![]() ![]() The other components -having low quality scores- are not assumed to represent real traits underlying our 16 questions. ![]() This is because only our first 4 components have Eigenvalues of at least 1. Our 16 variables seem to measure 4 underlying factors. Select components whose Eigenvalues are at least 1.Īpplying this simple rule to the previous table answers our first research question: So what's a high Eigenvalue? A common rule of thumb is to Only components with high Eigenvalues are likely to represent real underlying factors. Each component has a quality score called an Eigenvalue. Now, with 16 input variables, PCA initially extracts 16 factors (or “components”). Factor Analysis Output I - Total Variance Explained FACTOR /VARIABLES v1 v2 v3 v4 v5 v6 v7 v8 v9 v11 v12 v13 v14 v16 v17 v20 /MISSING PAIRWISE /* IMPORTANT!*/ /PRINT INITIAL CORRELATION EXTRACTION ROTATION /FORMAT SORT BLANK(.30) /PLOT EIGEN /CRITERIA MINEIGEN(1) ITERATE(25) /EXTRACTION PC /CRITERIA ITERATE(25) /ROTATION VARIMAX /METHOD=CORRELATION. *Initial factor analysis as pasted from menu. *Show both variable names and labels in output. So let's now set our missing values and run some quick descriptive statistics with the syntax below. But in this example -fortunately- our charts all look fine. If we see something unusual in a chart, we don't easily see which variable to address.
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