Structural Equation Modeling: June 7-11, 2010
Detailed information about the workshop is below. Registration for the workshop is available here and hotel reservations at the conference center are avaiable here.
Overview
Structural Equation Modeling is a five-day workshop focused on the application and interpretation of statistical models that are designed for the analysis of multivariate data with latent variables. Although the traditional multiple regression model is a powerful analytical tool within the social sciences, this is also highly restrictive in a variety of ways. Not only are all variables assumed to have no measurement error, but it is also limited to a single dependent variable with unidirectional effects. The structural equation model (SEM) generalizes multiple regression to include multiple dependent variables, reciprocal effects, indirect effects, and the estimation and removal of measurement error through the inclusion of latent variables. The SEM is a general framework that allows for the empirical testing of research hypotheses in ways not otherwise possible. In this workshop we provide a comprehensive exploration of the SEM with topics ranging from introductory to advanced. A detailed syllabus is presented below. The general structure of each day is lecture-based instruction from 9:00 to 3:00 (with morning, lunch, and afternoon breaks), and separate break-out sessions from 3:00 to 5:00 focused on model estimation using Mplus and Amos. Demonstration notes are also provided for SEMs estimated in LISREL and EQS. Although there is not a hands-on computer lab component to this workshop, we provide extensive live software demonstrations along with complete data and code for all examples. Further, participants are welcome to bring personal laptop computers to follow along with the software demonstrations or to work on their own data applications. A summary of participant reviews from prior workshops is available here.
Who Should Attend
Our workshop is designed for graduate students, post-doctoral fellows, faculty, and research scientists from the behavioral, social, and health sciences. Although it is recommended that participants have a working knowledge of the general regression model, these core concepts will be reviewed at the beginning of the workshop. Further, prior knowledge in either Mplus or Amos is useful but not required.
The Goals of the Workshop
Our motivating goal is to provide an intense yet enjoyable instructional experience that focuses on a large number of both introductory and advanced topics in structural equation modeling. We strive to strike an equal balance between core concepts of the underlying statistical model along with the practical application and interpretation of SEMs fitted to real empirical data. Our workshop is designed to provide participants with the materials and instruction needed to both develop a real understanding of the SEM and to be able to thoughtfully apply a variety of basic and advanced SEMs to their own data.
Daily Schedule
Each day begins at 8:00 with a continental-style breakfast provided at the conference center. Instruction then begins at 9:00, there is a mid-morning break, and then concludes at 12:00 for a full buffet lunch provided to all participants. Lecture continues from 1:30 to 3:00, there is a mid-afternoon break, and then two break-out sessions each lead by Dr. Curran and Dr. Bauer are held from 3:00 to 5:00 focused on the demonstration of fitting models in either Mplus or Amos. The day concludes at 5:00 (except on the final day, which concludes at 3:00 to allow for afternoon travel).
Tuition and Registration
Tuition for the five-day workshop is $1800 per registrant. This includes approximately 35 hours of total lecture time, a bound copy of the course notes and a bound copy of the computer demonstrations (approximately 500 pages total), a USB key containing all data and programs, and a continental breakfast and full lunch each day. Tuition is fully refundable (minus a $180 charge imposed by the credit card companies to process refunds) if cancellation is by May 1, 2010. There is no cancellation fee if paid by check if cancellation is made by the deadline. Registration is fully web-based and is accessed here.
Facilities & Accommodations
Our workshop will be held at the Rizzo Conference Center which provides a picturesque, retreat-like atmosphere with superb instructional facilities and is only minutes from historic downtown Chapel Hill. The Rizzo is owned by the Kenan-Flagler Business School at the University of North Carolina at chapel Hill and is a full service conference center including hotel rooms, dining, exercise facilities, jogging trails, and a free local shuttle service. The Rizzo was ranked #1 for food and accommodations in the 2008 Executive Development survey conducted by the Financial Times.
We have reserved a block of rooms on-site at the Rizzo Center, and participants can make reservations at the special conference rate of $179 per night (note that this includes a full buffet breakfast and full buffet lunch). There are two ways reservations can be made. First, you can call 919.913.2098 and identify yourself as part of the structural equation modeling group with group code SEM10 to receive the special room rate. Second, you can register online here and follow the automated registration instructions. The group code will be automatically populated with “SEM10”; the boxes for Corporate/Promotional Code and IATA number are not needed and can be left blank.
If a participant chooses to not stay at the Rizzo Conference Center itself (or if the reserved block of rooms is filled), there are a number of alternative hotels that are conveniently located near the conference center. A list of these hotels is located here.
Further Information
Please contact us either via email (info@cbanalytics.org) or by phone (919.533.9817) if you need additional information or have any further questions.
Syllabus
Day 1
•General introduction, organization, goals, and overview
•Brief introduction to matrix algebra
•Review of multiple regression
•review of one-predictor and multiple-predictor regression model
•introduction to path diagrams
•review of least squares estimation
•define regressionmodel in matrix notation
•describe model assumptions and limitations
•Introduction to path analysis (i.e., simultaneous equations or SEMs with observed variables)
•expand regression model to path model with multiple dependent variables
•define model in matrix notation
•define fundamental hypothesis: sigma=sigma[theta], mu=mu[theta]
Day 2
•Path analysis (continued)
•model parameterization: freeing & restricting structural paths
•model identification: necessary & sufficient rules for identification
•model estimation: direct ML under missing data with mean structures
•model evaluation: omnibus fit, local fit, & residual analysis
•model re-specification: modification indices & LaGrange Multipliers
•testing direct & indirect effects: total, direct, and specific indirect effects
•assumptions & limitations
•Introduction to confirmatory factor analysis (CFA)
•definition of a latent variable
•unidimensional vs. multidimensional models
•model identification
•model estimation
•model evaluation
•model re-specification
•factor score estimation
Day 3
•Structural equation models (SEMs) with latent variables
•the SEM as a CFA with structural relations
•combining manifest and latent factors
•model identification
•model estimation
•model evaluation
•model re-specification
•testing direct and indirect effects
•Multiple group SEMs
•conceptualization of parameter invariance & invariance typologies
•multiple group CFAs: factorial invariance
•multiple group SEMs: structural invariance
•formal testing of nested hierarchies of invariance
Day 4
•Latent curve models
•concept of a growth trajectory
•defining numerical measure of time in factor loading matrix
•unconditional linear growth curve model & testing of alternative time-specific error structures
•nonlinear trajectories: quadratic, piecewise, freed loading
•conditional growth model: time-invariant covariates
•conditional growth model: time-varying covariates
Day 5
•SEMS for binary and ordered-categorical data
•introduce concept of generalized linear model
•describe ML estimation via numerical integration
•distinguish binary vs. ordinal indicators (one vs. multiple thresholds)
•one factor CFA with binary as a two parameter logistic item response theory model
•multiple factor CFA
•multiple factor SEM
•review of limited information estimation via WLS & compare with ML
•Survey of advanced topics with recommended readings and resources
•mixture modeling, both general SEM and LCM & direct vs. indirect uses of mixtures
•interactions among latent variables
•multilevel SEMs