Advanced Quantitative Methods
The department faculty offer coursework in basic and advanced quantitative methods ranging across a wide variety of statistical techniques. The goal of this sequence of courses is to develop student knowledge and expertise in different methodological tools which will be beneficial in advancing their own substantive area of research. All courses are taught by CSP faculty.
To supplement a student’s education in the available coursework in quantitative methods, department faculty also sponsor the HSDg (Having Statistical Discussions Group), a methodology study group which meets on a monthly basis to discuss current directions in quantitative methods, offer support to students and faculty with questions pertaining to analyses, and demonstrate statistical applications to research questions of interest to faculty and students.
Finally, students enrolled in one of the graduate programs in the Department of Clinical and Social Sciences in Psychology may establish a Certificate in Quantitative Psychology. The intent of the certificate is to provide training and documentation of expertise in the application of advanced methodological and statistical methoods. The certificate will enable students to engage in high quality methodological practices within their substantive areas. Upon completion of the requirements, the certificate will be recorded on the student's official records and may be listed on the student's curriculum vitae.
CSP Quantitative Methods Courses
Data Analysis I
Issues of data analysis in experimental research. The course focuses on parametric techniques, specifically analysis of variance. Topics covered include simple and complex designs for between and within subjects factors, including mixed designs; analysis of covariance and trend and contrasts. The course includes a lab in which students are taught to use a popular statistical package for data analysis.
Data Analysis II
Topics include multiple regression, factor analysis, and an introduction to structural equation models. The emphasis is on conceptual insight into the General Linear Model, and well as flexible, practical application of regression models to the analysis of actual psychological data
Hierarchical Linear Modeling
This course covers the basic theory and equations underlying multilevel modeling techniques for analyzing hierarchical data. Lectures on the underlying statistics are paired with detailed in-class data-analysis examples and hands-on homework sets to ensure that students will leave the class fully competent to run and thoroughly interpret their own HLM analyses.
Structural Equation Modeling I
This course covers a range of statistical techniques that comprise Structural Equation Modeling: Confirmatory factor analysis, path analysis, and hybrid models (which include latent factors and the structural paths among them). The class will cover introductory material (e.g., identification, estimation) as well as some intermediate and advanced topics (e.g., measurement invariance and interactions between latent variables). Previous knowledge of regression is highly recommended.
Structural Equation Modeling II
This course will build upon methods covered in SEM I by covering advanced topics in SEM including advanced applications for growth modeling, categorical latent variable modeling in cross-sectional and longitudinal modeling settings, and growth mixture-modeling.
Dyadic Data Analysis
This course will cover methods suitable to data containing matching variables assessed on both members of a dyad. Topics include non-independence of data, the Actor-Partner Interdependence Model, mediation and moderation of dyadic effects, and longitudinal methods for dyadic data.
Research Methods in Psychology
Discussion of research design, reliability, and related topics in the first part. Consideration of data analysis with particular emphasis on analysis of variance, contrast analysis, and meta-analysis in the second part.
Developmental Research Methods
The goal of this course is to address the nature of different developmental methods and designs and their application to different programs of research, especially as they pertain to central disciplinary issues of stability and change in development. Course curriculum covers characteristics of measurement and methodology (e.g., questionnaires, interviews, observations, developmental assessments), research design (e.g., experiments, quasi-experiments, naturalistic and field research), and analytic models (e.g., multivariate, developmental).
Introduction to Clinical Research Methods
This course explores an array of methodological issues facing Clinical Psychology researchers - measure development and validation, sampling effects, power and type II error, efficacy vs. effectiveness, clinical vs. statistical significance, effects of method variance and non-specific treatment effects - providing a solid foundation in experimental design.