Criminal Justice-coas | Data Analysis in Criminal Justice II
P596 | 1436 | Verma


This is the second course in data analysis for graduate students in criminal justice.  This course
presumes a basic grounding in univariate models and non-parametric statistics including
probability theory.  We will study methods of analyses for data that involves more than one
variable.  The primary aim will be interpretation and manipulation of the data, starting with the
multivariate normal distribution and proceeding to the multivariate inference theory.  Sufficient
theory will be developed to facilitate an understanding of the main ideas and examples will be
given from criminal justice fields.  We will use computers routinely, and familiarity with
elementary use of SPSS will be assumed.  Topics covered will include matrix algebra,
multivariate normal population, inference about means and covariance, multivariate linear
models, principal component classification, factor analysis, cluster analysis and if time permits
path analysis.
Readings: To be announced
Requirements:  Grades will be based on a combination of quizzes and final examination,
plus routine homework.
Home Assignments         25%
Short Quizzes in Class   20%
Mid-Term exam       25%
Project Paper            30%
The project paper will involve developing a complete paper analyzing some
criminal justice data based upon one or more of the techniques learnt in class.  Students may take
the options of giving a final examination instead of the project.
Class Meeting: One 150-minute seminar each week (W, 2:30-5:00P, BH 308)
Course Will Satisfy:     CJUS Ph.D. research requirement
Instructor:    Professor Arvind Verma, Criminal Justice Department