2024 ISDSA Meeting

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

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.

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. Causual Mediation Analysis by Prof. Xu Qin of Unversity 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.

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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 -- 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: Structured Component Analysis Structural Equation Modeling

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

To add.

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: July 24
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 -- University of Pittsburgh

Dr. Xu Qin is an Assistant 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. Instructio

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

Regular registration for members: $399
Student registration for members: $199
Regular registration for non-members: $599
Student registration for non-members: $299

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

The subsided price covers the cost of all workshops.

For members
With meeting registration (regular members): $49
With meeting registration (student members): $25
Without meeting registration (regular members): $99
Without meeting registration (student members): $49

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 June 15, 2024.

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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.