Power analysis is a statistical method used in psychological research to determine the appropriate sample size for a study and is often required in grant proposals and by journals as part of the research submission process. WebPower is a software platform that provides a user-friendly interface for conducting power analyses for various statistical methods, including t-tests, ANOVA, regression, mediation analysis, multilevel modeling, longitudinal data analysis, and structural equation modeling. The workshop will focus on teaching participants how to use this software to conduct power analyses for their own research. The workshop was taught at the 2023 APS Annual Convention. You can find the slides and code used by the workshop here.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Instructor: Prof. Zhiyong Zhang. 

Dr. Zhiyong 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.

The class is free. Please register early.

You'll learn the fundamentals of data analysis with Python. Through the workshop, you will understand the six steps of data analysis processes as well as know how to read data from sources like CSV files and SQL, and how to use libraries like Numpy, Pandas, Matplotlib, and Seaborn to process and visualize data. 

This Training is design to introduce the foundamental on how to shape and transform your data before the data analysis using Power Query Editor. You will discover how to navigate this intuitive tool and get to grips with Power BI’s Data, Model, and Report views. You’ll load multiple datasets in the Data view, build a data model to understand the relationships between your tables in Model view, and create your first bar graph and interactive map visualization in Report view. You’ll also practice using Power Query Editor to prep your data for analysis.

Through hands-on exercises, you’ll learn how to change and format a wide range of visualizations, before moving on to sorting data and creating hierarchies—making it possible for you to drill into your reports.

This workshop is designed to introduce the fundamentals and practical applications of modeling data using data mining and machine learning methods. It surveys a series of topics starting from exploratory data analysis, unsupervised learning, supervised learning and other learning methods to uncover the patterns and structure of data targeting practical problem solving and identifying solutions. Students will learn by hands-on programming including such methods as regression, classification, logistic models, non-linear models, tree-based models, association rules, neural network and support vector machines. New developments in learning such as model visualization, deep learning and interpretable machine learning will also be introduced and illustrated.

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.

Modeling longitudinal data is one of the most active areas of research in social and behavioral sciences because longitudinal research provides valuable insights into change and causal relationships. The application of Bayesian methods in longitudinal research has gained increasing popularity. Taught by four quantitative researchers who are active in developing Bayesian methods and longitudinal data analytical methods, this workshop will teach participants how to analyze longitudinal data using Bayesian statistics. We will introduce the basic idea of Bayes' theorem first and move on to models including multilevel models, growth curve models, growth mixture models, and longitudinal structural equation models. Concrete examples will be provided to illustrate how to compute, report, and interpret Bayesian modeling results with empirical psychological data. Additionally, we will teach the application of Bayesian robust methods for nonnormal data ignorable and non-ignorable missing data.

This workshop teaches researchers how to conduct a statistical power analysis to determine the sample size for a planned study when applying structural equation modeling. Participants will learn statistical power analysis for mediation analysis, path analysis, and structural equation modeling using both traditional methods and Monte Carlo methods. Free software WebPower (https://webpower.psychstat.org) will be used to illustrate how to conduct power analysis in practice.