The Effect of Common Variance and Structure Pattern on Random Data Eigenvalues: Implications for the Accuracy of Parallel Analysis

Educational and Psychological Measurement - Tập 58 Số 4 - Trang 541-568 - 1998
Nigel E. Turner1
1Addiction Research Foundation, Clinical, Social & Evaluation Research Dept, Toronto, ON, US

Tóm tắt

Selecting the correct number of factors to retain in a factor analysis is a crucial step in developing psychometric tools or developing theories. The present study assessed the accuracy of parallel analysis, a technique in which the observed eigenvalues are compared to eigenvalues from simulated data in which no real factors are present. Study 1 investigated the effect of the presence of one real factor on the size of subsequent noise eigenvalues. The size of real factors and the sample size were manipulated. Study 2 examined the effect that the pattern of structure coefficients and continuousness of the variables have on the size of real and noise eigenvalues. Study 3 compared the results of Studies 1 and 2 to actual psychometric data. These examples illustrate the importance of modeling the data more closely when parallel analysis is used to determine the number of real factors.

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Thông tin xuất bản

Educational and Psychological Measurement

Tập 58 Số 4

541-568

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