Anda belum login :: 23 Jul 2024 10:22 WIB
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)
  • Perpustakaan Pusat (Semanggi)
    • Nomor Panggil: EE30.2
    • Non-tandon: 1 (dapat dipinjam: 0)
    • Tandon: tidak ada
    Lihat Detail Induk
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.
Opini AndaKlik untuk menuliskan opini Anda tentang koleksi ini!

Process time: 0.03125 second(s)