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Overview of CHAID (Decision Tree) Analysis

Overview of CHAID (Decision Tree) Analysis. Overview of CHAID Analysis. Ch i-squared A utomatic I nteraction D etector (CHAID) Similar to Regression analysis, in that it selects the best predictors that account for the most “explained” variance in employee satisfaction.

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Overview of CHAID (Decision Tree) Analysis

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  1. Overview of CHAID (Decision Tree) Analysis

  2. Overview of CHAID Analysis • Chi-squared Automatic Interaction Detector (CHAID) • Similar to Regression analysis, in that it selects the best predictors that account for the most “explained” variance in employee satisfaction. • However, CHAID analysis goes one step further, and identifies as the important elements of employee satisfaction those variables that most differentiate satisfied and dissatisfied employees.

  3. CHAID Analysis Steps • The CHAID procedure begins by finding survey variables that have a significant association with employee satisfaction. • It then assesses the category groupings, or interval breaks to pick the most significant combination of variables. • The variable having the strongest association with employee satisfaction becomes the first branch in a tree with a leaf for each category that is significantly different relative to satisfaction. • The process is repeated to find the predictor variable on each leaf most significantly related to satisfaction, until no significant predictors remain.

  4. Example of CHAID Analysis Org x • 41% of respondents who disagreed to statement 55 are not satisfied with Org X as a place to work. • Therefore, “Career Opportunities” is a key driver of employee satisfaction. Org x • 69% of respondents who disagreed to statements 59 and 55 are not satisfied with Org X as a place to work. • Therefore, “Hiring and Promotion” along with “Career Opportunities” are key drivers of employee satisfaction.

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