[wptab name=’Topic 1 | Intro to SEM’] In the first class, students will be introduced to the most fundamental concepts in SEM. Additionally, students will be exposed to basic syntax required to run data in the Mplus 7 software package. [/wptab]
[wptab name=’Topic 2 | Path Analysis’] Students will learn the principles of path analysis and the 5-step procedure for evaluating model-to-data fit. In doing so, students will become acquainted with the current conventions for reporting structural equation modeling methods within scientific manuscripts. [/wptab]
[wptab name=’Topic 3 | Exploratory Factor Analysis’] Students will be re-introduced to factor analysis through the SEM framework using a technique referred to as “exploratory structural equation modeling” or “ESEM.” Next, students will be shown how to interpret and report ESEM output. Students will then have an opportunity to test CFA and ESEM syntax with their own data and sample datasets. [/wptab]
[wptab name=’Topic 4 | Confirmatory Factor Analysis’] This class will emphasize traditional CFA and structural regression. We will take a closer look at some of the common problems users encounter when conducting measurement modeling and structural modeling. In addition, we will explore equivalent models, nested models, method effects, and MIMIC models. [/wptab]
[wptab name=’Topic 5 | Missing Data’] An important and often overlooked topic in SEM is what to do with missing data. This lecture will cover missing data assumptions, conventional and contemporary methods for handling missingness. [/wptab]
[wptab name=’Topic 6 | Fooling Yourself w/SEM’] SEM requires a vast knowledge-base. There are tons of places where practitioners can make mistakes. One may make errors in syntax or one could encounter errors in model testing that point to problems in data entry, coding, etc. Most frequently, researchers inaccurately interpret SEM output including model fit indices, parameter estimates, and modification indices. This lecture will cover the most common mistakes and problems researchers will encounter and how to avoid them. [/wptab]
[wptab name=’Topic 7 | Group Invariance’] To determine if measures are being interpreted the same way across groups (contexts or methods of delivery), researchers must learn the procedure for assessing group invariance. This procedure imposes constraints across groups in a sequential and progressive manner to test assumptions about equality of factor makeup, loadings, scales, and error. [/wptab]
[wptab name=’Topic 8 | Longitudinal Measurement Invariance’] Students will learn how to apply invariance testing to measures assessed over time [/wptab]
[wptab name=’Topic 9 | Growth Modeling’] ENTER [/wptab]
[wptab name=’Topic 11 | Partial Least Squares Modeling’] SEM is generally considered a large sample technique. However, latent variable models can be tested with very small sample sizes (e.g., <50) within a partial least squares (PLS) framework. This lecture will cover the fundamentals of PLS modeling, parameter estimation and evaluation, and software implementation with SmartPLS [/wptab]
[end_wptabset]