# Variable Selection.pptx - Variable Selection ISDS 7024 Dr...

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Variable Selection ISDS 7024 Dr. Van Scotter

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Variable Selection Approaches Theory Observation Inspiration Mechanical
Desirable Properties of Predictors Account for a large portion of the variance in the DV (Criterion-Related Validity). Represent the phenomena they are intended to represent (Construct Validity). Yield consistent results over time, situations, subjects, and measurement approaches (Construct Validity, Reliability, and Generalizability). Inexpensive to measure. Easily understood (Face Validity).

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Variable Selection Choosing an initial set of variables to include in a regression model. Adding or deleting variables from that set.
Basic Model where q is the number of variables Meaning of the regression is tied to the B’s so selecting the right predictors is critically important. If the goal is explanation: For a new theory, selection of predictors is based on our understanding of the phenomenon. Adding or deleting a variable has implications for the theory. Arbitrarily adding or deleting a variable disconnects the model from the theory it is supposed to test. For a well-established theory, selecting the right predictors is (mostly) a question of measurement. Measuring the same construct using different approaches can strengthen our confidence in the theory.

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