July 21–24, 2024 | Vienna, Austria

2024 ISDSA Meeting




Theme: Data Science and Psychology


About the Meeting

The Annual Meeting of the International Society for Data Science and Analytics (ISDSA) serves as a global platform for researchers and practitioners in the dynamic field of data science and analytics to exchange ideas and present cutting-edge research. This inclusive event welcomes participants with an interest in data science, data analytics, and broader quantitative research.

The 2024 Annual Meeting of ISDSA is scheduled to be held in the vibrant city of Vienna, Austria, focusing on the theme "Data Science and Psychology."

Submit an Abstract by Feb 1, 2024 (Closed)

We invite you to contribute to the intellectual discourse by submitting abstracts for the 2024 meeting. The theme, "Data Science and Psychology," encompasses theoretical and methodological papers, applications, case studies, and tutorials. Abstract submissions are open until Feb 1, 2024. Early submissions are encouraged. Presentation formats include regular 30-minute paper sessions and concise 10-minute speed presentations.

Workshops

The following workshops will be offered during this year's meeting. Thanks to the support of the University of Notre Dame, the workshops are free.

1. Conducting Meta-Analytic Structural Equation Modeling with R by Professor Mike Cheung of National University of Singapore
2. Structured Component Analysis Structural Equation Modeling by Prof. Heungsun Hwang of McGill University
3. Causal Mediation Analysis by Prof. Xu Qin of University of Pittsburgh

Awards

A paper will be selected for the best paper award. The award comes with a $500 check and a certificate.

We also offer a limited number of travel awards up to $500 to researchers based on need.

Share the meeting

You can share the meeting information with a colleague by inputting their email and name below.


Workshops

Three workshops will be 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.

Workshop 1: Conducting Meta-Analytic Structural Equation Modeling with R

Time: 1pm-4pm, July 21, Vienna Time / 8pm-11pm, July 21, Singapore Time / 8am-11am, July 21, US Eastern Time
Location: Virtual on Zoom (Register to get the link)

The workshop will cover meta-analytic structural equation modeling (MASEM), which uses the techniques of meta-analysis and structural equation modeling to synthesize correlation matrices and fit hypothesized models on the combined correlation matrix. It can be used to test path models, confirmatory factor analytic models, and structural equation models from a pool of correlation matrices. MASEM offers the benefits of both meta-analysis and SEM.

During the workshop, I will provide an introduction to the basic theory of MASEM and demonstrate how to conduct the analyses with R. While some familiarity with R would be beneficial, the workshop is designed to be accessible to those who are new to the programming language.

  • Mike Cheung

    Mike Cheung -- National University of Singapore

    Professor Mike Cheung specializes in quantitative methods and conducts research in structural equation modeling, meta-analysis, and multilevel modeling. His work focuses on the integration of meta-analysis and structural equation modeling. He is an Associate Editor of Research Synthesis Methods and Neuropsychology Review and serves on the editorial boards of several journals. For more information, please visit https://mikewlcheung.github.io/.

Workshop 2: An Introduction to Generalized Structured Component Analysis Structural Equation Modelling (GSCA-SEM) and its Applications Using Free Software

Time: 7pm-10pm, July 21, Vienna Time / 1pm-4pm, July 21, US/Canada Eastern Time
Location: Virtual on Zoom (Register to get the link)

Researchers in various fields are interested in studying the path-analytic relationships between constructs such as self-esteem, depression, socioeconomic status, etc. As constructs are abstract concepts that are not directly measurable, they are represented by proxies linked to empirical data or observed variables in statistical models. This enables researchers to test hypotheses about the relationships between constructs. There are two traditional ways of statistically representing constructs: (common) factors and components.

Structural equation modelling (SEM) is a general statistical framework for specifying and examining how such statistical representations as factors or components are related to observed variables and how they are related based on prior theory or knowledge. SEM has diverged into two domains, i.e., factor-based vs. component-based, depending on whether all constructs are represented as factors or components.

The two SEM domains include different statistical methods. Covariance structure analysis (CSA) has been a standard method for factor-based SEM, although there are other methods, including model-implied instrumental variable methods, factor score regression, structured factor analysis, and generalized structured component analysis with measurement errors incorporated (GSCA M ) . On the other hand, generalized structured component analysis (GSCA) is the most general method for component-based SEM, which can include a long- standing component-based method, partial least squares path modelling, as a special case. It has been shown that when an SEM method developed for one domain is used for the other domain, it results in biased solutions. For example, CSA will provide biased estimates of parameters in models with components (e.g., component loadings and path coefficients relating components), whereas GSCA will yield biased estimates of parameters in models with factors (e.g., factor loadings and path coefficients connecting factors).

Over the decades, all the methods have been used exclusively for either of the two SEM domains, permitting researchers to estimate models with factors or components only. However, researchers may often need to include both factors and components in the model to consider a broad array of constructs from different disciplines. The next generation of SEM methods has recently emerged that permits estimating models with both factors and components in a unified framework. It includes consistent partial least squares and integrated generalized structured component analysis (IGSCA).

GSCA-SEM (generalized structured component analysis structural equation modelling) is an umbrella term that includes three SEM methods—GSCA, GSCA M , and IGSCA—for estimating models with components only, with factors only, or with both factors and components, respectively. GSCA-SEM is highly versatile in accommodating the two statistical representations of constructs. GSCA Pro is a stand-alone software program for GSCA-SEM. The software can be freely downloaded from its website (www.gscapro.com). It provides a graphical user interface that allows users to draw their model as a path diagram easily, fit GSCA-SEM to the model, and obtain results.

This workshop begins by explaining the conceptual foundations of GSCA-SEM, focusing on model specification and evaluation. It then provides step-by-step illustrations of using the free software for various GSCA-SEM applications.

  • Heungsun Hwang

    Heungsun Hwang -- McGill University

    Prof. Hwang’s research program is generally devoted to the development and application of quantitative methods to address diverse issues in psychology and various other fields. His recent interests include the development of data integration tools for high-dimensional data collected from multiple sources; the development of a statistical methodology for investigating associations among genetic, brain, and behavioural/cognitive phenotypes; and the development and application of predictive models or machine learning algorithms for predicting behavioural and cognitive outcomes using genetic, physiological, and psychological data.

Workshop 3: Causal Moderated Mediation Analysis

Time: 1pm-4pm, July 24, Vienna Time / 8am-11am, July 24, US Eastern Time
Location: Virtual on Zoom (Register to get the link)

Research questions regarding how, for whom, and where a treatment achieves its effect on an outcome have become increasingly valued. Such questions can be answered by causal moderated mediation analysis, which assesses the heterogeneity of the mediation mechanism underlying the treatment effect across individual and contextual characteristics. The purpose of this three-hour virtual course is to introduce the general definition, identification, estimation, and sensitivity analysis for causal moderated mediation effects under the potential outcomes framework. Participants will also learn how to use a user-friendly R package to conduct the analysis and visualize analysis results. The method introduction and the package implementation will be illustrated with a re-analysis of the National Evaluation of Welfare-to-Work Strategies (NEWWS) Riverside data.

  • Xu Qin

    Xu Qin -- University of Pittsburgh

    Dr. Xu Qin is an Associate Professor of Research Methodology at the School of Education (primary) and an Assistant Professor of Biostatistics at the School of Public Health (secondary). She holds a Ph.D. from the Department of Comparative Human Development at the University of Chicago and a B.S. and an M.S. in Statistics from the Renmin University of China.

    Her research focuses on solving cutting-edge methodological problems in causal mediation analysis and multilevel modeling. She is also interested in using rigorous and innovative quantitative methods to evaluate the impacts of interventions and the underlying mechanisms. Methodologically, she has developed statistical methods and software for investigating the heterogeneity in causal mediation mechanisms in both multilevel and single-level settings, as well as sensitivity analysis and power analysis methods for causal mediation analysis. Substantively, she is interested in applying advanced statistical methods in developmental, educational, and health research.

Registration

Registration to attend the meeting and workshops is required. We will send you the receipt of registration through email, so make sure your email address is provided and correct.

To register, click the link here. It will take you to our meeting registration and management website.

Meeting Registration

If you are not an ISDSA member, you can register to become a member now. New membership till the end of 2024 is free.

Workshop Registration

Workshops are free for ISDSA members.

For members: Free

For non-members
With meeting registration (non-students): $99
With meeting registration (students): $49
Without meeting registration (non-students): $149
Without meeting registration (students): $99

For each workshop, we can only accommodate 200 participants. To register, please use the form below or our registration platform before July 1, 2024.

Refund Policy

For any reason that prevents you from attending the meeting or workshop, you can request a refund by contacting us. The refund amount depends on the time you make the request.
Before June 1: Refund full registration fee minus 5% of the processing fee (charged by the third party)
After June 1 & before June 25: Refund 80% of the full registration fee.
After June 25: no refund.

Hotel

We have reserved a block of rooms at the Hilton Vienna Park hotel. The rate is 209 euros per night with free breakfast and VAT included. You can book a room using the link here by July 10, 2024. The check in date is July 21, 2024 and Check out is July 24, 2024. You can cancel your booking free of charge until the July 16, 2024.

Sponsors

The meeting is jointly supported by the International Society for Data Science and Analytics, the Fudan Institute for Advanced Study in Social Sciences, the College of Management of National Taiwan Normal University, the Nanjing University of Posts and Telecommunications, and the University of Notre Dame.



Sponsoring Journals
Selected papers will be published in Journal of Behavioral Data Science.


Organizers


  • Haiyan Liu
    Assistant Professor
    University of California, Merced


  • Laura Lu
    Associate Professor
    University of Georgia

  • Wen Qu
    Assistant Professor
    Fudan University

  • Jiashan Tang
    Professor
    Nanjing University of Posts and Telecommunications

  • Xin Tong
    Associate Professor
    University of Virginia

  • Ke-Hai Yuan
    Professor
    University of Notre Dame

  • Zhiyong Zhang
    Professor
    University of Notre Dame

  • Contact information

    Telephone: 1-574-400-5868
    Email: meeting@isdsa.org
    Mail: P.O.Box 1471, ISDSA, Granger, IN 46530