Course Descriptions

Research Methods Courses

Advanced Methodology Courses from the Department of Criminal Justice and Criminology

CRIM 690 Advanced Regression Analysis (3 Credits)

This course focuses on statistical methods that are useful in the investigation of hypotheses in the social sciences and the analysis of public policies and programs. The bulk of the course is a detailed examination of the bivariate and multiple regression models estimated using Ordinary Least Squares (OLD), with an emphasis on constructing regression models to test social and economic hypotheses. Several special topics in regression analysis are addressed as well, including violations of OLD assumptions and the use of dummy variables, and interaction effects. Throughout the course, examples are dawn from the literature so students can see the models and methods in action. Prerequisite: CRIM 590.

CRIM 691 Advanced Research Design (3 Credits)

This course focuses on measurement and data development strategies and techniques to facilitate effective statistical analysis. Topics include the logic of causal inquiry and inference, the elaboration paradigm and model specification, handling threats to internal validity, hierarchies of design structure (experimental, quasi-experimental and non-experimental), linking design structure to affect estimation strategies, and analyzing design elements in published literature. Students will select a research topic in consultation with the instructor and prepare a written comparative design analysis. Prerequisite: CRIM 591.

CRIM 692 Qualitative Research Methods (3 Credits)

This course designed to increase students’ knowledge and understanding of the design and process of qualitative research in criminology. The material covered in this course includes the nature and uses of qualitative research; the design of qualitative research; grounded theory and the use of qualitative research to advance new theories and critically evaluate tenants or assumptions of widely held explanations of criminal behavior and justice system functioning; and the ethics of qualitative research. Qualitative research methodologies including ethnography, case studies, participant observation, interviewing, content analysis, and life history narrative / life course analysis will be studied. Students will develop and initiate their own qualitative research and learn first-hand about the conduct of such research, the sequencing of data collection, data analysis, and more data collection. Students will learn the uses of computer assisted software programs designed to assist qualitative data analysis. Prerequisite: CRIM 591.

CRIM 693 Survey Methods (3 Credits)

This course exposes students to the use of survey methods in social science research. Emphasis is placed on interview and questionnaire techniques and the construction and sequencing of survey questions as well as the use of Likert and Thurstone sales. Attention is also devoted to sampling theory, sampling designs, and sampling and non-sampling errors. Prerequisite: CRIM 591.

CRIM 695 Program Evaluation Methods (3 Credits)

An examination of the methods and techniques of evaluation research. Evaluation research includes the issues that characterize the generic research enterprise. In addition to the usual research concerns and problems, evaluation research must also address problems that are unique to determining whether a program, treatment, law, or policy, has had the desired effect when implemented in practice. This task is especially problematic with social policy contexts. The agenda for the course has two main components. First, the course will concern the structural features of designing and conducting a program evaluation. The second component will be an analysis of actual program evaluations in the literature.

CRIM 790 Categorical and Limited Dependent Variables (3 Credits)

The estimation of empirical models is essential to public policy analysis and social science research. Ordinary Least Squares (OLD) regression analysis is the most frequently used empirical model, and is appropriate for analyzing continuous dependent variables that meet certain distributional assumptions. This course examines several types of advanced regression models for dependent variables that violate one or more of the assumptions of the OLD regression model. For example, some dependent variables may be categorical, such as pregnant or not, employed or not, etc. Other dependent variables may be truncated or censored, such as contributions to an individual retirement account that are limited by law to certain dollar amounts. Still others may be counts of things, like the number of children born to a given woman or the number of traffic accidents on a given day. The principal models examined in the course are binary legit and probity, multinomial legit, ordinal legit and probity, tobit, and the family of Poisson regression models. The Heckman correction for selection and Event History Analysis are also addressed. All these models are estimated using maximum likelihood estimation (ML). The course focuses on the application and interpretation of the models, rather than statistical theory. Prerequisite: CRIM 690.

CRIM 791 Structural Equation Modeling (3 Credits)

This course is an introduction to structural equation modeling (SEM). SEM represents a general approach to the statistical examination of the fit of a theoretical model to empirical data. Topics include observed variable (path) analysis, latent variable models (e.g., confirmatory factor analysis), and latent variable SEM analyses. Prerequisite: CRIM 690.

CRIM 792 Survival Analysis and Longitudinal Data (3 Credits)

Criminological research often involves the study of change over time in both individuals and groups. Analyzing such over time poses a number of methodological and statistical challenges, however, and these must be addressed to derive valid inferences from data analysis. This course will examine several techniques that are appropriate for such analyses. These include the family of univariate, bivariate and multivariate techniques collectively known as “survival” or “event history analysis” that are appropriate for studying processes such as recidivism and length of time individuals spend in various programs. The course will also describe zero-inflated Poisson trajectory and latent growth curve models, as well as multilevel models for change. Emphasis will be on application as opposed to theory. Prerequisite: CRIM 690.

CRIM 793 Data Reduction and Factor Analysis (3 Credits)

Criminologists are often confronted with datasets containing numerous variables resulting from surveys and archival data extraction. It is advantageous to reduce the number of variables while still maintaining the integrity of the measurement of crucial concepts. Factor analysis is a valuable statistical technique for reducing the number of variables and detecting possible underlying structure (s) in the relationships among variables. This course will examine major factor analytic techniques such as Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) designed to find underlying unobservable (latent) variables that are reflected in the observed variables or manifest variables. In addition the course will examine the various factor rotation procedures commonly used to ensure that the derived factors or dimensions are orthogonal and do not either introduce multi-collinearity problems or exacerbate collinearity issues already present in the data. Given the number of factor analytic techniques and options, it is not surprising that different analysts could reach very different results analyzing the same data set. However, all analysts are looking for simple structure. Simple structure is a pattern of results such that each variable loads highly onto one and only one theoretically relevant factor. Prerequisite: CRIM 690.

Advanced Methodology Courses from the Graduate School of Education

07.642 Program Evaluation (3 Credits)

Evaluation tasks will be identified and the policy issues attendant to evaluation will be examined. Students will conduct an evaluation.

07.660 Ethnographic Inquiry (3 Credits)

This course provides the theoretical underpinnings of the nature, principles and processes of ethnographic research which focuses on the understanding of human cultures. Students will study how an ethnographic research project is developed and will conduct an aspect of a study during the semester. There will be particular emphasis on collecting and analyzing data in ethnographic research

07.704 Qualitative Research Methods (3 Credits)

This course concentrates on the use of qualitative methods for educational research. Strategies for conducting qualitative studies are described and techniques for analyzing and reporting findings are emphasized.

07.705 Survey Research (3 Credits)

Focusing on survey research methods, this course will familiarize students with the strategies, techniques, tactics, and issues in developing and administering questionnaires and interviews.

Advanced Methodology Courses from other Departments

19.680 Intro to SAS (3 Credits)

This course is designed for researchers who will be doing data analysis using SAS. No prior programming experience is necessary, though familiarity with and general experience in use of a PC (DOS and Windows) is required. The course covers topics including: basics of SAS, reading raw data and existing SAS data sets, modifying data, combining data sets, basic statistical procedures, sorting, summarizing, and printing data

19.674 Regression Methods (3 Credits)

This course is an intermediate-level statistics course focusing on regression models for both discrete and continuous outcomes. Our objective will be an understanding of statistical methods suitable for the practice of health sciences research (including epidemiology and clinical medicine). Main objectives will be the following: a solid practical understanding of multiple linear regression, a working understanding of logistic regression, a survey of additional topics in modern regression. The first goal includes F-tests, ANOVA, the construction and interpretation of indicator variables, methods of assessing model assumptions, problems of model selection for casual inference and comparison of alternative models. The second goal comprises the most common regression technique applied to binary event data (e.g. diseased vs. nondiseased, or treatment success vs. treatment failure). The final goal addresses the question of what to do when standard statistical assumptions fail, and entails an introduction to semiparametric models and robust methods.

19.689 Advanced Regression Modeling (3 Credits)

This course will cover introductions to several different regression methods used in environmental and occupational epidemiology to model exposure-response relationships. Topics include Poisson regression, Cox proportional hazards models, and nonparametric regression based on smoothed functions of exposure. Students should have working familiarity with STATA or SAS. Prerequisite: 19.674 or equivalent. 

47.611 Program Evaluation (3 Credits)

A skill-oriented approach that considers both formative and summative evaluation techniques. Emphasizes mastery of the technical aspects of the evaluation process, and includes consideration of the importance of program evaluation in community psychology, health, education, etc.

49.731 Statistics I (3 Credits)

This course covers descriptive statistics, random variables and expected value, discrete and continuous probability distributions, joint distribution functions, sampling distributions, point and interval estimation, and hypothesis testing, and non-parametric statistics.  This course will also provide a brief introduction to linear regression and analysis of variance (ANOVA).

49.733 Econometrics I (3 Credits)

After a brief review of the required mathematics for the course, the primary focus will be on the multivariate linear model. Topics include: consistency and asymptotic normality of the parameter estimates, sampling distributions, hypothesis testing, parameter restrictions, and specification tests and and corrections for violation of model assumptions. This course will also include working with various statistical packages. 

49.734 Econometrics II (3 Credits)

This course is a continuation of Econometrics II; the focus will be on the more advanced techniques used in estimation and inference problems in social science research. Possible topics include nonlinear models, the generalized method of moments, limited dependent variable and sample selection problems, multi-equation models, time-series models, and panel data analysis.  Statistical packages will be utilized for a hands-on approach to the techniques.

49.735 Cost-Benefit Analysis

The goal of this course will be to provide students with both the theoretical constructs and applications of tools for economic evaluation. Two basic approaches to economic evaluation will be discussed:  cost-benefit  analysis (CBA) and cost-effectiveness analysis (CEA).  This would entail identifying, measuring, valuing and comparing costs and consequences or outcomes of alternative courses of action. Each method would allow comparison of policies, programs or different intervention strategies based on the resources they consume and the outputs they generate. In CBA both costs and benefits are represented in monetary terms (e.g. dollars). CEA is most suitable when comparing interventions with similar intended outcomes. The results of such evaluations are summarized in a cost-effectiveness ratio where the denominator is represented in natural units and the numerator reflects costs in monetary terms.  Both CBA and CEA – will be constructed from a more limited perspective e.g. employers perspective (private resource cost, business case) or strictly from the workers’ point of view or from a broader societal perspective. The analyses will be critiqued both in terms of its merits and limitations. Difficult issues relating to discount rate, inflation adjustment, risk analysis, avoided costs, valuing human life and non-market goods (e.g. Hedonic Pricing, Contingent Valuation Methods), public goods (e.g. Travel Cost Method), uncertainty analysis will be discussed. The latter part of the course will emphasize student projects of an interdisciplinary nature where students will be required to perform case studies (evaluating programs, interventions or current policy issues that policy makers confront) in the area of energy and the environment, health care and prevention, community projects etc. This project will enable students to combine their insights from other disciplines with economic assessments of policy formulation and improve the quality of decision making.