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13 Cards in this Set

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 Reasons for utilizing multivariate techniques 1)Gathering additional information about a specific bivariate relationship by using another variable. 2)Multivariate statistical techniques cannot prove causation, but can provide evidence. Z variable Third variable that is controlled to see the relationship between X and Y. If the control variable has an effect, the relationship between X and Y will change under various conditions of Z. Partial Tables Tables that are split according to the categories of the Z variable. This controls for the effect of Z. Elaboration A more detailed form of multivariate analysis. Level of measurement for multivariate analysis Nominal and ordinal Direct Relationships The relationship between X and Y are the same in all partial tables and the bivariate tables. Spurious or intervening relationships The relationship between X and Y is the same in all partial tables, but much weaker than in the bivariate table. Interaction Each partial table and the bivariate table show different relationships between X and Y. Outcome of a Direct Relationship Z has no effect on the relationship between X and Y and can be ignored. Outcome of a Spurious Relationship Z occurs before both X and Y in time and is a common cause of both X and Y. Ignore X and focus on Y and Z. Outcome of an Intervening Relationship X is causally linked to Z which is causally linked to Y Continue to analyze all three variables. Outcome of an Interaction Incorporate Z and analyze Z's subgroups separately. Partial Gamma Strength of Association between X and Y once Z has been removed