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I'm running a simulation for an EFA with 50 continuous indicators and 5 factors, a residual variance of .4 for all indicators, and a correlation of .4 for factor 1 with factor 3. I looked at sample sizes of 100, 300, 500, and 1000 and noticed that for factor 3, the parater estimates became more biased as the sample size increased. Any suggestions? model population: f1 by y1-y10*.65 y11-y20*.05 y21-y30*.25 y31-y40*.3 y41-y50*.1; f2 by y1-y10*.05 y11-y20*.85 y21-y30*.1 y31-y40*.05 y41-y50*.05; f3 by y1-y10*.1 y11-y20*.05 y21-y30*.6 y31-y40*.1 y41-y50*.12; f4 by y1-y10*.05 y11-y20*.12 y21-y30*.1 y31-y40*.65 y41-y50*.1; f5 by y1-y10*.05 y11-y20*.12 y21-y30*.07 y31-y40*.15 y41-y50*.7; f1@1 f2@1 f3@1 f4@1 f5@1; f1 with f3@.4; y1-y50*.40; model: f1 by y1-y10*.65 y11-y20*.05 y21-y30*.25 y31-y40*.3 y41-y50*.1 (*1); [repeat as above for f2-f5] y1-y50*.40; |
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Please send the output for n=1000 to support. |
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