Criminal Justice-COAS | Data Analysis in Criminal Justice II
P596 | 1560 | Arvind 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. 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.

Home Assignments /Journal Article Evaluations	30%
Quizzes 			2		20%
Mid-Term Exam.					25%
Final Exam (Take Home)				25%

1. George, Darren and Mallery, Paul. 2001. SPSS for Windows Step by
Step: A Simple guide and Reference 11.0 Update. Boston: Allyn and
Bacon. 4th Ed.
2. Class Readings - will be provided by the instructor.
Recommended Reading:
Hair, Joseph F.; Anderson, Rolphe E.; Tatham, Ronald L.; and Black,
William C. 2000. Multivariate Data Analysis. New Jersey: Prentice
Hall. 5th Edition.

Class meeting:  R, 5:45 - 8:15, LH 025

Instructor:  Professor Arvind Verma, criminal justice department