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

Oleh:

Turner, Nigel E.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: Educational and Psychological Measurement vol. 58 no. 4 (1998), page 541-568.
Topik: accuracy; psychological tests; analysis; education
Fulltext: 541.pdf (1.26MB)

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Perpustakaan Pusat (Semanggi) Nomor Panggil: EE30.2 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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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 eigen values from simulated data on 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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