2023 ISDSA Meeting

Workshops

Six workshops are available. You will get a certificate of achievement for each workshop after completing it. Each workshop is 3-hour long and taught online through Zoom. Thanks to the support of the Society of Multivariate Experimental Psychology, we now offer all workshops for free. Please register to attend the workshops.

(1) Introduction to Social Network Analysis by Prof. Haiyan Liu from UC-Merced
(2) Longitudinal Data Analysis by Prof. Hongyun Liu from Beijing Normal University
(3) Practical Mediation Analysis by Prof. Laura Lu from University of Georgia and Prof. Qian Zhang from Florida State University
(4) Deep Learning Using R by Prof. Johnny Zhang from University of Notre Dame
(5) From Latent Class Model to Latent Transition Model Using Mplus by Prof. Hawjeng Chiou from National Taiwan Normal University
(6) Bayesian Longitudinal Data Modeling by Prof. Cynthia Tong from University of Virginia

Workshop 1: Introduction to Social Network Analysis

Time: 8am-11am, July 3, China Time / 8pm-11pm, July 2, US Eastern Time
Location： Virtual on Zoom (Register to get the link)

Social network analysis is becoming increasingly popular in social, educational, and psychological sciences. This interactive course intends to provide participants with a detailed introduction, practical examples, and demonstration of analyzing social network data using the free software R. Topics covered include (1) Network Data; (2) Network Visualization; (3) Network Statistics; (4) Basic and Advanced Network Models. Especially, we will cover classical models such as Stochastic Block Model, Exponential Random Graph Model, Latent Space Model, and the newly developed techniques such as Latent Factor Space Modeling and Network Mediation Analysis.

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  Haiyan Liu -- University of California, Merced

  Dr. Haiyan Liu is currently an assistant professor of Quantitative Methods, Measurement, and Statistics at the University of California, Merced. Dr. Liu’s research centers broadly on statistical modeling of psychological and behavioral data such as high-dimensional data, longitudinal data, and categorical data. Her recent research includes social network modeling, Bayesian structural equation modeling, and non-parametric modeling of growth curves.

Workshop 2: Longitudinal Data Analysis (In Chinese)

Time: 8pm-11pm, July 3, China Time / 8am-11am, July 3, US Eastern Time
Location： Virtual on Zoom (Register to get the link)

Longitudinal Data Analysis (LDA) is a popular statistical method in various fields, including psychology, education, sociology, management, economics, and medicine. This workshop aims to provide participants with an overview of the applications and capabilities of LDA. Topics include: (a) introduction of longitudinal designs and longitudinal data, (b) latent growth curve models and growth mixed models, (c) cross-lagged panel models, (d) separating within- and between-person effects models, and (e) advances in LDA. The workshop will consist of lectures and software demonstrations using Mplus and R with practical examples.

追踪数据分析是一种在心理学、教育学、社会学、管理学、经济学和医学等领域非常流行的统计方法。工作坊旨在从应用研究者的角度，介绍追踪数据分析在实际研究中的应用以及可以回答的研究问题。主题包括：（a）追踪研究设计和追踪数据简介；（b）潜在增长曲线模型和混合增长模型；（c）交叉滞后面板模型；（d）分离个体内和个体间效应的模型以及（e）追踪数据分析方法新进展。工作坊将以实际应用案例为主线，讲解不同统计分析模型的应用和软件操作。主要用到的软件包括Mplus和R。

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  Hongyun Liu -- Beijing Normal University

  Dr. Hongyun Liu is a professor in Faculty of Psychology at Beijing Normal University and has been working on the research of quantitative psychology for nearly 20 years. Her main research areas are psychological statistics, the theory and applications of psychological and educational measurement, and quantitative research methods. Her research interests involve the Internet-based test development, the theory and application of educational and psychological assessment, data analysis methods in large-scale assessments, and longitudinal data analysis.

Workshop 3: Practical Mediation Analysis

Time: 8pm-11pm, July 5, China Time / 8am-11am, July 5, US Eastern Time
Location： Virtual on Zoom (Register to get the link)

In social sciences, an input variable often affects an outcome variable via a third variable, or mediator. Mediation analysis is used to answer the question of “how”/ “why” the input variable affects the outcome. This workshop will introduce the principles and methods (e.g., multivariate regression, path analysis, multilevel models, etc.) for mediation analyses. Emphasis will be on data analysis and interpretation. After the workshop, attendants should be able to propose research questions related to mediation analyses, use advanced methods such as bootstrap to test mediation effects, decide which type of model is appropriate for mediation, interpret and display empirical findings, and understand some other important issues of mediation research such as longitudinal designs for assessing mediation and missing data problems.

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  Laura Lu -- University of Georgia

  Dr. Laura Lu is an associate professor in the Quantitative Methodologies (QM) program at the University of Georgia (UGA). In general, she has expertise in structural equation modeling (SEM), longitudinal data analysis, hierarchical linear modeling (HLM), and computational statistics such as Bayesian. During her professional career at UGA, her research has focused on developing innovative statistical approaches to address perennial challenges in statistical modeling such as mixture structure, reliabilities, model selection, missing data, outliers, topic modeling, and also promoting statistical models to applied research areas through collaborating, mentoring, and classroom teaching.

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  Qian Zhang -- Florida State University

  Dr. Qian (Jackie) Zhang is an associate professor in the Department of Educational Psychology and Learning Systems at Florida State University. Her research interests focus on causal inferences, longitudinal data analysis, and effect size synthesis using multilevel models and multilevel structural equation models. She is also interested in handling sub-optimal data conditions with missing data, measurement error, and/or confounding variables in statistical analyses. She collaborates with substantive researchers in the areas of early childhood development, sports psychology, and educational research. Her work has been published in prestigious journals such as Psychological Methods, Multivariate Behavioral Research, Structural Equation Modeling, British Journal of Mathematical and Statistical Psychology, and Behavioral Research Methods.

Workshop 4： Deep Learning Using R

Time: 8pm-11pm, July 6, China Time / 8am-11am, July 6, US Eastern Time
Location： Virtual on Zoom (Register to get the link)

Deep learning is a very active area of research in the machine learning and artificial intelligence communities. The foundation of deep learning is the neural network. In this workshop, we will teach how to practically carry out deep learning using the R package keras. Several real-world data sets will be used to illustrate how to build single layer, multiple layer, convolutional, and recurrent neural networks. Basic knowledge of R is expected.

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  Johnny Zhang -- University of Notre Dame

  Dr. Johnny Zhang is a professor in Quantitative Psychology at the University of Notre Dame. His research aims to develop better statistical methods and software in the areas of education, health, management and psychology. He has conducted research in the areas of Bayesian methods, Big data analysis, Structural equation modeling, Longitudinal data analysis, Mediation analysis, and Statistical computing and programming. His most recent research involves the development of new methods for social network and text analysis.

Workshop 5: From Latent Class Model to Latent Transition Model Using Mplus (In Chinese)

Time: 8am-11am, July 7, China Time / 8pm-11pm, July 6, US Eastern Time
Location： Virtual on Zoom (Register to get the link)

Latent class modeling (LCM), a branch of latent variable modeling, has become increasingly popular among social researchers in recent years because of its superior capability for detecting unobserved heterogeneity in data. Unlike the traditional latent variable models that focus on extracting factors, the latent variable in a LCM is discrete and categorical，and its groups, called latent classes, provide a classification structure or statistical taxonomy for recovering the sub-population behind the sample. This workshop will briefly introduce the methodological concepts of LCM and pay more attention to the analytic techniques, using Mplus, for implementing latent class analysis as well as latent profile analysis along with a major extension for longitudinal data analysis, entitled latent transition analysis (LTA), which is used to examine how individuals transition in the latent class membership over time.

潜在类别模式作为潜在变数模型的一个分支，近年来广泛被社会科学研究者重视，主要是因为LCM在估计资料背后的潜在异质性的优越能力。有别于传统潜在变数模型专注于因素的萃取，LCM所估计得到的潜在变数是离散的类别，称为潜在类别，可以作为观察资料背后的次母体统计分类系统。本工作坊将简要介绍LCM的原理与方法学概念，但着重于实际以Mplus来进行潜在类别分析、潜在剖面分析，以及在纵贯资料分析上的应用，也就是潜在移转分析，借以检验个体的潜在类别属性如何随着时间而变动。

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  Hawjeng Chiou -- National Taiwan Normal University

  Dr. Hawjeng Chiou is a distinguished professor of the College of Management at National Taiwan Normal University. His major interest is in applied psychometrics, particularly for the applications of advanced modeling techniques, such as Structural Equation Modeling, Multilevel Linear Modeling, and Latent Class Modeling, with substantial areas such as Human Resource Management, Organizational Behavior, and Psychological Testing as well as Creativity research. He has published multiple books on a variety of topics.

Workshop 6: Bayesian Longitudinal Data Modeling

Time: 1pm-4pm, July 3, China Time / 1am-4am, July 3, US Eastern Time
Location： In person at Fudan University and on Zoom (Free Registration)

This workshop is supported by the William K. and Katherine W. Estes Fund that is jointly overseen by the Association for Psychological Science and the Psychonomics Society.

Modeling longitudinal data is one of the most active areas of research in social, behavioral, and education sciences because longitudinal data can provide valuable insights into change and causal relationships. The application of Bayesian methods in longitudinal research has gained increasing popularity. This interactive workshop focuses on Bayesian methods in analyzing longitudinal data. Particularly, it will cover topics on growth mixture modeling, missing data analysis, and Bayesian model assessment. Concrete examples will be provided to illustrate how to compute, report, and interpret Bayesian modeling results with empirical psychological data.

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  Xin Cynthia Tong -- University of Virginia

  Dr. Tong is an associate professor at the University of Virginia. Her research focuses on developing and applying statistical methods in the areas of developmental and health studies. Methodologically, she is interested in Bayesian methodology, growth curve modeling, and robust structural equation modeling with nonnormal and missing data. Substantively, she is interested in analyzing the longitudinal development of cognitive ability and achievement skills.