| Summary: | 碩士 === 國立臺灣大學 === 心理學研究所 === 94 === Determining the number of factors is a critical step in factor analysis. Since Guttman (1954) and Kaiser (1960) proposed the eigenvalue-greater-than-one rule to determine the number of factors, this rule has been widely applied in different research fields. Besides this rule, other researchers have proposed different methods to determine the number of factors. Previous researches on the comparison of the performances of different methods have repeatedly shown that the rule of eigenvalue-greater-than-one is the least accurate and the most unstable method. However, due to its popularity, a thorough evaluation on the method is called for. This study was therefore designed to reexamine the performance of this rule in order to offer appropriate guidelines for its application. Factor loading, the ratio of number of variables to factors, the number of factors, sample size, and the complexity of factor model were manipulated in the present study to investigate the performance of the eigenvalue-greater-than-one rule. The results showed that when the factor loading was high (.8), and the ratio of number of variables to factors was equal to or greater than 4, eigenvalue-greater-than-one rule could correctly determine the number of factors in most conditions. When factor loading was equal to .6, and the ratio of number of variables to factors equaled to 4, this rule performed well in identifying the number of factors if the sample size was equal to or greater than 200. When the ratio of number of variables to factors was 6 or 8 with a factor loading of .6, only under large samples (n = 500, 1000) could this rule yield the correct number of factors. When factor loading was equal to .4, eigenvalue-greater-than-one rule performed poorly in most conditions.
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