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$ 945.00

IN PERSON – 5-day Statistics Short Course

Longitudinal Structural Equation Modeling (LSEM) Seminar Overview:

Do you have repeated measurements? Have you collected data over multiple timepoints? Do you need help designing your longitudinal study? If so, this is your course! Let us help you appropriately design your longitudinal study and analyze your data in the SEM latent variable framework using Longitudinal Structural Equation Modeling (LSEM). This framework will allow you more flexibility in evaluating your research questions over time as well as test assumptions that traditional techniques like ANOVA ignore.

Seminar Topics:

  • Design and measurement issues in cross-sectional and longitudinal research
  • Traditional panel designs
  • Overview of missing data
  • Latent growth curve modeling
  • Testing for Mediation and Moderation
  • Using Phantom Constructs
  • Longitudinal Measurement Invariance – Multiple Group LSEM
  • Growth Mixture Models

Seminar Description:

The seminar will be a series of lectures and computer workshops to provide participants with advanced training in the use of SEM for the analysis of longitudinal data.

Instructor: Todd D. Little, Ph.D.

Longitudinal SEM Training

Todd D. Little, PhD is a Professor and director of the Institute for Measurement, Methodology, Analysis and Policy at Texas Tech University. He is widely recognized for his quantitative work on various aspects of applied SEM (e.g., modern missing data treatments, indicator selection, parceling, modeling developmental processes) as well as his substantive developmental research (e.g., action-control processes and motivation, coping, and self-regulation). His work has garnered over 50,705 citations with an h-index of 98 and an i10-index of 263. In 2001, he was elected to membership in the Society for Multivariate Experimental Psychology, and in 2009, he was elected President of APA’s Division 5 (Evaluation, Measurement, and Statistics). He is a fellow in APA, APS, and AAAS. In 2013, he received the Cohen award from Division 5 of APA for distinguished contributions to teaching and mentoring and in 2015 he received the inaugural distinguished contributions award for mentoring developmental scientists from the Society for Research in Child Development. Both awards cited his founding of Stats Camp (Statscamp.org) in 2003 and its ongoing impact on shaping the quality of scientific inquiry for both past and future generations of researchers. Download Todd’s CV (PDF)

Instructor: Whitney Moore, Ph.D.

Longitudinal SEM Course

Whitney received her Ph.D. in the Psychosocial Aspects of Health and Physical Activity from the University of Kansas. She is currently an Assistant Professor at Wayne State University in the Division of Kinesiology, Health & Sport Studies where she teaches graduate courses in research methods, sport and exercise psychology, and statistics.

Instructor: Zachary Stickley, Ph.D.

Longitudinal Structural Equation Modeling

Zachary, Ph.D. is a researcher in the College of Education at Texas Tech University studying latent variable modeling and planned missing data. He received his Master of Education degree from Texas Tech University and his Bachelor of Science in Psychology from Tarleton State University. He has assisted Dr. Little in the instruction of Structural Equation Modeling courses at Texas Tech University as well as at numerous Stats Camp seminars and analysis retreats.

APA Continuing Education Credits:

Longitudinal Structural Equation Modeling (LSEM)

This course offers 26 hours of Continuing Education Credits. 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.

Seminar Includes:

Materials, downloads, recorded course video viewable for up to one year.

Learning Objectives:

The five-day training institute on Longitudinal Structural Equation Modeling (LSEM) will enable participants to:

  • Address design and measurement issues in longitudinal modeling.
  • Acquire understanding of the SEM concepts that are foundational to longitudinal SEM design.
  • Analyze longitudinal panel models in both a single group and multi-group configuration in CFA and SEM framework.
  • Incorporate mediation and moderation in a longitudinal framework.
  • Construct item parcels in a longitudinal framework.
  • Evaluate latent growth curve models.
  • Apply latent growth curve models in a multivariate and multiple group context.
  • Evaluate finite mixture models.
  • Interpret and evaluate covariance pattern mixture models.
  • Evaluate Growth mixture models.
  • Address missing data using FIML and MI methods.
  • Use modern missing data treatments to implement a planned missing data design.

Seminar Prerequisites:


  • Proficiency in multiple linear regression.
  • At least limited experience (e.g., graduate-level course) with continuous latent variable models, e.g., exploratory and confirmatory factor analysis (EFA; CFA) and structural equation modeling (SEM).
  • We strongly recommend that you attend our foundations of SEM as a pre-requisite to taking this advanced course. If you have not taken the foundations course, you should have extensive experience or have taken a graduate-level course on SEM before enrolling.
  • Intermediate proficiency with at least one statistical software package (e.g., SPSS, Stata, SAS, R, LISREL, Mplus, etc.).

Not required but advantageous:

  • At least limited experience (e.g., graduate-level course) with multivariate data analysis.

No level of proficiency beyond basic awareness is assumed for skills related to:

  • Longitudinal Structural Equation Modeling (LSEM).
  • Advanced mathematical or statistical topics such as matrix algebra or likelihood theory.

Software and Computer Support:

Participants need a laptop computer with Wi-Fi and webcam capabilities.

All statistical software used at Stats Camp will be available, free to participants, on our SMORS (statistical modeling on remote servers) system for the duration of camp.

All statistical software used at Stats Camp will be available, free to participants, on our SMORS (statistical modeling on remote servers) system for the duration of camp.

Seminar Audience:

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 seminar is for you. We strongly recommend that you attend our five-day intensive summer institute on the foundations of SEM as a pre-requisite to taking this five-day advanced seminar. If you have not taken the foundations Seminar, you should have extensive experience or have taken a graduate-level seminar on SEM before enrolling.

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.

The seminar will support LISREL, Mplus or Laavan. Some assistance will be available for questions related to other structural modeling packages. Previous knowledge of LISREL, Mplus or Laavan is preferred but not required.

Seminar Files

Instructor will provide password on first day of seminar.

All statistical software used at Stats Camp will be available, free to participants, on our SMORS (statistical modeling on remote servers) system for the duration of camp.

Summer Stats Camp 2023: Longitudinal Structural Equation Modeling (LSEM)
Monday June 12, 2023
9:00-10:45 Welcome and Introductions. Overview of Longitudinal Models
10:45-11:00 Rest Break
11:00-12:30 Design and Measurement Issues in Longitudinal Modeling
12:30-1:30 Rest Break
1:30-3:15 Review of Foundations of SEM
3:15-3:30 Rest Break
3:30-5:00 Longitudinal Panel Models: Basics
Tuesday June 13, 2023
9:00-10:45 Multiple-group Longitudinal Panel Models; CFA and SEM
10:45-11:00 Rest Break
11:00-12:30 Parcels and Parceling
12:30-1:30 Rest Break
1:30-3:15 Longitudinal Mediation & Moderation
3:15-3:30 Rest Break
3:30-5:00 Consultation
Wednesday June 14, 2023
9:00-10:45 Latent Growth Curve Modeling: Basics
10:45-11:00 Rest Break
11:00-12:30 Latent Growth Curve Modeling: Multivariate and Multiple Groups
12:30-1:30 Rest Break
1:30-3:15 Introduction to Finite Mixture Modeling
3:15-3:30 Rest Break
3:30-5:00 Introduction to growth mixture modeling / Consultation
Thursday June 15, 2023
9:00-10:45 Latent class growth analysis (LCGA)
10:45-11:00 Rest Break
11:00-12:30 Covariance Pattern Mixture Models
12:30-1:30 Rest Break
1:30-3:15 Growth mixture modeling (GMM)
3:15-3:30 Rest Break
3:30-5:00 Growth mixture modeling (GMM)
Friday June 16, 2023
9:00-10:45 Missing Data: Planned and Unplanned
10:45-11:00 Rest Break
11:00-12:30 Wrap-up then Individual Consultations
12:30-1:30 Rest Break
1:30-~3:30 Individual Consultations

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