Introduction to Lavaan

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00:00 Overview of lavaan
4:13 Getting started with lavaan
7:34 Beginning of lavaan tutorial
11:20 Model specification
16:20 Fitting models with cfa
18:10 Methods
22:00 Working with parameters
25:00 Fit measures
27:00 Extracting objects using inspect
30:00 Fitting and comparing multiple models
34:10 Residuals and modification indices
42:00 sem
44:35 model diagrams

Abstract: Lavaan is a free package in R used for performing structural equation modelling. Its intuitive and flexible syntax makes it a great option for estimating simple and complex models. The training will provide a hands-on introduction to lavaan. It will cover (a) preparing data, (b) specifying and estimating models, (c) modification indices, (d) model comparison, and (e) extracting parameters and fit statistics. The main focus will be on fitting confirmatory factor analytic models to multiple item psychological questionnaires. Lecture-style presentation will be combined with practical exercises. On successful completion of the training, participants should be able to perform confirmatory factor analysis on their own data. Prior experience using R will be helpful but is not essential.
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I’m really happy that you can so effortlessly click through RStudio and display your great uncommunicated awareness of the layout functionality and the fact that one needs to access specific data sets and libraries from some well-kept secret place using some very special clicks that only you are aware of.

Please don’t call your video an “Introduction to Lavaan” when you’re not up to taking the time and effort to appreciate that what seems self-explanatory to you, is not for the novice. Unless, of course, there’s something like a “Absolute Introduction to Lavaan” in your mind.

TheCyberklutz
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nice tutorial....can you do more on
How to intepret the SEM Model and what inferences can be drawn.
Also a bit more on SEMPLOT if possible

Great effort though :)

saurabhkhodake
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Hi,
I wanted to ask which is most appropriate software for conducting SEM along with moderation analysis, in case of categorical, nominal (binary and multinomial) and ordinal variables as outcome/dependent/endogenous variables ?
P.S:The predictor variables are scale, nominal and ordinal variables.
Regards
J

javeda