SYLLABUS
202:601 - DATA ANALYSIS IN CRIMINAL JUSTICE
Spring 2008
Wednesday
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Instructor: |
Jane
A. Siegel, Ph.D. |
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Office: |
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Phone: |
(856)
225-6143 |
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E-mail: |
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Website: |
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Office
Hours: |
Wednesday
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Teaching
assistant |
Mike
Epstein |
REQUIRED TEXT:
Statistics in
Criminal Justice, 2nd Ed., David Weisburd and
The
text is available in the bookstore and should include an SPSS student version
disk (v. 15) for use in a Windows environment (bound with the book). If
the data disk is not included, notify the bookstore immediately. Copies of the text without the SPSS disk are
also available at the bookstore. You
should purchase SPSS only if you want the convenience of using the program at
home. Otherwise, the program is
available in the school’s computer labs.
Used
copies of the book are also available from various on-line sellers, as are new
copies of SPSS Student Version (v. 15).
RECOMMENDED TEXT:
SPPS Companion Guide to Weisburd
and Britt’s Statistics in Criminal
Justice
This
text is available through various on-line booksellers. Although the book is not required, students
have found it useful for assistance with SPSS and I strongly recommend
purchasing it if you have never used SPSS before.
COURSE OBJECTIVES
This course is
intended to:
1. Introduce students to the basic means of measurement and statistical testing used most commonly in criminal justice and other social sciences and to the steps involved in data analysis;
2. Equip students with the skills required to
choose appropriate statistical procedures for research, execute those
procedures and correctly interpret the results; and
3. Provide an understanding of the issues involved
in statistical inference and analysis in order to enable students to examine
actual data analysis problems and be intelligent users of statistical studies.
COURSE DESCRIPTION
This course will
provide students with a grounding in the basic tools
used in quantitative analysis in the field of criminal justice and other social
sciences along with an introduction to the statistical issues involved in the
design and logic of research. Students
will learn to use various non-parametric measures of association as well as
parametric tests of significance and will be introduced to the fundamentals of
correlation and regression. Although students will make use of a standard
statistical software package (SPSS), they will also learn the computation of
several measures and statistical tests in order to enhance understanding of the
concepts that underlie them. The course will also provide students with an overview
of the steps involved in the data analysis process and the formulation and
testing of hypotheses.
COURSE REQUIREMENTS
Students are
expected to attend class regularly and to have read assigned material prior to
class. Statistics is a subject that builds upon existing knowledge and absences
interrupt that process. Problem sets will be assigned weekly and are due on the
date of the following week's class, unless otherwise noted. Late assignments will be accepted only with
prior permission of the instructor.
Problem
sets are to be done individually. (Please see the university's academic
integrity policy.) Answers to any problems that require computations should
show as much of the work performed as possible. Problems using SPSS procedures
should include printouts of the results where appropriate.
Datasets needed
for problem sets are available through a Sakai website that has been
established for the class.
In addition to
weekly problem sets, there will be a mid-term and final exam. Make-up exams will be given only if, prior
to the scheduled exam date, you have obtained permission to be excused from the
exam on that date.
GRADING
Grades will be
computed on the following basis:
|
Problem
sets |
30% |
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Mid-term |
30% |
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Final |
40% |
A final grade of
70 or above will be required to pass the course (i.e. obtain a grade of C or
above).
Assigned
readings should be done prior
to the date where they appear. Additional readings may be distributed in class.
Students are responsible for knowing the material in the readings, regardless
of whether it is discussed in class or not. In other words, your problem sets
and exams may include materials from both class lectures and your readings,
unless otherwise noted. Since I may not be able to discuss all of the subjects
covered in the readings, you should take careful notes as you read and ask
me about any topics you do not understand and that I have not reviewed in
class.
Note that the schedule is
subject to change!
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DATE |
TOPIC |
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1/23 |
Introduction.
Uses of statistics. Levels of measurement. Descriptive statistics. |
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1/30 |
Measures
of central tendency: mode, median and mean. Frequency distributions and the
graphical representation of data distributions. |
Chapters
1-4 |
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2/6 |
Measures
of dispersion: variance and standard deviation. |
Chapter
5 |
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2/13 |
Statistical
inference: using samples to make statements about populations. Hypothesis
testing. Sampling distributions. Steps in statistical tests of significance. |
Chapters
6-8 |
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2/20 |
Testing
for association between nominal-level measures: the chi square statistic. |
Chapter
9 |
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2/27 |
The
normal distribution and tests of statistical significance. |
Chapter
10 |
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3/5 |
MID-TERM
EXAM |
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3/12 |
Parametric
tests comparing means and proportions in two samples: t-tests. |
Chapter
11 |
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3/26 |
Comparing
means among more than two samples: Analysis of variance. |
Chapter
12 |
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4/2 |
Measuring
the strength of association between nominal-level or ordinal-level variables. |
Chapter
13 |
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4/9 |
Measuring
association between interval-level variables: the correlation coefficient. |
Chapter
14 |
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4/16 |
Estimating
the effect of one variable on another: the regression coefficient. |
Chapter
15 |
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4/23 |
Multivariate
regression. |
Chapter
16 |
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4/30 |
Confidence
intervals. |
Chapter
18 |
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5/14 |
FINAL
EXAM – |
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