ANCOVA Laboratory Exercise
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ANCOVA Laboratory Exercise – use the “clinical_ANCOVA.sav” data
Walk-through The purpose was compare depression levels of traditionally aged men and women college students.
1. Checking for Confounding a. Open the Clinical_ANCOVA.sav data set and select only those with a value of “1” on the “group” variable. b. Even as we were about to compare the mean depression of these two groups using an ANOVA, one of the research team members reminded us that there are gender differences on several variables that relate to depression, including social support, loneliness & stress,. Gender differences on these variables, she pointed out, might be “distorting” out gender comparison. c. Run an ANOVA comparing men and women on depression (BDI) and each of the suggested “control” variables.
Variable men’s mean (std) women’s mean (std) F df MSe p
Depression
SOSS
FRSS
RULS
Stress
SES
From the ANOVA’s… d. Is there a gender difference on depression for these groups?
e. Might any of these variables be “distorting this comparison”? Which ones? Explain your answers. 2. Separate ANCOVAs a. Get the ANCOVA using each of these variables as the single covariate. Note which give “a different answer” then the original (gender & depression) ANOVA.
Corrected means for Depression Tests of the “corrected Gender effect” men’s mean (std) women’s mean (std) F df MSe p
Gender
SOSS
Gender
FRSS
Gender
RULS
Gender
Stress
Gender
SES
b. Any surprises about which covariates change the picture about the gender difference? c. How does that happen???? d. Which of the individual covariates “change the results” when comparing men & women? 3. Multiple covariate ANCOVA a. Run a single ANCOVA with all of these variables as covariates
Corrected means for Depression Tests of the “corrected Gender effect” men’s mean (std) women’s mean (std) F df MSe p gender
SOSS
FRSS
RULS tress
SES
b. Is there a gender difference on depression after controlling for these variables?
c. What should you be worried about when treating these ANCOVA results as “definitive”??? .4. Checking for Interactions
Tests of the “corrected Gender effect” F df MSe p
Gender
SOSS
Interaction
Tests of the “corrected Gender effect” F df MSe p
Gender
FRSS
Interaction
Tests of the “corrected Gender effect” F df MSe p
Gender
RULS
Interaction
Tests of the “corrected Gender effect” F df MSe p
Gender
Stress
Interaction
Tests of the “corrected Gender effect” F df MSe p
Gender
SES
Interaction
a. Which models should include the interaction term? .5. Exploring the Interaction
a. Get the mean of SES b. Make a new variable SES_cen that is the mean-centered version of SES c. Obtain the ANCOVA with the gender*ses interaction using SES_cen (check all F-values match previous)
Tests of the “corrected Gender effect” F df MSe p b
Gender
SES
Interaction
Constant d. Put the values into the xls plotting program, and copy the picture below. e. Describe the pattern of the interaction! f. Describe the main effect of SES g. Finally, what we came for… Describe the “Gender effect” … Your Turn
Repeat this analysis using the group=2 (no traditionally aged students) -- adding age and SES (which is socio- economic status) to the list of covariates.
Variable men’s mean (std) women’s mean (std) F df MSe p
Depression
SOSS
FRSS
RULS
Stress
SES
From the ANOVA’s… f. Is there a gender difference on depression for these groups?
g. Might any of these variables be “distorting this comparison”? Which ones? Explain your answers. 2. Separate ANCOVAs e. Get the ANCOVA using each of these variables as the single covariate. Note which give “a different answer” then the original (gender & depression) ANOVA.
Corrected means for Depression Tests of the “corrected Gender effect” men’s mean (std) women’s mean (std) F df MSe p
Gender
SOSS
Gender
FRSS
Gender
RULS
Gender
Stress
Gender
SES
f. Any surprises about which covariates change the picture about the gender difference? g. How does that happen???? h. Which of the individual covariates “change the results” when comparing men & women? 3. Multiple covariate ANCOVA d. Run a single ANCOVA with all of these variables as covariates
Corrected means for Depression Tests of the “corrected Gender effect” men’s mean (std) women’s mean (std) F df MSe p gender
SOSS
FRSS
RULS tress
SES
e. Is there a gender difference on depression after controlling for these variables?
f. What should you be worried about when treating these ANCOVA results as “definitive”??? .4. Checking for Interactions
Tests of the “corrected Gender effect” F df MSe p
Gender
SOSS
Interaction
Tests of the “corrected Gender effect” F df MSe p
Gender
FRSS
Interaction
Tests of the “corrected Gender effect” F df MSe p
Gender
RULS
Interaction
Tests of the “corrected Gender effect” F df MSe p
Gender
Stress
Interaction
Tests of the “corrected Gender effect” F df MSe p
Gender
SES
Interaction
a. Which models should include the interaction term? .5. Exploring the Interaction
d. Get the mean of SES e. Make a new variable SES_cen that is the mean-centered version of SES f. Obtain the ANCOVA with the gender*ses interaction using SES_cen (check all F-values match previous)
Tests of the “corrected Gender effect” F df MSe p b
Gender
SES
Interaction
Constant d. Put the values into the xls plotting program, and copy the picture below. e. Describe the pattern of the interaction! f. Describe the main effect of SES
1. g. Finally, what we came for… Describe the “Gender effect” … Finally – let’s go back to the “alternative analysis lab” and apply the ANCOVA with interaction to that problem!
a. Get the mean and std of COV mean ______std ______b. Make a new variable COV_cen that is the mean-centered version of SES c. Obtain the ANCOVA with the gender*ses interaction using SES_cen
F df MSe p b
IV
COV
Interaction
Constant
Put the values into the xls plotting program, and copy the picture below.
Describe the pattern of the interaction!
Describe the main effect of COV
Finally, what we came for… Describe the “Treatment within this model