Mplus can estimate CFA models and CFA models with background variables for a single or multiple groups. Here we are going to move from fitting a measurement model to actually testing structural relationships between variables. Number of groups 1 you get convergence failures) where measures are on scales with high variance - where this is the case, rescaling predictors, e.g., standardising them, usually solves the … This line is not necessary if all of the variables in the data file will be used. To review, the model to be fit is the following: We’ll start out with a basic CFA model that does not have any constraints on the parameters nor any correlated errors. This page describes how to set up code in Mplus to fit a confirmatory factor analysis (CFA) model. In this example, it is assumed that the data are in the same folder as this input file. It not only provided me with the skills and confidence to conduct the analyses in MPlus but, also, the details of why, how, and what the analyses were about. This is the kind of comment statisticians find funny that leaves other people scratching their heads. By default, Mplus will assume that all error variances for the observed variables are independent of each other. ________ ________ _____ ________ ________ ... i would like to continue on the same topic of data output interpretation by EFA.05, especially if you have a large N. Most people look for CMIN, i.e., chisq/df, of <3, or the change in chi sq between nested models, i.e., two models with a minor change in structure, the chi sq for this being (diff in chisq) with (diff in df) df. As such, the objective of confirmatory factor analysis is to test whether the data fit a hypothesized measurement model. Also keep in mind that the number of characters in any row of the input file cannot exceed 80. ... 3. If students bring Mplus, it must have either the multilevel add-on or the combination add-on installed. As of this writing, SPSS for Windows does not currently support modules to perform the analyses you describe. Factor indicators for CFA models can be continuous, censored, binary, ordered categorical (ordinal), counts, or combinations of these variable types. Jones) which calls Mplus from within Stata and returns the results back to Stata. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives.