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ERIC Number: ED409325
Record Type: Non-Journal
Publication Date: 1997-Mar
Pages: 38
Abstractor: N/A
Reference Count: N/A
ISBN: N/A
ISSN: N/A
The Error of Accuracy for Two Regression Techniques: Does Psychometric Parallelism Matter?
Chang, Te-Sheng; Brookshire, William
The question of least-squares weights versus equal weights has been a subject of great interest to researchers for over 60 years. Several researchers have compared the efficiency of equal weights and that of least-squares weights under different conditions. Recently, S. V. Paunonen and R. C. Gardner stressed that the necessary and sufficient condition for equal-weights aggregation is that the predictors satisfy the requirements of psychometric parallelism. In this study, the effect of psychometric parallelism on the error of accuracy for equal weights and least-squares weights was investigated with the combination of different numbers of predictors, sample sizes, and intercorrelations. The findings indicate that equal weights always perform more precisely than least-squares weights as long as the following situations are satisfied: (1) the number of predictors is small; (2) the ratio of observation to predictor is small, less than or equal to 10; and (3) the magnitude of the mean intercorrelation is high, at least 0.6. Least-squares weights may perform more accurately than equal weights in the opposite situations of a large number of predictors, a high ratio of observation to predictor, and low intercorrelations. Nevertheless, the combination of a large number of predictors, large sample sizes, and a low mean of intercorrelation does not guarantee that least-squares weights are more accurate than equal weights. Equal weights are still more accurate than least-squares weights for the sample with a relatively high level of psychometric parallelism. (Contains 16 tables and 34 references.) (Author/SLD)
Publication Type: Reports - Evaluative; Speeches/Meeting Papers
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A