printer friendly pageNBIO 227: Understanding Techniques in Neuroscience
Students will learn to select and evaluate multidisciplinary techniques for approaching modern neuroscience questions. A combination of lectures and small group paper discussions will introduce techniques from molecular, genetic, behavioral, electrophysiological, imaging, and computational neuroscience. Students will be expected to complete homework assignments analyzing primary literature and attend optional laboratory demonstrations. Intended for graduate students, postdocs, and staff from any discipline; and for advanced undergraduates in the biosciences, engineering, or medicine.
Terms: Aut
| Units: 2
OB 670: Designing Social Research
This is a course in the design of social research, with a particular emphasis on research field (i.e., non-laboratory) settings. As such, the course is a forum for discussing and developing an understanding of the different strategies social theorists employ to explain social processes, develop theories, and make these theories as believable as possible. In general, these issues will be discussed in the context of sociological research on organizations, but this will not be the exclusive focus of the course. A range of topics will be covered, for example: formulating and motivating research questions; varieties of explanation; experimental and quasi-experimental methods, including natural experiments; counterfactual models; conceptualization and measurement; sampling and case selection; qualitative and quantitative approaches. This course is particularly oriented toward developing an appreciation of the tradeoffs of different approaches. It is well suited to Ph.D. students working on qualifying papers and dissertation proposals.
Terms: Aut
| Units: 3
Instructors:
Carroll, G. (PI)
POLISCI 421K: Questionnaire Design for Surveys and Laboratory Experiments: Social and Cognitive Perspectives (COMM 339, PSYCH 231, PUBLPOL 339)
The social and psychological processes involved in asking and answering questions via questionnaires for the social sciences; optimizing questionnaire design; open versus closed questions; rating versus ranking; rating scale length and point labeling; acquiescence response bias; don't-know response options; response choice order effects; question order effects; social desirability response bias; attitude and behavior recall; and introspective accounts of the causes of thoughts and actions.
Terms: Aut
| Units: 4
Instructors:
Krosnick, J. (PI)
PSYCH 231: Questionnaire Design for Surveys and Laboratory Experiments: Social and Cognitive Perspectives (COMM 339, POLISCI 421K, PUBLPOL 339)
The social and psychological processes involved in asking and answering questions via questionnaires for the social sciences; optimizing questionnaire design; open versus closed questions; rating versus ranking; rating scale length and point labeling; acquiescence response bias; don't-know response options; response choice order effects; question order effects; social desirability response bias; attitude and behavior recall; and introspective accounts of the causes of thoughts and actions.
Terms: Aut
| Units: 4
Instructors:
Krosnick, J. (PI)
PSYCH 252: Statistical Methods for Behavioral and Social Sciences
This course offers an introduction to advanced topics in statistics with the focus of understanding data in the behavioral and social sciences. It is a practical course in which learning statistical concepts and building models in R go hand in hand. The course is organized into three parts: In the first part, we will learn how to visualize, wrangle, and simulate data in R. In the second part, we will cover topics in frequentist statistics (such as multiple regression, logistic regression, and mixed effects models) using the general linear model as an organizing framework. We will learn how to compare models using simulation methods such as bootstrapping and cross-validation. In the third part, we will focus on Bayesian data analysis as an alternative framework for answering statistical questions. Please view course website: https://
psych252.github.io/. Open to graduate students only. Requirement:
Psych 10/
Stats 60 or equivalent
Terms: Win
| Units: 5
Instructors:
Gerstenberg, T. (PI)
;
Beller, A. (TA)
;
Grant, S. (TA)
...
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Instructors:
Gerstenberg, T. (PI)
;
Beller, A. (TA)
;
Grant, S. (TA)
;
Rispoli, B. (TA)
;
Schwartz, S. (TA)
SOC 280B: Introduction to Data Analysis (SOC 180B)
Preference to sociology majors, minors, and co-terms. Methods for analyzing and evaluating quantitative data in sociological research. Students will be taught how to run and interpret multivariate regressions, how to test hypotheses, and how to read and critique published data analyses.
Terms: Spr
| Units: 4
STATS 200: Introduction to Statistical Inference
Modern statistical concepts and procedures derived from a mathematical framework. Statistical inference, decision theory; point and interval estimation, tests of hypotheses; Neyman-Pearson theory. Bayesian analysis; maximum likelihood, large sample theory. Prerequisite:
STATS 116. Please note that students must enroll in one section in addition to the main lecture.
Terms: Aut, Win, Sum
| Units: 4
Instructors:
Hwang, J. (PI)
;
Johnstone, I. (PI)
;
Palacios, J. (PI)
...
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Instructors:
Hwang, J. (PI)
;
Johnstone, I. (PI)
;
Palacios, J. (PI)
;
Cherian, J. (TA)
;
Jang, J. (TA)
;
Jeong, Y. (TA)
;
Kanekar, R. (TA)
;
Lee, J. (TA)
;
Lu, S. (TA)
;
Ma, G. (TA)
;
Salerno, M. (TA)
;
Tanoh, I. (TA)
;
Xie, R. (TA)
;
Zhang, I. (TA)
;
Zhao, S. (TA)
STATS 202: Data Mining and Analysis
Data mining is used to discover patterns and relationships in data. Emphasis is on large complex data sets such as those in very large databases or through web mining. Topics: decision trees, association rules, clustering, case based methods, and data visualization. Prereqs: Introductory courses in statistics or probability (e.g.,
Stats 60), linear algebra (e.g.,
Math 51), and computer programming (e.g.,
CS 105). May not be taken for credit by students with credit in
STATS 216 or 216V.
Terms: Aut, Sum
| Units: 3
Instructors:
Erdmann-Pham, D. (PI)
;
Tran, L. (PI)
;
Chen, Z. (TA)
...
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Instructors:
Erdmann-Pham, D. (PI)
;
Tran, L. (PI)
;
Chen, Z. (TA)
;
Gonzalez, X. (TA)
;
Hu, A. (TA)
;
Ji, W. (TA)
;
Zhang, J. (TA)
STATS 203: Introduction to Regression Models and Analysis of Variance
Modeling and interpretation of observational and experimental data using linear and nonlinear regression methods. Model building and selection methods. Multivariable analysis. Fixed and random effects models. Experimental design. Prerequisites: A post-calculus introductory probability course, e.g.
STATS 116, basic computer programming knowledge, some familiarity with matrix algebra, and a pre- or co-requisite post-calculus mathematical statistics course, e.g.
STATS 200.
Terms: Win
| Units: 3
STATS 211: Meta-research: Appraising Research Findings, Bias, and Meta-analysis (CHPR 206, EPI 206, MED 206)
Open to graduate, medical, and undergraduate students. Appraisal of the quality and credibility of research findings; evaluation of sources of bias. Meta-analysis as a quantitative (statistical) method for combining results of independent studies. Examples from medicine, epidemiology, genomics, ecology, social/behavioral sciences, education. Collaborative analyses. Project involving generation of a meta-research project or reworking and evaluation of an existing published meta-analysis. Prerequisite: knowledge of basic statistics.
Terms: Win
| Units: 3
Instructors:
Ioannidis, J. (PI)
;
Kvarven, A. (PI)
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