Attachment | Size |
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LatentStructuralModels.pdf | 765.62 KB |

GenEpiHelperFunctions.R | 11.59 KB |

factorScaleExample1.csv | 19.71 KB |

factorScaleExample2.csv | 19.73 KB |

latentMultipleRegExample1.csv | 19.65 KB |

latentMultipleRegExample2.csv | 19.75 KB |

ThreeFactorScale1Test.R | 4.58 KB |

ThreeFactorScale2Test.R | 4.58 KB |

ThreeFactorSim.R | 1.32 KB |

ThreeLatentMultipleRegTest1.R | 5.28 KB |

ThreeLatentMultipleRegTest2.R | 5.28 KB |

ThreeLatentMultipleRegSim.R | 2.59 KB |

ThreeLatentMediationTest1.R | 5.03 KB |

ThreeLatentMediationTest2.R | 5.03 KB |

Week 6 of the course presents concepts in latent structure and measurement models. We take a look at 3 possible ways of using latent structure to test theories:

- the number of factors problem, i.e., can we collapse two scales into one;
- multiple regression using latent variables, i.e, how do we test the prediction of one latent variable from another and what are some limits on our abilities to test such theories; and
- mediation models with latent variables and some of the limitations on the conclusions that one can draw from fitting mediation models.

In order to run the examples, please create a project directory and download all of the files to that project directory. When you run R, be sure to change the working directory ("Change Directory") to your project directory.

Note that each of the R scripts named *Sim.R were used to produce the simulated data used in the examples. The causal conclusions most of us would like to make based on the outcome of fitting these models do not necessarily match the way that the simulated data were constructed. It is your assignment as a student to find out what is right and what is wrong about each of the analyses. It is important to understand the limits of the conclusions you can draw from latent variable regression and mediation modeling!