Workshop on Structural Equation Models for Longitudinal Data (July 13-15, Cork, Ireland)
The workshop will cover unconditional, conditional and multivariate latent curve models; models for non-normal and discrete outcomes; and modeling observed and unobserved heterogeneity (more details below), and will be held in the Western Gateway Building UCC. The workshop will be led by Professor Patrick Curran, Department of Psychology, University of North Carolina (UNC).
The Clinical Research Facility in Cork will be hosting a three-day workshop on Structural Equation Models for Longitudinal Data, July 13-15th, 2016.
The workshop will cover unconditional, conditional and multivariate latent curve models; models for non-normal and discrete outcomes; and modeling observed and unobserved heterogeneity (more details below). Models will be demonstrated with Mplus. There will be no formal tutorials, but participants are encouraged to being their own laptops with Mplus. Participants should be comfortable with standard statistical methods, including multiple linear regression. Familiarity with path analysis, factor analysis, or structural equation modelling will be helpful, but not necessary.
The three-day course fee is 600 EURO, and includes a full set of printed course materials, lunch each day, and tea and coffee throughout. There are also a limited number of 1-day registrations available for 250 EURO per day. The fee is already discounted for all participants, and there are no additional discounts available.
To register, please contact Dr Darren Dahly (email@example.com), who will then send you information on how to pay by credit card or purchase order. We kindly ask that attendees pay within 10 working days of registering to guarantee their place on the course. The registration deadline is the 6th of July. Refunds will be made at the discretion of the course organisers, and will not be possible under any circumstance after the 8th of July.
Title: Structural Equation Models for Longitudinal Data Dates: July 13-15, 2016 Location: Western Gateway Building, Room 107 Max participants: 30 Fees: Three-day course: 600 EUROS Single day(s): 250 EUROS per day
The workshop will be led by Professor Patrick Curran, Department of Psychology, University of North Carolina (UNC). Professor Curran is the Director of the L.L. Thurstone Psychometric Laboratory in the Department of Psychology and Neuroscience at the UNC. Patrick has dedicated much of his career to the teaching and dissemination of advanced quantitative methods and has won teaching awards from UNC and the American Psychological Association. Over the past 20 years he has taught over 50 national and international workshops on structural equation modeling, multilevel modeling, latent curve analysis, longitudinal data analysis, and general linear modeling. He draws on his experiences from his own program of research on high risk child development to guide and inform his quantitative teaching.
Patrick’s program of research is primarily focused on the development and evaluation of statistical models of change over time, particularly as applied to studies of adolescent substance use. He has published over 70 scientific papers and chapters and has co-authored a text book on latent curve modeling with Ken Bollen. Patrick has served as Associate Editor for Psychological Methods and currently serves on the editorial boards of seven scientific journals. For more details on Professor Curran’s work, see here. More information on his workshops can be found here:.
Chapter 1: The Unconditional Linear Latent Curve Model
1.1 Introduction and Organization of the Workshop
1.2 Defining a Latent Growth Curve
1.3 Latent Growth Curves as a Confirmatory Factor Model
1.4 Thinking More Closely About Time
1.5 Demonstration: Linear Trajectories of Negative Affect
Chapter 2: Nonlinear and Conditional Latent Curve Models
2.1 Modeling Nonlinear Trajectories
2.2 Demonstration: Seasonal Trajectories of Crime
2.3 Time-Invariant Covariates
2.4 Demonstration: Conditional LCM for Negative Affect
Chapter 3: Multivariate Latent Curve Models
3.1 Time-Varying Covariates
3.2 Demonstration: Simulated Math and Reading, Part 1
3.3 The Multivariate Latent Curve Model
3.4 Demonstration: Simulated Math and Reading, Part 2
3.5 Advanced Multivariate LCMs
Chapter 4: LCMs with Non-normal and Discrete Outcomes
4.1 Outcomes that are Continuous but Non-Normal
4.2 LCMs with Binary and Ordinal Outcomes: Concepts
4.3 LCMs with Binary and Ordinal Outcomes: Estimation
4.4 Demonstration: Parental Conflict During Adolescence
Chapter 5: Modeling Population Heterogeneity: Part 1
5.1 Population Heterogeneity in the Conventional LCM
5.2 The Multiple Groups Model
5.3 Demonstration: Development of Antisocial Behavior
5.4 Extensions of Multiple Groups LCM
Chapter 6: Modeling Population Heterogeneity: Part 2
6.1 Growth Mixture Models: Theory
6.2 Growth Mixture Models: Specification
6.3 Class Enumeration
6.4 Model Sensitivity
6.5 Demonstration: Developmental Taxonomy for Negative Affect?
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