The two exercises below utilize the data sets career-a.sav and career-f.sav, whi

Question

The two exercises below utilize the data sets career-a.sav and career-f.sav, which can be downloaded from this Web site: www.Pyrczak.com/data

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 (hrsl) 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 educ 2.

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?

d. 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 ap­propriate 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. Write a results statement.

2. Building on the previous problem, in which you investigated the effects of job satisfaction (satjobl) and age category (agecat4) on the combined dependent variable of hours worked per week (hrsl) 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- covariance 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? Write a results statement.

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

Explanation / Answer

oYou do a MANOVA instead of a series of one-at-a-time ANOVAs for two main reasons

nSupposedly to reduce the experiment-wise level of Type I error (8 F tests at .05 each means the experiment-wise probability of making a Type I error (rejecting the null hypothesis when it is in fact true) is 40%! The so-called overall test or omnibus test protects against this inflated error probability only when the null hypothesis is true. If you follow up a significant multivariate test with a bunch of ANOVAs on the individual variables without adjusting the error rates for the individual tests, there’s no “protection”

nAnother reasons to do MANOVA. None of the individual ANOVAs may produce a significant main effect on the DV, but in combination they might, which suggests that the variables are more meaningful taken together than considered separately

nMANOVA takes into account the intercorrelations among the DVs

Some of the things that we learned to look for on the ANOVA output:

A. The value of the F ratio (same line as the IV or “factor”)

B. The significance of that F ratio (same line)

C. The partial eta squared (an estimate of the amount of the “effect size” attributable to between-group differences (differences in levels of the IV (ranges from 0 to 1 where 1 is strongest)

D. The power to detect the effect (ranges from 0 to 1 where 1 is strongest)

Related Questions

Navigate

• Browse (All)
• Browse T
• Subjects

Integrity-first tutoring: explanations and feedback only — we do not complete graded work. Learn more.