1 / 8

Measurement Models and CFA; Chi-square and RMSEA

Measurement Models and CFA; Chi-square and RMSEA. Ulf H. Olsson Professor of Statistics. CFA and ML. k is the number of manifest variables.

media
Download Presentation

Measurement Models and CFA; Chi-square and RMSEA

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Measurement Models andCFA; Chi-square and RMSEA Ulf H. Olsson Professor of Statistics

  2. CFA and ML k is the number of manifest variables. If the observed variables comes from a multivariate normal distribution, and the model holds in the population, then Ulf H. Olsson

  3. Testing Exact Fit Ulf H. Olsson

  4. Problems with the chi-square test • The chi-square tends to be large in large samples if the model does not hold • It is based on the assumption that the model holds in the population • It is assumed that the observed variables comes from a multivariate normal distribution • => The chi-square test might be to strict, since it is based on unreasonable assumptions?! Ulf H. Olsson

  5. Alternative test- Testing Close fit Ulf H. Olsson

  6. How to Use RMSEA • Use the 90% Confidence interval for EA • Use The P-value for EA • RMSEA as a descriptive Measure • RMSEA< 0.05 Good Fit • 0.05 < RMSEA < 0.08 Acceptable Fit • RMSEA > 0.10 Not Acceptable Fit Ulf H. Olsson

  7. Other Fit Indices • CN • RMR • GFI • AGFI • Evaluation of Reliability • MI: Modification Indices Ulf H. Olsson

  8. Model evaluation • Does the model fit the data • Chi-square; RMSEA and other descriptive measures • Are the measures reliable? • Are the paths (coeffisients) significant? • Is it possible to improve fit? • Modification indices Ulf H. Olsson

More Related