Why is Measurement Equivalence Important?
HTML-код
- Опубликовано: 11 сен 2024
- QuantFish instructor and statistical consultant Dr. Christian Geiser explains why measurement equivalence (invariance) is important in the context of confirmatory factor analysis and structural equation modeling.
#Mplus #statistics #multigroup #geiser #quantfish #mplusforbeginners #statisticstutorials #CFA #SEM #psychometrics #psychometry
FREE weekly stats tips: christiangeise...
Get Dr. Geiser's FREE POWER ANALYSIS MINI-COURSE: www.goquantfis...
FREE PATH ANALYSIS & MEDIATION MINI-COURSE:
www.goquantfis...
MPLUS FACTOR ANALYSIS COURSE: www.goquantfis...
LONGITUDINAL SEM WITH MPLUS COURSE: www.goquantfis...
Dr. Geiser's BOOKS:
****Data Analysis with Mplus: amzn.to/3eg9vY4
****Longitudinal Structural Equation Modeling with Mplus: amzn.to/3ekOLOW
ON-DEMAND MPLUS COURSES:
****Mplus from Scratch: www.goquantfis...
****Multilevel Modeling with Mplus: www.goquantfis...
GET IN TOUCH with DR. GEISER:
****Facebook: / christiangeiserauthor
****Website: christiangeise...
ABOUT THIS INSTRUCTOR:
Dr. Christian Geiser is a quantitative psychologist, author of two books on Mplus, and a leader in the development of latent variable techniques for complex data. With his accessible books and sought-after workshops, he has helped thousands of researchers and students around the world to achieve their analytic goals. Sign up for his newsletter at christiangeiser.com.
ABOUT QUANTFISH:
We bring top-level stats training from the world's leading methodologists to researchers and students all over the world. Learn more at goquantfish.com.
Some affiliate links may be included at no additional cost to purchasers.
Great video, helped me read DATA for my annotated bibliography
Thank you so much for this question. I have a couple of questions about measurement equivalence across times.
1. After I test the configural invariance for the same latent factor, if I obtain poor model fit information from the following fit indices: CFI, TLI, RMSEA, and SRMR, can I explore modification indices and, for instance, add residual covariance based on M.I.? Alternatively, should I refrain from proceeding to calculate the metric invariance model due to the poor model fit of the configural invariance model?
2. If I am allowed to add residual covariances, should I specifically add covariances between residuals belonging to the same latent factor at both time points (time 1 and time 2)?
It definitely does not make sense to proceed to higher levels of invariance (e.g., metric) when the configural model is rejected (when it does not fit your data). One reason for misfit can be shared indicator-specific variance across time. Although this can indeed be modeled by allowing for autocorrelated error variables for the same variables across time, I prefer an approach with residual method (indicator-specific) factors. See my RUclips videos here:
ruclips.net/video/5kv4poKf6Cw/видео.html
ruclips.net/video/qQhmIMFFOns/видео.html
Best,
Christian Geiser