School of Public Affairs and Administration, Rutgers University-Newark
Applied Statistics, Fall 2015
Saturdays 1:30-4:25pm, Classroom:
Instructor: David Jancsics, PhD, Post-Doctoral Fellow
INTRODUCTION
This course introduces students to research and statistical methods with an emphasis on
applications to public policy and public administration decision-making. Its aim is to
train students to be comfortable in using quantitative research methods to answer policy,
management and performance related questions by using different types of quantitative
data such as official reports, organizational records or representative surveys.
In the first part of the course, we discuss the basic terms of statistics including variables,
measurement levels, operationalization and concepts of reliability and validity. We also
learn how to use descriptive statistics to summarize certain features of statistical data sets.
In the second part, we focus on inferential statistics for means and proportions and
discuss topics such as sampling distribution, population parameters, standard error and
hypothesis testing. In the third part, we introduce the concepts of regression analysis,
multivariate models as well as data reduction and classification techniques. Finally, we
discuss how to summarize and present results of statistical analyses and write a policy
research report for a non-technical audience.
We will primarily use Microsoft Excel and SPSS as statistical analysis package. Use of
simple calculator with a square root key will be necessary for many parts of this course.
REQUIRED TEXTBOOKS
Essential Statistics for Public Managers and Policy Analysts, 3 edition, by Evan M.
Berman and Xiaohu Wang, 2012. Thousand Oaks.
Performance Analysis for Public and Nonprofit Organizations by Xiaohu Wang, 2010.
Jones and Bartlett Publishers.
CLASS ATTENDANCE
This class builds steadily on materials learned in previous sessions. Therefore regular
class attendance and keeping up with assignments are essential. If you miss two or more
class sessions, you will be very likely to earn a course grade lower than “C+.”
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HOMEWORK ASSIGNMENTS
Your weekly homework assignments will include conceptual and data-based exercises.
Assignments will be posted along with lecture slides on Blackboard each week.
Homework is essential for understanding the material and doing well on the quizzes and
your paper.
QUIZZES (60%)
In order to check your understanding of the concepts you have learned I will frequently
give quizzes during the first 20-25 minutes of class. Quizzes will consist a mix of
true/false, short answer, calculation, and interpretation questions. You will take six (6)
quizzes over the semester. Each quiz is worth 10 points. I will drop your lowest quiz
score.
RESEARCH REPORT (40%)
You are required to write a 8-10 page (double-spaced) policy research report to
demonstrate how you can use statistical techniques to address real-world issues. This task
will allow you to demonstrate that you have learned how to choose appropriate statistics
to answer particular questions, how to interpret descriptive and inferential statistics, and
how to write a report for diverse audience. I will provide you a suggested outline and
organization of the research paper.
PROFESSIONAL CONDUCT
 Please arrive at class on time. You will have to miss a quiz otherwise.
 Cell phones as well as other electronic communication devices must be off or
silenced during class.
 Please do not use recording devices, audio and/or video in class.
 You will be held to the very highest standard of academic integrity in this course.
Please read “Rutgers University Academic Integrity Policy” here:
http://academicintegrity.rutgers.edu/files/documents/AI_Policy_9_01_2011.pdf
GRADING POLICY
 Quizzes 60%
 Applied Essay 40%
A
90 or above
B+
85-<90
B
80-<85
C+
75-<80
C
70-<75
F
below 70
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SCHEDULE
Week
1
2
Quiz
3
#1
4
5
#2
6
7
#3
8
9
Correlation analysis
10
11
#4
12
#5
13
14
Class topic
Introduction to analytical methods
Variable, measurement and data collection
Descriptive Statistics
 Percentage, ratio, frequency
distribution
Descriptive Statistics
 Central tendency
Descriptive Statistics
 Dispersion, Z Score
Confidence interval, sampling and
hypothesis testing
Contingency table and Chi square test
Compare Means
 T-test procedure
 ANOVA
#6
Bivariate linear regression
Multivariate linear regression
Logistic regression and more regression
topics
 Dummy variable
 Interaction effect
 Stepwise option etc. Data Reduction and Classification
 Factor Analysis
 Cluster Analysis
Presenting results
 Report
 Presentation
 Data visualization
Reading
[ES]Ch1, [PA]Ch1
[ES] Ch2, 3-5[PA]Ch2
Handout, [ES] Ch7
[ES] Ch6, [PA]Ch3
[ES] Ch7, [PA]Ch5
Handout, [PA]Ch7, [ES]
Ch2, [PA]Ch9
[ES] Ch8,10,[PA]Ch9
[ES] Ch12 ,13
[PA]Ch11-13
Handout, [PA]Ch9,
[PA]Ch10
[ES] Ch14, [PA]Ch13
[ES]Ch15, [PA]Ch10
Handout, [ES]Ch16
Handout
Handout, [PA]Ch14
Please note that this is a draft of the syllabus and it is subject to change as needed.
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