The concept of change, growth or discrepancy scores continues to persist in spite of documented statistical and measurement deficiencies. Recent writings support the continued use of change scores by identifying specific conditions where raw change scores have high predictive validity potential. The present paper expands the discussion of change score methodology by relating the concept of change to suppressor variable conditions in a least square regression model. The domain of conditions necessary for a weighted changed score composite to emerge as an underlying construct is mapped and the information loss through arbitrary assignment of weights to a change composite is explored.
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