THE EFFECT OF COMMON VARIANCE AND STRUCTURE PATTERN ON RANDOM DATA EIGENVALUES - IMPLICATIONS FOR THE ACCURACY OF PARALLEL ANALYSIS

Selecting the correct number of factors to retain in a factor analysis is a crucial step in developing psychometric tools or developing theo ries. The present study assessed the accuracy of parallel analysis, a technique in which the observed eigenvalues are compared to eigenvalue s from simulated data in which no real factors are present. Study 1 in vestigated the effect of the presence of one real factor on the size o f subsequent noise eigenvalues. The size of real factors and the sampl e 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 o f Studies 1 and 2 to actual psychometric data. These examples illustra te the importance of modeling the data more closely when parallel anal ysis is used to determine the number of real factors.