Final Exam Instructions: Write a research report on the following dataset. Your assignment is to come up with ONE interesting research questions that can be analyzed via ANY multivariable analysis we’ve covered in this class, analyze the hypotheses that result from the research question using SAS/JMP/R, and write up the results. I will give you a dataset, feel free to rename variables and label to make the data pertinent to YOUR research interests.
For full credit:
1. Include a Title Page appropriate for a journal manuscript submission. (5pts)
2. Write a short (~1 page) Literature Review for your area of interest. (5pts)
a) At the end of the literature review, come up with ONE research question (RQ) that can be tested with any multivariable analysis we’ve covered in this class (see #6b for more info). Write out what the RQ is and what hypotheses you’ll use to test it. (15pts)
3. Write a Methods section describing the data collection methods, the population you’re sampling from, and defining your independent and dependent variables and how they were measured with any reliability/validity information you have. (5pts)
4. Write up an Analyses section describing the program you’ll be using to run them, the statistical tests (descriptive and inferential) you’ll be running – remember to give specifics about the type of analysis you’re running, any assumptions you’ll be testing and any transformations, missing data analysis or changes you do to the dataset. (15pts)
5. In the analysis section, run and report whether your data meets the assumptions of the test you selected (10pts)
6. Write up a Results section including
a) Descriptive statistics to describe that data involved in your research question (not the entire data set I give you, but the variables you use to answer your research question). Remember to describe the RQ and whether the descriptive statistics support (or not) the RQ. Appropriately describe variables according to whether they are continuous or categorical as well as appropriate graphs. Make sure all variables are properly renamed/relabeled, and that you do NOT use automatic SAS or JMP tables/figures. (10pts)
b) Then, conduct a multivariable analysis as an inferential statistical test. This can include any multivariable analysis we conducted in class, including multiple regression, logistic regression, survival analysis, ANCOVA, MANOVA, MANCOVA, repeated measures, and related GLM models. You MUST remember to include results and interpretation for ALL pieces of the multivariable model. Be sure to include the important test statistics, p-values, confidence intervals and appropriate effect sizes as necessary throughout. (20pts)
7. Write a Discussion summarizing/integrating what you found from the analysis with your literature review. Interpret in terms of the hypotheses (did you reject or fail to reject?) (5pts)
8. Don’t forget sections for References, Figures, and Tables at the end of the manuscript. If you refer to tables and figures in the manuscript, they should be included in separate sections for Tables and Figures (not the appendix). Make sure all variables are properly renamed/relabeled. If you include tables, reformat as appropriate for your manuscript (don’t just copy and paste the SAS or JMP tables). (5pts)
9. Please include a separate Appendix with your SAS log and syntax or JMP dataset and script or R program including dataset and script, and any other work, including handwritten calculations, etc. (5pts). Do NOT refer to specific items in the Appendix in your report – if you have to do that, include the information either in text or in a table/figure. Consider this supplemental (not vital) material to a publishable manuscript.
This exam should showcase all the things you’ve learned in this class up to this point and should look like a manuscript you might submit to a journal. Include copies of all work or reasoning in an Appendix.
Your exam should be at minimum 5-6 WRITTEN pages double spaced ( ~1 page intro, ~1 page measures, ~1 page analysis plan, ~2 pages results, ~1 page discussion), not including the Appendix, Title page, references, tables and figures. There is no maximum page limit, but points may be taken off if parsimony and succinctness is not taken into account. As always, grammar and punctuation will be graded as well.
The final exam is due to me by: 8am Last day of class. Feel free to turn it in early to me. Post it under the Assignment tab on SOLE. Changes can be made up to the due date.
Honor code for the exam: You may use the following resources: your notes, textbook, calculator, Internet, homework, other exams, and excel/SPSS/SAS/JMP/R/other stats programs. You may NOT consult anything that is alive or communicates back to you (including, but not limited to: other students, professors, professional statisticians, tutors, parents, email or phone contacts, internet forums or chat rooms…).
You may ask the TA or myself if you have any questions.
Dataset is saved separately as an .csv file.
Description of Final Exam dataset
This dataset includes 4,434 responses and 84 variables and 1 ID variable. You do not have to use all of the given data. For many variables, they come in sets of three (for three different time points) so be cautious about picking variables that are autocorrelated! If you don’t wish to use repeated measures, just select the first variable in the series for your question. Variables X 77-84 may be suitable for survival analysis. Feel free to transform or normalize data, standardize data, make continuous data to categorical data, chuck out outliers, break up the data into groups, etc., as you see fit, but make sure you report it and do NOT make up data. IN SAS or JMP or R, You MUST RENAME these variables so they are relevant to your own research interests, but make sure the type of variable makes sense! You also MUST RELABEL values in categories so they are consistent with the research interests (also in SAS/JMP/R). You WILL have points taken off for NOT renaming/relabeling. As always, step 1: Look at the data – there may be univariate outliers, problematic missing, or improbable values you may wish to take into account.
The variables MUST be appropriate for the data requirements. For example, let’s say you want to predict weight, but you select a 1 to 4 variable. You want to run a MR on weight - and your results make no sense. This variable is ordinal, NOT continuous. It does not make sense to use for weight, and your results will be wrong. So be sure to match data type to your variable of interest, and analyze consistent with that analysis method’s requirements.
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