Fall 2022 1 Rutgers, The State University of New Jersey School of

Fall 2022

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Rutgers, The State University of New Jersey

School of Social Work

Advanced Statistical Methods I: Applied Regression Analysis

19:910:638, Fall 2022

Class

Instructor: Lenna Nepomnyaschy, Associate Professor

Email: [email protected]

Bio: https://socialwork.rutgers.edu/faculty-staff/lenna-nepomnyaschy

Time: Tuesdays, 1:00 – 3:40 pm

Space: 120 Albany St., Classroom A

Required Lab

Instructor: Addam Reynolds, PhD Candidate

Email: [email protected]

Bio: https://socialwork.rutgers.edu/admissions/phd-admissions/student-profiles

Time: Wednesdays, 2:00 – 3:30 pm

Space: 120 Albany St., Classroom A

Course Overview

This course, the first of the statistics sequence for social work doctoral students, will focus on

applied regression analysis and related multivariate methods. Linear regression will be covered

in depth, including regression assumptions, model specification, diagnostics, interaction

(moderation), and mediation (pathway) effects. Students will learn to use the Stata statistical

package for all analyses and class assignments. Each homework assignment will build on the

previous, with the final product being the back end of a journal-quality empirical paper for

publication.

Required Software:

This course requires that students learn and use the Stata Statistical Software Package for

hands-on data analysis and statistics applications for class assignments.

• Stata is available for all employees (GRAs, staff, faculty) for free download from the

OIRT software portal: https://software.rutgers.edu/

• Stata is available to all students in any Rutgers computer lab as well as the Doctoral

Student Computer lab in the SSW Annex AND in the Virtual Computer Lab through

Rutgers Libraries: https://labgateway.rutgers.edu/

• Students may also purchase their own version of Stata at discounted rates through the

Rutgers Office of Instructional Technology (Stata SE: student annual rate: $179)

http://www.stata.com/order/new/edu/gradplans/gp-direct.html

BEFORE MAKING ANY SOFTWARE PURCHASE, CONTACT PROFESSOR

Required Texts (3)

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•  (LB) Lewis-Beck, C. & Lewis-Beck, M. 2016. Applied Regression: An Introduction (2
nd
 
edition). Newbury Park, CA: Sage Publications. Pretty cheap to purchase ($20), BUT 
also available for free online through Rutgers Libraries. https://dx-doi-
org.proxy.libraries.rutgers.edu/10.4135/9781483396774 
 
•  (MJ) Mehmetoglu, M. & Jakobsen, T.G. 2016. Applied Statistics Using Stata: A Guide 
for the Social Sciences. Sage Publishing. Not available online, need to purchase ($35).  
Student Resources (access with Rutgers Netid): https://study-sagepub-
com.proxy.libraries.rutgers.edu/mehmetogluandjakobsen/student-resources 
 
•  (SW) Stock, J. & Watson, M. 2020. Introduction to Econometrics, 4
th
 ed. Pearson 
Education. Full text free online w/clickable chapters. Do not purchase: 
https://www.sea-stat.com/wp-content/uploads/2020/08/James-H.-Stock-Mark-W.-
Watson-Introduction-to-Econometrics-Global-Edition-Pearson-Education-Limited-
2020.pdf 
 
 
Supplementary Resources and Texts 
More In-Depth Applied Regression & Introductory Econometrics Texts 
Gelman, A., Hill, J. & Vehtari, A. 2021. Regression and Other Stories. Cambridge University 
Press. (This book is good – a little more than you are ready for at first, but could be a primary 
resource as you go forward. It is fully available for free online’) 
https://users.aalto.fi/~ave/ROS.pdf 
 
Wooldridge, J. 2006. Introductory Econometrics: A Modern Approach, 3
rd
 edition. Mason, OH: 
Thompson. (The bible of econometrics) 
 
Writing about Quantitative Analysis 
Miller, Jane E., 2013. The Chicago Guide to Writing about Multivariate Analysis (2
nd
 Edition).  
The Chicago Guides to Writing, Editing, and Publishing. University of Chicago Press.  
Study guide: http://www.press.uchicago.edu/books/miller/multivariate/index.html 
THIS IS SUPER HELPFUL 
See Jane Miller’s website for pdfs, videos and other material: 
http://policy.rutgers.edu/faculty/miller/ 
 
Specialized Regression Topics 
Jaccard, J. & Turrisi, R. 2003. Interaction Effects in Multiple Regression (2
nd
 Edition). Thousand  
Oaks, CA: Sage Publications. ISBN: 0761927425. (Highly useful) 
Available online through Rutgers Libraries: 
https://methods-sagepub-com.proxy.libraries.rutgers.edu/book/interaction-effects-in-multiple-
regression?fromsearch=true 
 
Hardy, Melissa. 1993. Regression with Dummy Variables. Newbury Park, CA: Sage 
Publications. Available online through Rutgers Libraries: 
https://dx-doi-org.proxy.libraries.rutgers.edu/10.4135/9781412985628 
 

Fall 2022

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General Stata Books

• Hamilton, L.C. 2006. Statistics with Stata, 6

th

edition. Cengage.

• Long, S.J. 2009. The Workflow of Data Analysis Using Stata. College Station, TX: Stata

Press.

• Kohler, U. & Kreuter, F. 2009. Data Analysis Using Stata (2

nd

ed). College Station, TX:

Stata Press.

• Mitchell, M. 2010. Data Management Using Stata: A Practical Handbook. College

Station, TX

Course Requirements

Students’ work will be evaluated based on the following course requirements (detailed

instructions to follow).

Homework Assignments (4 total) = 40%

There will be FOUR (4) homework assignments, which will be based on the skills and concepts

introduced during class and lab and on the required readings. Assignments will include writing

syntax to create Stata output from a dataset, creating tables and graphs from output, interpreting

output, and writing up methods and results of analyses. Assignments will build on one another

leading to the final assignment that will include most of the previous elements.

Homework #1: Univariate descriptive analysis & writing up descriptive analysis

Estimating, interpreting, and writing up results from descriptive tables

Homework #2: Multiple Regression

Writing a methods section and estimating, interpreting, and writing up results from a series of

multiple regression models with continuous dependent variables

Homework #3: Interpreting Interaction Effects Exercise

Interpreting interaction effects (moderation) models with continuous dependent variables

Homework #4: Estimating & Interpreting Interaction Effects in Your Models

Estimating, interpreting, and writing up results from interaction effects (moderation) models

with continuous dependent variables

Final Assignment = 40%

The final assignment will consist of a complete data analysis project which will build on all the

prior homework assignments. Analyses will include descriptive results, estimation of multiple

linear regression models and interaction effects models with a continuous outcome. The written

assignment will take the form of the Methods, Results, and (brief) Discussion sections of a

journal-style quantitative empirical paper. Students will describe their data, sample, measures,

and analytic strategy, describe the sample characteristics, interpret results from their bivariate

and multiple regression models, and provide a brief discussion of the answer to their question

and of the limitations of their analyses related to violations of regression assumptions and other

sources of bias.

Participation in Class Activities = 20%

This portion of the grade is made up of several components:

• In-class activities – small group work with peers to workshop student submitted

preliminary tables, small group work on interpreting and discussing sample papers, small

group work on interpreting results from sample models

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• Presentation of Final Assignment - students will present a working draft of their final

assignment to the class in a PowerPoint presentation in order to receive feedback and

provide feedback for peers regarding analyses and next steps (10-minute presentations,

similar to a conference presentation).

It goes without saying that students are expected to attend every class, come to class on time,

remain in class for the entire session, and to be prepared for class having read the required

readings and submitted the required materials (if something is due). While there is not specific

course credit associated with attendance, absences, being late to class, and lack of

participation and preparation will substantially impact students’ overall grades.

If, for some reason, class has to be held in a virtual (zoom) setting, students are similarly

expected to join the session on time, stay for the entire session, participate in discussions and

breakout exercises, and must have their cameras on. If there is some reason that a student

cannot have their camera turned on, they must inform the professor prior to class.

Grading

Grade cut-offs are as follows (scores of 0.5 and above will be rounded up):

A 92-100

B+ 87-91

B 82-86

C+ 77-81

C 70-76

F 0-69

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Applied Regression, Fall 2022 Outline of Class & Lab Topics & Homework Due Dates

Week &

Date

Assignment DUE Dates & Topics

Class Activity: Discussion of choosing a question to answer this semester & the FF Data

Class Activity: Discussion of issues in quantitative analyses & students' research questions

Class Activity: Discussion of sample papers

Class Activity: Discussion of sample papers, methods section of empirical paper & Homework #1

Class Activity: Calculating bivariate regression coefficients

Submit descriptive tables - day prior

Class Activity: Group workshopping of descriptive tables

Lab: Cont'd variable construction & bivariate regression

HW #1: Revised Descriptive Tables due

Class Activity: Interpreting regression coefficients

Class Activity: Discussion of regression models in sample papers

Submit multiple regression tables - prior day

Class Activity: Group workshopping of multiple regression tables

HW#2: Revised Multiple Regression Models Due

Class Activity: Interaction effects practice

Submit interaction tables/exercises - prior day

Class Activity: Group workshopping of interaction effects tables

Class: Catch up and review

HW #3: Interaction Effects Models Due

Class Activity: Discuss Sample Papers

Lab: Final Assignment

Students present & respond to peers

Class: Wrap Up & Discussion of Results of Analyses

Final Assignment Due (Friday)

Class & Lab Topics & Activities

Class: Bivariate Analysis & Intro to Linear Regression

Class: Dummy Variables & Mutliple Regression

Lab: Cont'd variable construction & bivariate regression

Class: Bivariate Linear Regression

Lab: Intro to Stata & Variable Construction

Class: Intro to Quantitative Analysis & Current Issues in Quantitative Methods

Week 1:

9/6/22

Lab: Intro to Stata & to FF data

Week 3:

9/20/22

Lab: Cont'd variable construction & descriptive statistics

Class: Quantitative Analyses & Descriptive Statistics

Class: Intro to Class & to the Fragile Families Study

Week 2:

9/13/22

Week 4:

9/27/22

Lab: Cont'd variable construction & bivariate analysis

Week 5:

10/4/22

Week 6:

10/11/22

Class: Bivariate Regression Cont'd

Class: Interaction Effects

Class: Interaction Effects Cont'd

Class: Wrap Up Interaction Effects

Week 10:

11/8/22

Lab: Interaction Effects

Week 11:

11/15/22

Week 12:

11/22/22

Lab: Interaction Effects

Week 7:

10/18/22

Lab: Multiple Regression

Week 8:

10/25/22

Lab: Multiple regression & tabling regression output

Week 9:

11/1/22

Lab: Multiple regression & creating figures

Class: Multiple Regression Cont'd

Class: Multiple regression Cont'd

Week 14:

12/6/22

Week 15:

12/13/22

Lab: Final Assignment

Week 13:

11/29/22

Class: Student Presentations

Lab: Final Assignment

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Detailed Course Outline  
Please note: In addition to the required readings for each week, there are sample empirical 
articles listed (some TBA). I will be adding (or substituting) relevant peer-reviewed empirical 
papers that use the various methods that we are covering as we go. Thus, each week there may 
be alternate journal articles that students will be required to read. 
 
Week 1:  September 6, 2022                                                                                                            
Topics: Intro to Course & Intro to Fragile Families Study 
•  Overview of course 
•  Discuss the FF Study & potential areas of interest 
 
Required Reading:  
•  Review syllabus 
•  Start to explore the Fragile Families & Child Wellbeing Study website (About, Data & 
Documentation): https://fragilefamilies.princeton.edu/ 
•  Fragile Families Study Fact Sheet: Key Findings from Baseline to the 5-year Follow-Up 
https://fragilefamilies.princeton.edu/sites/g/files/toruqf2001/files/ff_fact_sheet.pdf 
•  (skim this) Reichman, N., Teitler, J., Garfinkel, I. & McLanahan, S. 2001. Fragile 
Families:  Sample and Design. Children and Youth Services Review 23 (4/5): 303–26. 
https://doi.org/10.1016/S0190-7409(01)00141-4. 
 
In-Class Activity: 
Discussion of choosing a dataset for course assignments & the Fragile Families Data 
 
Week 2: September 13, 2022                                                                                                                 
Topics: Intro to quantitative analysis & current issues in quantitative analysis 
•  Legacy of racial and social injustice in quantitative analysis 
•  Replication crisis in science 
 
Required Readings:  
•  Boyd, R., Lindo, E., Weeks, L. & McLemore, M. 2020. On Racism: A new Standard for 
Publishing on Racial Health Inequities. Health Affairs Blog. 
https://www.healthaffairs.org/do/10.1377/hblog20200630.939347/full/ 
•  Zuberi, T. & Bonilla-Silva, E. 2008. White Logic, White Methods: Racism & 
Methodology. Rowman & Littlefield. Introduction (p. 1-16). Full book available free 
online through RU Library. https://ebookcentral-proquest-
com.proxy.libraries.rutgers.edu/lib/rutgers-ebooks/detail.action?pq-
origsite=primo&docID=1343788 
•  Piper, Kelsey. 10/14/2020. Science has been in a replication crisis for a decade. Have we 
learned anything? Vox.com. (Excellent links to all the key papers in this area) 
https://www.vox.com/future-perfect/21504366/science-replication-crisis-peer-review-
statistics 
•  Continue to explore FF data & website (Scales & Concepts Documentation, Publications) 
 
In-Class Activity: 
Discussion of issues in quantitative analysis & discussion of students’ research questions 

Fall 2022

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