Mplus only provides the variance, so we have the square root this to get the standard deviation. In the variance standardization method above, we only standardize by the predictor (the factor, X). In order to match the STDYX and variance standardization solutions, let’s first get the standard deviation of our outcome q01. The STDYX solution standardizes the loading by the standard deviation of both the predictor (the factor, X) and the outcome (the item, Y). TITLE: One Factor CFA Identifying Variance = 1īelow we show the STDYX solution, note that the loadings are different but the variances are the same. Mplus by default uses Option 2, marker method if nothing else is specified.
If the CFI and TLI are less than one, the CFI is always greater than the TLI. Mplus lists another fit statistic along with the CFI called the TLI Tucker Lewis Index which also ranges between 0 and 1 with values greater than 0.90 indicating good fit. If you reject the model, it means your model is not a close fitting model. In Mplus, you also obtain a p-value of close fit, that the RMSEA RMSEA is the root mean square error of approximation (values of 0.01, 0.05 and 0.08 indicate excellent, good and mediocre fit respectively, some go up to 0.10 for mediocre).CFI is the comparative fit index – values can range between 0 and 1 (values greater than 0.90, conservatively 0.95 indicate good fit).Model chi-square this is the chi-square statistic we obtain from the maximum likelihood statistic (similar to the EFA).The three main model fit indices in CFA are: TITLE: One Factor CFA SAQ-7 (Marker Method)
#Smartpls 3 model fit indices code
In Mplus the code is relatively simple, note the BY statement indicates the items to the right of the statement loading onto the factor to the left of the statement. Much like exploratory common factor analysis, we will assume that total variance can be partitioned into common and unique variance. The most fundamental model in CFA is the one factor model, which will assume that the covariance (or correlation) among items is due to a single common factor. As an exercise, let’s first assume that SPSS Anxiety is the only factor that explains common variance in all 7 items. Recall from our exploratory analysis that Items 1,2,3,4,5, and 8 load onto each other and Items 6 and 7 load onto the same factor. Computers are useful only for playing games.
However, from the exploratory factor analysis and talking to the Principal Investigator, we decided to remove Item 2 from the analysis. In this portion of the seminar, we will continue with the example of the SAQ. Confirmatory Factor AnalysisĬonfirmatory factor analysis borrows many of the same concepts from exploratory factor analysis except that instead of letting the data tell us the factor structure, we pre-determine the factor structure and perform a hypothesis test to see if this is true. Please refer to Confirmatory Factor Analysis (CFA) in R with lavaan for a much more thorough introduction to CFA.