methods II spring 13 Syllabus - Bloustein School of Planning and ...

Rutgers, the State University of New Jersey
Bloustein School of Planning and Public Policy
34:833:630 METHODS II
Spring, 2013
Professor: Andrea Hetling
Office: Civic Square Building, 33 Livingston Avenue, Room 542
E-mail: ahetling@rutgers.edu
Office Hours: Mondays 11:00am - 2:00pm and by appointment
Classroom: 33 Livingston Avenue, Civic Square Building, Room 253/ Comp Lab 372
Class Hours: Wednesdays, 1:10 – 3:50
Course Description and Teaching Style
This course is an introduction to basic quantitative data analysis and is designed to help you
develop the skills necessary to carry out elementary empirical analyses of policy issues. Methods
I is a pre-requisite, and I will assume a mastery of the topics covered in that course. The main
goal of Methods II is for you to learn to utilize statistics as an applied tool in researching policy
issues and thus an understanding of research design is critical. We will cover the techniques and
language of descriptive and inferential statistics. We will cover bivariate and multivariate
analyses with an introduction to multiple regression. You will write an original, empirical data
analysis paper using a secondary data set that you must find. In addition to conducting analyses,
we will discuss and interpret statistical results. This course will not make you into expert
statisticians, but it will give you the skills to be educated consumers (and often critics) of the
research of others and to continue your analytic studies in other classes if you so choose.
The class meets meets weekly for 2 hours and 40 minutes. The format for the class lecture
period will be lecture and facilitated discussion. I am a firm believer of active learning; thus, I
will attempt to use discussion in place of traditional lectures whenever possible and encourage
questions and expect class participation. We will also make use of the computer lab, often times
meeting in the classroom first and then walking to the lab for the last hour of class. These lab
sessions will allow you to gain some hands-on practice with the concepts we learn in lecture.
Course Objectives
The goals of this course is to provide students with:
1) An ability to conduct, interpret and present descriptive, inferential, and associative
statistics
2) The skill to use a statistical computer package - SPSS
3) The foundation necessary continue your studies in quantitative analyses if you so choose

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Required Readings
Mohr, Lawrence B. (1990). Understanding Significance Testing. Quantitative Applications in the
Social Sciences # 57. Thousand Oaks, CA: Sage Publications.
Pearson, Robert W. (2010). Statistical Persuasion. Thousand Oaks, CA: SAGE Publications.
Schroeder, Larry D., Sjoquist, David L., & Stephan, Paula E. (1986). Understanding Regression
Analysis: An Introductory Guide. Quantitative Applications in the Social Sciences # 57.
Newbury Park, CA: Sage Publications.
Books are available at the main Rutgers bookstore, in downtown New Brunswick.
Additional readings will be posted as PDFs under the resources tab on the course Sakai site.
Grading
Numerical grades will be calculated on a simple percentage basis as follows:
Midterm exam 20%
Weekly homeworks 30%
Class participation 5%
Data analysis paper 15%
Policy brief 10%
Final exam 20%
Total 100%
Letter grades will be assigned as follows:
90 to 100% = A; 86 to 89% = B+; 80 to 85% = B; 76 to 79% = C+
70 to 75% = C; 60 to 69% = D; 0 to 59% = F
Ground Rules
Collegial and respectful conduct is expected in class. Class members should consider themselves
colleagues who will collaborate to help each other develop a solid understanding of materials and
concepts. To facilitate this process and your learning, we will adhere to some basic rules:
• Attendance at all class sessions is expected. You will be granted two absences without
penalty. Any additional missed classes will count against your class participation grade.
If you miss a class, you must use the University absence reporting website
https://sims.rutgers.edu/ssra/ to indicate the date and reason for your absence. An email
is automatically sent to me.
• Class will start and end on time. Although I understand emergencies occur, timely
arrivals and departures should be the norm. Excessive and/or habitual lateness and/or
early departures will count against your class participation grade.
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• Please turn off (or silence and refrain from using) your cell phones and other electronic
devices during class.
• All assignments must be completed on time, typed in 12-point font, and submitted in hard
copy in class. Late work will be penalized one-quarter of a letter grade daily. For
example, an assignment submitted 4 days late will be docked an entire letter grade, e.g.,
from a B+ to a C+. Assignments cannot be submitted any later than one week after the
due date; missed assignments will receive a “0”.
• Late submissions for the final paper and policy memo are not permitted. Exam make-ups
are not permitted.
• Cheating, plagiarism and other forms of academic dishonesty will not be tolerated. Please
see the University’s Policy on Academic Integrity for Undergraduate and Graduate
Students located on the web at http://ctaar.rutgers.edu/integrity/policy.html. You should
also note that I use the Turnitin feature on Sakai to help me identify problems with
plagiarism.
• If any questions or concerns arise, please come see me! My office hours are listed on the
top of the syllabus. If you cannot make my office hours, please make an appointment.
Any student in this course who has a disability that may prevent him or her from fully
demonstrating his or her abilities should contact me as soon as possible so we can discuss
accommodations necessary to ensure full participation and to facilitate your educational
opportunities.
A Few Words to the Wise
• Come to class prepared! Complete readings prior to lecture.
• Seek help early! Don’t wait till the last minute if you are having difficulties.
• Get missed notes from a classmate! I will not hold individual meetings to provide
summaries or repeat class material.
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Schedule
Date Topic Readings and Assignments
1/23 Introductions and course overview;
Statistics in policy; Research ethics
1/30 SPSS review (Including review of
descriptive statistics)
Normal curve and inferential statistics
2/6 Estimation and confidence intervals
2/13 Hypothesis testing – one sample and
two samples
2/20 Hypothesis testing –ANOVA
Exam Review
2/27 EXAM
3/6 Secondary data
Bivariate association – review
Hypothesis testing – chi-square
3/13 Elaborating bivariate tables
Writing about numbers
3/20 SPRING BREAK
3/27 Bivariate association – choosing a
statistic - nominal and ordinal levels
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(Review Pearson, Chapters 1, 3, 5 &
6)
Pearson, Chapter 5, pp. 113-121
Urdan, Chapters 4 & 5 (pdf on Sakai)
HW 1 due
Pearson, Chapter 8
Mohr, pp 1-27
Vickers, Chapter 11 (pdf on Sakai)
HW 2 due
Vickers, Chapter 14 (pdf on Sakai)
Mohr, pp. (28-49) 49-74
Pearson, Chapter 9, pp. 207-215
HW 3 due
Pearson, Chapter 9, pp. 215-228
Urdan, Chapter 10 (pdf on Sakai)
HW 4 due
Pearson, Chapters 7 and 4
Urdan, Chapter 14 (pdf on Sakai)
TBA (pdf on Sakai)
Pearson, Chapter 14
HW 5 due
Pearson, Chapter 10, pp.22-240
HW 6 due
4/3 Correlations and Linear regression Pearson, Chapter 10, pp. 240-252 &
Chapter 11
Data analysis paper due
4/10 Multiple regression – assumptions and Pearson, Chapter 12
violations
HW 7 due
4/17 Dummy variables and interactions Schroeder, Sjoquist, & Stephan
Policy briefs due
4/24 Other models Vickers, Chapter 18
Pearson, Chapter 13
TBA (pdf on Sakai)
HW 8 due
5/1 Discussion/presentation of papers
Review
TBA FINAL EXAM