Structural Equation Modelling (SEM) increasingly is a ‘must’ for researchers in the social sciences and business analytics. However, the issue of how consistent the theoretical model is with the 
data, known as model fit, is by no means agreed upon: There is an abundance of fit indices available – and wide disparity in agreement on which indices to report and what the cut-offs for various indices actually are.
Assessing whether a specified model ‘fits’ the data is one of the most important steps in structural equation modelling, so it’s essential that researchers are comfortable using this technique.
New and improved indices that reflect some facet of model fit previously unaccounted for can entice a researcher to select those that indicate good model fit. But this is a risky practice that should be avoided, as it masks underlying problems that suggest possible misspecifications within the model.
In this webinar, we’ll review:
- A variety of fit indices to be used as a guideline to help you avoid errors
- The most widely respected and reported fit indices, including their interpretive value in assessing model fit and their availability in software packages such as SPSS Amos, SAS, Stata, Mplus, and the Lavaan Project in R
- Best practices on reporting structural equation modelling
Note: This training is an exclusive benefit to members of the Statistically Speaking Membership Program and part of the Stat’s Amore Trainings Series. Each Stat’s Amore Training is approximately 90 minutes long.
About the Instructor
With more than fourteen years of experience as a data scientist, Christos Giannoulis has developed statistical analyses that convert data into insights. He strives to advance statistical analyses from correlation to causation analyses using Frequentist and Bayesian methods.
Christos has extensive experience with advanced exploratory, predictive, and prescriptive analyses using a combination of graphical user interface software (e.g., SPSS modeler, Jmp, Statistica, and Stata) as well as programming languages (e.g., R and Python) on desktop and cloud environments. He has authored and co-authored 5 reports, published more than 30 papers using statistical analysis, and presented at conferences in North America and Europe.
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