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QUALITY INDEX |
Pch 594 Guidelines | Statistical Reporting for the Special Project |
Purpose
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.
Introduction to Statistical Reporting
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.
Collecting Data
Developing Data Shells
Before you even start to analyze the data, you should develop
templates (shells) for your statistical tables. These shells will help you
to organize your statistical analysis activities so you don't miss anything. Creating these shells will provide you with the freedom to organize the tables in a logical way, without being distracted by the data, and will enhance your statistical reporting. Once you have completed your analysis, you can just plug in the numbers.
You can also develop data shells for the reporting of qualitative data. This would include tables that would have at least 2 columns, in which you would be recording the actual statements, or, short answers in one column, and the category you are grouping the statement under in the second column.
Precoding
For each data collection instrument, include a Codebook in the Appendix.
Postcoding
For each data collection instrument, include a Codebook
in the Appendix.
Recoding
For each data collection instrument, include a Codebook
in the Appendix.
Gender
Gender
Depending on what kinds of data you collect, you would have to enter the data into some program, process it so you can then analyze it.
Finally, with Epi Info, you can develop data entry screens that look like your data collection forms, edit in spread sheet format, modify the database structure at will without losing data, analyze numeric and string data, perform a whole range of statistics, and develop some charts as well.
While data processing can be considered by some to be data analysis, I am making a differentiation. Raw data that are unusable without some intermediate steps to make them "analyzable" remain raw. Thus, data processing include those procedures in which raw data are redefined, or, categorized with the use of derived variables.
For example, qualitative statements make for colorful anecdotal narratives, but they are practically useless (qualitative researchers, don't kill me yet) without some categorization (processing). Categorization facilitates the generating of descriptive statistics. Examples include:
STATEMENT | CATEGORY - TONE (Derived Variable) |
---|---|
I would definitely read this | Positive |
Suitable for teenagers | Neutral |
Not very useful | Negative |
"OTHER" COUNTRIES | CATEGORY - CONTINENT (Derived Variable) |
---|---|
Botswana | Africa |
China | Asia |
France | Europe |
Malaysia | Asia |
Venezuela | South America |
In this instance, because of the variety in responses, you may decide to go back and recode the original choices using the derived variable, Continent, as this would reduce the number of categories (in this case, countries) you would have to analyze, during data analysis, and may be just as useful as having all the countries' names.
Include preliminary data processing of qualitative statements and "Other" responses in the Appendix.
Include the analyses of derived variables in Section 4's "Results" section, using APA format.
Most of the data collected for developing and evaluating your Product Prototype will most likely fall into one of the following:
Therefore, the MINIMUM data analyses I expect to see include:
Demographic Variable Gender | Total (N = 100) | Percent | Mean Age (Std Dev) |
---|---|---|---|
Female | 48 | 48% | 28.9 yrs (+ 15.0) |
Male | 52 | 52% | 31.8 yrs (+ 19.6) |
Total | 100 | 100% | 31.1 yrs (+18.8) |
Choice Variable (N = 20) | Total Percent |
---|---|
Font Choice (n = 20) | Choice A (n=10) 50%
Choice B (n=5) 25% Choice C (n=5) 25% |
Graphic Choice (n = 20) | Choice A (n= 2) 10%
Choice B (n= 8) 40% Choice C (n=10) 50% |
Title Choice (n = 18) | Choice A (n = 0) 0%
Choice B (n = 15) 83.3% Choice C (n = 3) 16.7% |
Category Variable (N = 10) | Percent |
---|---|
Useful (n = 5) | 50% |
Not Useful (n = 3) | 30% |
Don't Know (n = 2) | 20% |
Statements (N = 50) | Strongly Agree (%) | Agree (%) | Neutral (%) | Disagree (%) | Strongly Disagree (%) |
---|---|---|---|---|---|
The program was useful (n=35) | 10 (28.6%) | 20 (57.1%) | 0 (0.0%) | 5 (14.3%) | 0 (0.0%) |
I will be a better health educator (n=50) | 0 (0.0%) | 50 (100.0.%) | 0 (0.0%) | 0 (0.0%) | 0 (0.0%) |
Since the data collected for the Special Project are mostly from surveys, or other methods using some sort of survey-type instrument or template, for purposes of developing and modifying your Product Prototype, the presentation of descriptive statistics, with some basic bivariate analyses is usually sufficient. You would not need to perform statistical procedures necessary to test a hypothesis, since this is not the purpose of the Special Project.
Because you have collected categorical data, along with some demographic data, you can perform some cross-tabulations to see if any relationships exist between two variables. Cross-tabs are most useful in seeing whether or not demographics can be used to explain differences in your other variables. For example, cross-tabs may show that the choice in fonts, colors, or topics may be determined by gender, age, or even geographic location.
This is why it always important to collect some basic demographics as Age, Gender, and Geographic Location because such characteristics could explain differences that may show up in the other variables you collect. It will also help you to better tailor your product to the various populations in your audience.
In addition to providing descriptive statistics, you should perform some basic statistical procedures to explore any relationships that may exist between variables, especially between your independent and your dependent variables. Think of your demographic variables as the independent variables. Does gender affect the responses to the questions you have asked? Does location affect the responses to the questions you have asked?
Note: Dependent Variable=Agreement Status ; Independent Variable=Gender
If you treat your Likert Scale responses as linear numeric, then you
can calculate means for your scales and then compare these means across the series of questions you asked. You would report this as follows:
For example, the last statement seems to have received low ratings.
You may want to see if gender could explain the difference in ratings.
Oh, oh. Looks like the men in your audience didn't like the explanations. You may want to think about revising the explanations. And, you should be cautious when interpreting the mean for the graphics statement. Even though the graphics statement had the highest mean rating, only 1/3 of your population answered that question. It could be that the 20 who didn't like the graphics just didn't respond.
Published on the Net: January 17, 2001
© Copyright 1999 - 2023 Betty C. Jung All rights
reserved.
Gender Agree (%) Disagree (%) Total Female 75 (68.2%) 35 (31.2%) 110 (51.2%)
Male 95 (90.2%) 10 (9.5%) 105 (48.8%)
Total 170 (79.1%) 45 (20.9%) 215 (100.0%)
Note: Chi-Square (Yates Corrected) = 14.82; p value=0.0001
Agreement status is statistically significantly different by gender.
Calculate percentages by row (independent variable); Compare
percentages by column (dependent variable).
Statements (N = 30) Mean (Std Dev) The graphics add to the brochure (n = 10) 4.5 (+ 0.4)
The fonts add to the brochure (n = 24) 4.2 (+ 0.3)
Explanations add to the brochure (n = 30) 3.3 (+ 1.27)
Note: Strongly Agree=5, Strongly Disagree=1
Gender (N = 30) Mean (Std Dev) Female (n = 15) 3.9 (+ 1.06)
Male (n = 15) 2.8 (+ 1.3)
Note: Strongly Agree=5, Strongly Disagree=1
F statistic = 6.266; p value= 0.017
Mean ratings are statistically significantly different by gender.
Pch 594 Guidelines
Statistical Reporting for the Special Project
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Updated: 12/27/2028 R249