing a solid foundation for critical conclusions and assisting the researcher to complete the dissertation proposal. 

4. Qualitative Data Analysis

Qualitative data refers to data that does not involve numbers. You are required to carry out an analysis of the data collected through experiments, focus groups, and interviews. This can be a time-taking process because it requires iterative examination and sometimes demanding the application of hermeneutics. Note that using qualitative technique doesn’t only mean generating good outcomes but to unveil more profound knowledge that can be transferrable.

Presenting qualitative data analysis in a dissertation  can also be a challenging task. It contains longer and more detailed responses. Placing such comprehensive data coherently in one chapter of the dissertation can be difficult due to two reasons. Firstly, we cannot figure out clearly which data to include and which one to exclude. Secondly, unlike quantitative data, it becomes problematic to present data in figures and tables. Making information condensed into a visual representation is not possible. As a writer, it is of essence to address both of these challenges.

          Qualitative Data Analysis Methods

Following are the methods used to perform quantitative data analysis. 

•   Deductive Method

This method involves analyzing qualitative data based on an argument that a researcher already defines. It’s a comparatively easy approach to analyze data. It is suitable for the researcher with a fair idea about the responses they are likely to receive from the questionnaires.

•  Inductive Method

In this method, the researcher analyzes the data not based on any predefined rules. It is a time-taking process used by students who have very little knowledge of the research phenomenon.

5. Quantitative Data Analysis

Quantitative data contains facts and figures obtained from scientific research and requires extensive statistical analysis. After collection and analysis, you will be able to conclude. Generic outcomes can be accepted beyond the sample by assuming that it is representative – one of the preliminary checkpoints to carry out in your analysis to a larger group. This method is also referred to as the “scientific method”, gaining its roots from natural sciences.

The Presentation of quantitative data  depends on the domain to which it is being presented. It is beneficial to consider your audience while writing your findings. Quantitative data for  hard sciences  might require numeric inputs and statistics. As for  natural sciences , such comprehensive analysis is not required.

                Quantitative Analysis Methods

Following are some of the methods used to perform quantitative data analysis. 

• Trend analysis:  This corresponds to a statistical analysis approach to look at the trend of quantitative data collected over a considerable period.
• Cross-tabulation:  This method uses a tabula way to draw readings among data sets in research.  
• Conjoint analysis :   Quantitative data analysis method that can collect and analyze advanced measures. These measures provide a thorough vision about purchasing decisions and the most importantly, marked parameters.
• TURF analysis:  This approach assesses the total market reach of a service or product or a mix of both. 
• Gap analysis:  It utilizes the  side-by-side matrix  to portray quantitative data, which captures the difference between the actual and expected performance. 
• Text analysis:  In this method, innovative tools enumerate  open-ended data  into easily understandable data. 

6. Data Presentation Tools

Since large volumes of data need to be represented, it becomes a difficult task to present such an amount of data in coherent ways. To resolve this issue, consider all the available choices you have, such as tables, charts, diagrams, and graphs. 

Tables help in presenting both qualitative and quantitative data concisely. While presenting data, always keep your reader in mind. Anything clear to you may not be apparent to your reader. So, constantly rethink whether your data presentation method is understandable to someone less conversant with your research and findings. If the answer is “No”, you may need to rethink your Presentation. 

7. Include Appendix or Addendum

After presenting a large amount of data, your dissertation analysis part might get messy and look disorganized. Also, you would not be cutting down or excluding the data you spent days and months collecting. To avoid this, you should include an appendix part. 

The data you find hard to arrange within the text, include that in the  appendix part of a dissertation . And place questionnaires, copies of focus groups and interviews, and data sheets in the appendix. On the other hand, one must put the statistical analysis and sayings quoted by interviewees within the dissertation. 

8. Thoroughness of Data

It is a common misconception that the data presented is self-explanatory. Most of the students provide the data and quotes and think that it is enough and explaining everything. It is not sufficient. Rather than just quoting everything, you should analyze and identify which data you will use to approve or disapprove your standpoints. 

Thoroughly demonstrate the ideas and critically analyze each perspective taking care of the points where errors can occur. Always make sure to discuss the anomalies and strengths of your data to add credibility to your research.

9. Discussing Data

Discussion of data involves elaborating the dimensions to classify patterns, themes, and trends in presented data. In addition, to balancing, also take theoretical interpretations into account. Discuss the reliability of your data by assessing their effect and significance. Do not hide the anomalies. While using interviews to discuss the data, make sure you use relevant quotes to develop a strong rationale. 

It also involves answering what you are trying to do with the data and how you have structured your findings. Once you have presented the results, the reader will be looking for interpretation. Hence, it is essential to deliver the understanding as soon as you have submitted your data.

10. Findings and Results

Findings refer to the facts derived after the analysis of collected data. These outcomes should be stated; clearly, their statements should tightly support your objective and provide logical reasoning and scientific backing to your point. This part comprises of majority part of the dissertation. 

In the finding part, you should tell the reader what they are looking for. There should be no suspense for the reader as it would divert their attention. State your findings clearly and concisely so that they can get the idea of what is more to come in your dissertation.

11. Connection with Literature Review

At the ending of your data analysis in the dissertation, make sure to compare your data with other published research. In this way, you can identify the points of differences and agreements. Check the consistency of your findings if they meet your expectations—lookup for bottleneck position. Analyze and discuss the reasons behind it. Identify the key themes, gaps, and the relation of your findings with the literature review. In short, you should link your data with your research question, and the questions should form a basis for literature.

The Role of Data Analytics at The Senior Management Level

From small and medium-sized businesses to Fortune 500 conglomerates, the success of a modern business is now increasingly tied to how the company implements its data infrastructure and data-based decision-making. According

The Decision-Making Model Explained (In Plain Terms)

Any form of the systematic decision-making process is better enhanced with data. But making sense of big data or even small data analysis when venturing into a decision-making process might

13 Reasons Why Data Is Important in Decision Making

Wrapping Up

Writing data analysis in the dissertation involves dedication, and its implementations demand sound knowledge and proper planning. Choosing your topic, gathering relevant data, analyzing it, presenting your data and findings correctly, discussing the results, connecting with the literature and conclusions are milestones in it. Among these checkpoints, the Data analysis stage is most important and requires a lot of keenness.

In this article, we thoroughly looked at the tips that prove valuable for writing a data analysis in a dissertation. Make sure to give this article a thorough read before you write data analysis in the dissertation leading to the successful future of your research.

Oxbridge Essays. Top 10 Tips for Writing a Dissertation Data Analysis.

Emidio Amadebai

As an IT Engineer, who is passionate about learning and sharing. I have worked and learned quite a bit from Data Engineers, Data Analysts, Business Analysts, and Key Decision Makers almost for the past 5 years. Interested in learning more about Data Science and How to leverage it for better decision-making in my business and hopefully help you do the same in yours.

Recent Posts

Causal vs Evidential Decision-making (How to Make Businesses More Effective) 

In today’s fast-paced business landscape, it is crucial to make informed decisions to stay in the competition which makes it important to understand the concept of the different characteristics and...

Bootstrapping vs. Boosting

Over the past decade, the field of machine learning has witnessed remarkable advancements in predictive techniques and ensemble learning methods. Ensemble techniques are very popular in machine...

Surviving and Thriving in Postgraduate Research pp 555–687 Cite as

What Data Gathering Strategies Should I Use?

• Ray Cooksey 3 &
• Gael McDonald 4  
• First Online: 28 June 2019

1835 Accesses

1 Citations

In this chapter, we review many of the data gathering strategies that can be used by postgraduates in social and behavioural research. We explore three major domains of data gathering strategies: strategies for connecting with people (encompassing interaction-based and observation-based strategies), exploring people’s handiworks (encompassing participant-centred and artefact-based strategies) and structuring people’s experiences (encompassing data-shaping and experience-focused strategies). In light of our pluralist perspective, we consider each data gathering strategy, not only as a distinct and self-contained strategy (which may encompass a range of more specific data gathering approaches), but also as part of a larger more interconnected and dynamic toolkit. Our goal is to highlight some key considerations and issues associated with each strategy that might be relevant to your decision making about which might be appropriate for you to use as part of your research journey, given your research frame, pattern(s) of guiding assumptions, contextualisations, positionings, research questions/hypotheses, scoping and shaping considerations and MU configuration.

This is a preview of subscription content, log in via an institution .

Buying options

• Available as PDF
• Read on any device
• Instant download
• Own it forever
• Available as EPUB and PDF
• Durable hardcover edition
• Dispatched in 3 to 5 business days
• Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Anderson, L. (2006). Analytic autoethnography. Journal of Contemporary Ethnography, 5 (4), 373–395.

Article   Google Scholar  

Anderson, V. (1997). Systems thinking basics: From concepts to causal loops . Cambridge, MA: Pegasus Communications.

Google Scholar  

Angrosino, M. (2007). Doing ethnographic and observational research . London: Sage Publications.

Book   Google Scholar  

Athanasou, J. A. (1997). Introduction to educational testing . Wentworth Falls, NSW: Social Science Press.

Axelrod, R. (2007). Simulation in the social sciences. In J.-P. Rennard (Ed.), Handbook of research on nature inspired computing for economy and management (pp. 90–100). Hershey, PA: Idea Group Reference.

Babbie, E. (2011). The basics of social research (5th ed.). Belmont, CA: Wadsworth Cengage Learning.

Banks, M. (2007). Using visual data in qualitative research . London: Sage Publications.

Barbour, R. (2007). Doing focus groups . London: Sage Publications.

Beach, D., & Pedersen, R. B. (2013). Process tracing methods: Foundations and guidelines . Ann Arbor, MI: University of Michigan Press.

Bechtel, R. B. (1967). Hodometer research in architecture. Milieu, 2 , 1–9.

Bennett, A., & Checkel, J. T. (Eds.). (2014). Process tracing: From metaphor to analytic tool . Cambridge, UK: Cambridge University Press.

Bickel, R. (2007). Multilevel analysis for applied research . New York: The Guilford Press.

Boddy, C. (2005). Projective techniques in market research: Valueless subjectivity or insightful reality? A look at the evidence for the usefulness, reliability and validity of projective techniques in market research. International Journal of Market Research, 47 (3), 239–254.

Boje, D. M. (1991). The storytelling organization: A study of story performance in an office-supply firm. Administrative Science Quarterly, 36 (1), 106–126.

Bond, D., & Ramsey, E. (2007). Going beyond the fence: Using projective techniques as survey tools to meet the challenges of bounded rationality. In Proceedings of the 2007 Association for Survey Computing Meeting, The Challenges of a Changing World: Developments in the Survey Process , Southampton, UK (pp. 259–270).

Bond, T. G., & Fox, C. M. (2015). Applying the Rasch model: Fundamental measurement in the human sciences (3rd ed.). New York: Routledge.

Boslaugh, S. (2007). Secondary data sources for public health: A practical guide . New York: Cambridge University Press.

Bowen, G. A. (2009). Document analysis as a qualitative research method. Qualitative Research Journal, 9 (2), 27–40.

Bowerman, B. L., O’Connell, R. T., & Koehler, A. B. (2005). Forecasting, time series, and regression: An applied approach (4th ed.). Belmont, CA: Brooks/Cole.

Boyle, G. J., Saklofske, D. H., & Matthews, G. (Eds.). (2015). Measures of personality and social psychological constructs . Amsterdam: Academic Press.

Boyd, D., & Crawford, K. (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, Communication & Society, 15 (5), 662–679.

Britten, N., Campbell, R., Pope, C., Donovan, J., Morgan, M., & Pill, R. (2002). Using meta ethnography to synthesise qualitative research: A worked example. Journal of Health Services Research & Policy, 7 (4), 209–215.

Brown, R. V. (2005). Rational choice and judgment: Decision analysis for the decider . Hoboken, NJ: Wiley.

Bryant, A., & Charmaz, K. (Eds.). (2007). The Sage handbook of grounded theory . Los Angeles: Sage Publications.

Bryman, A., & Bell, E. (2015). Business research methods (4th ed.). New York: Oxford University Press.

Bryman, A., & Cramer, D. (2004). Constructing variables. In M. Hardy & A. Bryman (Eds.), Handbook of data analysis (pp. 17–34). London: Sage Publications.

Buzan, T. (2003). The mind map book (Rev ed.). London: BBC Books.

Buzan, T. (2018). Mind map mastery . London: Watkins.

Byrne, B. M. (2010). Structural equation modelling with AMOS: Basic concepts, applications, and programming (2nd ed.). New York: Routledge.

Campbell, R., Pound, P., Morgan, M., Daker-White, G., Britten, N., Pill, R., et al. (2011). Evaluating meta-ethnography: systematic analysis and synthesis of qualitative research. Health Technology Assessment, 15 (43).

Canter, D., Brown, J., & Groat, L. (1985). A multiple sorting procedure for studying conceptual systems. In M. Brenner, J. Brown, & D. Canter (Eds.), The research interview: Uses and approaches (pp. 79–114). London: Academic Press.

Carmichael, G. A. (2016). Fundamentals of demographic analysis: Concepts, measures and methods . Switzerland: Springer.

Carsey, T. M., & Harden, J. J. (2013). Monte Carlo simulation and resampling methods for the social sciences . Los Angeles: Sage Publications.

Cassell, C., & Walsh, S. (2004). Repertory grids. In C. Cassell & G. Symon (Eds.), Essential guide to qualitative methods in organizational research (pp. 61–72). London: Sage Publications.

Chapter   Google Scholar  

Catterall, M., & Ibbotson, P. (2000). Using projective techniques in education research. British Educational Research Journal, 26 (2), 245–256.

Charmaz, K. (2014). Constructing grounded theory (2nd ed.). London: Sage Publications.

Chang, H. (2016). Autoethnography as method . London: Routledge.

Chase, S. E. (2005). Narrative inquiry: Multiple lenses, approaches, voices. In N. K. Denzin & Y. S. Lincoln (Eds.), The Sage handbook of qualitative research (3rd ed., pp. 651–679). Thousand Oaks, CA: Sage Publications.

Checkel, J. T. (2008). Process Tracing. In A. Klotz & D. Prakash (Eds.), Qualitative methods in international relations (Research Methods Series) (pp. 114–127). London: Palgrave Macmillan.

Checkland, P., & Poulter, J. (2006). Learning for action: A short definitive account of soft systems methodology and its use for practitioners, teachers and students . Chichester, UK: Wiley.

Chell, E. (2004). Critical incident technique. In C. Cassell & G. Symon (Eds.), Essential guide to qualitative methods in organizational research (pp. 45–60). London: Sage Publications.

Chi, M. T. (2006). Laboratory methods for assessing experts’ and novices’ knowledge. In A. Ericsson, N. Charness, P. Feltovich, & R. Hoffman (Eds.), The Cambridge handbook of expertise and expert performance (pp. 167–184). Cambridge. MA: Cambridge University Press.

Chilisa, B. (2012). Indigenous research methodologies . Los Angeles: Sage Publications.

Chilisa, B., & Tsheko, G. N. (2014). Mixed methods in indigenous research: Building relationships for sustainable intervention outcomes. Journal of Mixed Methods Research, 8 (3), 222–233.

Chrzanowska, J. (2002). Interviewing groups and individuals in qualitative marketing research . London: Sage Publications.

Clandinin, D. J. (Ed.). (2007). Handbook of narrative inquiry: Mapping a methodology . Thousand Oaks, CA: Sage Publications.

Cochran, W. G., & Cox, G. M. (1957). Experimental designs (2nd ed.). New York: John Wiley & Sons.

Cohen, I. G., Amarasingham, R., Shah, A., Xie, B., & Lo, B. (2014). The legal and ethical concerns that arise from using complex predictive analytics in health care. Health Affairs, 33 (7), 1139–1147.

Cohen, L., Manion, L., & Morrison, K. (2011). Research methods in education (7th ed.). New York: Routledge.

Collis, J., & Hussey, R. (2009). Business research: A practical guide for undergraduate and postgraduate students . London: Palgrave Macmillan.

Cook, T. D., & Campbell, D. T. (1979). Quasi-experimentation: Design and analysis issues for field settings . Boston: Houghton Mifflin.

Cook, T. D., Campbell, D. T., & Peracchio, L. (1990). Quasi-experimentation. In M. D. Dunnette & L. M. Hough (Eds.), Handbook of industrial and organizational psychology (Vol. 4, pp. 491–576). Palo Alto, CA: Consulting Psychologists Press Inc.

Cooksey, R. W. (1996). Judgment analysis: Theory, methods, and applications . San Diego, CA: Academic Press.

Cooksey, R. W. (2000). Mapping the texture of managerial decision making: A complex dynamic decision perspective. Emergence: A Journal of Complexity Issues in Organizations and Management, 2 (2), 102–122.

Cooksey, R. W. (2014). Illustrating statistical procedures: Finding meaning in quantitative data (2nd ed.). Prahran, VIC: Tilde University Press.

Cooksey, R. W., Freebody, P., & Wyatt-Smith, C. (2007). Assessment as judgment-in-context: Analyzing how teachers evaluate students’ writing. Educational Research and Evaluation, 13 (5), 401–434.

Cooksey, R. W., & Loomis, R. J. (1979). Visitor locomotor exploration of a museum gallery . Paper presented at the 50th Annual Meeting of the Rocky Mountain Psychological Association, Las Vegas, NV.

Cooper, H., Hedges, L. V., & Valentine, J. C. (Eds.). (2009). The handbook of research synthesis and meta-analysis . New York: Russell Sage Foundation.

Corti, L., Thompson, P., & Fink, J. (2004). Preserving, sharing and reusing data from qualitative research: Methods and strategies. In C. Cassell & G. Symon (Eds.), Essential guide to qualitative methods in organizational research (pp. 288–300). London: Sage Publications.

Cowton, C. J. (1998). The use of secondary data in business ethics research. Journal of Business Ethics, 17, 423–434.

Czarniawska, B. (2004). Narratives in social science research . Thousand Oaks, CA: Sage Publications.

DeVellis, R. F. (2016). Scale development: Theory and applications . Los Angeles: Sage Publications.

Dick, P. (2004). Discourse analysis. In C. Cassell & G. Symon (Eds.), Essential guide to qualitative methods in organizational research (pp. 203–213). London: Sage Publications.

Duncan, M. (2004). Autoethnography: Critical appreciation of an emerging art. International Journal of Qualitative Methods, 3 (4), 28–39.

Durlak, J. A. (1995). Understanding meta-analysis. In L. G. Grimm & P. R. Yarnold (Eds.), Reading and understanding multivariate statistic (pp. 319–352). Washington, DC: American Psychological Association.

Eden, C., & Ackermann, F. (2002). A mapping framework for strategy making. In A. Huff & M. Jenkins (Eds.), Mapping strategic knowledge (pp. 173–195). London: Sage Publications.

Edmunds, H. (1999). The focus group research handbook . Lincolnwood, IL: NTC Business Books.

Elliott, J. (2005). Using narrative in social research: Qualitative and quantitative approaches . London: Sage Publications.

Elliott, H. (1997). The use of diaries in sociological research on health experience. Sociological Research Online , 2 (2). Retrieved August 11, 2018, form http://www.socresonline.org.uk/2/2/7.html .

Emerson, R. M., Fretz, R. I., & Shaw, L. L. (2011). Writing ethnographic fieldnotes (2nd ed.). Chicago: University of Chicago Press.

Enders, W. (2014). Applied econometric time series (4th ed.). New York: John Wiley & Sons.

Eppler, M. J. (2006). A comparison between concept maps, mind maps, conceptual diagrams, and visual metaphors as complementary tools for knowledge construction and sharing. Information Visualization, 5, 202–210.

Ericsson, K. A. (2003). Valid and non-reactive verbalization of thoughts during performance of tasks: Towards a solution to the central problems of introspection as a source of scientific data. In A. Jack & A. Roepstorff (Eds.), Trusting the subject: The use of introspective evidence in cognitive science (Vol. 1, pp. 1–18). Exeter, UK: Imprint Academic.

Ericsson, K. A., & Simon, H. A. (1993). Protocol analysis: verbal reports as data (Rev ed.). Cambridge, MA: The MIT Press.

Evans, W. (2015). Test wiseness: An examination of cue-using strategies. The Journal of Experimental Education, 52 (3), 141–144.

Farrington, D. P., & Knight, B. J. (1979). Two non-reactive field experiments on stealing from a ‘lost’ letter. British Journal of Social and Clinical Psychology, 18 (3), 277–284.

Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. AI magazine, 17 (3), 37.

Fielding, N. G., Lee, R. M., & Blank, G. (Eds.). (2008). The Sage handbook of online research methods . Los Angeles: Sage Publications.

Finch, H., & Lewis, J. (2003). Focus groups. In J. Ritchie & J. Lewis (Eds.), Qualitative research practice (pp. 170–198). Los Angeles: Sage Publications.

Flanagan, J. C. (1954). The critical incident technique. Psychological Bulletin, 51 (4), 327–358.

Flick, U. (2014). An introduction to qualitative research (5th ed.). Los Angeles: Sage Publications.

Fogarty, G. (2008). Principles and applications of education and psychological testing. In J. A. Athanasou (Ed.), Adult educational psychology (pp. 351–384). Rotterdam, Netherlands: Sense Publishers.

Fothergill, S., Loft, S., & Neal, A. (2009). ATC-lab advanced: An air traffic control simulator with realism and control. Behavior Research Methods & Instrumentation, 41 (1), 118–127.

Frazer, L., & Lawley, M. (2000). Questionnaire design & administration . Milton, QLD: Wiley.

Frumkin, N. (2015). Guide to economic indicators (4th ed.). London: Routledge.

Gabriel, Y., & Griffiths, D. S. (2004). Stories in organizational research. In C. Cassell & G. Symon (Eds.), Essential guide to qualitative methods in organizational research (pp. 114–126). London: Sage Publications.

Galletta, A. (2013). Mastering the semi-structured interview and beyond: From research design to analysis and publication . New York: New York University Press.

Gamst, G. C., Liang, C. T. H., & Der-Karabetian, A. (2011). Handbook of multicultural measures . Thousand Oaks, CA: Sage Publications.

Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35 (2), 137–144.

Gilbert, N. (2008). Agent-based models . Thousand Oaks, CA: Sage Publications.

Gilbert, N., & Troitzsch, K. G. (2005). Simulation for the social scientist (2nd ed.). New York: McGraw-Hill International.

Gillham, B. (2005). Research interviewing: The range of techniques . Berkshire, UK: Open University Press.

Glass, G. V., McGaw, B., & Smith, M. L. (1981). Meta-analysis in social research . Beverly Hills, CA: Sage Publications.

Glass, G. V., Willson, V. L., & Gottman, J. M. (2008). Design and analysis of time-series experiments . Charlotte, NC: Information Age Publishing.

Goodwin, J. (Ed.). (2012a). Sage secondary data analysis: Volume 1: Using secondary sources and secondary analysis . London: Sage Publications.

Goodwin, J. (Ed.). (2012b). Sage secondary data analysis: Volume 2: Quantitative approaches to secondary analysis . London: Sage Publications.

Goodwin, J. (Ed.). (2012c). Sage secondary data analysis: Volume 3: Qualitative data and research in secondary analysis . London: Sage Publications.

Goodwin, J. (Ed.). (2012d). Sage secondary data analysis: Volume 4: Ethical, methodological and practical issues in secondary analysis . London: Sage Publications.

Graco, W. J. (2001). Research into identification and classification of patterns of non-compliance in data using a doctor-shopper sample . Unpublished PhD thesis, School of Marketing and Management, University of New England, Armidale, NSW, Australia.

Gray, D. E. (2014). Doing research in the real world (3rd ed.). Los Angeles: Sage Publications.

Grimmer, J., & Stewart, B. M. (2013). Text as data: The promise and pitfalls of automatic content analysis methods for political texts. Political analysis, 21 (3), 267–297.

Guest, G., MacQueen, K. M., & Namey, E. E. (2012). Applied thematic analysis . Los Angeles: Sage Publications.

Guillemin, M. (2004,). Understanding illness: Using drawings as a research method. Qualitative Health Research , 14 (2), 272–289.

Gupta, V., & Lehal, G. S. (2009). A survey of text mining techniques and applications. Journal of Emerging Technologies in Web Intelligence, 1 (1), 60–76.

Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2014). A primer on partial least squares structural equation modelling (SEM) . Los Angeles: Sage Publications.

Han, J., Kamber, M., & Pei, J. (2012). Data mining: Concepts and techniques (3rd ed.). Waltham, MA: Morgan Kaufmann Publishers.

Hand, D. J. (2004). Measurement theory and practice: The world through quantification . New York: John Wiley & Sons.

Hancock, G. R. (2004). Experimental, quasi-experimental and nonexperimental design with latent variables. In D. Kaplan (Ed.), The Sage handbook of quantitative methodology for the social sciences (pp. 317–334). Thousand Oaks, CA: Sage Publications.

Haladyna, T. M. (2004). Developing and validating multiple-choice test items (3rd ed.). London: Routledge.

Hamm, R. M. (1988). Moment-by-moment variation in experts’ analytic and intuitive cognitive activity. IEEE Transactions on Systems, Man, and Cybernetics, 18 (5), 757–776.

Hammond, K. R., Frederick, E., Robillard, N., & Victor, D. (1989). Application of cognitive theory to the student-teacher dialogue. In D. A. Evans & V. L. Patel (Eds.), Cognitive science in medicine. Biomedical modeling . Cambridge, MA: M1T Press (pp. 173–210).

Hammond, K. R., & Stewart, T. R. (2001). The essential Brunswik: Beginnings, explications, applications . New York: Oxford University Press.

Hammond, K. R., & Wascoe, N. E. (1980). Realizations of Brunswik’s representative design . San Francisco: Jossey-Bass.

Harper, D. (2005). What’s new visually? In N. K. Denzin & Y. S. Lincoln (Eds.), The Sage handbook of qualitative research (3rd ed., pp. 747–763). Thousand Oaks, CA: Sage Publications.

Heritage, J. (1984). Garfinkel and ethnomethodology . Cambridge, UK: Polity Press.

Hinds, P. S., Vogel, R. J., & Clarke-Steffen, L. (1997). The possibilities and pitfalls of doing a secondary analysis of a qualitative data set. Qualitative Health Research, 7 (3), 408–424.

Hine, D. W., Montiel, C. J., Cooksey, R. W., & Lewko, J. (2005). Mental models of poverty in developing nations: A Canada-Philippines contrast. Journal of Cross-Cultural Psychology, 36 (3), 283–303.

Hine, D. W., Gifford, R., Heath, Y., Cooksey, R., & Quain, P. (2009). A cue utilization approach for investigating harvest decisions in commons dilemmas. Journal of Applied Social Psychology, 39 (3), 564–588.

Hodson, R. (1999). Analyzing documentary accounts . Thousand Oaks, CA: Sage Publications.

Hofferth, S. L. (2005). Secondary data analysis in family research. Journal of Marriage and Family, 67 (4), 891–907.

Hughes, J. & Goodwin, J. (Eds). (2014a). Documentary & archival research: Volume 1: Human documents – Perspectives and approaches . London: Sage Publications.

Hughes, J. & Goodwin, J. (Eds). (2014b). Documentary & archival research: Volume 3: Human documents in social research . London: Sage Publications.

Hughes, J. & Goodwin, J. (Eds). (2014c). Documentary & archival research: Volume 4: Archival research . London: Sage Publications.

Hyland, M. (1981). Introduction to theoretical psychology . London: Macmillan Press.

Irwin, S. (2013). Qualitative secondary data analysis: Ethics, epistemology and context. Progress in Development Studies, 13 (4), 295–306.

Jasper, J. D., & Shapiro, J. (2002). MouseTrace: A better mousetrap for catching decision processes. Behavior Research Methods, Instruments, & Computers, 34 (3), 364–374.

Johnson, B. (2001). Towards a new classification of nonexperimental quantitative research. Educational Researcher, 30 (2), 3–13.

Jones, S. H. (2005). Autoethnography: Making the personal political. In N. K. Denzin & Y. S. Lincoln (Eds.), The Sage handbook of qualitative research (3rd ed., pp. 763–791). Thousand Oaks, CA: Sage Publications.

Jorgensen, D. (1989). Participant observation: A methodology for human studies . Newbury Park, CA: Sage Publications.

Kane, M., & Trochim, W. M. K. (2007). Concept mapping for planning and evaluation . Thousand Oaks, CA: Sage Publications.

Kamberelis, G., Dimitriadis, G., & Welker, A. (2018). Focus group research and/in figured worlds. In N. K. Denzin & Y. S. Lincoln (Eds.), The Sage handbook of qualitative research (5th ed., pp. 692–716). Los Angeles: Sage Publications.

Kaptchuk, T. J. (2001). The double-blind, randomized, placebo-controlled trial: Gold standard or golden calf? Journal of Clinical Epidemiology, 54 (6), 541–549.

Keeves, J. P. (1997). Educational research, methodology, and measurement: An international handbook (2nd ed.). Oxford, UK: Pergamon.

Kellehear, A. (1993). The unobtrusive observer: A guide to methods . St Leonards, NSW: Allen & Unwin.

Keppel, G., & Wickens, T. D. (2004). Design and analysis: A researcher’s handbook (4th ed.). Upper Saddle River, NJ: Prentice Hall.

Ker, J. S., Hesketh, E. A., Anderson, F., & Johnston, D. A. (2006). Can a ward simulation exercise achieve the realism that reflects the complexity of everyday practice junior doctors encounter? Medical Teacher, 28 (4), 330–334.

King, N. (2004). Using templates in the thematic analysis of data. In C. Cassell & G. Symon (Eds.), Essential guide to qualitative methods in organizational research (pp. 256–270). London: Sage Publications.

Kirk, R. E. (2013). Experimental design: Procedures for behavioral sciences (4th ed.). Thousand Oaks, CA: Sage Publications.

Konstantopoulos, S., & Hedges, L. (2004). Meta-analysis. In D. Kaplan (Ed.), The Sage handbook of quantitative methodology for the social sciences (pp. 281–300). Thousand Oaks, CA: Sage Publications.

Kovach, M. (2009). Indigenous methodologies: Characteristics, conversations, and contexts . Toronto: University of Toronto Press.

Kovach, M. (2018). Doing indigenous methodologies: A letter to a research class. In N. K. Denzin & Y. S. Lincoln (Eds.), The Sage handbook of qualitative research (5th ed., pp. 214–234). Los Angeles: Sage Publications.

Krippendorf, K. (2004). Content analysis: An introduction to its methodology (2nd ed.). Thousand Oaks, CA: Sage Publications.

Kumar, L., & Bhatia, P. K. (2013). Text mining: concepts, process and applications. Journal of Global Research in Computer Science, 4 (3), 36–39.

Kvale, S. (2007). Doing interviews . London: Sage Publications.

L’Eplattenier, B. E. (2009). An argument for archival research methods: thinking beyond methodology. College English, 72 (1), 67–79.

Lamprianou, I. (2008). Introduction to psychometrics: The case of Rasch models. In J. A. Athanasou (Ed.), Adult educational psychology (pp. 385–418). Rotterdam, Netherlands: Sense Publishers.

Lane, D. C. (1995). On a resurgence of management simulations and games. Journal of the Operations Research Society, 46 (5), 604–625.

Lane, S., Raymond, M. R., & Haladyna, T. M. (2015). Handbook of test development (2nd ed.). London: Routledge.

Laukkanen, M. (1998). Conducting causal mapping research: Opportunities and challenges. In C. Eden & J. Spender (Eds.), Managerial and organizational cognition: Theory, methods and research (pp. 168–191). London: Sage Publications.

Lee, A. T. (2005). Flight simulation: virtual environments in aviation . London: Routledge.

Legard, R., Keegan, J., & Ward, K. (2003). In-depth interviews. In J. Ritchie & J. Lewis (Eds.), Qualitative research practice (pp. 138–169). Los Angeles: Sage Publications.

Lipsey, M. W., & Wilson, D. B. (2001). Practical meta-analysis . Thousand Oaks, CA: Sage Publications.

Louviere, J. J. (1988). Analyzing decision making: Metric conjoint analysis . Newbury Park, CA: Sage Publications.

Maani, K. E., & Cavana, R. Y. (2007). Systems thinking, systems dynamics: Managing change and complexity (2nd ed.). North Shore, NZ: Pearson Education New Zealand.

Malhotra, N. K., Hall, J., Shaw, M. & Oppenheim, P. P. (2008). Essentials of marketing research: An applied approach (2nd Ed.). French’s Forest, NSW: Pearson Education.

Margolis, E., & Pauwels, L. (Eds.). (2011). The Sage handbook of visual research methods . Los Angeles: Sage Publications.

Margolis, E., & Zunjarwad, R. (2018). Visual research. In N. K. Denzin & Y. S. Lincoln (Eds.), The Sage handbook of qualitative research (5th ed., pp. 600–626). Los Angeles: Sage Publications.

Mathison, S. (1988). Why triangulate? Educational Researcher, 17 (2), 13–17.

McAuley, J. (2004). Hermeneutic understanding. In C. Cassell & G. Symon (Eds.), Essential guide to qualitative methods in organizational research (pp. 192–202). London: Sage Publications.

McDonald, S., Daniels, K., & Harris, C. (2004). Cognitive mapping in organizational research. In C. Cassell & G. Symon (Eds.), Essential guide to qualitative methods in organizational research (pp. 73–85). London: Sage Publications.

Meier, P. S. (2007). Mind-mapping: A tool for eliciting and representing knowledge held by diverse informants. Social Research Update, 52, 1–4.

Mennecke, B., Roche, E., Bray, D., Konsynski, B., Lester, J., Rowe, M., et al. (2008). Second Life and other virtual worlds: A roadmap for research. Communications of the Association for Information Systems , 22 (Article 20), 371–388. Retrieved August 11, 2018, from https://lib.dr.iastate.edu/cgi/viewcontent.cgi?article=1015&context=scm_pubs .

Meyer, B. D. (1995). Natural and quasi-experiments in economics. Journal of Business and Economic Statistics, 13 (2), 151–161.

Miller, J. H., & Page, S. E. (2007). Complex adaptive systems: An introduction to computational models of social life . Princeton, NJ: Princeton University Press.

Milligan, G. W. (1981). A Monte Carlo study of thirty internal criterion measures for cluster analysis. Psychometrika, 46 (2), 187–199.

Minichiello, V., Aroni, R., & Hays, T. (2008). In-depth interviewing: Principles, techniques, analysis . Sydney: Pearson Education.

Montgomery, P., & Bailey, P. H. (2007). Field notes and theoretical memos in grounded theory. Western Journal of Nursing Research, 29 (1), 65–79.

Mooney, C. Z. (1997). Monte Carlo simulation . Thousand Oaks, CA: Sage Publications.

Muchiri, M. (2006). Transformational leader behaviours, social processes of leadership and substitutes for leadership as predictors of employee commitment, efficacy, citizenship behaviours and performance outcomes . Unpublished PhD thesis, New England Business School, University of New England.

Murdock, S. H., Kelley, C., Jordan, J., Pecotte, B., & Luedke, A. (2016). Demographics: A guide to methods and data sources for media, business, and government . London: Routledge.

Nisbett, R. E., & Ross, L. (1980). Human inference: Strategies and shortcomings of social judgment . Upper Saddle River, NJ: Prentice Hall.

Noblit, G. W., & Hare, R. D. (1988). Meta-ethnography: Synthesizing qualitative studies (Vol. 11). Newbury Park, CA: Sage Publications.

North, M. (2012). Data mining for the masses . Athens: Global Text Project. Retrieved August 11, 2018, from https://s3.amazonaws.com/academia.edu.documents/39657706/North_-_Data_Mining_for_the_Masses_-_2012.pdf?AWSAccessKeyId=AKIAIWOWYYGZ2Y53UL3A&Expires=1533962896&Signature=3h603Aq1BUPPSz2Svg0lC9RvwU0%3D&response-content-disposition=inline%3B%20filename%3DData_Mining.pdf .

Nunnally, J. C. (1978). Psychometric theory (2nd ed.). New York: McGraw-Hill.

O’Cass, A. (1998). Reconceptualising and reconstructing consumer involvement: modelling involvement in a nomological network of relevant constructs: Casting the net wider or just fishing around . Unpublished PhD thesis, School of Marketing and Management, University of New England, Armidale, NSW, Australia.

Olaru, D., Purchase, S., & Denize, S. (2009). Using docking/replication to verify and validate computational models. In Proceedings of the 18th World IMACS/MODSIM Congress , Cairns, Queensland (pp. 4432–4438). Retrieved August 11, 2018, from https://pdfs.semanticscholar.org/0edc/80bb4313dab4a0e0ee94a508a5a2883d4ebd.pdf .

Omodei, M. M., & Wearing, A. J. (1995). The Fire Chief microworld generating program: An illustration of computer-simulated microworlds as an experimental paradigm for studying complex decision-making behavior. Behavior Research Methods, Instruments, & Computers, 27 (3), 303–316.

Onwuegbuzie, A. J., Leech, N. L., & Collins, K. M. (2010). Innovative data collection strategies in qualitative research. The Qualitative Report, 15 (3), 696–726.

Ortlipp, M. (2008). Keeping and using reflective journals in the qualitative research process. The Qualitative Report, 13 (4), 695–705.

Paterson, B. L., Thorne, S. E., Canam, C., & Jillings, C. (2001). Meta-study of qualitative health research: A practical guide to meta-analysis and meta-synthesis . Thousand Oaks, CA: Sage Publications.

Payne, J. W., Bettman, J. R., & Johnson, E. J. (1993). The adaptive decision maker . New York: Cambridge University Press.

Phellas, C. N., Bloch, A., & Seale, C. (2012). Structured methods: interviews, questionnaires and observation. In C. Seale (Ed.), Researching society and culture (3rd ed., pp. 181–205). London: Sage Publications.

Pidd, M. (2009). Tools for thinking: Modelling in management science (3rd ed.). Chichester, UK: John Wiley & Sons.

Pierce, C. A., & Aguinis, H. (1997). Using virtual reality technology in organizational behavior research. Journal of Organizational Behavior, 18 (5), 407–410.

Pink, S. (2013). Doing visual ethnography . Los Angeles: Sage Publications.

Place, U. T. (1992). The role of the ethnomethodological experiment in the empirical investigation of social norms and its application to conceptual analysis. Philosophy of the Social Sciences, 22 (4), 461–474.

Plowright, D. (2011). Using mixed methods: Frameworks for an integrated methodology . Los Angeles: Sage Publications.

Prasad, A. (2002). The contest over meaning: Hermeneutics as an interpretive methodology for understanding texts. Organizational Research Methods, 5 (1), 12–33.

Proctor, T. (2010). Creative problem solving for managers (3rd ed.). New York: Routledge.

Provost, F., & Fawcett, T. (2013). Data science and its relationship to big data and data-driven decision making. Big Data, 1 (1), 51–59.

Punch, K. (2003). Survey research: The basics . London: Sage Publications.

Railsback, S. F., & Grimm, V. (2012). Agent-based and individual-based modeling: A practical introduction . Princeton, NJ: Princeton University Press.

Rapley, T. (2007). Doing conversation, discourse and document analysis . London: Sage Publications.

Reynolds, C. R., Livingston, R. B., Willson, V. L., & Willson, V. (2010). Measurement and assessment in education (2nd ed.). Boston: Pearson Education International.

Richmond, B. (2004). An introduction to systems thinking: STELLA software . Lebanon, OH: IEEE Systems.

Robinson, J. P., Shaver, P. R., & Wrightsman, L. S. (Eds.). (1991). Measures of personality and social psychological attitudes . San Diego: Academic Press.

Rosenthal, R. (1984). Meta-analytic procedures for social research . Beverly Hills, CA: Sage Publications.

Ross, G. (2001). Visual methodologies: An introduction to the interpretation of visual materials . London: Sage Publications.

Rowlinson, M. (2004). Historical analysis of company documents. In C. Cassell & G. Symon (Eds.), Essential guide to qualitative methods in organizational research (pp. 301–311). London: Sage Publications.

Rymaszewski, M., Au, W. J., Wallace, M., Winters, C., Ondrejka, C., & Batstone-Cunningham, B. (2007). Second life: The official guide . Hoboken, NJ: Wiley.

Sandall, J. L. (2006). Navigating pathways through complex systems of interacting problems: Strategic management of native vegetation policy . Unpublished PhD thesis, New England Business School, University of New England, Armidale, NSW, Australia.

Sandelowski, M., Docherty, S., & Emden, C. (1997). Qualitative metasynthesis: Issues and techniques. Research in Nursing & Health, 20 (4), 365–371.

Samra-Fredericks, D. (2004). Talk-in-interaction/conversation analysis. In C. Cassell & G. Symon (Eds.), Essential guide to qualitative methods in organizational research (pp. 214–227). London: Sage Publications.

Sapsford, R. (2007). Survey research (2nd ed.). London: Sage Publications.

Schembri, S., & Boyle, M. V. (2013). Visual ethnography: Achieving rigorous and authentic interpretations. Journal of Business Research, 66 (9), 1251–1254.

Schkade, D. A., & Payne, J. W. (1994). How people respond to contingent valuation questions: A verbal protocol analysis of willingness to pay for an environmental regulation. Journal of Environmental Economics and Management, 26 (1), 88–109.

Schmidt, F. L., & Hunter, J. E. (2014). Methods of meta-analysis: Correcting error and bias in research findings . Los Angeles: Sage Publications.

Schmitt, R. (2005). Systematic metaphor analysis as a method of qualitative research. The Qualitative Report, 10 (2), 358–394.

Schreiber, R., Crooks, D., & Stern, P. N. (1997). Qualitative meta-analysis. In J. Morse (Ed.), Completing a qualitative project: Details and dialogue (pp. 311–326). Thousand Oaks, CA: Sage Publications.

Schreier, M. (2012). Qualitative content analysis in practice . Los Angeles: Sage Publications.

Shadish, W. R., Cook, T. D., & Campbell, D. T. (2001). Experimental and quasi-experimental designs for generalized causal inference (2nd ed.). Boston: Cengage.

Shum, D., O’Gorman, J., Creed, P., & Myors, B. (2017). Psychological testing and assessment ebook (3rd ed.). New York: Oxford University Press.

Senge, P., Kleiner, A., Roberts, C., Ross, R., & Smith, B. (1994). The fifth discipline field book . London: Nicholas Brealey.

Shiratori, R., Arai, K., & Kato, F. (2005). Gaming, simulations and society: Research scope and perspective . Tokyo: Springer.

Sloan, L., & Quan-Haase, A. (Eds.). (2017). The Sage handbook of social media research methods . Los Angeles: Sage Publications.

Small, S. D., Wuerz, R. C., Simon, R., Shapiro, N., Conn, A., & Setnik, G. (1999). Demonstration of high-fidelity simulation team training for emergency medicine. Academic Emergency Medicine, 6 (4), 312–323.

Smith, A. E., & Humphreys, M. S. (2006). Evaluation of unsupervised semantic mapping of natural language with Leximancer concept mapping. Behavior Research Methods, 38 (2), 262–279.

Smith, E. (2008). Pitfalls and promises: The use of secondary data analysis in educational research. British Journal of Educational Studies, 56 (3), 323–339.

Smith, M. (2007). Research methods in accounting . Los Angeles: Sage Publications.

Solórzano, D. G., & Yosso, T. J. (2002). Critical race methodology: Counter-storytelling as an analytical framework for education research. Qualitative Inquiry, 8 (1), 23–44.

Stengel, D. N., & Chaffe-Stengel, P. (2012). Working with economic indicators: Interpretation and sources . New York: Business Expert Press.

Sterman, J. S. (2000). Business dynamics: Systems thinking and modeling for a complex world . New York: McGraw-Hill/Irwin.

Stewart, D., Shamdasani, P. N., & Rook, D. W. (2007). Focus groups: Theory and practice (2nd ed.). Thousand Oaks, CA: Sage Publications.

Stiles, D. (2004,). Pictorial representation. In C. Cassell & G. Symon (Eds.), Essential guide to qualitative methods in organizational research (pp. 127–140). London: Sage Publications.

Stuart, E. A., & Rubin, D. B. (2008). Best practice in quasi-experimental designs: Matching methods for causal inference. In J. W. Osborne (Ed.), Best practices in quantitative methods (pp. 155–176). Los Angeles: Sage Publications.

Sue, V. M., & Ritter, L. A. (2012). Conducting online surveys (2nd ed.). Los Angeles: Sage Publications.

Suri, H. (2011). Purposeful sampling in qualitative research synthesis. Qualitative Research Journal, 11 (2), 63–75.

Symon, G. (2004). Qualitative research diaries. In C. Cassell & G. Symon (Eds.), Essential guide to qualitative methods in organizational research (pp. 98–113). London: Sage Publications.

Taber, C. S., & Timpone, R. J. (1996). Computational modeling . Thousand Oaks, CA: Sage Publications.

Tansey, O. (2007). Process tracing and elite interviewing: A case for non-probability sampling. PS: Political Science & Politics , 40 (4), 765–772.

Thornton III, G. C., & Kedharnath, U. (2013). Work sample tests. In K. F. Geisinger et al. (Eds.), APA handbook of testing and assessment in psychology, Vol. 1. Test theory and testing and assessment in industrial and organizational psychology (pp. 533–550). Washington, DC: American Psychological Association.

Trenor, J. M., Miller, M. K., & Gipson, K. G. (2011). Utilization of a think-aloud protocol to cognitively validate a survey instrument identifying social capital resources of engineering undergraduates. In Electronic Proceedings of the American Society for Engineering Education Annual Conference and Exposition , Vancouver, CA (pp. 22.1656.1–22.1656.15). Retrieved August 11, 2018, from https://peer.asee.org/utilization-of-a-think-aloud-protocol-to-cognitively-validate-a-survey-instrument-identifying-social-capital-resources-of-engineering-undergraduates .

van Someren, M. W., Barnard, Y. F., & Sandberg, J. A. C. (1994). The think aloud method: A practical approach to modeling cognitive processes . London: Academic Press.

Veal, A. J. (2005). Business research methods: A managerial approach (2nd ed.). French’s Forest, NSW: Pearson Education.

Walker, S. J. (2014). Big data: A revolution that will transform how we live, work, and think. International Journal of Advertising, 33 (1), 181–183.

Walsh, J. J. (1997). Projective testing techniques. In J. P. Keeves (Ed.), Educational research, methodology and measurement: An international handbook (2nd ed., pp. 954–958). Oxford, UK: Pergamon.

Walsh, S., & Clegg, C. (2004). Soft systems analysis: Reflections and update. In C. Cassell & G. Symon (Eds.), Essential guide to qualitative methods in organizational research (pp. 334–348). London: Sage Publications.

Walsh, D., & Downe, S. (2005). Meta-synthesis method for qualitative research: A literature review. Journal of Advanced Nursing, 50 (2), 204–211.

Waddington, D. (2004). Participant observation. In C. Cassell & G. Symon (Eds.), Essential guide to qualitative methods in organizational research (pp. 154–164). London: Sage Publications.

Weber, R. P. (1990). Basic content analysis (2nd ed.). Newbury Park, CA: Sage Publications.

Webb, E. J., Campbell, D. T., Schwartz, R. D., & Sechrest, L. (2000). Unobtrusive measures (Rev ed.). Thousand Oaks, CA: Sage Publications.

Webster, J. G. (2015). The physiological measurement handbook . Boca Raton: CRC Press.

Wilcox, R. R. (1997). Simulation as a research technique. In J. P. Keeves (Ed.), Educational research, methodology and measurement: An international handbook (2nd ed., pp. 150–154). Oxford, UK: Pergamon.

Wilensky, U. (1999). NetLogo . Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL. Retrieved August 11, 2018, from http://ccl.northwestern.edu/netlogo/ .

Wilensky, U. (2003). NetLogo Traffic Grid model . Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL. Retrieved August 11, 2018, from http://ccl.northwestern.edu/netlogo/models/TrafficGrid .

Williams, J. H. (2014). Defining and measuring nature: The make of all things . San Raphael, CA: Morgan & Claypool.

Wybo, J. L. (2008). The role of simulation exercises in the assessment of robustness and resilience of private or public organizations. In H. J. Pasman & I. A Kirillov (Eds.), Resilience of cities to terrorist and other threats (pp. 491–507). Dordrecht: Springer.

Yamarone, R. (2017). The economic indicator handbook: How to evaluate economic trends to maximize profits and minimize losses . Hoboken, NJ: Wiley.

Yin, R. K. (2011). Qualitative research from start to finish . New York: The Guilford Press.

Download references

Author information

Authors and affiliations.

UNE Business School, University of New England, Armidale, NSW, Australia

Ray Cooksey

RMIT University Vietnam, Ho Chi Minh City, Vietnam

Gael McDonald

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Ray Cooksey .

Appendix: Clarifying Experimental/Quasi-experimental Design Jargon

These contrasting concepts provide insights into the way that researchers, who implement the Manipulative experience-focused strategy under the positivist pattern of guiding assumptions, talk or write about certain features of their research.

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this chapter

Cite this chapter.

Cooksey, R., McDonald, G. (2019). What Data Gathering Strategies Should I Use?. In: Surviving and Thriving in Postgraduate Research. Springer, Singapore. https://doi.org/10.1007/978-981-13-7747-1_14

Download citation

DOI : https://doi.org/10.1007/978-981-13-7747-1_14

Published : 28 June 2019

Publisher Name : Springer, Singapore

Print ISBN : 978-981-13-7746-4

Online ISBN : 978-981-13-7747-1

eBook Packages : Education Education (R0)

Share this chapter

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Publish with us

Policies and ethics

• Find a journal
• Track your research

Study at Cambridge

About the university, research at cambridge.

• Undergraduate courses
• Events and open days
• Fees and finance
• Postgraduate courses
• How to apply
• Postgraduate events
• Fees and funding
• International students
• Continuing education
• Executive and professional education
• Courses in education
• How the University and Colleges work
• Term dates and calendars
• Visiting the University
• Annual reports
• Equality and diversity
• A global university
• Public engagement
• Give to Cambridge
• For Cambridge students
• For our researchers
• Business and enterprise
• Colleges & departments
• Email & phone search
• Museums & collections
• Open Research
• Share Your Research
• Open Research overview
• Share Your Research overview
• Open Research Position Statement
• Scholarly Communication overview
• Join the discussion overview
• Author tools overview
• Publishing Schol Comm research overview
• Open Access overview
• Open Access policies overview
• Places to find OA content
• Open Access Monographs overview
• Open Access Infrastructure
• Repository overview
• How to Deposit overview
• Digital Object Identifiers (DOI)
• Request a Copy
• Copyright overview
• Third party copyright
• Licensing options
• Creative Commons
• Authorship and IP
• Copyright and VLE
• Copyright resources
• Outreach overview
• Training overview
• Events overview
• Contact overview
• Governance overview

Data and your thesis

• Scholarly Communication
• Open Access
• Training, Outreach and Events

What is research data?

Research data are the evidence that underpins the answer to your research question and can support the findings or outputs of your research. Research data takes many different forms. They may include for example, statistics, digital images, sound recordings, films, transcripts of interviews, survey data, artworks, published texts or manuscripts, or fieldwork observations. The term 'data' is more familiar to researchers in Science, Technology, Engineering and Mathematics (STEM), but any outputs from research could be considered data. For example, Humanities, Arts and Social Sciences (HASS) researchers might create data in the form of presentations, spreadsheets, documents, images, works of art, or musical scores. The Research Data Management Team in the University Library aim to help you plan, create, organise, share, and look after your research materials, whatever form they take. For more information about the Research data Management Team, visit their website .

Data Management Plans

Research Data Management is a complex issue, but if done correctly from the start, could save you a lot of time and hassle when you are writing up your thesis. We advise all students to consider data management as early as possible and create a Data Management Plan (DMP). The Research Data Management Team offer help in creating your DMP and can offer advice and training on how to do this. There are some departments that have joined a pilot project to include Data Management Plans in the registration reviews of PhD students. As part of the pilot, students are asked to complete a brief Data Management Plan (DMP) and supervisors and assessors ensure that the student has thought about all the issues and their responses are reasonable. If your department is taking part in the pilot or would like to, see the Data Management Plans for Pilot for Cambridge PhD Students page. The Research Data Management Team will provide support for any students, supervisors or assessors that are in need.

Submitting your digital thesis and depositing your data

If you have created data that is connected to your thesis and the data is in a format separate to the thesis file itself, we recommend that you deposit it in the data repository and make it open access to improve discoverability. We will accept data that either does not contain third party copyright, or contains third party copyright that has been cleared and is data of the following types:

•     computer code written by the researcher
•     software written by the researcher
•     statistical data
•     raw data from experiments

If you have created a research output which is not one of those listed above, please contact us on the [email protected] address and we will advise whether you should deposit this with your thesis, or separately in the data repository. If you are ready to deposit your data in the data repository, please do so via symplectic elements. More information on how to deposit can be found on the Research Data Management pages . If you wish to cite your data in your thesis, we can arranged for placeholder DOIs to be created in the data repository before your thesis is submitted. For further information, please email:  [email protected]  

Third party copyright in your data

For an explanation of what is third party copyright, please see the OSC third party copyright page . If your data is based on, or contains third party copyright you will need to obtain clearance to make your data open access in the data repository. It is possible to apply a 12 month embargo to datasets while clearance is obtained if you need extra time to do this. However, if it is not possible to clear the third party copyrighted material, it is not possible to deposit your data in the data repository. In these cases, it might be preferable to deposit your data with your thesis instead, under controlled access, but this can be complicated if you wish to deposit the thesis itself under a different access level. Please email [email protected] with any queries and we can advise on the best solution.

Open Research Newsletter sign-up

Please contact us at  [email protected]   to be added to the mailing list to receive our quarterly e-Newsletter.

The Office of Scholarly Communication sends this Newsletter to its subscribers in order to disseminate information relevant to open access, research data management, scholarly communication and open research topics. For details on how the personal information you enter here is used, please see our  privacy policy . 

Privacy Policy

© 2024 University of Cambridge

• Contact the University
• Accessibility
• Freedom of information
• Privacy policy and cookies
• Statement on Modern Slavery
• Terms and conditions
• University A-Z
• Undergraduate
• Postgraduate
• Research news
• About research at Cambridge
• Spotlight on...

• Deutschland
• United Kingdom

• PhD Dissertations
• Master’s Dissertations
• Bachelor’s Dissertations
• Scientific Dissertations
• Medical Dissertations
• Bioscience Dissertations
• Social Sciences Dissertations
• Psychology Dissertations
• Humanities Dissertations
• Engineering Dissertations
• Economics Dissertations
• Service Overview
• Revisión en inglés
• Relecture en anglais
• Revisão em inglês

Manuscript Editing

• Research Paper Editing
• Lektorat Doktorarbeit
• Dissertation Proofreading
• Englisches Lektorat
• Journal Manuscript Editing
• Scientific Manuscript Editing Services
• Book Manuscript Editing
• PhD Thesis Proofreading Services
• Wissenschaftslektorat
• Korektura anglického textu
• Akademisches Lektorat
• Journal Article Editing
• Manuscript Editing Services

PhD Thesis Editing

• Medical Editing Sciences
• Proofreading Rates UK
• Medical Proofreading
• PhD Proofreading
• Academic Proofreading
• PhD Proofreaders
• Best Dissertation Proofreaders
• Masters Dissertation Proofreading
• Proofreading PhD Thesis Price
• PhD Dissertation Editing
• Lektorat Englisch Preise
• Lektorieren Englisch
• Wissenschaftliches Lektorat
• Thesis Proofreading Services
• PhD Thesis Proofreading
• Proofreading Thesis Cost
• Proofreading Thesis
• Thesis Editing Services
• Professional Thesis Editing
• PhD Thesis Editing Services
• Thesis Editing Cost
• Dissertation Proofreading Services
• Proofreading Dissertation

PhD Dissertation Proofreading

• Dissertation Proofreading Cost
• Dissertation Proofreader
• Correção de Artigos Científicos
• Correção de Trabalhos Academicos
• Serviços de Correção de Inglês
• Correção de Dissertação
• Correção de Textos Precos
• Revision en Ingles
• Revision de Textos en Ingles
• Revision de Tesis
• Revision Medica en Ingles
• Revision de Tesis Precio
• Revisão de Artigos Científicos
• Revisão de Trabalhos Academicos
• Serviços de Revisão de Inglês
• Revisão de Dissertação
• Revisão de Textos Precos
• Corrección de Textos en Ingles
• Corrección de Tesis
• Corrección de Tesis Precio
• Corrección Medica en Ingles
• Corrector ingles
• Choosing the right Journal
• Journal Editor’s Feedback
• Dealing with Rejection
• Quantitative Research Examples
• Number of scientific papers published per year
• Acknowledgements Example
• ISO, ANSI, CFR & Other
• Types of Peer Review
• Withdrawing a Paper
• What is a good h-index
• Appendix paper
• Cover Letter Templates
• Writing an Article
• How To Write the Findings
• Abbreviations: ‘Ibid.’ & ‘Id.’
• Sample letter to editor for publication
• Tables and figures in research paper
• Journal Metrics
• Revision Process of Journal Publishing
• JOURNAL GUIDELINES

Select Page

Writing the Data Analysis Chapter(s): Results and Evidence

Posted by Rene Tetzner | Oct 19, 2021 | PhD Success | 0 |

4.4 Writing the Data Analysis Chapter(s): Results and Evidence

Unlike the introduction, literature review and methodology chapter(s), your results chapter(s) will need to be written for the first time as you draft your thesis even if you submitted a proposal, though this part of your thesis will certainly build upon the preceding chapters. You should have carefully recorded and collected the data (test results, participant responses, computer print outs, observations, transcriptions, notes of various kinds etc.) from your research as you conducted it, so now is the time to review, organise and analyse the data. If your study is quantitative in nature, make sure that you know what all the numbers mean and that you consider them in direct relation to the topic, problem or phenomenon you are investigating, and especially in relation to your research questions and hypotheses. You may find that you require the services of a statistician to help make sense of the data, in which case, obtaining that help sooner rather than later is advisable, because you need to understand your results thoroughly before you can write about them. If, on the other hand, your study is qualitative, you will need to read through the data you have collected several times to become familiar with them both as a whole and in detail so that you can establish important themes, patterns and categories. Remember that ‘qualitative analysis is a creative process and requires thoughtful judgments about what is significant and meaningful in the data’ (Roberts, 2010, p.174; see also Miles & Huberman, 1994) – judgements that often need to be made before the findings can be effectively analysed and presented. If you are combining methodologies in your research, you will also need to consider relationships between the results obtained from the different methods, integrating all the data you have obtained and discovering how the results of one approach support or correlate with the results of another. Ideally, you will have taken careful notes recording your initial thoughts and analyses about the sources you consulted and the results and evidence provided by particular methods and instruments as you put them into practice (as suggested in Sections 2.1.2 and 2.1.4), as these will prove helpful while you consider how best to present your results in your thesis.

Although the ways in which to present and organise the results of doctoral research differ markedly depending on the nature of the study and its findings, as on author and committee preferences and university and department guidelines, there are several basic principles that apply to virtually all theses. First and foremost is the need to present the results of your research both clearly and concisely, and in as objective and factual a manner as possible. There will be time and space to elaborate and interpret your results and speculate on their significance and implications in the final discussion chapter(s) of your thesis, but, generally speaking, such reflection on the meaning of the results should be entirely separate from the factual report of your research findings. There are exceptions, of course, and some candidates, supervisors and departments may prefer the factual presentation and interpretive discussion of results to be blended, just as some thesis topics may demand such treatment, but this is rare and best avoided unless there are persuasive reasons to avoid separating the facts from your thoughts about them. If you do find that you need to blend facts and interpretation in reporting your results, make sure that your language leaves no doubt about the line between the two: words such as ‘seems,’ ‘appears,’ ‘may,’ ‘might,’ probably’ and the like will effectively distinguish analytical speculation from more factual reporting (see also Section 4.5).

You need not dedicate much space in this part of the thesis to the methods you used to arrive at your results because these have already been described in your methodology chapter(s), but they can certainly be revisited briefly to clarify or lend structure to your report. Results are most often presented in a straightforward narrative form which is often supplemented by tables and perhaps by figures such as graphs, charts and maps. An effective approach is to decide immediately which information would be best included in tables and figures, and then to prepare those tables and figures before you begin writing the text for the chapter (see Section 4.4.1 on designing effective tables and figures). Arranging your data into the visually immediate formats provided by tables and figures can, for one, produce interesting surprises by enabling you to see trends and details that you may not have noticed previously, and writing the report of your results will prove easier when you have the tables and figures to work with just as your readers ultimately will. In addition, while the text of the results chapter(s) should certainly highlight the most notable data included in tables and figures, it is essential not to repeat information unnecessarily, so writing with the tables and figures already constructed will help you keep repetition to a minimum. Finally, writing about the tables and figures you create will help you test their clarity and effectiveness for your readers, and you can make any necessary adjustments to the tables and figures as you work. Be sure to refer to each table and figure by number in your text and to make it absolutely clear what you want your readers to see or understand in the table or figure (e.g., ‘see Table 1 for the scores’ and ‘Figure 2 shows this relationship’).

Beyond combining textual narration with the data presented in tables and figures, you will need to organise your report of the results in a manner best suited to the material. You may choose to arrange the presentation of your results chronologically or in a hierarchical order that represents their importance; you might subdivide your results into sections (or separate chapters if there is a great deal of information to accommodate) focussing on the findings of different kinds of methodology (quantitative versus qualitative, for instance) or of different tests, trials, surveys, reviews, case studies and so on; or you may want to create sections (or chapters) focussing on specific themes, patterns or categories or on your research questions and/or hypotheses. The last approach allows you to cluster results that relate to a particular question or hypothesis into a single section and can be particularly useful because it provides cohesion for the thesis as a whole and forces you to focus closely on the issues central to the topic, problem or phenomenon you are investigating. You will, for instance, be able to refer back to the questions and hypotheses presented in your introduction (see Section 3.1), to answer the questions and confirm or dismiss the hypotheses and to anticipate in relation to those questions and hypotheses the discussion and interpretation of your findings that will appear in the next part of the thesis (see Section 4.5). Less effective is an approach that organises the presentation of results according to the items of a survey or questionnaire, because these lend the structure of the instrument used to the results instead of connecting those results directly to the aims, themes and argument of your thesis, but such an organisation can certainly be an important early step in your analysis of the findings and might even be valid for the final thesis if, for instance, your work focuses on developing the instrument involved.

The results generated by doctoral research are unique, and this book cannot hope to outline all the possible approaches for presenting the data and analyses that constitute research results, but it is essential that you devote considerable thought and special care to the way in which you structure the report of your results (Section 6.1 on headings may prove helpful). Whatever structure you choose should accurately reflect the nature of your results and highlight their most important and interesting trends, and it should also effectively allow you (in the next part of the thesis) to discuss and speculate upon your findings in ways that will test the premises of your study, work well in the overall argument of your thesis and lead to significant implications for your research. Regardless of how you organise the main body of your results chapter(s), however, you should include a final paragraph (or more than one paragraph if necessary) that briefly summarises and explains the key results and also guides the reader on to the discussion and interpretation of those results in the following chapter(s).

Why PhD Success?

To Graduate Successfully

This article is part of a book called "PhD Success" which focuses on the writing process of a phd thesis, with its aim being to provide sound practices and principles for reporting and formatting in text the methods, results and discussion of even the most innovative and unique research in ways that are clear, correct, professional and persuasive.

The assumption of the book is that the doctoral candidate reading it is both eager to write and more than capable of doing so, but nonetheless requires information and guidance on exactly what he or she should be writing and how best to approach the task. The basic components of a doctoral thesis are outlined and described, as are the elements of complete and accurate scholarly references, and detailed descriptions of writing practices are clarified through the use of numerous examples.

The basic components of a doctoral thesis are outlined and described, as are the elements of complete and accurate scholarly references, and detailed descriptions of writing practices are clarified through the use of numerous examples. PhD Success provides guidance for students familiar with English and the procedures of English universities, but it also acknowledges that many theses in the English language are now written by candidates whose first language is not English, so it carefully explains the scholarly styles, conventions and standards expected of a successful doctoral thesis in the English language.

Individual chapters of this book address reflective and critical writing early in the thesis process; working successfully with thesis supervisors and benefiting from commentary and criticism; drafting and revising effective thesis chapters and developing an academic or scientific argument; writing and formatting a thesis in clear and correct scholarly English; citing, quoting and documenting sources thoroughly and accurately; and preparing for and excelling in thesis meetings and examinations. 

Completing a doctoral thesis successfully requires long and penetrating thought, intellectual rigour and creativity, original research and sound methods (whether established or innovative), precision in recording detail and a wide-ranging thoroughness, as much perseverance and mental toughness as insight and brilliance, and, no matter how many helpful writing guides are consulted, a great deal of hard work over a significant period of time. Writing a thesis can be an enjoyable as well as a challenging experience, however, and even if it is not always so, the personal and professional rewards of achieving such an enormous goal are considerable, as all doctoral candidates no doubt realise, and will last a great deal longer than any problems that may be encountered during the process.

Interested in Proofreading your PhD Thesis? Get in Touch with us

If you are interested in proofreading your PhD thesis or dissertation, please explore our expert dissertation proofreading services.

Rene Tetzner

Rene Tetzner's blog posts dedicated to academic writing. Although the focus is on How To Write a Doctoral Thesis, many other important aspects of research-based writing, editing and publishing are addressed in helpful detail.

Related Posts

PhD Success – How To Write a Doctoral Thesis

October 1, 2021

Table of Contents – PhD Success

October 2, 2021

The Essential – Preliminary Matter

October 3, 2021

The Main Body of the Thesis

October 4, 2021

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, automatically generate references for free.

• Knowledge Base
• Methodology
• 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.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the ‘Cite this Scribbr article’ button to automatically add the citation to our free Reference Generator.

Bhandari, P. (2022, May 04). Data Collection Methods | Step-by-Step Guide & Examples. Scribbr. Retrieved 9 April 2024, from https://www.scribbr.co.uk/research-methods/data-collection-guide/

Is this article helpful?

Pritha Bhandari

Other students also liked, qualitative vs quantitative research | examples & methods, triangulation in research | guide, types, examples, what is a conceptual framework | tips & examples.

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

• Knowledge Base

Methodology

• Types of Interviews in Research | Guide & Examples

Types of Interviews in Research | Guide & Examples

Published on March 10, 2022 by Tegan George . Revised on June 22, 2023.

An interview is a qualitative research method that relies on asking questions in order to collect data . Interviews involve two or more people, one of whom is the interviewer asking the questions.

There are several types of interviews, often differentiated by their level of structure.

• Structured interviews have predetermined questions asked in a predetermined order.
• Unstructured interviews are more free-flowing.
• Semi-structured interviews fall in between.

Interviews are commonly used in market research, social science, and ethnographic research .

Table of contents

What is a structured interview, what is a semi-structured interview, what is an unstructured interview, what is a focus group, examples of interview questions, advantages and disadvantages of interviews, other interesting articles, frequently asked questions about types of interviews.

Structured interviews have predetermined questions in a set order. They are often closed-ended, featuring dichotomous (yes/no) or multiple-choice questions. While open-ended structured interviews exist, they are much less common. The types of questions asked make structured interviews a predominantly quantitative tool.

Asking set questions in a set order can help you see patterns among responses, and it allows you to easily compare responses between participants while keeping other factors constant. This can mitigate   research biases and lead to higher reliability and validity. However, structured interviews can be overly formal, as well as limited in scope and flexibility.

• You feel very comfortable with your topic. This will help you formulate your questions most effectively.
• You have limited time or resources. Structured interviews are a bit more straightforward to analyze because of their closed-ended nature, and can be a doable undertaking for an individual.
• Your research question depends on holding environmental conditions between participants constant.

Here's why students love Scribbr's proofreading services

Discover proofreading & editing

Semi-structured interviews are a blend of structured and unstructured interviews. While the interviewer has a general plan for what they want to ask, the questions do not have to follow a particular phrasing or order.

Semi-structured interviews are often open-ended, allowing for flexibility, but follow a predetermined thematic framework, giving a sense of order. For this reason, they are often considered “the best of both worlds.”

However, if the questions differ substantially between participants, it can be challenging to look for patterns, lessening the generalizability and validity of your results.

• You have prior interview experience. It’s easier than you think to accidentally ask a leading question when coming up with questions on the fly. Overall, spontaneous questions are much more difficult than they may seem.
• Your research question is exploratory in nature. The answers you receive can help guide your future research.

An unstructured interview is the most flexible type of interview. The questions and the order in which they are asked are not set. Instead, the interview can proceed more spontaneously, based on the participant’s previous answers.

Unstructured interviews are by definition open-ended. This flexibility can help you gather detailed information on your topic, while still allowing you to observe patterns between participants.

However, so much flexibility means that they can be very challenging to conduct properly. You must be very careful not to ask leading questions, as biased responses can lead to lower reliability or even invalidate your research.

• You have a solid background in your research topic and have conducted interviews before.
• Your research question is exploratory in nature, and you are seeking descriptive data that will deepen and contextualize your initial hypotheses.
• Your research necessitates forming a deeper connection with your participants, encouraging them to feel comfortable revealing their true opinions and emotions.

A focus group brings together a group of participants to answer questions on a topic of interest in a moderated setting. Focus groups are qualitative in nature and often study the group’s dynamic and body language in addition to their answers. Responses can guide future research on consumer products and services, human behavior, or controversial topics.

Focus groups can provide more nuanced and unfiltered feedback than individual interviews and are easier to organize than experiments or large surveys . However, their small size leads to low external validity and the temptation as a researcher to “cherry-pick” responses that fit your hypotheses.

• Your research focuses on the dynamics of group discussion or real-time responses to your topic.
• Your questions are complex and rooted in feelings, opinions, and perceptions that cannot be answered with a “yes” or “no.”
• Your topic is exploratory in nature, and you are seeking information that will help you uncover new questions or future research ideas.

Prevent plagiarism. Run a free check.

Depending on the type of interview you are conducting, your questions will differ in style, phrasing, and intention. Structured interview questions are set and precise, while the other types of interviews allow for more open-endedness and flexibility.

Here are some examples.

• Semi-structured
• Unstructured
• Focus group
• Do you like dogs? Yes/No
• Do you associate dogs with feeling: happy; somewhat happy; neutral; somewhat unhappy; unhappy
• If yes, name one attribute of dogs that you like.
• If no, name one attribute of dogs that you don’t like.
• What feelings do dogs bring out in you?
• When you think more deeply about this, what experiences would you say your feelings are rooted in?

Interviews are a great research tool. They allow you to gather rich information and draw more detailed conclusions than other research methods, taking into consideration nonverbal cues, off-the-cuff reactions, and emotional responses.

However, they can also be time-consuming and deceptively challenging to conduct properly. Smaller sample sizes can cause their validity and reliability to suffer, and there is an inherent risk of interviewer effect arising from accidentally leading questions.

Here are some advantages and disadvantages of each type of interview that can help you decide if you’d like to utilize this research method.

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

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Quartiles & Quantiles
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Prospective cohort study

Research bias

• Implicit bias
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic
• Social desirability bias

The four most common types of interviews are:

• Structured interviews : The questions are predetermined in both topic and order. 
• Semi-structured interviews : A few questions are predetermined, but other questions aren’t planned.
• Unstructured interviews : None of the questions are predetermined.
• Focus group interviews : The questions are presented to a group instead of one individual.

The interviewer effect is a type of bias that emerges when a characteristic of an interviewer (race, age, gender identity, etc.) influences the responses given by the interviewee.

There is a risk of an interviewer effect in all types of interviews , but it can be mitigated by writing really high-quality interview questions.

Social desirability bias is the tendency for interview participants to give responses that will be viewed favorably by the interviewer or other participants. It occurs in all types of interviews and surveys , but is most common in semi-structured interviews , unstructured interviews , and focus groups .

Social desirability bias can be mitigated by ensuring participants feel at ease and comfortable sharing their views. Make sure to pay attention to your own body language and any physical or verbal cues, such as nodding or widening your eyes.

This type of bias can also occur in observations if the participants know they’re being observed. They might alter their behavior accordingly.

A focus group is a research method that brings together a small group of people to answer questions in a moderated setting. The group is chosen due to predefined demographic traits, and the questions are designed to shed light on a topic of interest. It is one of 4 types of interviews .

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

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

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

George, T. (2023, June 22). Types of Interviews in Research | Guide & Examples. Scribbr. Retrieved April 9, 2024, from https://www.scribbr.com/methodology/interviews-research/

Is this article helpful?

Tegan George

Other students also liked, unstructured interview | definition, guide & examples, structured interview | definition, guide & examples, semi-structured interview | definition, guide & examples, unlimited academic ai-proofreading.

✔ Document error-free in 5minutes ✔ Unlimited document corrections ✔ Specialized in correcting academic texts

Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.

4 Gathering and Analyzing Qualitative Data

Gathering and analyzing qualitative data.

As the role of clinician researchers expands beyond the bedside, it is important to consider the possibilities of inquiry beyond the quantitative approach. In contrast to the quantitative approach, qualitative methodology is highly inductive and relies on the background and interpretation of the researcher to derive meaning from the gathering and analytic processes central to qualitative inquiry.

Chapter 4: Learning Objectives

As you explore the research opportunities central to your interests to consider whether qualitative component would enrich your work, you’ll be able to:

• Define what qualitative research is
• Compare qualitative and quantitative approaches
• Describe the process of creating themes from recurring ideas gleaned from narrative interviews

What Is Qualitative Research?

Quantitative researchers typically start with a focused research question or hypothesis, collect a small amount of numerical data from a large number of individuals, describe the resulting data using statistical techniques, and draw general conclusions about some large population. Although this method is by far the most common approach to conducting empirical research in fields such as respiratory care and other clinical fields, there is an important alternative called qualitative research. Qualitative research originated in the disciplines of anthropology and sociology but is now used to study psychological topics as well. Qualitative researchers generally begin with a less focused research question, collect large amounts of relatively “unfiltered” data from a relatively small number of individuals, and describe their data using nonstatistical techniques, such as grounded theory, thematic analysis, critical discourse analysis, or interpretative phenomenological analysis. They are usually less concerned with drawing general conclusions about human behavior than with understanding in detail the experience of their research participants.

Consider, for example, a study by researcher Per Lindqvist and his colleagues, who wanted to learn how the families of teenage suicide victims cope with their loss (Lindqvist, Johansson, & Karlsson, 2008). They did not have a specific research question or hypothesis, such as, What percentage of family members join suicide support groups? Instead, they wanted to understand the variety of reactions that families had, with a focus on what it is like from their perspectives. To address this question, they interviewed the families of 10 teenage suicide victims in their homes in rural Sweden. The interviews were relatively unstructured, beginning with a general request for the families to talk about the victim and ending with an invitation to talk about anything else that they wanted to tell the interviewer. One of the most important themes that emerged from these interviews was that even as life returned to “normal,” the families continued to struggle with the question of why their loved one committed suicide. This struggle appeared to be especially difficult for families in which the suicide was most unexpected.

The Purpose of Qualitative Research

The strength of quantitative research is its ability to provide precise answers to specific research questions and to draw general conclusions about human behavior. This method is how we know that people have a strong tendency to obey authority figures, for example, and that female undergraduate students are not substantially more talkative than male undergraduate students. But while quantitative research is good at providing precise answers to specific research questions, it is not nearly as good at generating novel and interesting research questions. Likewise, while quantitative research is good at drawing general conclusions about human behavior, it is not nearly as good at providing detailed descriptions of the behavior of particular groups in particular situations. And quantitative research is not very good at communicating what it is actually like to be a member of a particular group in a particular situation.

But the relative weaknesses of quantitative research are the relative strengths of qualitative research. Qualitative research can help researchers to generate new and interesting research questions and hypotheses. The research of Lindqvist and colleagues, for example, suggests that there may be a general relationship between how unexpected a suicide is and how consumed the family is with trying to understand why the teen committed suicide. This relationship can now be explored using quantitative research. But it is unclear whether this question would have arisen at all without the researchers sitting down with the families and listening to what they themselves wanted to say about their experience. Qualitative research can also provide rich and detailed descriptions of human behavior in the real-world contexts in which it occurs. Among qualitative researchers, this depth is often referred to as “thick description” (Geertz, 1973) .

Similarly, qualitative research can convey a sense of what it is actually like to be a member of a particular group or in a particular situation—what qualitative researchers often refer to as the “lived experience” of the research participants. Lindqvist and colleagues, for example, describe how all the families spontaneously offered to show the interviewer the victim’s bedroom or the place where the suicide occurred—revealing the importance of these physical locations to the families. It seems unlikely that a quantitative study would have discovered this detail. The table below lists some contrasts between qualitative and quantitative research

Data Collection and Analysis in Qualitative Research

Data collection approaches in qualitative research are quite varied and can involve naturalistic observation, participant observation, archival data, artwork, and many other things. But one of the most common approaches, especially for psychological research, is to conduct interviews. Interviews in qualitative research can be unstructured—consisting of a small number of general questions or prompts that allow participants to talk about what is of interest to them—or structured, where there is a strict script that the interviewer does not deviate from. Most interviews are in between the two and are called semi-structured interviews, where the researcher has a few consistent questions and can follow up by asking more detailed questions about the topics that come up. Such interviews can be lengthy and detailed, but they are usually conducted with a relatively small sample. The unstructured interview was the approach used by Lindqvist and colleagues in their research on the families of suicide victims because the researchers were aware that how much was disclosed about such a sensitive topic should be led by the families, not by the researchers.

Another approach used in qualitative research involves small groups of people who participate together in interviews focused on a particular topic or issue, known as focus groups. The interaction among participants in a focus group can sometimes bring out more information than can be learned in a one- on-one interview. The use of focus groups has become a standard technique in business and industry among those who want to understand consumer tastes and preferences. The content of all focus group interviews is usually recorded and transcribed to facilitate later analyses. However, we know from social psychology that group dynamics are often at play in any group, including focus groups, and it is useful to be aware of those possibilities. For example, the desire to be liked by others can lead participants to provide inaccurate answers that they believe will be perceived favorably by the other participants. The same may be said for personality characteristics. For example, highly extraverted participants can sometimes dominate discussions within focus groups.

Data Analysis in Qualitative Research

Although quantitative and qualitative research generally differ along several important dimensions (e.g., the specificity of the research question, the type of data collected), it is the method of data analysis that distinguishes them more clearly than anything else. To illustrate this idea, imagine a team of researchers that conducts a series of unstructured interviews with people recovering from alcohol use disorder to learn about the role of their religious faith in their recovery. Although this project sounds like qualitative research, imagine further that once they collect the data, they code the data in terms of how often each participant mentions God (or a “higher power”), and they then use descriptive and inferential statistics to find out whether those who mention God more often are more successful in abstaining from alcohol. Now it sounds like quantitative research. In other words, the quantitative-qualitative distinction depends more on what researchers do with the data they have collected than with why or how they collected the data.

But what does qualitative data analysis look like? Just as there are many ways to collect data in qualitative research, there are many ways to analyze data. Here we focus on one general approach called grounded theory (Glaser & Strauss, 1967) . This approach was developed within the field of sociology in the 1960s and has gradually gained popularity in psychology. Remember that in quantitative research, it is typical for the researcher to start with a theory, derive a hypothesis from that theory, and then collect data to test that specific hypothesis. In qualitative research using grounded theory, researchers start with the data and develop a theory or an interpretation that is “grounded in” those data. They do this analysis in stages. First, they identify ideas that are repeated throughout the data. Then they organize these ideas into a smaller number of broader themes. Finally, they write a theoretical narrative—an interpretation of the data in terms of the themes that they have identified. This theoretical narrative focuses on the subjective experience of the participants and is usually supported by many direct quotations from the participants themselves.

As an example, consider a study by researchers Laura Abrams and Laura Curran, who used the grounded theory approach to study the experience of postpartum depression symptoms among low-income mothers (Abrams & Curran, 2009) . Their data were the result of unstructured interviews with 19 participants. The table below hows the five broad themes the researchers identified and the more specific repeating ideas that made up each of those themes. In their research report, they provide numerous quotations from their participants, such as this one from “Destiny:”

“Well, just recently my apartment was broken into and the fact that his Medicaid for some reason was cancelled so a lot of things was happening within the last two weeks all at one time. So that in itself I don’t want to say almost drove me mad but it put me in a funk….Like I really was depressed. (p. 357)”

Their theoretical narrative focused on the participants’ experience of their symptoms, not as an abstract “affective disorder” but as closely tied to the daily struggle of raising children alone under often difficult circumstances. The table below illustrates the process of creating themes from repeating ideas in the qualitative research gathering and analysis process.

Given their differences, it may come as no surprise that quantitative and qualitative research do not coexist in complete harmony. Some quantitative researchers criticize qualitative methods on the grounds that they lack objectivity, are difficult to evaluate in terms of reliability and validity, and do not allow generalization to people or situations other than those actually studied. At the same time, some qualitative researchers criticize quantitative methods on the grounds that they overlook the richness of human behavior and experience and instead answer simple questions about easily quantifiable variables.

In general, however, qualitative researchers are well aware of the issues of objectivity, reliability, validity, and generalizability. In fact, they have developed a number of frameworks for addressing these issues (which are beyond the scope of our discussion). And in general, quantitative researchers are well aware of the issue of oversimplification. They do not believe that all human behavior and experience can be adequately described in terms of a small number of variables and the statistical relationships among them. Instead, they use simplification as a strategy for uncovering general principles of human behavior.

Many researchers from both the quantitative and qualitative camps now agree that the two approaches can and should be combined into what has come to be called mixed-methods research (Todd, Nerlich, McKeown, & Clarke, 2004). In fact, the studies by Lindqvist and colleagues and by Abrams and Curran both combined quantitative and qualitative approaches. One approach to combining quantitative and qualitative research is to use qualitative research for hypothesis generation and quantitative research for hypothesis testing. Again, while a qualitative study might suggest that families who experience an unexpected suicide have more difficulty resolving the question of why, a well-designed quantitative study could test a hypothesis by measuring these specific variables in a large sample. A second approach to combining quantitative and qualitative research is referred to as triangulation. The idea is to use both quantitative and qualitative methods simultaneously to study the same general questions and to compare the results. If the results of the quantitative and qualitative methods converge on the same general conclusion, they reinforce and enrich each other. If the results diverge, then they suggest an interesting new question: Why do the results diverge and how can they be reconciled?

Using qualitative research can often help clarify quantitative results via triangulation. Trenor, Yu, Waight, Zerda, and Sha (2008) investigated the experience of female engineering students at a university. In the first phase, female engineering students were asked to complete a survey, where they rated a number of their perceptions, including their sense of belonging. Their results were compared across the student ethnicities, and statistically, the various ethnic groups showed no differences in their ratings of their sense of belonging.

One might look at that result and conclude that ethnicity does not have anything to do with one’s sense of belonging. However, in the second phase, the authors also conducted interviews with the students, and in those interviews, many minority students reported how the diversity of cultures at the university enhanced their sense of belonging. Without the qualitative component, we might have drawn the wrong conclusion about the quantitative results.

This example shows how qualitative and quantitative research work together to help us understand human behavior. Some researchers have characterized qualitative research as best for identifying behaviors or the phenomenon whereas quantitative research is best for understanding meaning or identifying the mechanism. However, Bryman (2012) argues for breaking down the divide between these arbitrarily different ways of investigating the same questions.

Key Takeaways

• The qualitative approach is centered on an inductive method of reasoning
• The qualitative approach focuses on understanding phenomenon through the perspective of those experiencing it
• Researchers search for recurring topics and group themes to build upon theory to explain findings
• A mixed methods approach uses both quantitative and qualitative methods to explain different aspects of a phenomenon, processes, or practice
• This chapter can be attributed to Research Methods in Psychology by Rajiv S. Jhangiani, I-Chant A. Chiang, Carrie Cuttler, & Dana C. Leighton is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License, except where otherwise noted. This adaptation constitutes the fourth edition of this textbook, and builds upon the second Canadian edition by Rajiv S. Jhangiani (Kwantlen Polytechnic University) and I-Chant A. Chiang (Quest University Canada), the second American edition by Dana C. Leighton (Texas A&M University-Texarkana), and the third American edition by Carrie Cuttler (Washington State University) and feedback from several peer reviewers coordinated by the Rebus Community. This edition is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. ↵

Gathering and Analyzing Qualitative Data Copyright © by megankoster is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

Share This Book

I Help to Study

Useful information for students

Home » Proposal » Data gathering tools in thesis proposal

• Academic Writing
• Assignments
• Business Plans
• Buy Services
• Custom Writing
• Dissertations
• For Professionals
• Help & Assistance
• Useful Services
• Various Papers

Data gathering tools in thesis proposal

A good way of gathering valuable data for your dissertation is actually by developing a effective survey. In what is a simple process you may get a lot of data and from verifiable sources. But you will need to know the easiest method to do a highly effective survey since the data collected will most likely be reflected directly in your dissertation. Erroneous information frequently signifies that all your jobs are called into question and it is disastrous for that academic career.

• Identify your survey’s objectives. The first step of conducting a great survey for your dissertation should be to define your objectives. The details you’ve always wondered? Should generate a regular or fewer-than-focused survey, you’re results won’t offer you anything helpful. That’s why it’s imperative that you focus your queries around the certain point.
• Choose your target survey audience. You may have a specific group inside your ideas, but consider narrowing your focus and that means you your results reveal something accurate of the particular group. Would you like to concentrate on males or females? Would you like to ask only people 35-years of age or maybe more youthful? History students? Physics students? Statistically, bigger sample sizes provides you with more results, however a focused audience provides you with better results.
• Prioritize your queries. Remove your set of questions and acquire them organized by topics or subjects. Consider removing questions that appear to repeat themselves. In every single section, list your queries by order worth addressing to check out strategies to edit questions or eliminate probably the most trivial ones. Your target survey group is often more ready to produce a survey that’s short and to the stage. Extended surveys usually offer a sophisticated of skipped questions or generic solutions (i.e. all “yes” or all “no” solutions) from participants wanting to connect to the conclusion.
• Pre-test out your survey obtaining a little sample. This might appear like a no-brainer, quite a few make mistake of beginning their survey without first testing it obtaining a select handful of. Whether you employ buddies or family, pre-test out your survey to make certain the issue you’ve written appear sensible which your survey questions flow naturally. Request feedback and do something positive about suggested changes whether it would improve your data gathering process.
• Communicate your personal purpose. It’s required for provide your survey group know why they’re being surveyed. Explain why notebook is relevant for that work and exactly how you anticipate it will also help you build up your argument round the particular subject. Your survey group is often more conscious to every question and provides you honest, thoughtful solutions.

Surveys make uncertainty in the specific question or quantity of problems you’ll need clarified. Remaining using the the following tips when performing your survey can help you gather better data for your dissertation regardless of whether you conduct your survey by email, web or personally.

Thesis writing guides

It’s possible to rely on them totally free.

Dissertation help sources

Related Articles:

Latest Posts

• Privacy Policy

© 2016 | IHelptoStudy.Com

Please Wait!

Why You Should Read a Data Gathering Procedure Example

The data gathering procedure you employ in your paper determines if you receive a piece that is trustworthy or not. Therefore, it is crucial to employ the best procedure to get the perfect results. It improves the quality of the paper and makes you sound scholarly.

Most people struggle when need to gather data. While some do not know the data collection methodologies to follow, the majority do not have the experience in data handling. Eventually, they prepare papers that only earn them low grades. What is the remedy in such cases? Have a look at a perfect data gathering procedure example to be well-versed with the procedure that can work for your situation. In the process, you can make your work easier and improve the general quality of the papers you can prepare.

The best remedy for those without the sills in data gathering is to hire experts who are proficient in this field. Fortunately, we stand out as the company that can assist you with such issues. We have worked on a variety of papers that require verifiable data and understand what can work perfectly for you. With our assistance, you do not strain with data collection and handling. You follow every stage to ensure what you receive is perfect.

Table of Contents

What Is the Definition of Data Gathering Procedure?

Dissertation writing involves the handling of statistical data. Therefore, you need to know the best data to use in your paper. The definition of data gathering procedure is that it is the technique used to obtain the information used in a dissertation to substantiate the claims made by a writer. To get the perfect outcome, you should use the best procedure. If you are unsure of how to obtain your data, it is advisable to hire experts in this field to offer assistance. We have data experts who can help with these tasks.

What are the data collection methods that you can use? They are explained below:

• Use of surveys

The method is mainly effective for those who need qualitative data to use in their academic documents. In surveys, open-ended questions are used. What kind of information can be collected using this method? They include the perception people have on a product, attitudes towards government policy, the beliefs people hold, or the knowledge people have on a given issue, among other information types. For the exact information needed, the questions should not be leading and should cover the exact areas needed by the researchers. The data is later analyzed to obtain the conclusions needed.

• Conducting interviews

This quantitative research data gathering procedure is used to obtain from people on a one-on-one basis. In this case, the researcher should have several predetermined questions. The interview questions can be close-ended, like in the case where the interviewees are expected to provide the ‘YES’ or ‘NO’ type of responses. It can also have open-ended questions in which the respondent has the freedom to provide a response they are comfortable with. To ensure the data collected is rich in the content required, the interviewer should ensure there are follow-up questions for areas where the respondent may provide ambiguous information.

There are different ways the interviews can be conducted. The first way is to do it face-to-face. As the respondent provides the answers, the interviewee can record them by writing or tape-recording. The data collected is later sorted and written in the paper. The other method is through phone conversations. Your respondent should provide the answers required as you keep a clean record that you can use later to write the paper needed.

• Taking a focus group

In this case, the interviewee can take a group and get the information from them. There is a set of predetermined questions that are inquired from the respondents in turns. The method is effective when different people hold varied opinions on the same issue. Focus groups differ depending on the type of responses required in the probe. To get the most reliable results from this method, the number of people in the group should be between 5 and 10 people.

• Direct observation

The data gathering procedure for qualitative research applies the sensory organs such as the eyes to see what is going on, ears to hear the things going on, and the ears to smell. The method helps the researcher to avoid bias in what people say.

• Content Analysis

The researcher uses data that is already available and supports their point of view. Different documents can be used in this case, including newspapers with reputation, research articles from known experts, approved government reports, and other online data sources that can be of help in this case. For the reliability of the data, different sources should be used for research.

It is you to determine the methodology that can work for your case when it comes to data collection. Choosing a wrong procedure may mean that you obtain unreliable or irrelevant data. You do not want to face the frustrations of presenting data that is unrelated to your topic. Therefore, it is advisable to hire an expert who understands how things work as far as data is concerned. We come in handy in such situations. Do not use faulty data gathering procedures when we can assist you in collecting the best data using our proven collection techniques.

What Determines the Sample of Data Gathering Procedure

Not all the procedures are effective for your paper. What applies to one paper may not be recommended for another. What are the factors in assessing to settle on the best procedure? Get answers:

The Course and Topic of Study Handled

Different courses require varying procedures when it comes to the collection and handling of data. While there are those courses where secondary information sources can work, others need data that one obtains first first-hand. For example, the type of data that is acceptable for those handling engineering courses is not the same as what works for those pursuing psychology. The same applies to the topic. The data needs for different subjects vary. Therefore, you must analyze the needs of your course and topic before selecting a procedure for data gathering.

The Specific Faculty Guidelines on Data Gathering

Your department has its instructions when it comes to the sample of data gathering procedure. Failure to adhere to what is specified may mean you miss important marks because your paper may not be as good as what is expected from you. Therefore, it is crucial to be well-versed with your faculty guidelines. Where the rules seem too strict for you, it is advisable to get experts who are comfortable with the specifications. We are the best company when it comes to adherence to the rules. The professionals assess all the guidelines you submit to ensure the data obtained meet the specifications you submit.

Personal Preferences in Data Gathering

The convenience encountered in data gathering varies from one person to the next. What one person considers to be hard may be easy for another. On a personal level, you should opt for a procedure that you are comfortable with. It is you who decide on the topic, settle on the data, analyze and come up with the conclusion. Therefore, selecting a procedure you are sure can work for you is fundamental. A convenient information gathering procedure saves you from stress.

What Should You Do Before Data Gathering?

You should not embark on the data gathering if you are unsure of what is required. The first step is to analyze and understand the topic you have. The keywords encountered determine whether you need a quantitative or qualitative type of data. Where you are expected to settle on your own topic, take something you are sure you can easily obtain data to defend.

The next procedure is to study the guidelines that are provided for doing the paper and collection of the data. For example, some professors insist that a student should use a given method of data collection. Your grade depends on whether you adhere to that specification or not.

Prepare adequately before you begin the gathering. For instance, you have to settle on a given method and determine the tools you need for data gathering. You can read an approved data gathering procedure pdf to understand what to do.

Need Example of Data Gathering Procedure in Thesis? Buy Here

Apart from getting the best example of data gathering procedure in thesis, we can also help with the whole data gathering work. Hire us for the best results.

15% OFF Your first order!

Aviable for the first 1000 subscribers, hurry up!

You might also like:

150 Qualitative and Quantitative Nursing Research Topics for Students

What Is Culture and What Are Some Popular Culture Essay Topics?

Money-back guarantee

24/7 support hotline

Safe & secure online payment