Six courses including topics like mediation, moderation, SEM, R, and missing data imputation, Washington, DC, 25-27 Feb 2022


Author: Zachary Stickley

Accelerated Statistical Continuing Education
Stats Camp Foundation is approved by the American Psychological Association to sponsor continuing education for psychologists. Stats Camp Foundation maintains responsibility for this program and its content.
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FEBRUARY 25-27, 2022
Virtual and In-Person Options Available

Latent Profile Analysis
Instructor: Whitney G. Moore, Ph.D.
This hands-on seminar will introduce participants to the prevailing “best practices” for direct applications of basic latent profile analysis to cross-sectional data, specifically latent profile analysis (LPA) also known as latent class cluster analysis (LCCA), including model assumptions, specification, estimation, evaluation, selection, and interpretation. Models that allow for the inclusion of correlates and predictors of latent profile membership, as well as distal outcomes of latent profile membership, will be presented.

Longitudinal Structural Equation Modeling
Instructor: Todd D. Little, Ph.D.
Instructor: Elizabeth Grandfield, Ph.D.

An advanced intensive short course in the analysis of longitudinal data using SEM. Live lectures along with individual and group consultation. If you already have a strong background in the application of SEM to analyze the covariance structure of multivariate data and you need to learn how to apply more advanced models to longitudinal data, this course is for you. Participants from a variety of fields, including sociology, psychology, education, human development, marketing, business, biology, medicine, political science, and communication, will benefit from the course.

Mediation and Moderation
Instructor: Mwarumba Mwavita, Ph.D.
In many scientific fields research questions have become more complex. Researchers are no longer simply interested in if one variable (X) is related to another (Y). Instead, research questions such as: “Why is X related to Y?” and “When is X related to Y?” abound. This course addresses methods to test why two variables are related (mediation) and when two variables are related (moderation).

Missing Data Treatment with Multiple Imputation and PcAux
Instructor: Daniel E. Bontempo, Ph.D.
This 3-day class will review fundamental concepts of missing data treatment with multiple imputation, cover applied examples using the R package MICE (multiple imputation with chained equations), review options for checking imputation performance, and also introduce PcAux software that can facilitate the use of MICE with very large datasets. Important concepts such as missing data mechanisms will be explained. MICE will be contrasted with other imputation methods that require specification of a joint multivariate distribution. The use of MICE will be demonstrated, and user-options will be discussed.

Structural Equation Modeling (SEM) using R
Instructor: Alex Schoemann, Ph.D.
An introduction of the theoretical and applied understandings of latent variable models also called SEM. Topics include confirmatory factor analysis models, multiple group models, mediation, moderation, and longitudinal models. Using real datasets provided in the seminar, participants will learn how to use the lavaan package in the R software program to analyze data and interpret results. Participants from a variety of fields, including sociology, psychology, education, human development, marketing, business, biology, medicine, political science, and communication, will benefit from the seminar.

Systematic Review and Meta-Analysis
Instructor: Joshua R. Polanin, Ph.D.
Systematic review and meta-analysis are techniques used to synthesize and summarize large bodies of research literature. Compared to results from a single primary study, results from a meta-analysis provide greater generalizability, increased precision, and the ability to explore heterogeneity across studies.  You will use the latest techniques and technical tools to conduct a high-quality systematic review and meta-analysis while learning hands-on, practical, and applied approaches to conducting reviews by combining lectures with practice material designed to enable you to conduct future meta-analyses.

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