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

mplus bootstrapping | 1.4 | 0.5 | 1741 | 86 | 19 |

mplus | 0.65 | 0.7 | 1860 | 94 | 5 |

bootstrapping | 0.22 | 0.6 | 1396 | 76 | 13 |

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

mplus bootstrapping | 1.93 | 0.2 | 1142 | 52 |

Consider this seemingly unrelated regression using Stata. You could preface the command with the bootstrap prefix, as illustrated below, to obtain bias corrected bootstrap standard errors based on 20,000 replications. The same analysis can be run in Mplus and obtaining bias corrected standard errors.

Mplus creates an output file which contains the original data used in the analysis (i.e., item1 to item9) followed by the probability that Mplus estimates that the observation belongs to Class 1, Class2, and Class 3. Next, the class with the highest probability (the modal class) is shown.

Each of the models have the provided diagrams, model equations, and the Mplus code. The code includes the requisite DEFINE:, ANALYSIS:, MODEL:, and OUTPUT: principal commands, as well as preceding USEVARIABLES:

Mplus, on the other hand, can handle unlimited numbers and configurations of mediators (in series and/or in parallel) and moderators, and can also fit and test models with multiple IVs and DVs. Also, unlike PROCESS, it can handle mediation and moderation where the data structure is multilevel, and can incorporate latent variables.