Please answer, I posted this last week and they said more info was needed but this is the entire question.
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. Implications for Further Analysis Compared with Bivariate Table, Partial Tables Show Pattern Likely Next Step Analyze another Z variable Same relationship between X and Y (gammas for partial tables within 0.10 of bivariate gamma) Direct Focus on relationship Weaker relationship between X and Y (gammas from partial tables at least 0.10 weaker than bivariate gamma) Interaction between Z and X or among X, Y, and z Spurious/Intervening 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. Monthly Sales (Y) Totals Low Frequency of Absence (X) Low High 14 (28.0%) 27 (58.7%) 36 (72.0%) 19 (41.3%) 50 (100.0%) 46 (100.0%) 41 High 55 Totals 96 Gamma = -0.5703 association between the number of missed work days (X) and the amount of monthly sales The data in the table suggest that there is a negative (Y). Here are the partial tables showing the bivariate relationship between X and Y after controlling for level of education. A. High School Monthly Sales (Y) Totals Low 37 Frequency of Absence (X) Low High 11 (68.8%) 26 (72.2%) 5 (31.3%) 10 (27.8%) 16 (100.0%) 36 (100.0%) Gamma = -0.0833 High 15 Totals 52 B. College Frequency of Absence (X) Low High Monthly Sales (Y) Totals Low 1 (10.0%) 4 High 3 (8.8%) 31 (91.2%) 34 (100.0%) 9 (90.0%) 40 Totals 10 (100.0%) 44 Gamma = -0.0690 Compared with the bivariate table, the partial tables show a weaker 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. 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. Implications for Further Analysis Compared with Bivariate Table, Partial Tables Show Pattern Likely Next Step Analyze another Z variable Same relationship between X and Y (gammas for partial tables within 0.10 of bivariate gamma) Direct Focus on relationship Weaker relationship between X and Y (gammas from partial tables at least 0.10 weaker than bivariate gamma) Interaction between Z and X or among X, Y, and z Spurious/Intervening 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. Monthly Sales (Y) Totals Low Frequency of Absence (X) Low High 14 (28.0%) 27 (58.7%) 36 (72.0%) 19 (41.3%) 50 (100.0%) 46 (100.0%) 41 High 55 Totals 96 Gamma = -0.5703 association between the number of missed work days (X) and the amount of monthly sales The data in the table suggest that there is a negative (Y). Here are the partial tables showing the bivariate relationship between X and Y after controlling for level of education. A. High School Monthly Sales (Y) Totals Low 37 Frequency of Absence (X) Low High 11 (68.8%) 26 (72.2%) 5 (31.3%) 10 (27.8%) 16 (100.0%) 36 (100.0%) Gamma = -0.0833 High 15 Totals 52 B. College Frequency of Absence (X) Low High Monthly Sales (Y) Totals Low 1 (10.0%) 4 High 3 (8.8%) 31 (91.2%) 34 (100.0%) 9 (90.0%) 40 Totals 10 (100.0%) 44 Gamma = -0.0690 Compared with the bivariate table, the partial tables show a weaker 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
