Keyword | CPC | PCC | Volume | Score | Length of keyword |
---|---|---|---|---|---|

mplus baseline model | 1 | 0.9 | 1828 | 41 | 20 |

mplus | 1.91 | 0.2 | 1288 | 87 | 5 |

baseline | 0.97 | 0.6 | 1209 | 17 | 8 |

model | 0.61 | 0.1 | 9283 | 18 | 5 |

Keyword | CPC | PCC | Volume | Score |
---|---|---|---|---|

mplus baseline model | 1.32 | 0.2 | 9922 | 54 |

The model, which consists of two latent variables and eight manifest variables, is described in our previous post setting up a running CFA and SEM example. Mplus only reads data in text format, see this post for details on how to prepare a data file for Mplus. The data can be accessed from Github. To review, the model to be fit is the following:

The baseline model Mplus prints the chi-square statistic for is a model where all of the structural (regression) paths are assumed to be zero (i.e., a null model).

CFA Mplus. This page describes how to set up code in Mplus to fit a confirmatory factor analysis (CFA) model. The model, which consists of two latent variables and eight manifest variables, is described here. Mplus only reads data in text format, see this post for details on how to prepare a data file for Mplus.

By default, Mplus identifies the model by constraining the first loading for each factor to equal one. By default, Mplus will assume that all error variances for the observed variables are independent of each other. We can relax this constraint with some additional syntax: The WITH statement specifies which error variances covary.