Question

Please note that for all problems in this course, the standard cut-off (alpha) for a test of significance will be .05, and you always report the exact power unless SPSS output states p=.000 (you’d report p<.001). Also, remember that we divide the p value in half when reporting one-tailed tests with 1 – 2 groups.

Problem Set 1: Two-way ANOVA (8 pts) Research Scenario An Industrial/Organizational psychologist conducted a study examining differences in sex (women and men) and primary mode of communication with superiors (face, email, indirect) on perceived likelihood of receiving a raise in the next 6 months. Perceived likelihood was measured in percent likelihood of expecting a raise (e.g., 0 indicates they absolutely do not expect a raise in the next 6 months). Twenty-eight participants completed the study. Their results are in the table below. Conduct a two-way ANOVA to determine whether perceived likelihood of receiving a raise in the next 6 months is affected by sex and/or primary mode of communication. Remember to name and define your variables (your two independent variables and your one dependent variable) under the “Variable View,” then return to the “Data View” to enter and analyze the data. (Note the data set is small to ease your burden – use a two-way ANOVA regardless!) Face Email Indirect Men 85 100 90 95 100 99 90 100 80 85 90 95 40 30 Women 50 75 80 70 25 10 5 50 75 75 5 10 25 10

9. Paste the relevant SPSS output, including post hocs if necessary. (2 pts)

10. Create the most appropriate graph(s) for this data given the results. (2 pts)

11. Write an APA-style Results section based on your analysis. All homework “Results sections” should follow the examples provided in the presentations and textbooks. They should include the statistical statement within a complete sentence that mentions the type of test conducted, whether the test was significant, and if relevant, effect size and/or post hoc analyses. Don’t forget to include a decision about the null hypothesis. (4 pts)

Answer 1

SPSS output:

Tests of Between-Subjects Effects
Dependent Variable:RAISE
Source	Type III Sum of Squares	df	Mean Square	F	Sig.
Corrected Model	21252.631a	5	4250.526	8.994	.000
Intercept	96217.338	1	96217.338	203.594	.000
SEX	11870.330	1	11870.330	25.117	.000
COMMUNICATION_MODE	7228.342	2	3614.171	7.648	.003
SEX * COMMUNICATION_MODE	1035.940	2	517.970	1.096	.352
Error	10397.083	22	472.595
Total	140276.000	28
Corrected Total	31649.714	27
a. R Squared = .671 (Adjusted R Squared = .597)

Multiple Comparisons
RAISE Tukey HSD
(I) COMMUNICATION_MODE	(J) COMMUNICATION_MODE	Mean Difference (I-J)	Std. Error	Sig.	95% Confidence Interval
					Lower Bound	Upper Bound
FACE	EMAIL	29.8545*	9.49856	.013	5.9936	53.7155
	INDIRECT	41.5429*	10.71322	.002	14.6306	68.4551
EMAIL	FACE	-29.8545*	9.49856	.013	-53.7155	-5.9936
	INDIRECT	11.6883	10.51079	.517	-14.7155	38.0921
INDIRECT	FACE	-41.5429*	10.71322	.002	-68.4551	-14.6306
	EMAIL	-11.6883	10.51079	.517	-38.0921	14.7155
Based on observed means. The error term is Mean Square(Error) = 472.595.
*. The mean difference is significant at the .05 level.

Graphs:

Results

The null hypothesis, ho: the main effect of sex is not significant. The alternative hypothesis, h1: the main effect of sex is significant. WIth F=25.117, P<5%, I REJECT ho and conclude that the main effect of sex is significant.

The null hypothesis, ho: the main effect of COMMUNICATION_MODE is not significant. The alternative hypothesis, h1: the main effect of COMMUNICATION_MODE is significant. WIth F=7.648, P<5%, I REJECT ho and conclude that the main effect of COMMUNICATION_MODE is significant.

The null hypothesis, ho: interaction effect is not significant. The alternative hypothesis, h1: the interaction effect is significant. WIth F=1.096, P>5%, I fail to REJECT ho and conclude that the interaction effect IS NOT significant.

From posr Hoc analysis, with p<5%, there is significant difference in the mean raise between (face, indirect) and (email and face).