July 4–6, 2023 | Shanghai, China

2023 ISDSA Meeting

Theme: Applied Data Science

About the Meeting

The Annual Meeting of the International Society for Data Science and Analytics (ISDSA) aims to provide a global forum for researchers and practitioners in the field of data science and data analytics to communicate and showcase their latest research. The ISDSA Meeting is for anyone who is interested in data science and data analytics, and more generally, quantitative research.

The 2023 Annual Meeting of ISDSA will take place in Shanghai, China, from July 4-6, 2023. The format of the meeting is primarily in person and the meeting is in English. However, to accommodate those who cannot travel, we also accept up to 15 virtual talks. This year's meeting is organized and hosted by the Fudan Institute for Advanced Study in Social Sciences.

Abstract submission closed on March 15, 2023

The theme of this year's meeting is "Applied Data Science". We welcome the submission of abstracts on theoretical and method papers, applications and case studies, and tutorials. Abstract submission is open till March 15, 2023. You are encouraged to submit your abstract early and the decision on your submission is typically made one week after we receive your submission. The first 50 accepted presenters will receive three days of free hotel (July 3-5) and food in Shanghai, China. We accept regular 30-minute paper presentations and 10-minute speed paper presentations. If you are interested in participating in the meeting, please submit an abstract now. You will be asked to register as a user to use the online abstract submission system if you haven't done so. A complete submission needs a title, an abstract (anywhere between 100 to 500 words with adequate information for evaluation), and a complete list of the authors.

We particularly welcome submissions on the following two topics.
(1) Data Science in Humanities and Social Sciences. Selected papers will be published in the Springer journal Fudan Journal of the Humanities and Social Sciences (Q2; Citescore 2021 is 1.9 and ranks 91 out of 264 journals in General Social Sciences category).
(2) Data Science Methods in Political Science and Public Policy Research. Selected papers will be published in the Springer journal Chinese Political Science Review (Q1; Citescore 2021 is 2.1 and ranks 147 out of 608 journals in Political Science and International Relations category).


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. The travel awards go to:
∎ Yilan Chen, Beijing Normal University, China. Talk title: A Two-Step Method based on lz* for Identifying Effortful Respondents
∎ Joshua Chiroma Gandi, University of Jos, Nigeria. Talk title: Psychoperiscope: A data ingestion tool and modeling scale for social and behavioral data science
∎ Dibaba Bayisa Gemechu, Namibia University of Science and Technology, Namibia. Talk title: Ordinal Logistic Regression Model in Determining Factors Associated with Household Food Insecurity in Namibia
∎ Md Hasinur Rahaman Khan, University of Dhaka, Bangladesh. Talk title: Implementation of Gradient Boosting for Survival Analysis with Competing Risk
∎ Xiaolan Liao, University of Oklahoma Health Sciences Center, United States. Talk title: Using Baseline Case Characteristics to Predict Successful Treatment Completion
∎ Mingqing Zheng, Ningxia University, China. Talk title: How Does Self-Compassion Associate with Coping Self-Efficacy in Daily Life? A Dynamic Structural Equation Model Analysis

Share the meeting

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

Visa information

You might need a visa to visit China. Please visit the official website of the Department of Consular Affairs of the Ministry of Foreign Affairs of China to find out whether and which type of visa you need. Typically, the L-visa or F-visa is what you need. If you plan to apply for the F-visa, please contact us at visa@isdsa.org with the following information: name, affiliation, email address, gender, birth date, password number, and country of citizenship so that we can provide you an invitation letter to assist with your visa application. Your information will be kept confidential and removed after we issue you the invitation letter. You are encouraged to visit the website of the Chinese embassy in your home country for specific requirements.

Note that neither the ISDSA nor the local organizing university will provide emergency medical insurance. It is the responsibility of the attendees to have their own medical insurance.


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.

  • Haiyan Liu

    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.


  • Hongyun Liu

    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.

  • Laura Lu

    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.

  • Qian Zhang

    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.

  • Johnny Zhang

    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.


  • Hawjeng Chiou

    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.

  • Tong

    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.


Registration to attend the meeting is required. The first 50 registered presenters will receive three days of free hotel and food in Shanghai. Register early to take advantage of this. We can waive the registration fee for up to 15 presenters. We also provide travel awards up to $500 on a competitive basis. Students and those from low-income countries will be given priority. If you are interested in the waiver of the registration fee and/or travel award, please send a short justification statement and your CV to meeting@isdsa.org.

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: $299
Student registration for members: $149
Regular registration for non-members: $399
Student registration for non-members: $199

If you are not an ISDSA member, you can register to become a member now. Membership till the end of 2023 is free.

Special note for participants from China

If you are from China, you can also pay your registration fee through bank transfer. To do so, first send your money to the following account (make sure to include the note "isdsa2023年会会务费"):
Then, send us an email to meeting@isdsa.org and we will provide you with a code and instruction for registration.

The following rate is used for registration fee in RMB.
Regular registration for members: RMB 2100
Student registration for members: RMB 1050
Regular registration for non-members: RMB 2800
Student registration for non-members: RMB 1400

Workshop Registration

You can choose to register for one or multiple workshops.Thanks to the support of the Society of Multivariate Experimental Psychology, we now offer all workshops for free.

For members
With meeting registration (regular members): $99 Now $0
With meeting registration (students): $49 Now $0
Without meeting registration (regular members): $149 Now $0
Without meeting registration (students): $99 Now $0

For non-members
With meeting registration (regular members): $99 Now $0
With meeting registration (students): $49 Now $0
Without meeting registration (regular members): $199 Now $0
Without meeting registration (students): $149 Now $0

For each workshop, we can only accommodate 200 participants. To register, please use our registration platform before June 15, 2023.

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.


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 the following journals: Fudan Journal of the Humanities and Social Sciences, Chinese Political Science Review, and Journal of Behavioral Data Science.


  • Hawjeng Chiou
    Distinguished Professor
    National Taiwan Normal University

  • Sujian Guo
    Distinguished Professor
    Fudan University

  • Karl Ho
    Professor of Instruction
    University of Texas at Dallas

  • Hongyun Liu
    Beijing Normal University

  • Wen Qu
    Assistant Professor
    Fudan University

  • Jiashan Tang
    Nanjing University of Posts and Telecommunications

  • Ke-Hai Yuan
    University of Notre Dame

  • Zhiyong Zhang
    University of Notre Dame

  • Contact information

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