![]() ![]() Taken together, these tests provide a minimum standard which should be passed before a factor analysis (or a principal components analysis) should be conducted.Ī. An identity matrix is matrix in which all of the diagonal elements are 1 and all off diagonal elements are 0. Bartlett’s Test of Sphericity – This tests the null hypothesis that the correlation matrix is an identity matrix. Kaiser-Meyer-Olkin Measure of Sampling Adequacy – This measure varies between 0 and 1, and values closer to 1 are better. If the determinant is 0, then there will be computational problems with the factor analysis, and SPSS may issue a warning message or be unable to complete the factor analysis.Ī. The table above is included in the output because we used the det option on the /print subcommand. Analysis N – This is the number of cases used in the factor analysis. Deviation – These are the standard deviations of the variables used in the factor analysis.Ĭ. Mean – These are the means of the variables used in the factor analysis.ī. Standard deviations (which is often the case when variables are measured on different scales).Ī. If the factor analysis is being conducted on the correlations (as opposed to the covariances), it is not much of a concern that the variables have very different means and/or The number of cases used in the analysis will be less than the total number of cases in the data file if there are missing values on any of the variables used in the factor analysis, because, by default, SPSS does a listwise deletion of incomplete cases. Please note that the only way to see how many cases were actually used in the factor analysis is to include the univariate option on the /print subcommand. The table above is output because we used the univariate option on the /print subcommand. print initial det kmo repr extraction rotation fscore univariate variables item13 item14 item15 item16 item17 item18 item19 item20 item21 item22 item23 item24 First open the file M255.sav and then copy, paste and run the following syntax into the SPSS Syntax Editor. Let’s start with orthgonal varimax rotation. General information regarding the similarities and differences between principalĬomponents analysis and factor analysis, see Tabachnick and Fidell (2001), for example. We have also created a page ofĪnnotated output for a principal components analysis that parallels this analysis. To use all of these options, we have included them here to aid in theĮxplanation of the analysis. Reproduced correlation matrix, the scree plot and the plot of the rotated In this example we have included many options, including the original and Number of factors yields the most interpretable results. The number of factors to extract should be guided by theory, but also informedīy running the analysis extracting different numbers of factors and seeing which We will use iterated principal axis factor with three factors as our method of extraction, a varimax rotation, andįor comparison, we will also show the promax oblique solution. As a rule of thumb,Ī bare minimum of 10 observations per variable is necessary to avoidįor the example below, we are going to do a rather “plain vanilla” factorĪnalysis. Good, 500 is very good, and 1000 or more is excellent. Regarding sample size: 50 cases is very poor, 100 is poor, 200 is fair, 300 is Tabachnick and Fidell (2001, page 588) cite Comrey and Lee’s (1992) advise Structure is pattern of results such that each variable loads highly onto oneįactor analysis is a technique that requires a large sample size.įactor analysis is based on the correlation matrix of the variables involved,Īnd correlations usually need a large sample size before they stabilize. ![]() However, all analysts are looking for simple structure. Given the number of factor analytic techniques and options, it is not surprisingĭifferent analysts could reach very different results analyzing the same data You also need to determine the number of factors that you want to extract. Oblique rotations, such as promax, which allow the factors to be correlated with Impose the restriction that the factors cannot be correlated, and Of factors, including orthogonal rotations, such as varimax and equimax, which Many different types of rotations that can be done after the initial extraction Likelihood, generalized least squares, unweighted least squares), There are also There are many different methods thatĬan be used to conduct a factor analysis (such as principal axis factor, maximum Underlying unobservable (latent) variables that are reflected in the observed Overview: The “what” and “why” of factor analysisįactor analysis is a method of data reduction. Professor James Sidanius, who has generously shared them with us. The data used in this example were collected by This page shows an example of a factor analysis with footnotesĮxplaining the output. ![]()
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