2. Patterns in partial tables

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)			Analyze another Z variable
Weaker relationship between X and Y (gammas from partial tables at least 0.10 weaker than bivariate gamma)			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)			Analyze subgroups (categories of Z) separately

Consider the following bivariate table showing the relationship between the number of missed work days (frequency of absence: low or high) and the amount of monthly sales (classified as either low or high, based on company-wide averages) among sales personnel at a car dealership.

	Frequency of Absence (X)
Monthly Sales (Y)	Low	High	Totals
Low	43 (51.2%)	31 (22.8%)	74
High	41 (48.8%)	105 (77.2%)	146
Totals	84 (100.0%)	136 (100.0%)	220
	Gamma = +0.5607

The data in the table suggest that there is association between the number of missed work days (X) and the amount of monthly sales (Y).

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

A. High School

	Frequency of Absence (X)
Monthly Sales (Y)	Low	High	Totals
Low	24 (48.0%)	9 (22.0%)	33
High	26 (52.0%)	32 (78.0%)	58
Totals	50 (100.0%)	41 (100.0%)	91
	Gamma = +0.5329

B. College

	Frequency of Absence (X)
Monthly Sales (Y)	Low	High	Totals
Low	19 (55.9%)	22 (23.2%)	41
High	15 (44.1%)	73 (76.8%)	88
Totals	34 (100.0%)	95 (100.0%)	129
	Gamma = +0.6156

Compared with the bivariate table, the partial tables show relationship between the number of missed work days and the amount of monthly sales. This is evidence of relationship between the X and Y variables and implies that you should the variable Z (level of education) in further analysis.