Moving Beyond the 'Average' Family: Analyzing Unique Groups With Latent Class Analysis
We know that not all individuals and families are alike. Yet, nearly all statistical methods used in Family Science research are derived from measures of central tendency.
With mixture modeling analysis, we can identify latent (unobserved) groups of individuals or families within a larger population, allowing for a much more nuanced understanding of the diversity of families and family relationships.
In this interactive webinar, you’ll learn from presenter Adam M. Galovan, Ph.D., how to conduct a latent class analysis so you can identify and explore latent classes or groups — the ways they are unique, factors that predict membership in unmeasured groups, and the outcomes for those in various unmeasured groups.
During the webinar, Dr. Galovan will discuss conceptual issues that justify the use of mixture models, focusing on the limitations of modeling techniques based on measures of central tendency.
He will then guide participants step by step through an example of latent class analysis and provide instruction about the different analysis options for conducting the analysis in Mplus. Dr. Galovan also will discuss how to interpret Mplus outputs, and common errors or warnings you might encounter in mixture model analysis.
Webinar participants should already have a basic understanding of the topic.
By attending this webinar, you'll be able to:
- explain the value of latent class techniques in understanding families;
- conduct latent class analyses using your own data; and
- evaluate the results of latent class analyses.
Approved for 1.5 hours of CFLE continuing education credit.
What Attendees Said About This Webinar:
"Great presentation! It will definitely be helpful to enhance my statistical skills."
"Helpful, clear explanations."
"Great webinar for beginners."
About the Presenter
Adam M. Galovan, Ph.D., is an assistant professor of Family Science in the University of Alberta's Department of Human Ecology. He has authored and presented several articles and papers employing mixture models, including latent class, latent profile, growth mixture, and latent transition analysis models. He strongly advocates for the integration of theory and methodology in family research, and in 2019 he was a co-chair of the Theory Construction and Research Methodology (TCRM) Workshop, a preconference workshop of the NCFR Annual Conference.
Dr. Galovan received his doctoral degree from the University of Missouri's Department of Human Development and Family Science, with a collateral focus on research methodology and statistics.
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