PowerPoint slides for the seminar given on are here: PowerPoint Slides for Intro to CFA You may download the complete R code here: cfa.rĪfter clicking on the link, you can copy and paste the entire code into R or RStudio. Please also make sure to have the following R packages installed, and if not, run these commands in R (RStudio). (Optional) Obtaining the parameter tableīefore beginning the seminar, please make sure you have R and RStudio installed.(Optional) Warning message with second-order CFA.Two Factor Confirmatory Factor Analysis.(Optional) Model test of the baseline or null model.One factor CFA with more than three items (SAQ-8).(Optional) Degrees of freedom with means.(Optional) How to manually obtain the standardized solution.Identification of a three-item one factor CFA.Known values, parameters, and degrees of freedom.One Factor Confirmatory Factor Analysis.Motivating example SPSS Anxiety Questionairre.Proceed through the seminar in order or click on the hyperlinks below to go to a particular section: Latent Growth Models (LGM) and Measurement Invariance with R in lavaan. The third seminar goes over intermediate topics in CFA including latent growth modeling and measurement invariance. Introduction to Structural Equation Modeling (SEM) in R with lavaan.However, in the second seminar we necessitate distinguishing between $x$-side and $y$-side variables for instructional purposes. Since $y$-side notation is more common in the literature, we use $\eta$ and $\epsilon$ for the respective factor and observed residual parameters. Traditionally, CFA models should be $x$-side variables with parameters $\xi$ for the latent factor and $\delta$ for the observed residuals. In this first seminar, all variables are presumed to be $y$-side variables and the direction of the arrows are unconventional (pointing to the left). The second seminar goes over a broader range of observed and latent variable models. This seminar is the first in a three-part series on latent variable modeling. A rudimentary knowledge of linear regression is required to understand some of the material in this seminar. For exploratory factor analysis (EFA), please refer to A Practical Introduction to Factor Analysis: Exploratory Factor Analysis. Its emphasis is on understanding the concepts of CFA and interpreting the output rather than a thorough mathematical treatment or a comprehensive list of syntax options in lavaan. This seminar will show you how to perform a confirmatory factor analysis using lavaan in the R statistical programming language.
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