SOSC 13200 Social Science Inquiry (college core sequence)
CHDV 20100 Human Development Research Designs (cross-listed as PSYC 20100)
CHDV 20101/30101 Applied Statistics in Human Development Research
CHDV 30102 Introduction to Causal Inference (cross-listed as PBHS 43201, PLSC 30102, SOCI 30318, STAT 31900, MACS 51000)
CHDV 32411 Mediation, Moderation, and Spillover Effects (cross-listed as CCTS 32411, PBPL 29411, PSYC 32411, STAT 33211)
CHDV 40102 Advanced Topics in Causal Inference (cross-listed as SOC 40202)
CHDV 40203 Youth of the Great Recession
COURSE DESCRIPTIONS
SOSC 13200 Social Science Inquiry (college core sequence, Winter Quarter)
To what extent do social environments facilitate or constrain human development? There are many competing theories about how parenting and schooling are structured by social-cultural contexts and whether parenting and schooling actually contribute to a child’s well-being. While descriptive comparisons of child experiences across social-cultural contexts are often revealing; the causal relationships between various environmental input and child well-being are not easy to establish. Yet understanding not only the associational but also the causal relationships is crucial for decisions with regard to resource allocation within households, schools, and in a society at large. We will empirically examine some dominant theories with regard to the above issues. In particular, we will scrutinize the existing evidence and evaluate the validity of inferences. The course will cover basic statistical concepts and methods including sampling, descriptive statistics, and inferential statistics, and will place an emphasis on defining causality in terms of potential outcomes. The primary objective of this course is to apply statistical concepts and methods in empirical investigations of some theoretical questions that have puzzled social scientists at least for decades. The course will not only enable students to critically evaluate the social science literature but also prepare them to plan and conduct empirical studies of their own.
CHDV 20100 Human Development Research Designs (cross-listed as PSYC 20100)
The purpose of this course is to expose CHD majors in college to a broad range of methods in social sciences with a focus on human development research. The faculty in Comparative Human Development is engaged in interdisciplinary research encompassing anthropology, biology, psychology, sociology, and applied statistics. The types of data and methods used by faculty span the gamut of possible methodologies for addressing novel and important research questions. In this course, students will study how appropriate research methods are chosen and employed in influential research and will gain hands-on experience with data collection and data analysis.
In spring 2021, 73 students enrolled in this course participated in a study on “Coping with COVID-19.” With IRB approval, the students collected survey data from more than 400 respondents and conducted nearly 70 semi-structured interviews. In summer 2020, a research team of six college students at the University of Chicago, under the supervision of Prof. Guanglei Hong and Jose Eos Trinidad (2nd year CHD doctoral student), prepared the data and other content for dissemination through a website. The data are likely suitable for BA and MA thesis studies. You may ask research questions including: (1) What is the multitude of challenges perceived by individuals given their demographics and social positions in relation to the labor force? (2) What resources or strategies do individuals employ to cope with the perceived challenges? In particular, how do individuals relate to others during the pandemic? (3) How may the perceived challenges and coping strategies contribute to or undermine psychological well-being? (4) Does social connectedness mitigate the impacts of perceived challenges on anxiety and depressive symptoms?
CHDV 20101/30101 Applied Statistics in Human Development Research
This course provides an introduction to quantitative methods of inquiry and a foundation for more advanced courses in applied statistics for students in social sciences who are interested in studying human development in social contexts. The course covers univariate and bivariate descriptive statistics, an introduction to statistical inference, t test, two-way contingency table, analysis of variance, simple linear regression, and multiple regression. All statistical concepts and methods will be illustrated with applications to a series of scientific inquiries organized around describing and understanding adolescent transitions into adulthood across demographic subpopulations in the contemporary American society. We will use the National Longitudinal Survey of Youth 1997 (NLSY97) throughout the course to reveal disparities between subpopulations in opportunities and life course outcomes. At the end of the course, students should be able to define and use descriptive and inferential statistics to analyze data and to interpret analytical results. No prior knowledge in statistics is assumed. High school algebra and probability are the only mathematical pre-requisites. Every student is required to participate in a lab section. Students will review the course content and learn to use the Stata software in the lab under the TA’s guidance.
CHDV 30102 Introduction to Causal Inference (cross-listed as PBHS 43201, PLSC 30102, SOCI 30318, STAT 31900, MACS 51000)
This course is designed for graduate students and advanced undergraduate students from the social sciences, education, public health science, public policy, social service administration, and statistics who are involved in quantitative research and are interested in studying causality. The goal of this course is to equip students with basic knowledge of and analytic skills in causal inference. Topics for the course will include the potential outcomes framework for causal inference; experimental and observational studies; identification assumptions for causal parameters; potential pitfalls of using ANCOVA to estimate a causal effect; propensity score based methods including matching, stratification, inverse-probability-of-treatment-weighting (IPTW), marginal mean weighting through stratification (MMWS), and doubly robust estimation; the instrumental variable (IV) method; regression discontinuity design (RDD) including sharp RDD and fuzzy RDD; difference in difference (DID) and generalized DID methods for cross-sectional and panel data, and fixed effects model. Intermediate Statistics or equivalent such as STAT 224/PBHS 324, PP 31301, BUS 41100, or SOC 30005 is a prerequisite. This course is a pre-requisite for “Advanced Topics in Causal Inference” and “Mediation, moderation, and spillover effects.”
CHDV 32411 Mediation, Moderation, and Spillover Effects (cross-listed as CCTS 32411, PBPL 29411, PSYC 32411, STAT 33211)
This course is designed for graduate students and advanced undergraduate students from social sciences, statistics, public health science, public policy, and social services administration who will be or are currently involved in quantitative research. Questions about why a treatment works, for whom, under what conditions, and whether one individual’s treatment could affect other individuals’ outcomes are often key to the advancement of scientific knowledge. We will clarify the theoretical concepts of mediated effects, moderated effects, and spillover effects under the potential outcomes framework. The course introduces cutting-edge methodological approaches and contrasts them with conventional strategies including multiple regression, path analysis, and structural equation modeling. The course content is organized around application examples. The textbook “Causality in a Social World: Moderation, Mediation, and Spill-Over” (Hong, 2015) will be supplemented with other readings reflecting latest developments and controversies. Weekly labs will provide tutorials and hands-on experiences. All students are expected to contribute to the knowledge building in class through participation in presentations and discussions. Students are encouraged to form study groups, while the written assignments are to be finished and graded on an individual basis. Intermediate Statistics such as STAT 224/PBHS 324, PP 31301, BUS 41100, or SOC 30005 and Introduction to Causal Inference or their equivalent are prerequisites.
CHDV 40102 Advanced Topics in Causal Inference (cross-listed as SOC 40202)
This course provides an in-depth discussion of selected topics in causal inference that are beyond what are covered in the introduction to causal inference course. The course is intended for graduate students and advanced undergraduate students who have taken the “introduction to causal inference” course or its equivalent and want to extend their knowledge in causal inference. The course is particularly suitable for students who plan to conduct scientific research that involve investigations of causal relationships as well as for those with strong methodological interests. Topics will include (1) alternative matching methods, randomization inference for testing hypothesis and sensitivity analysis; (2) marginal structural models and structural nested models for time-varying treatment; (3) Rubin Causal Model (RCM) and Heckman’s scientific model of causality; (4) latent class treatment variable; (5) measurement error in the covariates; (6) the M-estimation for the standard error of the treatment effect for the use of IPW; (7) the local average treatment effect (LATE) and its problems, sensitivity analysis to examine the impact of plausible departure from the IV assumptions, and identification issues of multiple IVs for multiple/one treatments; (8) multilevel experimental designs and observational data for treatment evaluation; (9) nonignorable missingness and informative censoring issues. Intermediate Statistics such as STAT 224 and Introduction to causal inference or their equivalent are prerequisites.
CHDV 40203 Youth of the Great Recession
This research seminar is designed for graduate students who are eager to investigate how the Great Recession in the past decade has affected the life course trajectories of people, especially children and youth, in various demographic groups defined by the intersections of social class, race/ethnicity, gender, and urbanisity. Dramatic changes in global and local economies have posed challenges to individuals, families, and communities to various degrees, which offer research opportunities to revisit and reconsider theories about human development in social contexts. The class will raise big theoretical questions substantiated by the literature and will narrow down to specific questions for empirical investigation. These questions will then evolve into research projects to be carried out collectively or individually through analyzing large-scale longitudinal data sets. The process will involve discussions of appropriate research designs, development of data analytic plans, and interpretations of empirical evidence. Throughout the course, students will receive hands-on training on how to write an empirical paper for an academic journal. Students are expected to produce single-authored or co-authored manuscripts at the end of the course. Prerequisites for this course are at least one and preferably two applied statistics courses.