Criminal Justice-coas | Data Analysis in Criminal Justice II
P596 | 1570 | 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, 5:45-8:15P, BH 308)

Course Will Satisfy:	CJUS Ph.D. research requirement

Instructor:	Professor Arvind Verma, Criminal Justice Department