Criminal Justice-COAS | Data Analysis in Criminal Justice II
P596 | 24743 | Verma


COURSE DESCRIPTION

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. Grades will
be based on a combination of quizzes and final examination, plus
routine homework. Topics covered will include matrix algebra,
multivariate normal population, inference about means and
covariance, multivariate linear models, principal component
analysis, canonical correlation analysis, some discussion of
Discriminant and classification, factor analysis, cluster analysis
and if time permits, path analysis.



Format: There will be lectures, discussion of journal papers
and ‘lab work’ on the computer that will involve practical handling
of crime data through the SPSS system software.



Evaluation:

Home Assignments /Journal Article Evaluations 30%

Quizzes 2     20%

Mid-Term Exam.  25%

Final Exam (Take Home)   25%

Class Meeting:  Tuesday, 2:30-5:00 p.m.

Instructor:  Professor Arvind Verma, criminal justice department