This chapter discusses preparing to write up quantitative data for a doctoral dissertation or thesis. It addresses some common mistakes made in using complex statistical models that are unnecessary. Organizing data through statistical significance, variance, direction of findings, and effect size is important. Quantitative arguments should include a claim, evidence, and warrant linked together. Standards for quantitative research include having a clear "golden thread" and ensuring objectivity, clarity, and replicability. The chapter provides tips on effective presentation and interpretation of quantitative results and findings through tables, charts, and explanation of statistical procedures. Resources for further learning on quantitative analysis are also suggested.

1. Writing Your Doctoral Dissertation
or Thesis Faster
A Proven Map to Success
by E. Alana James and Tracesea Slater
Chapter 12:
Are You Ready to Write Up Your Quantitative
Data?

3. The Main Challenges Advisors See With
Qualitative Work
• Complex statistical models and tests
are not always necessary or
preferable
• Use the right tools
Figure 12.1 One of the most common mistakes made by
students doing quantitative studies is that they use
complex statistical models and tests, thinking this
automatically makes for a better analysis.
Source: Jupiterimages, Brand X Pictures/Brand X Pictures/Thinkstock

4. Organizing Quantitative Evidence
• Statistical significance and variance of
results
• Directions of the findings
• Effect size
• Use tables and figures
Figure 12.2 Be prepared to work
through your data many times.
Source: Jorge Cham: A Story Told In File Names, PhD Comic, originally
published by www.phdcomic.com

5. Building a Quantitative Argument
• Argumentation
• Claim
• Evidence
• Warrant
• Backing
Figure 11.5 Make sure there is
a strong clear link between
your evidence and your claim.
Source: Stockbyte/Stockbyte/Thinkstock

6. Assessment Standards for Quantitative
Research
• Golden thread
• Three standards apply to all doctoral
research: “objectivity, clarity, and
replicability” (Bryant, 2004, p. 117).
• Outstanding quantitative discussion
Figure 12.3 Award-winning dissertations often have
tables and charts that display complex results that
help the reader understand the data and analysis.
Source: Ryan McVay/ Photodisc/Thinkstock

7. Quantitative Research Tips: Do…
• Be clear on the difference between your results and your findings.
• Tell your reader at the beginning of each section how the results and findings will be presented and then follow
that order.
• Superimpose a routine on your explanation of results, repeating that routine throughout your argumentation in
order for your reader to understand the sequence of thoughts and how they develop.
• Use the tables and charts to present your data.
• Clearly explain the statistical models and tests you used and why they are best suited to your study.
• Include a substantive conversation about the statistical procedures used to arrive at the findings.
• Conclude with a summary through which the reader can follow the course of your argument and will be
convinced with the internal validity of your study.

8. Qualitative Research Tips: Don’t…
• Spend too much space on written descriptions, especially paragraph-long
descriptions of frequencies of responses; instead organize quantitative
evidence using tables and charts and show your readers how the qualitative
data compare with quotations from that evidence.
• Forget to consider the internal validity of your findings, discussion or
conclusions, and arguments in terms of both types of evidence. This
discussion should be considered as part of your final chapter.

9. Where Should I Go to Dig Deeper?
Suggested Resources to Consider
• Gliner, J. A., Morgan, G. A., & Leech, N. L. (2009). Research methods in applied settings: An integrated approach to design and analysis (2nd ed.). New York:
Routledge/Psychology Press. This is an excellent text recommended by one of our reviewers as a great basic for quantitative research.
• Huck, S. W. (2012). Reading statistics and research (6th ed.). Boston: Pearson. Also recommended by one of our reviewers, this book is known to be a “uniquely
accessible text [that] shows precisely how to decipher and critique statistically-based research reports. Praised for its non-intimidating writing style, the text
emphasizes concepts over formulas.”
• Morgan, G. A., Leech, N. L., Gloeckner, G. W., & Barrett, K. C. (2011). IBM SPSS for introductory statistics: Use and interpretation. New York: Routledge Taylor & Francis
Group. Chapters 1, 3, and 6 respectively are a must read for quantitative researchers covering variables, research problems, questions, measurement, and descriptive
statistics, and the selection of and interpretation of inferential statistics. Written in down-to-earth language and augmented by a thorough example that uses data from
the High School and Beyond study. Appendix B has an especially good discussion of how to ask questions in quantitative studies.
• Roberts, C. M. (2004). The dissertation journey. Thousand Oaks, CA: Corwin Press. Chapter 14 and 15 give a brief but useful description of the path from data gathering,
through analysis, and on to the final discussion or conclusions. Many pullout boxes offer helpful hints and additional resources.
• Rudestam, K. E., & Newton, R. R. (2007). Surviving your dissertation: A comprehensive guide to content and process (3rd ed.). Thousand Oaks, CA: Sage. This book contains an
excellent set of appendices that discuss ways in which to present data in tables.
• Salkind, N. J. (2012). 100 questions (and answers) about research methods. Thousand Oaks, CA: SAGE. This book is laid out in, usually single-paged, questions and answers
and has direct help for many of the most common challenges faced by the beginning quantitative researcher.
• Thompson, B. (2006). Foundations of behavioral statistics: An insight-based approach. New York: Guilford Press. This book shows readers how to interpret research
outcomes and make statistical decisions.