Statistical Analysis Exercise for Identifying Associations between Variables - Prof. Vale, Assignments of Geriatrics

A class exercise focused on identifying appropriate statistical procedures and tests for analyzing associations between variables. The exercise covers various scenarios, including gender and mph program tracks, midterm exam scores, football interest, and attitude changes towards contraception. Students are required to determine the appropriate statistical tests and any necessary transformations or procedures for each question.

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Pre 2010

Uploaded on 11/08/2009

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Class Exercise on Statistical Analysis
PM 536
December 3, 2020
This exercise is review for choosing the appropriate statistical procedures and tests. For each
question below write the appropriate statistical test and any transformations or other procedures
needed to conduct the analysis. Then write a sentence describing the result.
1) The association between gender (1=male 2=female) and MPH program track (1=Bio/Epi,
2=HC, 3=HP, & 4=Nutrition).
2) The association between gender (1=male 2=female) and MPH program track (midterm exam
score). Which variable is independent and which dependent?
3) The association between gender (1=male 2=female) and interest in football rated as low,
medium and high.
4) Whether attitude toward contraception, measured as a 7 item likert scale (alpha=.75) increased
significantly between baseline and follow up among a two-wave panel sample of 1,000 randomly
selected respondents?
5) In (4) above, how would you test whether it increased differently for men and women?
6) In (5) above, how would you test whether a media campaign was associated with changes in
attitudes assuming you had a variable which asked whether respondents had seen or heard the
campaign?
7) In (6) above, how would you test whether the media campaign was more effective among
women than men?
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Class Exercise on Statistical Analysis

PM 536

December 3, 2020

This exercise is review for choosing the appropriate statistical procedures and tests. For each

question below write the appropriate statistical test and any transformations or other procedures

needed to conduct the analysis. Then write a sentence describing the result.

1) The association between gender (1=male 2=female) and MPH program track (1=Bio/Epi,

2=HC, 3=HP, & 4=Nutrition).

2) The association between gender (1=male 2=female) and MPH program track (midterm exam

score). Which variable is independent and which dependent?

3) The association between gender (1=male 2=female) and interest in football rated as low,

medium and high.

4) Whether attitude toward contraception, measured as a 7 item likert scale (alpha=.75) increased

significantly between baseline and follow up among a two-wave panel sample of 1,000 randomly

selected respondents?

5) In (4) above, how would you test whether it increased differently for men and women?

6) In (5) above, how would you test whether a media campaign was associated with changes in

attitudes assuming you had a variable which asked whether respondents had seen or heard the

campaign?

7) In (6) above, how would you test whether the media campaign was more effective among

women than men?

Factor Loadings Variable | 1 2 Uniqueness -------------+-------------------------------- q10a | 0.58102 -0.35716 0. q10b | 0.66227 -0.45192 0. q10c | 0.59314 -0.08275 0. q10d | 0.08049 0.77817 0. q10e | 0.31485 0.56020 0. q10f | 0.51183 0.09332 0. q10g | 0.49089 0.30261 0. q10h | 0.56381 -0.32552 0. q10i | 0.34078 0.37732 0. q10j | 0.50707 -0.16623 0. q10k | 0.49777 0.17450 0. q10l | 0.30303 0.57073 0.

. egen bf_1=rmean(q10a q10b q10c q10f q10g q10h q10j q10k) (85 missing values generated) . egen bf_2=rmean(q10d q10e q10l) (88 missing values generated) reg bf_1 q1gen q3age q4b, b Source | SS df MS Number of obs = 1773 -------------+------------------------------ F( 3, 1769) = 2. Model | 1.33829501 3 .446098336 Prob > F = 0. Residual | 272.293069 1769 .153924855 R-squared = 0. -------------+------------------------------ Adj R-squared = 0. Total | 273.631364 1772 .154419506 Root MSE =.


bf_1 | Coef. Std. Err. t P>|t| Beta 

-------------+---------------------------------------------------------------- q1gen | .0379335 .0215399 1.76 0.078. q3age | .0016037 .0007975 2.01 0.044. q4b | -.013156 .008224 -1.60 0.110 -. _cons | 3.401938 .0623882 54.53 0..


. reg bf_2 q1gen q3age q4b, b Source | SS df MS Number of obs = 1770 -------------+------------------------------ F( 3, 1766) = 8. Model | 10.3783104 3 3.45943679 Prob > F = 0. Residual | 699.324817 1766 .395993668 R-squared = 0. -------------+------------------------------ Adj R-squared = 0. Total | 709.703128 1769 .401188879 Root MSE =.


bf_2 | Coef. Std. Err. t P>|t| Beta 

-------------+---------------------------------------------------------------- q1gen | -.1024162 .0346114 -2.96 0.003 -. q3age | -.0026681 .0012803 -2.08 0.037 -. q4b | -.0531411 .0132106 -4.02 0.000 -. _cons | 2.29904 .1002629 22.93 0..