Statistical Reporting for the Special Project

Pch 594 Guidelines Statistical Reporting 101

Suggested Citation: Jung, BC (2006 - 2017). Statistical Reporting for the Special Project.


The reason why I am writing this additional set of guidelines for PCH 594 - Special Project Seminar II is because I have found, over the years, that statistical reporting is one area that is not done very well. This is also one area that I have spent the most advisement time on. Therefore, my hope in writing these guidelines is to help you better understand why statistical reporting is important, and why it can and should be done well while you are in school and when you will be working in the field of Public Health.

Of course, I cannot cover everything, but do provide enough detail so that you can submit a presentable Section 4 for your final Special Project Report. Basically, this documentation will deal with how to report data collected with the use of surveys, telephone interviews and focus groups. These are the most common methods used for the Special Project's data collection activities.

Keep in mind that the Special Project Seminar II is a culminating course in which you get to apply what you have learned in previous courses. The courses pertinent to statistical reporting include: PCH 516 Public Health Research, PCH 515 Biostatistics and PCH 551 Epidemiology.

Therefore, my assumption is that you have mastered the content for these courses, and, PCH 594 - Special Project Seminar II is providing you with the opportunity to apply the skills you have learned in those courses to a real-life project -- developing a health education product for a sponsoring agency. If anything sounds unfamiliar to you, then it is your responsibility to become familiar with the material.

You are welcome to explore any number of textbooks I have reviewed in my Annotated Bibliographies in the areas of Biostatistics, Epidemiology, Public Health Practic, Research Practice & Methodologies, and Statistics. If there are particular areas you feel you need guidance on, please let me know and I will direct you to the most appropriate resources.

Introduction to Statistical Reporting

Since the development and refinement of your Product is dependent on the data you collect to help you in the process, it is vital that you report your data collection and analysis activities in an understandable manner. Your readers should be able to decide for themselves if the data you have collected were appropriate for the task. Since you developed your instruments based on a review of the literature, and from conferring with experts in the field of practice, this should not be an issue. However, because this is an academic research exercise as well, then it should also contribute to the field in a way that others can build upon. This can only be done when what has already been done is documented in a professional manner.

In essence, the purpose of reporting the results of your data collection is to share your experiences and your findings so future researchers can use them as resources for future research endeavors. You should always provide the appropriate context with which your reader will need to truly understand what your data collection and analysis were all about.

Briefly, your readers should only have to look at your appendices with your data collection instruments and documentation, and the "Results" section of your Report's Section 4 to know what you did. If you lost your readers at this point you can forget about having them bother to read the rest of the report you have slaved over during many sleepless weeks. In essence, your statistical presentation has to be perfect for the serious reader (like me).

The instruments and documentation should be self-explanatory so that any data analyst, regardless of what statistical software s/he uses, would be able to analyze the data and make statistical sense of the data. This should also allow the data analyst to compare the results with those that were collected by others with different populations, using the same instrument.

How to Report Your Stats & Analyses in Your Special Project Report

Reporting Basics

I would like for you to organize your statistical reporting, as described below, to meet the critical eye of those who would be most interested in this kind of information, namely, diehard research-oriented professionals (like me). These people are usually the most meticulous about accuracy and elegant presentation of data. Furthermore, not everyone processes this kind of information the same way. Some people like narratives and others just like statistical tables so they can make up their own minds about how they want to interpret the data.

  • Use APA format for all statistical tables presented in the body of the paper.
  • Combine the reporting of variables that are similar in type and content (i.e., group multiple choice responses together, Likert Scale responses together, yes/no responses together) into one table so you don't have 3000 tables.
  • Use descriptive headings.
  • In all your data tables, provide the "N" or "n" depending on whether you are talking about the entire survey population or just a sample of the survey population.
  • Make sure percentages total to 100.
  • Report decimals to only one place (i.e., 10.3, not 10.26) for your data. When reporting the results of statistical procedures (i.e., Chi-Squares, F statistics, p values) report to 3 decimal places.
  • If the stats need an explanation, then provide them as notes right under the table.
  • Each table should be self-explanatory and be able to stand alone.

Where Data Collection Documentation Go

All data collection instruments should be included in your final report's Appendix. They should be organized sequentially, in the order you conducted them. Include everything pertaining for a particular data collection instrument into one appendix, as follows:

  1. Data collection instrument, including any scripts, letters, etc.
  2. Variable definition listing (or, the results of Epi Info's Analysis program command, "variables") - this is so a data analyst can reconstruct the data file structure in cases where s/he does not have the program you used to store your data.
  3. Data Entry Templates (Screens) (or, Epi Info *.qes file) for closed-ended questions.
  4. Codebooks (or, Epi Info *.chk file) for closed-ended questions. This is so the reader will know how you defined (operationalized) your variables.
  5. Data Analysis Protocols for open-ended, qualitative responses. You should have a protocol for such data collection instruments as focus groups, interviews, etc. Here you define what you were collecting for each question from your script, and what categories you are using for each question. This is so the data analyst will know how you categorized these responses, and the rationale you used for developing the derived variables.
  6. Raw data (i.e., statements from open-ended, qualitative surveys, interviews, focus groups) - with preliminary data processing (i.e., statement categorization) - so the reader has a better idea of how you analyzed the qualitative data.

Where Statistical Tables Go

For each variable you collect, you should provide a brief descriptive statistical summary. Your statistical tables should go into one of two places:

  • Raw data tables with preliminary data processing should go into the Appendix, as part of the appendix that includes the data collection instrument and its documentation.
  • The analyzed data tables should go into Section 4's "Results" section, at the end of Section 4. All tables should be numbered sequentially. The "Results" section should make sense in and of itself. These tables should be in APA format.

Keep in mind that each table should be self-explanatory, so that if your paper were to fall apart and I picked up a page with a statistical table on it, I would know what it was all about.

And don't forget to include all your tables in the LIST OF TABLES that goes right after the TABLE OF CONTENTS.

What Your Narrative Should Say

Because the Special Project is a "Research and Development" course, and your Report is really a research report, it should be written for the research audience - scientific in approach, cognizant of the time constraints of professionals who just want the facts, and are not necessarily interested in a lot of narrative. Therefore, the narrative should not duplicate any data already presented in tables, but should seek to provide context, supplement and enlighten.

Your narrative should present a general overview of the results, without repeating everything already presented in a table. You should talk about how you used your findings to develop and modify your Product Prototype.

You should emphasize pertinent findings you want to call to the reader's attention, which may not be that obvious when just viewing the statistical tables. Section 4 should only report factually your findings and how you used those findings. Your interpretations of the findings should go into Section 5.

Pertinent findings include important and interesting findings. Important findings include everything you used for modifying and refining your Product Prototype. For example, you decided to use font size 16 because results from the pre-development survey indicated the majority who responded to the question on font size chosed "Font Size 16".

Interesting findings include surprising results (i.e., surveyed males 15-19 liked hot pink for brochure background). Of course, you would need to explain why (in Section 5, not in Section 4) you found this to be interesting, and how it relates to the literature (Dr. X found females 15-19 years of age favored hot pink, and males 15-19 years of age favored blue. This unusual finding from your survey may be attributed to the fact that the males you surveyed were urban graffiti artists who favor the use of blatant colors to draw attention to the important societal messages they think everyone should pay attention to.)

Remember, this document is meant to supplement Guidelines for Writing Sections 4 & 5. Review that document for the "big picture."

Pch 594 Guidelines Statistical Reporting 101

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Published on the Net: January 17, 2001
Updated: 12/22/2016 R106


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