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1 - 10 of 65 results for: VPGE::Specialized

BIOS 205: Introduction to R for Data Analysis

Autumn quarter enrollment limited to ADVANCE students; instructor consent required for enrollment. Topics include: basics of R (widely used, open-source programming and data analysis environment) programming language and data structures, reading/writing files, graphics tools for figure generation, basic statistical and regression operations, survey of relevant R library packages. Interactive format combining lectures and computer lab. For course and enrollment information, see https://web.stanford.edu/~sbagley2/bios205/
Terms: Aut, Win, Spr | Units: 1 | Grading: Medical Satisfactory/No Credit
Instructors: Bagley, S. (PI)

COMM 318: Quantitative Social Science Research Methods

An introduction to a broad range of social science research methods that are widely used in PhD work. Prerequisite: consent of instructor.
Terms: Spr | Units: 1-5 | Grading: Letter (ABCD/NP)
Instructors: Krosnick, J. (PI)

COMM 339: Questionnaire Design for Surveys and Laboratory Experiments: Social and Cognitive Perspectives (POLISCI 421K, PSYCH 231)

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 | Grading: Letter (ABCD/NP)
Instructors: Krosnick, J. (PI)

DESINST 215: The Design of Data

Our world is increasingly complex and laden with many forms of measurable data. Infographics abound, but whether explicit or not, the stories they tell are all designed. In this hyper-concentrated, hands-on course, students will learn to use mapping and design techniques to sort and synthesize data, unlock insights and communicate information. We will create four different types of maps and infographics and students will practice finding insight from both qualitative and quantitative information. Take this course if you are interested in learning how to navigate through and create for the complicated intersection of data and design.nnAdmission by application. See dschool.stanford.edu/classesn for more information.
Terms: Spr | Units: 3-4 | Grading: Letter or Credit/No Credit
Instructors: Carter, C. (PI)

EARTH 202: PhD Students on the PhD

This seminar is designed for coterms and upperclassmen who are considering pursuing a PhD in earth science fields but want to know what that path really entails. Consisting of small-group discussions with current PhD students, this course will feature conversations on a range of PhD research topics and will also delve into the substance of the PhD experience itself. We will explore PhD students' programs and career paths: the milestones, processes, and issues that guide their decisions and shape their PhD experiences. Discussion themes will be determined partly in advance and partly based on the interests of participants and could include topics such as choosing a PhD program or research question, interdisciplinarity, community engagement, or work/life balance.
Terms: not given this year | Units: 1 | Grading: Satisfactory/No Credit

ECON 292: Quantitative Methods for Empirical Research

This is an advanced course on quantitative methods for empirical research. Students are expected to have taken a course in linear models before. In this course I will discuss modern econometric methods for nonlinear models, including maximum likelihood and generalized method of moments. The emphasis will be on how these methods are used in sophisticated empirical work in social sciences. Special topics include discrete choice models and methods for estimating treatment effects.
Terms: Aut | Units: 2-5 | Grading: Letter or Credit/No Credit
Instructors: Imbens, G. (PI)

EDUC 242: Workshop on Instrument Development for Assessment, Research or Evaluation Purposes I

This course is designed with the belief that collecting information is a routine activity in which most researchers and educators are involved. Developing and improving instruments to gather information for descriptive, assessment, research, or evaluation purposes is a goal that unites all social sciences. Therefore, this course focuses on the technical skills required to develop, judge, and/or select quality instruments in diverse domains. The course will focus on your personal journey to develop or judge an instrument on something that is important for you.
Terms: Aut | Units: 3 | Repeatable for credit | Grading: Letter or Credit/No Credit

EDUC 252: Introduction to Test Theory

Concepts of reliability and validity; derivation and use of test scales and norms; mathematical models and procedures for test validation, scoring, and interpretation.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Domingue, B. (PI)

EDUC 252L: Introduction to Test Theory - Lab

This course will cover the material from 252A in an applied setting. Emphasis will be in developing a capacity for applying and interpreting psychometrics techniques to real-world and simulated data.
Terms: Win | Units: 2 | Grading: Letter or Credit/No Credit
Instructors: Domingue, B. (PI)

EDUC 260A: Statistical Methods for Group Comparisons and Causal Inference (HRP 239, STATS 209)

See http://rogosateaching.com/stat209/. Critical examination of statistical methods in social science and life sciences applications, especially for cause and effect determinations. Topics: mediating and moderating variables, potential outcomes framework, encouragement designs, multilevel models, heterogeneous treatment effects, matching and propensity score methods, analysis of covariance, instrumental variables, compliance, path analysis and graphical models, group comparisons with longitudinal data. Prerequisite: intermediate-level statistical methods.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
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