Select the correct entries from the dropdown menus to complete the following table of possible results when analyzing the effect of a third variable Z on the relationship between two variables X and Y.

Compared with Bivariate Table, Partial Tables Show	Pattern	Implications for Further Analysis	Likely Next Step
Same relationship between X and Y (gammas for partial tables within 0.10 of bivariate gamma)	Interaction, Direct, Spurious/intervening	Incorporate Z, Disregard Z	Analyze another Z variable
Weaker relationship between X and Y (gammas from partial tables at least 0.10 weaker than bivariate gamma)	Interaction, Direct, Spurious/intervening	Incorporate Z, Disregard Z	Focus on relationship between Z and X or among X, Y, and Z
Mixed (at least 0.10 difference in gammas between partial tables and between partial tables and bivariate table)	Interaction, Direct, Spurious/intervening	Incorporate Z, Disregard Z	Analyze subgroups (categories of Z) separately

Consider the following bivariate table showing the relationship between job satisfaction (low or high) and longevity, that is, the time working for the company (low = less than 2 years, high = 2 years or more), among workers for a fast-food chain.

	Job Satisfaction (X)
Longevity (Y)	Low	High	Totals
Low	14 (28.0%)	27 (58.7%)	41
High	36 (72.0%)	19 (41.3%)	55
Totals	50 (100.0%)	46 (100.0%)	96
	Gamma = -0.5703

The data in the table suggest that there is no, a positive, a negative, or a perfect association between job satisfaction (X) and longevity (Y).

Here are the partial tables showing the bivariate relationship between X and Y after controlling for gender.

	Job Satisfaction (X)
Longevity (Y)	Low	High	Totals
Low	11 (68.8%)	26 (72.2%)	37
High	5 (31.3%)	10 (27.8%)	15
Totals	16 (100.0%)	36 (100.0%)	52
	Gamma = -0.0833

B. Women

	Job Satisfaction (X)
Longevity (Y)	Low	High	Totals
Low	3 (8.8%)	1 (10.0%)	4
High	31 (91.2%)	9 (90.0%)	40
Totals	34 (100.0%)	10 (100.0%)	44
	Gamma = -0.0690

Compared with the bivariate table, the partial tables show the same, a mixed, a weaker, or a stronger relationship between job satisfaction and longevity. This is evidence of a direct, an interactive, a spurious or intervening, or a fallaciousrelationship between the X and Y variables and implies that you should incorporate, modify, disregard, elaborate the variable Z (gender) in further analysis.