Biostatistics Curriculum
Courses in Biostatistics and Statistics
The Center for Clinical Epidemiology and Biostatistics, the Department of Biostatistics and Epidemiology, and the Graduate Group in Epidemiology and Biostatistics offer a wide range of courses; a brief description of current offerings is provided below. Not all courses are offered every year. Additional information may be found in the University of Pennsylvania Graduate Studies Catalog. The program may revise these courses over time; the descriptions given here are for guidance only.
BSTA 509 Introductory Epidemiology
This course is a series of lectures designed to teach basic principles of epidemiologic research. It provides an overview of the types of research questions that can be addressed by epidemiologic methods. Topics covered include definitions of epidemiology; measures of disease frequency; measures of effect and association; epidemiologic study designs, both experimental and non-experimental; data collection methods; and an overview of analysis of epidemiologic studies. (The lectures for this course are identical to those in EP 501.) (0.5 course unit, fall.) Prerequisites: permission of instructor.
BSTA 510 Introduction to Human Health and Diseases
The purpose of this course is to introduce students who have a limited background in medicine and biology to the basic vocabulary and principles of human health and disease, in preparation for collaborative research in biostatistics. The course begins with an overview of basic human biochemistry and cell biology. Later topics focus on the major organ systems (including circulation, digestion and excretion, neurophysiology, and reproduction) and major disease areas (such as cancer and heart disease). For students with sufficient backgrounds, this course may be waived with the permission of the Director of Graduate Programs. (0.5 course unit, fall.) Prerequisites: permission of instructor.
BSTA 512 Database Management for Clinical Epidemiology I (EP 532)
This course offers an introduction to the techniques of database management as applied to clinical research. Students learn how to design and implement computerized databases; perform basic query and reporting operations; migrate data between various file formats; prepare databases for statistical analysis; and perform data quality assurance procedures. The course focuses on the practical issues of database management and is intended to support the student's planned research enterprise. (0.5 course unit, spring/fall.) Prerequisites: Limited to MSCE/PhD in Epidemiology and MS/PhD in Biostatistics students.
BSTA 513 Measurement of Health in Epidemiology (EP 542)
This course is a series of lectures and discussion sessions designed to introduce the student to the concepts of health measurement as applied to epidemiologic studies. Topics include the basics of health measurement theory; critical evaluation of the current status of health measurement in a chosen field; and techniques for developing and using measurement scales, including item analysis, validity and reliability testing, and qualitative methods. (1.0 course unit, fall.) Prerequisites: permission of instructor.
BSTA 514 Clinical Economics and Clinical Decision Making (EP 550)
This course focuses on the application of decision analysis and economic analysis to clinical and policy research. The course begins by covering the selection, use, and analysis of diagnostic tests using two-by-two tables, likelihood ratios, and ROC curves. It continues with the introduction of more general tools for decision analysis, including decision trees and other mathematical models. Special emphasis is placed on the assessment and use of utilities in these models. A major focus is the application of economic principles to the evaluation of health outcomes. During seminars, students will carry out practical exercises that include problem solving, critically analyzing published articles, and learning to use computer software that facilitates decision and economic analyses. (1.0 course unit, spring.) Prerequisites: None.
BSTA 550 Applied Regression and Analysis of Variance (STAT 500)
This is an applied graduate level course in multiple regression and analysis of variance for students who have completed an undergraduate course in basic statistical methods. Emphasis is on practical methods of data analysis and interpretation. Model building, tests of the general linear hypothesis, residual analysis, leverage and influence, one-way ANOVA, two-way ANOVA, and factorial ANOVA are covered. This course is primarily for doctoral students in the managerial, behavioral, social and health sciences. (1.0 course unit, fall.) Prerequisites: STAT 112 or equivalent.
BSTA 620 Probability
See Graduate Studies Catalog for Statistics at the Wharton School (1.0 course unit, fall). Prerequisites: Two semesters of calculus (through multivariable calculus) and linear algebra.
BSTA 621 Statistical Inference I
See Graduate Studies Catalog for Statistics at the Wharton School (1.0 course unit, spring.) Prerequisites: BSTA 620.
BSTA 622 Statistical Inference II
This course is a survey of statistical inference including estimation, confidence intervals, hypothesis tests and nonparametric methods. (1.0 course unit, fall.) Prerequisites: BSTA 621.
BSTA 630 Statistical Methods and Data Analysis I
This first course in statistical methods for data analysis is aimed at first-year Biostatistics students. It focuses on the analysis of continuous data. Topics include descriptive statistics (measures of central tendency and dispersion, shapes of distributions, graphical representations of distributions, transformations, and testing for goodness of fit); populations and sampling (hypotheses of differences and equivalence, statistical errors); one- and two-sample t tests; analysis of variance; correlation; nonparametric tests on means and correlations; estimation (confidence intervals and robust methods); and regression modeling (simple linear regression, multiple regression, model fitting and testing, partial correlation, residuals, multicollinearity). Examples of medical and biologic data will be used throughout the course, and use of computer software demonstrated. (1.0 course unit, fall.) Prerequisites: Multivariable calculus and linear algebra, BSTA 620 (may be taken concurrently); permission of instructor.
BSTA 631 Statistical Methods and Data Analysis II
This is the second half of the methods sequence, where the focus shifts to methods for categorical and survival data. Topics in categorical include defining rates; incidence and prevalence; the chi-squared test; Fisher's exact test and its extension; relative risk and odds-ratio; sensitivity; specificity; predictive values; logistic regression with goodness of fit tests; ROC curves; the Mantel-Haenszel test; McNemar's test; the Poisson model; and the Kappa statistic. Survival analysis will include defining the survival curve, censoring, and the hazard function; the Kaplan-Meier estimate, Greenwood's formula and confidence bands; the log rank test; and Cox's proportional hazards regression model. Examples of medical and biologic data will be used throughout the course, and use of computer software demonstrated. (1.0 course unit, spring.) Prerequisites: linear algebra, calculus, BSTA 630, BSTA 620, BSTA 621 (may be taken concurrently); permission of instructor.
BSTA 651 Introduction to Linear Models and Generalized Linear Models
This course extends the content on linear models in BSTA 630 and BSTA 631 to more advanced concepts and applications of linear models. Topics include the matrix approach to linear models including regression and analysis of variance; tests of the general linear hypothesis; estimability; polynomial, piecewise, ridge, and weighted regression; regression and collinearity diagnostics; multiple comparisons; fitting strategies; simple experimental designs (block designs, split plot); random effects models; and prediction. In addition, generalized linear models will be introduced with emphasis on the binomial, logit and Poisson log-linear models. Applications of methods to example datasets will be emphasized. (1.0 course unit, spring.) Prerequisites: linear algebra, calculus, BSTA 630, BSTA 620, BSTA 621 (may be taken concurrently); permission of instructor.
BSTA 652 Categorical Data Analysis
This course elaborates on the treatment of categorical data analysis in Statistical Methods I and II. Topics include probability models for contingency tables, estimation of odds ratios, exact and asymptotic tests of independence, generalized linear models (logit, complementary log-log, and loglinear), ordinal regression models, Mantel-Haenszel tests, and estimation. (1.0 course unit, fall.) Prerequisites: BSTA 621, BSTA 631, BSTA 651; permission of instructor.
BSTA 653 Survival Analysis
This course extends the methods for the analysis of time to event data or survival analysis covered in BSTA 631. Concepts include survival distributions, hazard distributions, censoring mechanisms and truncation mechanisms. Parametric and nonparametric methods for estimation and inference will be covered, including the Kaplan-Meier estimator, exponential and Weibull models, logrank tests, the generalized Wilcoxon test, the Cox proportional hazards regression and extensions to time-dependent covariates. (1.0 course unit, fall.) Prerequisites: BSTA 621, BSTA 631, BSTA 651; permission of instructor.
BSTA 656 Longitudinal Data Analysis
This course covers both the applied aspects and recent methods developments in longitudinal data analysis. In the first part, we review the properties of the multivariate normal distribution and cover basic methods in longitudinal data analysis, such as exploratory data analysis, two-stage analysis and mixed-effects models. Focus is on the linear mixed-effects models, where we cover restricted maximum likelihood estimation, estimation and inference for fixed and random effects and models for serial correlations. The second part covers advanced topics, including nonlinear mixed-effects models, GEE, generalized linear mixed-effects models, nonparametric longitudinal models, functional mixed-effects models, and joint modeling of longitudinal data and the dropout mechanism. (1.0 course unit, spring.) Prerequisites: BSTA 621, BSTA 631, BSTA 651, BSTA 652, BSTA 653; permission of instructor.
BSTA 657 Design of Biomedical Studies I
This course is an introduction to aspects of the statistical planning and design of biomedical investigations. It introduces the classical theory of experimental design using case studies in biomedical research as illustrations. Topics include bias and precision, sample-size estimation, randomization and random sampling, (0.5 course unit, spring.) Prerequisites: BSTA 621, BSTA 631, BSTA 651, BSTA 652, BSTA 653; permission of instructor.
BSTA 658 Design of Biomedical Studies II
This course builds on the basic theory of experimental design in biomedical investigations by focusing on statistical topics in observational studies and randomized trials. Case studies will be used to illustrate the methods. (0.5 course unit, spring.) Prerequisites: BSTA 621, BSTA 631, BSTA 651, BSTA 652, BSTA 653; BSTA 657 highly recommended; permission of instructor.
BSTA 670 Statistical Computing
This is a course in computing algorithms useful in statistical research and advanced statistical applications. Topics include computer arithmetic; matrix algebra; numerical optimization methods with application to maximum-likelihood estimation and GEEs; spline smoothing and penalized likelihood; numerical integration, random number generation and simulation; Gibbs sampling; bootstrap methods; missing data problems and EM; imputation; data augmentation algorithms; and Fourier transforms. (1.0 course unit, fall.) Prerequisites: BSTA 621, BSTA 651; permission of instructor.
BSTA 752 Categorical Data Analysis II
In this course, students present and discuss methodological papers chosen by the instructor from the literature on advanced categorical methods in a variety of areas. These areas include accounting for correlated data with population-averaged and random and fixed effects effects models fitted with estimation procedures including generalized estimating equations, maximum likelihood, penalize quasilikelihood, and conditional likelihood methods. Additional topics including accommodating non-ignorable missing data, confounding by cluster, treatment non-adherence in randomized trials, mediation analysis, and latent class and latent variable models. Software for implementing these methods will also be considered in the context of some examples from medical research. The student presentations will be reviewed by peer students in the course, who will provide feedback. Grades will be based on the instructor evaluations of these presentations and ensuing discussion. In addition, a data analysis project will be handed in as part of the final grade.
(1.0 course unit/spring.) Prerequisites: BSTA 652; permission of instructor.
BSTA 753 Survival Analysis II
This course discusses the theoretical basis of concepts and methodologies associated with survival data and censoring, nonparametric tests, and competing risk models. Much of the theory is developed using counting processes and martingale methods. Material is drawn from recent literature. (1.0 course unit/semester TBA.) Prerequisites: BSTA 653, BSTA 622 (may be taken concurrently).
BSTA 770 Nonparametric Inference (STAT 915)
This course introduces various nonparametric statistical methods that are used in practice. Both classical rank-based inference techniques and modern smoothing-based curve procedures are treated. Topics include nonparametric hypothesis testing, efficiency, asymptotic power theory, partial likelihood, nonparametric curve estimation (including density estimation), nonparametric regression, and survival analysis. (1.0 course unit/semester TBA.) Prerequisites: TBA.
BSTA 771 Applied Bayesian Analysis
This course compares and contrasts Bayesian, empirical Bayes, and frequentist approaches to statistical inference. Core topics include Bayes's theorem, the likelihood principle, selection of prior distributions (both informative and non-informative), and simulation techniques for obtaining estimates of posterior distributions. Key statistical techniques including linear models, generalized linear models, and survival models are presented from a Bayesian perspective, along with methods for model checking and model choice such as posterior predictive distributions and Bayes factors. The course emphasizes the development and estimation of hierarchical models as a means of modeling complicated real-world problems. Bayesian methods in the design and analysis of clinical trials are also considered, with emphasis on better incorporating uncertainty and the effects of missing data and non-compliance into inference. (1.0 course unit/spring.) Prerequisites: permission of instructor.
BSTA 774 Statistical Methods for Evaluating Diagnostic Tests
Topics include estimation of ROC curves; comparison of multiple diagnostic tests; development of diagnostic tests using predictive models; effects of measurement errors; random-effects models for multi-reader studies; verification bias in disease classification; methods for time-dependent disease classifications; study design; related software; meta-analyses for diagnostic test data; and current topics in the statistical literature. (1.0 course unit, fall.) Prerequisites: BSTA 652, BSTA 621 or equivalent; permission of instructor.
BSTA 775 Sample Survey Methods (STAT 920)
This course covers the design and analysis of sample surveys. The focus is on the latter - specifically, classical analyses of data from studies involving random sampling, stratified sampling, or cluster sampling; large-sample results; and other topics as time and interests dictate (1.0 course unit, spring.) Prerequisites: STAT 511 or equivalent with permission of instructor.
BSTA 777 Forecasting and Time Series (STAT 910)
This course covers Fourier analysis of data, stationary time series, properties of autoregressive/moving average models and estimation of their parameters, spectral analysis, and forecasting. Applications to problems in economics, engineering, physical science, and life science are discussed. (1.0 course unit.) Prerequisite: STAT 511 or STAT 541 or equivalent.
BSTA 779: Semiparametric Inferences and Biostatistics
This course will expose students to semiparametric inference theory through its applications to cutting-edge research topics in biostatistics, including two-phase design problems and modeling problems in genetic epidemiology. Thus, this course will benefit those who wish to advance their theoretical statistical training, those who wish to explore biostatistics research in the area of two-phase design problems and in genetic epidemiology, and those who wish to deepen their understanding of commonly used semiparametric biostatistical methods such as partial likelihood inference for Cox regression and the prospective analysis of retrospective case-control studies. (1.0 course unit, spring.) Prerequisites: The course is designed for students in biostatistics, statistics, or other strongly quantitative disciplines.BSTA 621/622 or equivalent; ability to program in R/S-Plus, SAS, Stata or Matlab; permission of the instructor.
BSTA 781 Asymptotic Theory with Biomedical and Psychosocial Applications
This course is an introduction to the asymptotic theory of statistics, with an array of applications to motivate as well as demonstrate its utility in addressing problems in biomedicine and psychosocial research. Notions of convergence of random sequences and common asymptotic techniques are introduced without measure theory. In addition to classical likelihood-based asymptotic theory, this course also focuses on distribution-free inference from estimating equations and U-statistics. Examples from AIDS, genetic, and psychosocial research are presented to motivate the methods development and to demonstrate the utility of the asymptotic theory. (1.0 course unit, fall.) Prerequisites: BSTA 621, BSTA 622, BSTA 630, BSTA 631, BSTA 651, BSTA 652 or equivalent; permission of instructor.
BSTA 782 Statistical Methods for Incomplete Data
This course reviews the theory and methodology of incomplete data, covering ignorability and the coarse-data model, including MAR, MCAR and their generalizations; computational methods such as the EM algorithm and its extensions; methods for handling missing data in commonly used models such as the generalized linear model and the normal mixed model; methods based on imputation; diagnostics for sensitivity to nonignorability; and nonignorable modeling and current topics. (1 course unit, spring.) Prerequisites: BSTA 621 required; BSTA 670 recommended; permission of instructor.
BSTA 783 Multivariate and Functional Data Analysis
This course covers both the classical theory and recent methods for multivariate exploratory analysis, as well as techniques for handling functional data. The first part reviews classical multivariate exploratory methods such as principal component analysis, factor analysis, cluster analysis and discriminant analysis, as well more recent methods, such as structural equations models, neural networks and classification trees. The second part covers the more advanced topic of functional data analysis, including graphical representations, principal component analysis and linear models for functional data. (1.0 course unit, fall.) Prerequisites: BSTA 621, BSTA 651; BSTA 656; permission of instructor.
BSTA 784 Analysis of Biokinetic Data
The time-course of a drug monitored via circulation samples gives us a comprehensive account of the number and sizes of body pools within which the drug distributes before its eventual elimination. Furthermore, the pattern of change of the time-course with increasing drug doses will expose the nature of the mechanisms facilitating that transport and metabolism. How these features are elucidated falls under the general topic of Compartmental Analysis, and the tools and technique of kinetics as well as those of drug dynamics form a part of this topic investigating 'the analysis of biokinetic data'. Additionally we will be exploring how metabolic challenges, such as the glucose challenge, the TRH challenge, and the epinephrine challenge expose aspects of the functionality of their targeted tissues, and, most specifically, we will show how indices relating to insulin resistance are derived. (0.5 course unit, fall) not offered every year. Prerequisites: Introductory statistics including regression and hypothesis testing; EPID 520, BSTA 630 or equivalent; permission of instructor
BSTA 785 Statistical Methods for Genomic Data Analysis
This course covers statistical, probabilistic and computational methods
for analyzing high-throughput genomic data. In particular, the following
topics will be covered: (1) analysis of gene expression data (model-based
gene expression index for Affymetrix arrays, normalization, transformation,
identification of differentially expressed genes, false discovery rate, analysis of
time course gene expression data, gene ontology) (2) analysis of
protein-protein interaction data (Markov models for modeling protein-protein
interactions, methods for analyzing graphs/pathways). (3) methods for
identification of transcriptional factor binding sites and transcriptional modules
(MEME, Gibbs, comparative genomics etc.) (4) methods for studying
genetic networks and for integrating different data sources. (5) methods for
analyzing other high-throughput genomic data (array CGH, SELDI-TOF
proteomics, tiling arrays for genome-wide transcription studies, SiRNA
and miRNA). (1.0 course unit, spring). Prerequisites: BSTA 620, BSTA
621, these courses can be taken concurrently with this course; permission of
the instructor.
BSTA 786 Advanced Topics in Clinical Trials
This course will cover in some depth selected topics of interest in clinical trials that are discussed only minimally in the introductory clinical trials courses. Topics may include methods of treatment allocation and blinding, sequential and/or adaptive trial designs, methods of handling missing data, design of active control/noninferiority trials, constructed endpoints, and other topics based on interest of registrants. (0.5 course unit; spring) Prerequisites: BSTA 658 or BSTA 659, permission of instructor.
BSTA 787 Methods for Statistical Genetics in Complex Human Disease
This is an introductory course for graduate students in Biostatistics, Statistics, Epidemiology, Bioinformatics and other BGS disciplines which will cover statistical methods for the analysis of family and population based genetic data. Topics covered will include allele frequency estimation, classical segregation and linkage analysis, multipoint linkage tests, general pedigree analysis, family-based association analysis and population based haplotype analysis. Students will be exposed to the latest statistical methodology and computer tools on gene mapping in complex human disease. They will also read and evaluate current statistical human genetics literature. (1.0 course unit, spring) Prerequisites: Introductory graduate-level courses in statistics (such as BSTA 630-631or EPID 520-521) are required; permission of the instructor.
BSTA 790 Causal Inference in Biomedical Research
This course considers approaches to defining and estimating causal effects in various settings. The potential-outcomes approach provides the framework for the concepts of causality developed here, although we will briefly consider alternatives. Topics considered include: the definition of effects of scalar or point treatments; nonparametric bounds on effects; identifying assumptions and estimation in simple randomized trials and observational studies; alternative methods of inference and controlling confounding; propensity scores; sensitivity analysis for unmeasured confounding; graphical models; instrumental variables estimation; joint effects of multiple treatments; direct and indirect effects; intermediate variables and effect modification; randomized trials with simple noncompliance; principal stratification; effects of time-varying treatments; time-varying confounding in observational studies and randomized trials; nonparametric inference for joint effects of treatments; marginal structural models; and structural nested models. (1.0 course unit, fall.) Prerequisites: TBA.
BSTA 798 Advanced Topics in Biostatistics I
This seminar will be taken by doctoral candidates after the completion of most of their coursework. Topics in biostatistical methodology will vary from year to year. Methodology related to clinical trials, missing data, functional data analysis, generalized linear models, statistical genetics, advances in Bayesian methodology are examples of areas that may be covered. (0.5 course unit, fall, spring.) Prerequisites: TBA; permission of instructor.
This seminar will be taken by doctoral candidates after the completion of most of their coursework. Topics in biostatistical methodology will vary from year to year. Methodology related to clinical trials, missing data, functional data analysis, generalized linear models, statistical genetics, advances in Bayesian methodology are examples of areas that may be covered. (0.5 course unit, fall, spring.) Prerequisites: TBA; permission of instructor.
BSTA 812 Seminar in Probability Theory (STAT 955)
Selected topics in the theory of probability and stochastic processes are discussed. (1.0 course unit/semester TBA.)
BSTA 820 Statistical Inference III (STAT 552)
Statistical inference including estimation, confidence intervals, hypothesis tests and nonparametric methods are covered. (1.0 course unit/semester, fall.) Prerequisites: STAT 550 and STAT 551 (a continuation of STAT 550).
BSTA 852 Forecasting and Time Series (STAT 910)
This course covers Fourier analysis of data, stationary time series, properties of autoregressive-moving average models and estimation of their parameters, spectral analysis, and forecasting. Applications to problems in economics, engineering, physical science, and life science are discussed. (1.0 course unit/semester TBA.) Prerequisites: TBA.
BSTA 870 Seminar in Advanced Applications of Statistics (STAT 991)
This seminar is for doctoral candidates who have completed their coursework. Topics vary from year to year and are chosen from advanced probability, statistical inference, robust methods, and decision theory; the principal emphasis is on applications. (1.0 course unit/ semester TBA.) Prerequisites: TBA.
BSTA 910 TBD Guided Tutorial: Teaching
BSTA 920 Guided Tutorial: Research (0.5 - 3.0 course units)
BSTA 930 TBD Guided Tutorial: Applications
BSTA 940 Dissertation Research (0.5 - 3.0 course units)
Consulting I Workshop
Participation in the consulting laboratory is a requirement for both the MS and PhD degrees in biostatistics. This course covers general principles of statistical consulting and includes some statistical consulting experience. Students attend and participate in consulting meetings at which strategies and plans for data analysis are discussed in the context of actual clinical research. Students prepare reports summarizing these meetings and outlining the proposed analyses. They also attend several protocol development classes in which the early stages of planning a study are presented and critiqued. The course emphasizes developing communication skills, learning to work with clinical investigators, and developing an approach to understanding what the scientific questions are and how to translate those questions into plans for statistical analyses. (Spring.) Prerequisites: BSTA 510, BSTA 511, BSTA 630; permission of instructor. (Biostatistics students only.)
Consulting II Project (Master's Thesis)
This course continues the consulting experience in Consulting I Workship and provides the course structure for preparation of a Master's thesis (or equivalent for PhD students). (Fall/spring.) Prerequisites: BSTA 631. (Biostatistics students only.)
BSTA 999 Independent Study (0.5 - 1.0 course units)
Other Courses at The University of Pennsylvania
There are several statistics-related courses offered throughout the University of Pennsylvania. The following partial list, which excludes those in the Biostatistics and Statistics Departments, is limited to those that the Biostatistics program finds acceptable as electives for PhD or MS students. Students who wish to take these courses for graduate credit should consult with their faculty advisors or the graduate program chair.
DEMG 604 (SOCI 604) Methodology of Social Research
This course is an introduction to measurement theory, research design, and survey research methods. Topics include validity, reliability, scaling, experimental design, the elaboration model, causal analysis, sampling and questionnaire construction. A combination of lectures, problem sets, and examples from sociological research will be employed to introduce the various methods.
DEMG 609 (SOCI 609) Basic Methods of Demography
This course features discussions and laboratory assignments on principal population research methods.
DEMG 707 (SOCI 707) Seminar in Demographic Research
Each student must complete a major research paper, under individualized faculty supervision and generally on a topic related to the expected dissertation topic. This is required for all second-year demography students.
DEMG 708 (SOCI 708) Seminar in Demographic Research
This is a continuation of DEMG 707 required of all second-year demography students.
DEMG 757 (SOCI 757) Mathematical Demography
Mathematical models in demography and their applications are covered.
Education
EDUC 684 Psychological AssessmentThis course covers analysis of primary assessment concepts including psychometric theory, clinical and actuarial decision making, bias, and prediction; application of standards for educational and psychological tests and testing of aptitude, achievement, behavior, attitude, and personality. Prerequisite(s): Undergraduate statistics or tests and measurement.
EDUC 768 Test Construction
This course covers design of evaluation instruments; derivation of basic formulas in psychometric theory; planning of tests; item writing and analysis; and procedures for evaluation. Prerequisite(s): EDUC 767.
EDUC 771. Advanced Psychometric Methods
This course covers classic true-score theory; item analysis; exploratory and confirmatory item factoring and clustering; and reliability and validity analyses, scaling, and replication. A practicum on the design of education and psychological scales is included. Prerequisite(s): EDUC 684 or equivalent and permission of instructor.
EDUC 782 Advanced Psychological Assessment I
This course offers a critical analysis of tests and clinical methods in assessment as related to theories of intelligence as well as practical administration of the Wechsler Intelligence Scale for Children, the Stanford-Binet, and other individual intelligence tests. Prerequisite(s): Admission to doctoral program in Professional Psychology or permission of instructor.
EDUC 783 Advanced Psychological Assessment II
This course includes review and administration of assessment instruments in the areas of adaptive behavior, perceptual abilities, neurological functioning, diagnostic and achievement measures, vocational interests, and objective personality measures. Integration and interpretation of results and intervention are included. Prerequisite(s): Admission to doctoral program in Professional Psychology and EDUC 782, or permission of instructor.
Sociology
SOCI 128 Introduction to Demographic MethodsThis course introduces the basic methods and materials of demographic analysis, focusing on practical problems and using US data for examples and class assignments. The main sources of demographic data, including censuses, surveys, and vital statistics are covered, as are the measures that can be applied to each. Through the use of contemporary and historical US data, students learn the fundamental measures of fertility, mortality, migration and population composition, and how to apply these measures to study demographic structure and change in human population.
