The two exercises that follow utilize the data sets career-a.sav and career-f.sav, which can be down- loaded from this website:

www.routledge.com/9781138289734

1. You are interested in evaluating the effect of job satisfaction (satjob2) and age category (agecat4) on

the combined DV of hours worked per week (hrs1) and years of education (educ).Use career-a.sav

for steps a and b.

a. Develop the appropriate research questions and/or hypotheses for main effects and interaction.

b. Screen data for missing data and outliers. What steps, if any, are necessary for reducing missing

data and outliers?

For all subsequent analyses in Question 1, use career-f.sav and the transformed variables of hrs2 and educ2.

c. Test the assumptions of normality and linearity of DVs.

i. What steps, if any, are necessary for increasing normality?

ii. Are DVs linearly related?

Conduct MANOVA with post hoc (be sure to test for homogeneity of variance-covariance).

i.Can you conclude homogeneity of variance-covariance? Which test statistic is most ap- propriate for interpretation of multivariate results?

ii. Is factor interaction significant? Explain.

iii. Are main effects significant? Explain.

iv. What can you conclude from univariate ANOVA and post hoc results?

e. Conclude with a results statement.

Building on the previous problem, in which you investigated the effects of job satisfaction (satjob2)

and age category (agecat4) on the combined dependent variable of hours worked per week (hrs1) and

years of education (educ), you are now interested in controlling for respondents' income such that rin-

com91 will be used as a covariate. Complete the following using career-a.sav.

a. Develop the appropriate research questions and/or hypotheses for main effects and interaction.

b. Screen data for missing data and outliers. What steps, if any, are necessary for reducing missing

data and outliers?

For all subsequent analyses in Question 2, use career-f.sav and the transformed variables of hrs2, educ2, and rincom2.

c. Test the assumptions of normality and linearity of DVs and covariate.

i. What steps, if any, are necessary for increasing normality?

ii. Are DVs and covariate linearly related?

d.Conduct a preliminary MANCOVA to test the assumptions of homogeneity of variance-covari-

ance and homogeneity of regression slopes/planes.

i. Can you conclude homogeneity of variance-covariance? Which test statistic is most

appropriate for interpretation of multivariate results?

ii. Do factors and covariate significantly interact? Explain.

e. Conduct MANCOVA.

i. Is factor interaction significant? Explain.

ii. Are main effects significant? Explain.

iii. What can you conclude from univariate ANOVA results?

f. Conclude with a results statement.

3. Compare the results from Question 1 and Question 2. Explain the differences in main

effects.