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