Criminal Justice-coas | Data Analysis in Criminal Justice II
P596 | 1594 | 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