Note: You must establish a session for the correct term (Spring, Summer, or Spring/Summer Academic Term 2003) on wolverineaccess.umich.edu in order to use the link "Check Times, Location, and Availability". Once your session is established, the links will function.
This page was created at 8:47 PM on Mon, Jul 14, 2003.
Spring HalfTerm Courses
POLSCI 990. Dissertation/Precandidate.
Instructor(s):
Prerequisites & Distribution: Election for dissertation work by doctoral student not yet admitted as a Candidate. Graduate standing. (14). (INDEPENDENT). May be repeated for credit.
Credits: (14).
Course Homepage: No homepage submitted.
Election for dissertation work by doctoral student not yet admitted as a Candidate.
Spring/Summer Term Courses
POLSCI 692. Directed Reading.
Instructor(s):
Prerequisites & Distribution: Graduate standing and permission of instructor required. (16). (INDEPENDENT). May not be repeated for credit.
Credits: (16).
Course Homepage: No homepage submitted.
A direct reading on a topic of the student's choice.
POLSCI 892. Directed Research.
Instructor(s):
Prerequisites & Distribution: Graduate standing. Permission of instructor required. (16). (INDEPENDENT). May not be repeated for credit.
Credits: (16).
Course Homepage: No homepage submitted.
Directed research on a topic of the student's choice.
POLSCI 990. Dissertation/Precandidate.
Instructor(s):
Prerequisites & Distribution: Election for dissertation work by doctoral student not yet admitted as a Candidate. Graduate standing. (18). (INDEPENDENT). May be repeated for credit.
Credits: (18; 14 in the halfterm).
Course Homepage: No homepage submitted.
Election for dissertation work by doctoral student not yet admitted as a Candidate.
POLSCI 995. Dissertation/Candidate.
Instructor(s):
Prerequisites & Distribution: Graduate School authorization for admission as a doctoral Candidate. Graduate standing. (8). (INDEPENDENT). May be repeated for credit.
Credits: (8; 4 in the halfterm).
Course Homepage: No homepage submitted.
Graduate School authorization for admission as a doctoral Candidate. N.B. The defense of the dissertation (the final oral examination) must be held under a full term Candidacy enrollment period.
Summer HalfTerm Courses
POLSCI 592. Advanced Internship in Political Science.
Section 201 — [3 credits].
Prerequisites & Distribution: Two courses in political science at the 400level or above and concentration in political science; or graduate standing. Permission of supervising instructor and review by the Department's internship advisor. (26). No more than four credits of internship may be included as part of a concentration plan in political science. (EXPERIENTIAL). May be repeated for credit for a maximum of 8 credits.
Credits: (26).
Course Homepage: No homepage submitted.
This course will focus on the process by which public policy is created and how various actors participate in and influence that process at the national level. We will also explore how political science and "real
life" experience can inform one another. This will be a very "handson" course, since students will use their internship offices as research sites for their class projects. Guest speakers from the Washington political
community will be scheduled to share their insights with the class. Requirement: Basic knowledge of American government. The course will be limited to 15 students on a first come/first served basis and will meet in Washington, DC once a week. Location, day and time TBA.
POLSCI 592. Advanced Internship in Political Science.
Section 202.
Instructor(s):
Lawrence Greene
(lrgre@umich.edu)
Prerequisites & Distribution: Two courses in political science at the 400level or above and concentration in political science; or graduate standing. Permission of supervising instructor and review by the Department's internship advisor. (26). No more than four credits of internship may be included as part of a concentration plan in political science. (EXPERIENTIAL). May be repeated for credit for a maximum of 8 credits.
Credits: (26).
Course Homepage: No homepage submitted.
No Description Provided. Contact the Department.
POLSCI 594. Introduction to Statistics and Data Analysis I.
Methods
Section 211 — ICPSR COURSE. COURSE MEETS 6/237/18.
Instructor(s):
Pedro Sanchez
Prerequisites & Distribution: College algebra. ICPSR Summer Program. Permission of instructor required. (2). May not be repeated for credit.
Credits: (2 in the halfterm).
Course Homepage: No homepage submitted.
The only prerequisite for this course is skill in basic algebra. Participants who have
weak mathematical backgrounds are advised to enroll in Mathematics for Social Scientists I simultaneously with
this course. The instructional format for this fourweek course is lecture combined with daily analyses performed by
the participants, some using handheld calculators and some using statistical software on a computer. Topics include:
data acquisition, classification, and summarization; basic probability; random variables and their distributions; and
confidence intervals and tests of hypotheses for means, variances, and proportions from one or two populations. The course will be taught at the level of the first 13 chapters of Applied Statistics by Neter, Wasserman, and
Whitmore. Participants should bring calculators with additive memory and a square root function.
POLSCI 595. Introduction to Statistics and Data Analysis II.
Methods
Section 222 — ICPSR COURSE. COURSE MEETS 7/218/15.
Instructor(s):
Merle Barbara Feldbaum
Prerequisites & Distribution: POLSCI 594. ICPSR Summer Program. Permission of instructor required. (2). May not be repeated for credit.
Credits: (2 in the halfterm).
Course Homepage: No homepage submitted.
This course is a continuation of Introduction to Statistics and Data Analysis I, and that course or its equivalent is a prerequisite. Course content will include the study of regression, chi square, and analysis of variance. Other topics will be discussed as time permits. Each concept will be illustrated by numerous substantive
examples drawn from social research. The course will be taught at the level of Neter et al., Applied Statistics.
POLSCI 596. Introduction to Regression Analysis.
Methods
Section 211 — ICPSR COURSE. COURSE MEETS 6/237/18.
Instructor(s):
Prerequisites & Distribution: Introductory statistics. ICPSR Summer Program. Permission of instructor required. (3). May not be repeated for credit.
Credits: (3 in the halfterm).
Course Homepage: No homepage submitted.
Students entering this workshop should have had at least one semester of basic introductory statistics. The workshop will provide an introduction to bivariate and multivariate regression models. Topics will include the development of the regression model, analysis of variance, parameter estimation, hypothesis testing, applications, and interpretation.
POLSCI 692. Directed Reading.
Instructor(s):
Prerequisites & Distribution: Graduate standing and permission of instructor required. (16). (INDEPENDENT). May not be repeated for credit.
Credits: (16).
Course Homepage: No homepage submitted.
A direct reading on a topic of the student's choice.
POLSCI 695. Regression Analysis.
Methods
Section 211 — ICPSR COURSE. MEETS 6/237/18.
Instructor(s):
Timothy Elton Mcdaniel
Prerequisites & Distribution: Introductory statistics and a background in elementary mathematics sufficient for the study of matrix algebra, and POLSCI 599. ICPSR Summer Program. Permission of instructor required. (3). May not be repeated for credit.
Credits: (3).
Course Homepage: No homepage submitted.
The prerequisites for the workshop are an introductory course in applied statistics at the level of Neter et al., Applied Statistics, and a background in elementary mathematics sufficient for the study of matrix algebra. The content of this course will include the nature of a linear model, least squares and maximum likelihood estimation, analysis of residuals, the general linear model, violation of assumptions (multicollinearity, heteroskedasticity, autocorrelation, measurement error, specification error), models with dummy variables, analysis of variance, and analysis of covariance. These concepts will be motivated by and illustrated with numerous substantive examples. Although knowledge of matrix arithmetic is not a prerequisite for this course, some concepts in matrix algebra will be introduced as appropriate. Wonnacott and Wonnacott's Econometrics, Neter and Wasserman's Applied Linear Statistical Models, and Weisberg's Applied Regression Analysis are three of a large number of texts that could be used for this course.
POLSCI 695. Regression Analysis.
Methods
Section 212 — ICPSR COURSE. MEETS 6/23  7/18.
Instructor(s):
Regina M Baker
Prerequisites & Distribution: Introductory statistics and a background in elementary mathematics sufficient for the study of matrix algebra, and POLSCI 599. ICPSR Summer Program. Permission of instructor required. (3). May not be repeated for credit.
Credits: (3).
Course Homepage: No homepage submitted.
See Political Science 695.211.
POLSCI 695. Regression Analysis.
Methods
Section 213 — ICPSR COURSE. MEETS 6/23  7/18.
Instructor(s):
Robert William Andersen
Prerequisites & Distribution: Introductory statistics and a background in elementary mathematics sufficient for the study of matrix algebra, and POLSCI 599. ICPSR Summer Program. Permission of instructor required. (3). May not be repeated for credit.
Credits: (3).
Course Homepage: No homepage submitted.
See Political Science 695.211.
POLSCI 695. Regression Analysis.
Methods
Section 222 — ICPSR COURSE. COURSE MEETS 6/23 7/18.
Instructor(s):
Brian Michael Pollins
Prerequisites & Distribution: Introductory statistics and a background in elementary mathematics sufficient for the study of matrix algebra, and POLSCI 599. ICPSR Summer Program. Permission of instructor required. (3). May not be repeated for credit.
Credits: (3).
Course Homepage: No homepage submitted.
See Political Science 695.211.
POLSCI 811. Advanced Multivariate Statistical Methods.
Methods
Section 211 — ICPSR COURSE. COURSE MEETS 6/23  7/18
Instructor(s):
Razia Azen
Prerequisites & Distribution: Graduate standing. Permission of instructor required. (3). May not be repeated for credit.
Credits: (3).
Course Homepage: No homepage submitted.
A very strong background in statistics at least at the level of the ICPSR courses Regression Analysis and Mathematics for Social Scientists II is necessary for this course. The purpose of this fourweek workshop is to discuss linear models
that are useful for analyzing multivariate data. After briefly reviewing univariate linear models, the course will cover multivariate hypothesis testing, principal components analysis, discriminant analysis, canonical correlation analysis, multivariate analysis of variance, and factor analysis. The level and breadth of coverage is roughly equivalent to that found in the multivariate texts Cooley and Lohnes, Multivariate Data Analysis; Tatzuoka, Multivariate Analysis; and Johnson and Wichern, Applied Multivariate Statistical Analysis.
POLSCI 812. Scaling and Dimensional Analysis.
Methods
Section 211 — ICPSR COURSE. COURSE MEETS 6/23  7/18.
Instructor(s):
William Jacoby
Prerequisites & Distribution: Regression analysis / POLSCI 699. ICPSR Summer Program. Permission of instructor required. (3). May not be repeated for credit.
Credits: (3).
Course Homepage: No homepage submitted.
Participants who elect this course should have had an introductory course in applied statistics at about the level of Neter et al., Applied Statistics, and they should have a mathematical competency consistent with the content of the ICPSR course Mathematics for Social Scientists II. This workshop will focus on analytic techniques designed to provide geometric representations of data. Topics to be covered include brief examinations of measurement theory, Likert scaling, magnitude scaling, more detailed investigations of the unfolding model, factor analysis, and several varieties of multidimensionalscaling. If time permits, several additional techniques will be covered, including cluster analysis and optimal scaling procedures. No single text satisfactorily covers all of these topics; instead, the course will rely on Davison's Multidimensional Scaling, articles from professional journals, and several of the Sage University Papers on Quantitative Applications in the Social Sciences (e.g., McIver and Carmine's Unidimensional Scaling, and Kim and Mueller's two volumes on factor analysis).
POLSCI 813. Structural Equation (Causal) Modeling.
Methods
Section 222 — ICPSR COURSE. COURSE MEETS 7/218/15.
Instructor(s):
Prerequisites & Distribution: Regression analysis / POLSCI 699. ICPSR Summer Program. Permission of instructor required. (3). May not be repeated for credit.
Credits: (3).
Course Homepage: No homepage submitted.
This course centers on simultaneous equation models — models of more than one equation, to account for more than one dependent variable — formerly called "causal models." The workshop will focus on linear models of the standard econometric type, continuing on to nonlinear models, models with discrete dependent variables, and models with measurement error ("covariance structure models") as time permits. The course will cover the nature of simultaneous equation models, their parameters, and the "effects" they imply; the assumptions under which one would customarily analyze them; the identification problem and criteria for identifiability; and such simultaneous equations estimators as two and threestage least squares, and limitedand fullinformation maximum likelihood.
POLSCI 814. Time Series Analysis.
Methods
Section 221 — ICPSR COURSE. COURSE MEETS 7/218/15.
Instructor(s):
Prerequisites & Distribution: Regression analysis / POLSCI 699. ICPSR Summer Program. Permission of instructor required. (3). May not be repeated for credit.
Credits: (3).
Course Homepage: No homepage submitted.
Both the ICPSR courses Regresssion Analysis and Mathematics for Social Scientists II are prerequisites for this course. This fourweek workshop begins by focusing on the autoregressive and moving average components of time series, and then turns to estimation of univariate time series models using the BoxJenkins approach. Intervention analysis and more general transfer function models build on this tradition, often referred to as the statistical analysis of time series. The course then focuses on the econometric (regression) analysis of time series, historically quite distinct from the statistical tradition. In recent years, regression analysis has borrowed much from the statistical tradition, and the connections between the two are important for understanding how social scientists should analyze time series data. Analysis of integrated time series, including unit root econometrics and error correction models, focuses on recent econometric advances in dealing with nonstationary data. Mill's Time Series Analysis for Economists, McCleary and Hay's Applied Time Series Analysis for Social Scientists, and Harvey's Econometric Analysis of Time Series include much of the material that will be covered in this course.
POLSCI 815. Categorical Analysis.
Methods
Section 222 — ICPSR COURSE. COURSE MEETS 7/218/15.
Instructor(s):
SIMON CHENG
Prerequisites & Distribution: Regression analysis / POLSCI 699. ICPSR Summer Program. Permission of instructor required. (3). May not be repeated for credit.
Credits: (3).
Course Homepage: No homepage submitted.
Which candidate did you vote for?" "Which diagnosis is correct given the patient's symptoms?" "Will that offender avoid crime in the future?" Responses to questions of this kind are recorded in unordered categories whose statistical analysis is the topic of this workshop. When observations from individual cases are available, they may be treated within a regression framework in which coefficients for the explanatory variables are found under the assumptions of linear probability, linear discriminant, probit, logit, multinomial logit, or conditional logit models. When individual cases have been grouped into a contingency table, cell proportions rather than individual responses constitute the dependent variable, and linear probability, loglinear, or logistic models may be employed. The statistical justification of models for both situations will be presented, and their application to survey data will be illustrated in class and through computer exercises. Participants should enter this workshop with an active working knowledge of the topics covered in Regression Analysis and Mathematics for Social Scientist II. Readings will be drawn from texts such as Agresti's Categorical Data Analysis and Long's Regression Models for Categorical and Limited Dependent Variables.
POLSCI 816. (LISREL) Models: General Structural Equations.
Methods
Section 221 — ICPSR COURSE. COURSE MEETS 7/218/15.
Instructor(s):
Douglas Edward Baer
Prerequisites & Distribution: Multivariate statistics / POLSCI 787. ICPSR Summer Program. Permission of instructor required. (3). May not be repeated for credit.
Credits: (3).
Course Homepage: No homepage submitted.
Individuals who enroll in this workshop should have taken the ICPSR courses Regression Analysis, Simultaneous Equation Models, Scaling and Dimensional Analysis, and Mathematics for Social Scientists II, or their equivalents. This course provides an introduction to estimation techniques for structural equation models that contain latent or unmeasured variables. These models, commonly referred to as "LISREL" models (named for the computer program most widely used to estimate them), are more general than the usual econometric/regression models. They allow for measurement error in the observed variables as well as multiple indicators for the latent variables. The "LISREL" models' relationships with measurement theory are discussed.
Topics treated include path analysis, confirmatory factor analysis, and consequences of measurement error. Much of the content of the course is covered in Bollen's Structural Equations with Latent Variables.
POLSCI 817. Advanced Analysis of Variance.
Methods
Section 222 — ICPSR COURSE. COURSE MEETS 7/218/15.
Instructor(s):
Michael L Berbaum
Prerequisites & Distribution: Aan introduction to analysis of variance and linear regression models / Regression analysis / POLSCI 699. ICPSR Summer Program. Permission of instructor required. (3). May not be repeated for credit.
Credits: (3).
Course Homepage: No homepage submitted.
Researchers in the behavioral sciences who undertake treatment and impact studies in nonlaboratory settings face difficult design and analysis choices. This course focuses on the statistical tools appropriate for designs with repeated measures, many outcomes, and multiple levels of observation (e.g., classrooms, schools, school districts, or sites in a clinical or evaluation study). Among the techniques to be covered are univariate and multivariate analysis of (co)variance for repeated measures, mixed models and multilevel analysis, and adjustment for selection and dropout. Both omnibus tests and contrasts to evaluate specific hypotheses (i.e., effects, trends, and interactions) will be presented for these situations. Other topics may include remedies for violations of assumptions, planning sample sizes, and gauging effect sizes. The use of computer packages to carry out analyses will be integral to the course. Participants should have had an introduction to analysis of variance and linear regression models. The technical level of the course will correspond to that of Kirk's Experimental Designs.
POLSCI 818. Mathematical Models: Game Theory.
Methods
Section 211 — ICPSR COURSE. Course meets 6/237/18.
Instructor(s):
Mark Fey
Prerequisites & Distribution: Regression analysis / POLSCI 699. ICPSR Summer Program. Permission of instructor required. (3). May not be repeated for credit.
Credits: (3).
Course Homepage: No homepage submitted.
This course introduces individuals to one of the most important areas of decision theory, interactive choices between actors. The fundamental concepts linked with the
criteria of individual rationality will be thoroughly explained and integrated into a broad overview of the theory of games. Individual topics will include discussions of both continuous and matrix games. Topics particularly relevant to matrix games will include twoperson, nperson, and zero and nonzero sum, as well as cooperative and noncooperative games. Applications to a variety of substantive fields will be discussed. The prerequisite for the workshop is an introductory course in applied statistics at the level of Neter et al., Applied Statistics.
POLSCI 819. Mathematical Models: Rational Choice in a Social Context.
Methods
Section 222 — ICPSR COURSE. COURSE MEETS 7/218/15.
Instructor(s):
James D Johnson
Prerequisites & Distribution: An understanding of game theory modeling and a course in applied statistics, Regression analysis / POLSCI 699. ICPSR Summer Program. Permission of instructor required. (3). May not be repeated for credit.
Credits: (3).
Course Homepage: No homepage submitted.
"Rational choice theory" actually consists of a set of theories, usually but not always mathematical, that investigate the ways that actions taken by rational individual decisionmakers can interact in often surprising ways to generate stable aggregate outcomes. This workshop is an introduction to rational choice theories
and their uses in social science. It focuses on the logic of rational choice analysis in both explanatory and, to a lesser extent, normative contexts. The aims of the workshop are to impart the basic techniques of rational choice modeling
and to explore the intuitive and theoretical issues that motivate and limit any use of those techniques. The workshop especially is concerned with matters of interpretation and empirical testing, and with the problem of determining just
what any particular class of rational choice theories tells us about the social and political world and how it purports to do so. Topics include models of voting, bargaining, collective action, social norms, institutions, and even culture. Readings are drawn from economics, political science, sociology, and anthropology. Class format throughout combines lecture and discussion, but the balance shifts from the former to the latter as the session progresses. Although the workshop does not presuppose familiarity with either game theory or the mathematics needed to solve
gametheoretic problems, some prior knowledge of those topics will be an advantage. Students interested in this workshop are strongly advised to take a game theory course prior to enrolling.
POLSCI 820. Maximum Likelihood Estimation for Generalized Linear Models.
Methods
Section 211 — ICPSR COURSE. COURSE MEETS 6/237/18.
Instructor(s):
Charles H Franklin
Prerequisites & Distribution: Regression analysis / POLSCI 699. ICPSR Summer Program. Permission of instructor required. (3). May not be repeated for credit.
Credits: (3).
Course Homepage: No homepage submitted.
This course introduces and develops a number of new and very useful statistical models that move beyond standard linear regression. Among the topics covered are logit and probit models for both binary and ordinal dependent variables, event count models, models for heteroskedastic regressions, and more. Maximum likelihood unifies these models by providing a single, coherent approach to estimation and a way of thinking about how data are generated. The background needed for the course is multiple regression in matrix form. Attendance at the Mathematics for Social Scientists II lectures should prove useful.
POLSCI 822. Dynamic/Longitudinal Analysis.
Methods
Section 222 — ICPSR COURSE. MEETS 6/23  7/18.
Instructor(s):
Prerequisites & Distribution: Regression analysis / POLSCI 699. ICPSR Summer Program. Permission of instructor required. (2). May not be repeated for credit.
Credits: (2).
Course Homepage: No homepage submitted.
This course introduces the basic rationale of current approaches to the analysis of longitudinal data focusing on the analysis of mutual influence of variables over time. It covers issues of stability of measurement, analysis of change in the multiple regression framework, and dynamic modeling using the latent growth approach. Its purpose is to provide both a basic statistical background and practical experience in analyzing data with widespread statistical software (SPSS/SAS and LISREL).
POLSCI 822. Dynamic/Longitudinal Analysis.
Methods
Section 231 — ICPSR COURSE. COURSE MEETS 6/23  7/18.
Instructor(s):
Michael L Berbaum
Prerequisites & Distribution: Regression analysis / POLSCI 699. ICPSR Summer Program. Permission of instructor required. (2). May not be repeated for credit.
Credits: (2).
Course Homepage: No homepage submitted.
Longitudinal analysis is the study of short series of observations obtained from many
respondents over time and is also referred to as panel analysis (of a crosssection of time series), or repeated measures, or growth curve analysis (polynomials in time), or multilevel analysis (where one level is a sequence of observations from respondent). Longitudinal analysis is used for panel surveys, experiments, and quasiexperiments in health and biomedicine, education and psychology, and the evaluation of prevention and treatment programs. This course treats the statistical basis and practical application of linear models for longitudinal normal data and generalized
linear models for longitudinal binary, count, and ordinal data. The approach involves inclusion of random effects in linear models to reflect withinperson crosstime correlation. Techniques for irregularly observed (unequally spaced) data will be covered. Other ICPSR courses focus on time series and structural equations approaches, including latent growth curve models, which are only briefly discussed in this course. The technical level will be at Track II, with interludes at Track III (matrix algebra, probability distributions). Examples and exercises will use both standard and specialpurpose software. Participants should have a good understanding of linear regression or analysis of variance.
POLSCI 822. Dynamic/Longitudinal Analysis.
Methods
Section 241 — ICPSR COURSE. COURSE MEETS 6/24  7/18.
Instructor(s):
Prerequisites & Distribution: Regression analysis / POLSCI 699. ICPSR Summer Program. Permission of instructor required. (2). May not be repeated for credit.
Credits: (2).
Course Homepage: No homepage submitted.
This course introduces the basic rationale of current approaches to the analysis of longitudinal data focusing on the analysis of mutual influence of variables over time. It covers issues of stability of measurement, analysis of change in the multiple regression framework, and dynamic modeling using the latent growth approach. Its purpose is to provide both a basic statistical background and practical experience in analyzing data with widespread statistical software (SPSS/SAS and LISREL).
POLSCI 824. NonLinear Systems II.
Methods
Section 202 — ICPSR COURSE. COURSE MEETS 7/218/15.
Prerequisites & Distribution: Regression analysis / POLSCI 699. ICPSR Summer Program. Permission of instructor required. (3). May not be repeated for credit.
Credits: (3).
Course Homepage: No homepage submitted.
The nonlinear dynamics exhibited by complex systems often pose difficult problems for modelers of those systems, especially when the complex systems are adaptive. The growing availability of computers has led to a recent proliferation of "bottomup, agentbased" models of complex adaptive systems. These models consist of a number of interacting agents, and each agent's behavior is governed by a small set of simple rules. However, the interaction of the agents can produce complex "emergent" structures and dynamic behaviors of individuals and groups. These lectures will give an introduction to bottomup approaches to computer modeling and compare them to more traditional mathematical (analytical) approaches and to topdown computer models (e.g., typical macroeconomic models). The lectures will also offer a survey of the field of Evolutionary Computation (EC), including a discussion of the role of EC in agentbased models. A number of social science applications will also be reviewed and analyzed.
POLSCI 826. Computing for Social Science.
Methods
Section 201 — ICPSR COURSE. MEETS 6/237/18.
Instructor(s):
Michael Hawthorne
Prerequisites & Distribution: ICPSR Summer Program. Permission of instructor required. (2). May not be repeated for credit.
Credits: (2).
Course Homepage: No homepage submitted.
This series of integrated lectures covering various aspects of computer usage in the social sciences, introduces three major statistical packages, SAS, SPSS, and STATA. Comparisons of the capabilities of these packages and other computing software will be developed.
POLSCI 826. Computing for Social Science.
Methods
Section 212 — ICPSR COURSE. MEETS 6/23  7/18.
Instructor(s):
Phillip Ardoin
Prerequisites & Distribution: ICPSR Summer Program. Permission of instructor required. (2). May not be repeated for credit.
Credits: (2).
Course Homepage: No homepage submitted.
This series of integrated lectures covering various aspects of computer usage
in the social sciences is designed so that a participant can attend a module in one topic without prior attendance at other lectures. However, students can obtain a broad overview of computer issues by attending the complete series. Initial lectures will introduce techniques of computing at ICPSR. In later lectures, three major statistical packages, SAS, SPSS, and Stata, will be discussed. Comparisons of the capabilities of these packages and other computing software will be developed. An abbreviated version of this course will also be offered during the second fourweek session of the Program, when introductions to SPSS, SAS, and Stata will be repeated.
POLSCI 827. Research Design for Social Science.
Methods
Section 201 — ICPSR COURSE. COURSE MEETS 6/237/18.
Instructor(s):
Prerequisites & Distribution: ICPSR Summer Program. (2). May be repeated for credit. May be elected more than once in the same term.
Credits: (2).
Course Homepage: No homepage submitted.
This course provides a basic introduction to the challenges of research design and research method in political science. One major aim is to foster selfconsciousness about the tradeoffs that necessarily attend every stage of research. Another is to help participants develop their own model research proposals. The course reviews some principles of theory building first. Then it considers important debates about research design as well as practical matters that affect methods of data collection, the conduct of fieldwork, and entry into the profession as a specialist.
POLSCI 827. Research Design for Social Science.
Methods
Section 211 — ICPSR COURSE. MEETS 6/23  7/18.
Instructor(s):
Prerequisites & Distribution: ICPSR Summer Program. (2). May be repeated for credit. May be elected more than once in the same term.
Credits: (2).
Course Homepage: No homepage submitted.
See Political Science 827.201.
POLSCI 827. Research Design for Social Science.
Methods
Section 213 — ICPSR COURSE. MEETS 6/23  7/18.
Instructor(s):
Prerequisites & Distribution: ICPSR Summer Program. (2). May be repeated for credit. May be elected more than once in the same term.
Credits: (2).
Course Homepage: No homepage submitted.
See Political Science 827.201.
POLSCI 827. Research Design for Social Science.
Methods
Section 214 — ICPSR COURSE. MEETS 6/23  7/18.
Instructor(s):
Prerequisites & Distribution: ICPSR Summer Program. (2). May be repeated for credit. May be elected more than once in the same term.
Credits: (2).
Course Homepage: No homepage submitted.
See Political Science 827.201.
POLSCI 827. Research Design for Social Science.
Methods
Section 222 — ICPSR COURSE. MEETS 7/218/15.
Instructor(s):
Prerequisites & Distribution: ICPSR Summer Program. (2). May be repeated for credit. May be elected more than once in the same term.
Credits: (2).
Course Homepage: No homepage submitted.
See Political Science 827.201.
POLSCI 827. Research Design for Social Science.
Methods
Section 223 — ICPSR COURSE. MEETS 7/21  8/15.
Instructor(s):
Prerequisites & Distribution: ICPSR Summer Program. (2). May be repeated for credit. May be elected more than once in the same term.
Credits: (2).
Course Homepage: No homepage submitted.
See Political Science 827.201.
POLSCI 829. Mathematics for Social Science.
Methods
Section 201 — ICPSR COURSE. COURSE MEETS 6/237/18.
Instructor(s):
Stephen G Bringardner
Prerequisites & Distribution: Introductory statistics/POLSCI 599. ICPSR Summer Program. Permission of instructor required. (2). May be repeated for credit. May be elected more than once in the same term.
Credits: (2).
Course Homepage: No homepage submitted.
This fourweek series of lectures is designed to review those basic mathematical skills necessary for a meaningful understanding of elementary statistics, data analysis, and social methodology. Course content includes a discussion of mathematical notation, basic set theory, various number systems, the algebra of
numbers, the notion of a function, several important classes of functions, and solutions to systems of linear equations. In addition, several approaches to the specification of probability will be examined, and some basic statistical concepts will be introduced. The general discourse will be at about the level of W.L. Bashaw's
Mathematics for Statistics. This lecture series is most suitable as a review for those who have been exposed to this material previously, but it is also intended to serve as a brief and limited introduction.
POLSCI 829. Mathematics for Social Science.
Methods
Section 202 — ICPSR COURSE. MEETS 6/23  7/18.
Instructor(s):
Prerequisites & Distribution: Introductory statistics/POLSCI 599. ICPSR Summer Program. Permission of instructor required. (2). May be repeated for credit. May be elected more than once in the same term.
Credits: (2).
Course Homepage: No homepage submitted.
Designed to review those basic mathematical skills necessary for a meaningful understanding of elementary statistics, data analysis, and social methodology. Course content includes a discussion of mathematical notation, basic set theory, various number systems, the algebra of numbers, the notion of a function, several important classes of functions, and solutions to systems of linear equations. In addition, several approaches to the specification of probability will be examined, and some basic statistical concepts will be introduced.
POLSCI 829. Mathematics for Social Science.
Methods
Section 212 — ICPSR COURSE. MEETS 7/21  8/15.
Instructor(s):
Pedro Sanchez
Prerequisites & Distribution: Introductory statistics/POLSCI 599. ICPSR Summer Program. Permission of instructor required. (2). May be repeated for credit. May be elected more than once in the same term.
Credits: (2).
Course Homepage: No homepage submitted.
This lecture series is intended to provide a background in matrix algebra for participants in ICPSR workshops. Approximately nine onehour lectures will be devoted to matrices and linear algebra. The lecture series is most suitable as a review for those who have been exposed to this material previously, but it is also intended to serve as a brief and limited introduction.
POLSCI 829. Mathematics for Social Science.
Methods
Section 213 — ICPSR COURSE. MEETS 7/21  8/15.
Instructor(s):
Prerequisites & Distribution: Introductory statistics/POLSCI 599. ICPSR Summer Program. Permission of instructor required. (2). May be repeated for credit. May be elected more than once in the same term.
Credits: (2).
Course Homepage: No homepage submitted.
See Political Science 829.202.
POLSCI 830. Advanced Topics in Social Research.
Methods
Section 201 — Topic? ICPSR COURSE. MEETS 6/237/18.
Instructor(s):
Henry A Heitowit
Prerequisites & Distribution: Permission of instructor. ICPSR Summer Program.(2). May be repeated for credit. May be elected more than once in the same term.
Credits: (2).
Course Homepage: No homepage submitted.
No Description Provided. Contact the Department.
POLSCI 830. Advanced Topics in Social Research.
Methods
Section 202 — Topic? ICPSR COURSE.
Instructor(s):
Henry A Heitowit
Prerequisites & Distribution: Permission of instructor. ICPSR Summer Program.(2). May be repeated for credit. May be elected more than once in the same term.
Credits: (2).
Course Homepage: No homepage submitted.
No Description Provided. Contact the Department.
POLSCI 830. Advanced Topics in Social Research.
Methods
Section 211 — Topic? ICPSR COURSE. MEETS 6/23  7/15.
Instructor(s):
Henry A Heitowit
Prerequisites & Distribution: Permission of instructor. ICPSR Summer Program.(2). May be repeated for credit. May be elected more than once in the same term.
Credits: (2).
Course Homepage: No homepage submitted.
No Description Provided. Contact the Department.
POLSCI 830. Advanced Topics in Social Research.
Methods
Section 222 — Topic? ICPSR COURSE. COURSE MEETS 7/218/15.
Instructor(s):
Henry A Heitowit
Prerequisites & Distribution: Permission of instructor. ICPSR Summer Program.(2). May be repeated for credit. May be elected more than once in the same term.
Credits: (2).
Course Homepage: No homepage submitted.
No Description Provided. Contact the Department.
POLSCI 832. Mathematical Statistics for Social Science.
Methods
Section 222 — ICPSR COURSE. COURSE MEETS 7/218/15.
Instructor(s):
Jefferson M Gill
Prerequisites & Distribution: Regression Analysis / POLSCI 699. ICPSR Summer Program.Permission of instructor required. (3). May not be repeated for credit.
Credits: (3).
Course Homepage: No homepage submitted.
No Description Provided. Contact the Department.
POLSCI 838. Mathematical Models: Advanced Game Theory.
Methods
Section 222 — ICPSR COURSE. COURSE MEETS 7/218/15.
Instructor(s):
Prerequisites & Distribution: Regression Analysis / POLSCI 699 and Game Theory / POLSCI 818. ICPSR Summer Program.(3). May not be repeated for credit.
Credits: (3).
Course Homepage: No homepage submitted.
This course seeks to develop skills in the use and evaluation of game
theory models. Much of the course will focus on game theory applications in political science, though social scientists from other disciplines will find the material useful as well.
Students should have a comfortable facility with algebra and with the basic concepts in linear algebra and statistics. Knowledge of calculus is not necessary.
Those students taking the course for credit will be evaluated through homeworks and one takehome test. Homeworks will count for 60% the final grade, and the takehome test will count for 40% of the final grade. Auditors are welcome, and those who complete homeworks and keep up with the lectures and reading will be entitled to seek our help with problems and with the material.
There is one required text:
James Morrow. 1995. Game Theory for Political
Scientists. Princeton: Princeton University Press.
There are other game theory texts that may be helpful, some of which are more advanced than Morrow (Fudenberg and Tirole, Myerson, Osborne and Rubinstein), and some of which are less advanced than Morrow (Ordeshook's Primer). I encourage students who want to supplement their reading with other material to consult with us about the appropriate texts.
Other required readings will be made available to you for photocopying.
Basic Assumptions of Rational Choice; Decision Theory, Optimization; Representing Games, Strategic Form Games, Dominance; Nash Equilibrium, Mixed Strategies; Extensive Form Games, Backwards Induction; Subgame Perfection, Forward Induction; Bayesian Games, Bayesian Equilibrium; Perfection, Sequential Equilibrium, Refinements; Signaling Games; Repeated Games; Applications.
POLSCI 841. Advanced Topics in Maximum Likelihood Estimation.
Methods
Section 222 — ICPSR COURSE. COURSE MEETS 7/218/15.
Instructor(s):
Charles H Franklin
Prerequisites & Distribution: POLSCI 821. ICPSR Summer Program. Permission of instructor required. (3). May not be repeated for credit.
Credits: (3).
Course Homepage: No homepage submitted.
This course introduces and develops a number of new and very useful statistical models that move beyond standard linear regression. Among the topics covered are logit and probit models for both binary and ordinal dependent variables, event count models, models for heteroskedastic regressions, and more. Maximum likelihood unifies these models by providing a single, coherent approach to estimation and a way of thinking about how data are generated. The background needed for the course is
multiple regression in matrix form. Attendance at the Mathematics for Social Scientists II lectures should prove useful.
POLSCI 892. Directed Research.
Instructor(s):
Prerequisites & Distribution: Graduate standing. Permission of instructor required. (16). (INDEPENDENT). May not be repeated for credit.
Credits: (16).
Course Homepage: No homepage submitted.
Directed research on a topic of the student's choice.
POLSCI 990. Dissertation/Precandidate.
Instructor(s):
Prerequisites & Distribution: Election for dissertation work by doctoral student not yet admitted as a Candidate. Graduate standing. (14). (INDEPENDENT). May be repeated for credit.
Credits: (18; 14 in the halfterm).
Course Homepage: No homepage submitted.
Election for dissertation work by doctoral student not yet admitted as a Candidate.
POLSCI 995. Dissertation/Candidate.
Instructor(s):
Prerequisites & Distribution: Graduate School authorization for admission as a doctoral Candidate. Graduate standing. (4). (INDEPENDENT). May be repeated for credit.
Credits: (8; 4 in the halfterm).
Course Homepage: No homepage submitted.
Graduate School authorization for admission as a doctoral Candidate. N.B. The defense of the dissertation (the final oral examination) must be held under a full term Candidacy enrollment period.
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