SEM in AMOS when you have incomplete data (new, 2018)

This video provides an overview of SEM (using path analysis) in AMOS when you have missing data. It demonstrates how to estimate the basic model using FIML estimation (enacted by clicking on Estimate Means and Intercepts under Analysis Properties). It also demonstrates the use of the Regression Imputation approach to generate a complete dataset (which will allow you to use other options in AMOS such as Modification indices and Bootstrapping). The video does not cover the theory behind missing data mechanisms or the approaches recommended with particular patterns of missingness. The viewer is encouraged to read more on those topics. A copy of the SPSS data file used in the video can be downloaded here: https://drive.google.com/file/d/18azDsnEoPGBozyF4dMBrw3dh4QKcS4R1/view A copy of the .amw file containing the path model generated for the video can be downloaded here: https://drive.google.com/file/d/10ryZkcsNacq_60BWaao7f-ftqkOtqYad/view A pdf copy of the page referenced in the video on interpreting fit statistics can be obtained here: https://drive.google.com/file/d/14zM-5fZUpN2drO3ZwTfm18ET2ybZBVOF/view You can also access another video on dealing with missing data by going here: https://youtube.com/watch?v=N-v_PFI98MI&feature=youtu.be For more instructional videos and other materials on various statistics topics, be sure to my webpages at the links below: Introductory statistics: https://sites.google.com/view/statisticsfortherealworldagent/home Multivariate statistics: https://sites.google.com/view/statistics-for-the-real-world/home