Results for VPGE::Specialized

An introduction to a broad range of social science research methods that are widely used in PhD work. Prerequisite: consent of instructor.

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.

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.

Primarily for master's students in the School of Education. Focus is on reading literature and interpreting descriptive and inferential statistics, especially those commonly found in education. Topics: basic research design, instrument reliability and validity, descriptive statistics, correlation, t-tests, one-way analysis of variance, and simple and multiple regression. All offerings of this course (whether meeting on Mon & Weds or Tues & Thurs) will be taught identically.

(Formerly EDUC 151.) Primarily for master's students: An introduction to the core concepts and methods of qualitative research. Through a variety of hands-on learning activities, readings, field experiences, class lectures, and discussions, students will explore the processes and products of qualitative inquiry. This is a graduate level course. No undergraduates may enroll. Priority will be given to GSE students, and final enrollment depends on instructor approval after the first day of class.

Concepts of reliability and validity; derivation and use of test scales and norms; mathematical models and procedures for test validation, scoring, and interpretation.

This course will cover the material from EDUC 252 in an applied setting. Emphasis will be in developing a capacity for applying and interpreting psychometrics techniques to real-world and simulated data.

Major periods of evolution, particularly since the mid-19th century. Premise: insights into contemporary higher education can be obtained through its antecedents, particularly regarding issues of governance, mission, access, curriculum, and the changing organization of colleges and universities.

The purpose of Introduction to Program Evaluation (EDUC 278) is to provide an introduction to the field of program evaluation. Students taking this course will learn basic concepts and participate in intellectual debates in the field. This course is intended to examine issues and challenges faced by evaluators of educational and social programs. We will be working with real evaluation tasks throughout the course. The class will meet once a week for 2hrs 50 min. It is critical that you commit to reading all the material before class, so that the discussion is well-focused. During the last weeks of the course, an evaluation proposal of a real-world program will be developed. The proposal will become the final paper.

Social science researchers often deal with complex data and research questions that traditional statistics models like linear regression cannot adequately address. This course offers the opportunity to understand and apply two widely used types of advanced regression analysis that allow the examination of 1) multilevel data structures (multilevel models) and 2) multivariate research questions (structural equation models).

For doctoral students. How to develop a researchable question and research design, identify data sources, construct conceptual frameworks, and interpret empirical results. Presentation by student participants and scholars in the field. May be repeated for credit.

We examine unprecedented scrutiny and critiques of US colleges and universities. How have expectations changed? How have campus leaders and faculty responded? Who is served, what is taught, what research is prioritized, and what public impact is envisioned-contributing to democracy via access, education for citizenship and employment, producing reliable knowledge, serving as a community partner. Sources are readings, group work, and speakers. The seminar is designed for graduate students across the university. Undergraduates and postdocs need the instructor's permission.

American higher education has had to continually struggle with issues of difference, particularly racial and ethnic cultural difference, throughout its history. While the civil rights and student protest era of the 1960s are easily recognized as moments of cultural struggle, they evolve and take new forms, extending and re-framing ideological and material conflict in the academy. These include battles over: the content of the curriculum; access to college and admissions; and the domains of legitimate knowledge. In this course, we will critically examine and discuss the research and discourse concerning issues of diversity and equity in the current era. We will examine the political, cultural, and social contours of these issues and as well as the efficacy of campus responses to them such as race-conscious admissions policies, identity-based offices, ethnic studies programs, and other DEI (diversity, equity, and inclusion) initiatives.

This workshop/course is designed for students who are designing a survey to collect quantitative data for a research project. The workshop content draws on relevant cognitive processing theories and research related to development of good survey questions. In addition to some readings and a few mini lectures, this workshop is designed to be highly interactive and practical. By the end of the course students will have designed and pilot tested their survey instrument. Course enrollment is limited to 12 students and may be repeated for credit.

The goal of this course is to familiarize students with the Stata statistical software package for use in quantitative research. By the end of the course, students should be able to import and export data, clean and manage data, conduct standard statistical tests (e.g., correlation, t-test, regression), and produce a graph.

Addressing the theoretical underpinnings of qualitative interviews as well as the application of theory to practice, this course considers different approaches to interviewing. Interview types covered will range from group interviews to individual interviews, and from unstructured, ethnographically oriented interviews to highly structured interviews. Students will move from theory to interview design, implementation, and initial stages of analysis, with an emphasis on consistency in approach and utility in graduate-level research.

Strategies for communicating effectively in social and academic settings. Informal and formal language used in campus settings, including starting and maintaining conversations, asking questions, making complaints, and contributing ideas and opinions. Simulations and discussions, with feedback on pronunciation, grammar, and usage.

Skills for effective participation in classroom settings, seminars, and research group meetings. Pronunciation, grammar, and appropriateness for specific tasks. Feedback on language and communication style. May be repeated once for credit. Prerequisite: EFSLANG 690A or consent of instructor.

For advanced graduate students. Practice in academic presentation skills; strategy, design, organization, and use of visual aids. Focus is on improving fluency and delivery style, with videotaping for feedback on language accuracy and usage. May be repeated once for credit.

For advanced graduate students. Practice in academic presentation skills; strategy, design, organization, and use of visual aids. Focus is on improving fluency and delivery style, with video recording for feedback on language accuracy and usage. Fulfills the requirement for EFSLANG 691.

This course focuses on strategies for effective listening to university lectures and other academic content, such as seminars and group discussions. It extends beyond listening for main ideas and details, providing practice in identifying discourse markers common in academic settings and in recognizing and accommodating implied information, hesitations, and reduced forms, such as contractions. It also covers challenging areas such as processing numbers and adapting to unfamiliar vocabulary. Listening practice is complemented by instruction in effective note-taking and study strategies to retain and review comprehended information. Additionally, the course has a significant discussion component, giving students the opportunity to interpret what they hear to develop a deeper and more critical understanding of the content and to link that understanding to their own spoken English production and interaction. Materials include recorded lectures from Stanford faculty and other relevant sources.

Listening strategies and vocabulary for understanding English in academic and non-academic contexts. Discussion and interpretation of communicative intent. Computer-based and video exercises across a range of genres; individual project. May be repeated once for credit. Prerequisite: EFSLANG 693A or consent of instructor.

This course provides training in recognizing and practicing American English sounds, stress, and intonation patterns in connected speech in order to improve comprehension and enhance intelligibility in a variety of settings. After receiving an individualized analysis of speech patterns, students engage in directed practice both with online software and in class, receiving immediate feedback. Through these in-class activities and practice assignments, students will improve their ability to pronounce English clearly and to self-monitor and self-correct. The instructor will meet with students regularly throughout the quarter for one-on-one tutorials. Enrollment limited to 12.

Focus is on improving grammatical accuracy and vocabulary, building fluency, and learning the structure and conventions of English correspondence, reports, and short academic papers. Enrollment limited to 14.

Strategies and conventions for graduate writing. Emphasis is on fluency, organization, documentation, and appropriateness for writing tasks required in course work. May be repeated once for credit.

Focus on clarity, accuracy, and appropriate style. For graduate students experienced in English writing and currently required to write for courses and research. Class meetings and individual conferences. Prerequisite: EFSLANG 698A. May be repeated once for credit.

Strategies and conventions for graduate writing. Emphasis is on fluency, organization, documentation, and appropriateness for writing tasks required in course work and in producing research papers. Fulfills the requirement for EFSLANG 698A.

To be effective as an engineer or scientist, you must communicate your cutting-edge research and projects effectively to a broad range of audiences: your professors, your fellow students, your colleagues in the field, and sometimes the public. ENGR. 202W offers a collaborative environment in which you will hone your communication skills by writing and presenting about a project of your choosing and working on your CV/resume. ENGR202W is a practicum (supervised practical application) that helps you build toward a complete skillset for technical communication in the twenty-first century. Through interactive presentations and activities, group workshops, and individual conferences, you will learn best practices for communicating to academic and professional audiences for a range of purposes.

Stanford graduates will play important roles in solving many of today's and tomorrow's major societal problems-in areas such as education, health, energy, and domestic and global poverty-that call for actions by nonprofit, business, and hybrid organizations as well as governments. This course teaches skills and bodies of knowledge relevant to these roles, covering topics such as designing, implementing, scaling, and evaluating social strategies; systems thinking; decision making under risk; psychological biases that adversely affect people's decisions; methods for influencing behavior; and pay-for-success programs. The large majority of the course will be devoted to students' working in teams to apply these concepts and tools to an actual problem, with teams choosing whatever problem interests them.

Businesses are increasingly expected to have a positive social impact, as evidenced by the rapid growth of impact investing. Yet, even as the impact investing market has taken off, impact measurement has lagged, undermining the credibility of the sector. Impact measurement is also critical to individual firms, not-for-profits and governments, as they face increased pressure to generate quantifiable results. How can we measure impact? What are some of the most effective frameworks, tools and approaches for impact measurement? How does one choose the most appropriate measurement tool based on factors such as the size, maturity and sector of an organization? Can impact be distilled down to a single monetary measure, such as a dollar? These and other questions will be explored in considerable depth, through analysis and discussion of case studies as well as hand-on use of impact measurement tools. This course is a good match for students interested in impact measurement, impact investing, profit-with-purpose businesses or the role of business in society. The course will be co-taught by Matt Bannick, who led the impact investing firm, Omidyar Network, and served as the President of PayPal and of eBay International, and Neil Malhotra, Director of the Center for Social Innovation and an expert in the statistical measurement of impact.

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.

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.

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.

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.

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

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.

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.

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.

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.

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.