Social Psychology

PSYC 121 Social Psychology | Syllabus

PSYC 398 Psychology of Social Change

PSYC 406 Social Cognition | Syllabus

SPI 502 Psychology for Policy Analysis & Implementation

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Social psychology is one of the major social science fields that uses scientific methods to discover psychology mechanisms underlying social phenomena. These courses cover both undergraduate and graduate level in-depth examination of specific topics in social psychology. Topics includes key concepts, theories, methods, and findings in social psychology, with particular attention to attitudes, social influence, group dynamics, intergroup relations, identity, as well as recent research on computational approaches to cognition, behaviors, groups, and culture. 

Computational Social Science

2020 Fall

PSYC 469 Undergrad credit

PSYC 800 Grad credit

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Computational social science is a new interdisciplinary frontier in the social sciences. This course is a seminar-format survey of computational approaches to social science research, with emphasis on methods, tools, software frameworks, and complexity theory as these apply to the investigation of social phenomena.

Agent-Based Modeling

2020 Spring, 2021 Spring

PSYC 469 Undergrad credit

PSYC 800 Grad credit

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Agent-based modeling is a computer modeling technique that simulates the interactions of agents to gain insights into system behaviors. This stochastic model is built from the bottom up and validates generative causality. It has established itself as a powerful research method in the formal, physical, and especially the social sciences. This is a 3-unit one-semester elective that offers students a hands-on introduction to some landmark models, the core concepts, and the techniques of agent-based modeling.

Data Science

2020 Fall, 2021 Spring

PSYC 500 Undergrad credit | Course website

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We (with Dr. Tim Pleskac, Dr. Marsha McCartney, and Xiaohong Cai) redesign PSYC 500 (formerly, Intermediate Statistics for Psychological Research) to Introduction to Data Science. This course offers computational and statistical thinking, mathematical foundations, model building and software foundation, data curation, and knowledge transference, as well as a hands-on introduction to Python and Google Colab. This is funded by the course transformation grant from KU center for teaching excellence.