1 Social Statistics SOCI-A336/A339 Loyola University New Orleans Spring 2013 Class: Monday and Wednesday 3:30-4:45 (Mercy 105) Lab: Wednesday 4:55-5:45 (Mercy 105)
Dr. Carol Ann MacGregor [email protected] 30 Modular Office Building (a.k.a. Former Mercy Parking Lot) (504) 865-2571
Office Hours: Tuesday 8:30-12 and Thursday 12-3:30, or by appointment
Lab Assistant: Anna Edelman [email protected] Office Hours: Location:
Course Description Chances are you are taking this class because you are required to as a Sociology major. Although I hope you are excited about the possibilities, I recognize some of you may also be approaching the class with a mixture of fear and loathing. To be certain, statistics can be both dry and intimidating. However, in this class I hope to show you that statistics can be exciting and useful when applied to a topic that interests you. Fortunately, statistics can be applied to almost anything-- including the kinds of social phenomena and questions that probably attracted you to sociology in the first place. Understanding some basic statistics will not simply fulfill a requirement toward your degree in sociology. Instead, a basic understanding of fundamental concepts in statistics can help you be a more critical consumer of statistics produced and reported by scholars, governments, special interests, and the media. Data analysis is a skill—one that is increasingly in demand in the contemporary economy. Mastering these skills might even land you a job (and if it doesn’t, you’ll be much better prepared for graduate school). This class is designed as a first course in statistics for students in the social sciences. Most substantive examples will be drawn from work in Sociology. We begin where your research methods class (hopefully) left off—with concepts, variables and measurement. We then look at descriptive statistics—considering univariate and bivariate measures. Next, we consider bivariate and multivariate relationships—moving from the foundation of inferential statistics, probability theory, through to linear and logistic regression. Throughout the course, you will analyze data using SPSS (Statistical Program for the Social Sciences) and learn how to effectively write up your statistical findings for scholarly and non-scholarly audiences.
Required Texts 1. Eric J. Krieg Statistics and Data Analysis for Social Science ISBN 978-0205728275 ($81.70) 2
2. Stephen Sweet and Karen Grace-Martin Data Analysis with SPSS: A First Course in Applied Statistics (4th Edition) ISBN 978-0205019670 ($39.99) NOTE: One copy of each of these books is available on 2-hour reserve at Monroe Library 3. iClicker 4. Any other readings will be posted to Blackboard
Assignments and Assessment Weekly Lab Assignments 45% Midterm Exam 15% Final Project 30% Attendance and Participation 10%
Weekly Lab Assignments—Each week in the lab section we will undertake a small hands-on activity designed to let you explore the concepts covered in the lecture. If the activity will extend beyond lab time or involve a written assignment, a handout describing the exercise will be circulated in class at least two classes prior to the lab.
Final Project—A separate handout details the formal expectations of this 10 to 12 page (double spaced) final project. In this project you will develop a research question based on your interests, choose a dataset or datasets, conduct an analysis using the techniques covered in this course, and write up your results.
Class Attendance and Lateness—Attendance will be taken at all sessions (beginning in Week 3 this will be done via iClicker in the first 5 minutes of class). I always appreciate courtesy emails if you are ill or otherwise unable to attend a session.
You are required to purchase an iClicker remote for in-class participation. i>clicker is a response system that allows you to respond to questions I pose during class; you will be graded on that feedback and/or your in-class participation. In order to receive this credit, you will need to register your iClicker remote in class. I will project a Registration screen with 3 steps to follow (look for your email ID which will alphabetically scroll down the screen). Once your remote is registered, your email ID will no longer appear on that scrolling list and you are registered for the entire semester. If for some reason, you cannot follow these steps, I will need to register you by manually adding your clicker. Attendance and participation points will not be added retroactively. iClicker will be used every day in class, and you are responsible for bringing your remote daily beginning in Week 3.
Class and Lab Grade You will receive the same grade in both the class and the lab as they operate symbiotically. This also generally benefits students. However, if you would like to receive separate grades for each section please come discuss this with me.
Late Assignments This class is based in many ways on cumulative knowledge. I strongly discourage you from getting behind in your weekly lab assignments. Except for cases of severe health problem or 3 other emergency, lab assignments will not be accepted more than two weeks after the original session. The penalty is 0.25 (out of 3) for each day of lateness.
The Final Project will not be accepted after May 6th. The penalty is one third of a letter grade for each 24 hours of lateness after 5pm on the last day of classes.
Grading Scale A 95-100 A- 90-95 B+ 85-89 B 80-84 B- 75-79 C+ 70-74 C 65-69 C- 60-65 D 50-59 F <50
Schedule of Classes
Week 1- Getting Started January 7- Introduction and Course Overview
January 9- The Quantitative Research Process • Freese, Jeremy (2009) “Secondary Analysis of Large Social Surveys” in Eszter Hargittai (Editor) Research Confidential: Solutions to Problems Most Social Scientists Pretend They Never Have Ann Arbor: University of Michigan Press.
Lab #1- Finding Data
Week 2- Concepts, Variables and Measurement January 15 • Krieg Chapter 1, “Concepts, Variables and Measurement”
January 17 • Sweet and Grace-Martin Chapter 1, “Key Concepts in Social Research”
Lab #2 Identifying Concepts and Measures
Week 3- Getting Started with SPSS January 21- Dr. Martin Luther King, Jr. Day
January 23- Getting Started with SPSS • Sweet and Grace-Martin Chapter 2, “Getting Started: Accessing, Examining and Saving Data”
Lab #3- Introduction to SPSS
Week 4- Univariate Statistics January 28- Frequency Tables • Kreig Chapter 2, “Frequency Tables”
January 30- Measures of Central Tendency • Kreig Chapter 3, “Measures of Central Tendency” 4
• Sweet and Grace-Martin Chapter 3, “Univariate Analysis: Descriptive Statistics”
Lab #4 Descriptive Statistics
Week 5 Univariate Statistics (continued) February 4- Measures of Dispersion • Kreig Chapter 4, “Measures of Dispersion • Sweet and Grace-Martin Chapter 3, “Univariate Analysis: Descriptive Statistics”
February 6- Constructing Variables • Sweet and Grace-Martin Chapter 4, “Constructing Variables”
Lab #5- Constructing Variables
Week 6 Mardi Gras
Week 7- Probability February 18- Probability and the Normal Curve • Kreig Chapter 5, “Probability and the Normal Curve”
February 20- From Samples to Statistics • Kreig Chapter 6, “Probability—From Samples to Statistics”
Lab #6- Probability Exercises
Week 8- Bivariate Analysis February 25- Crosstabs and Chi-Square • Kreig Chapter 7, “Cross-tabulation Tables and Chi-Square”
February 27- Measures of Association for Categorical Variables • Kreig Chapter 8, “Measures of Association for Categorical Variables” • Sweet and Grace-Martin Chapter 5, “Assessing Association through Bivariate Analysis”
Lab #7- Cross-tabs
Week 9- Midterm Review March 4- Review for Midterm
March 6- IN CLASS MIDTERM [No Lab: Lab time available for those who want extra time for the midterm]
Week 10- ANOVA March 11- Analysis of Variance: Theory • Kreig Chapter 9, “Analysis of Variance” 5
March 13- Analysis of Variance: Application • Sweet and Grace-Martin Chapter 6, “Comparing Group Means through Bivariate Analysis”
Lab #8- ANOVA
Week 11- Linear Regression: Foundations March 18- Correlation • Kreig, Chapter 10 pp. 303-319.
March 20- Bivariate Example • Small and Grace-Martin Chapter 7, pp. 161-170.
Lab #9- Correlation
Week 12 Easter Break
Week 13- Linear Regression April 1- Easter Monday Holiday
April 3- Linear Regression • Kreig Chapter 10, “Correlation and Regression” • Small and Grace-Martin Chapter 7, “Modeling Relationships of Multiple Variables with Linear Regression”
Lab #10- OLS Regression
Week 14- Logistic Regression April 8- Logistic Regression (Introduction) • Small and Grace-Martin Chapter 8, “Logistic Regression”
April 10- Logistic Regression (Practice) • Small and Grace-Martin Chapter 8, “Logistic Regression”
Lab #11- Logistic Regression
Week 15- Final Review April 15- Question and Answers (you submit the questions) • Re- read any material you struggled with over the whole semester
April 17- Additional Practice • Re- read any material you struggled with in Week 13 and Week 14
Lab #12- More Regression 6
Week 16- Presenting Your Results April 22- Writing about Quantitative Analysis • Selections from Miller, Jane (2005) The Chicago Guide to Writing About Multivariate Analysis Chicago: University of Chicago Press.
April 24- The Visual Display of Quantitative Information • Booth, Wayne C., Gregort Colomb, and Joseph M. Williams (2008) “Communicating Evidence Visually” in The Craft of Research Chicago: The University of Chicago Press.
Lab #13- Making Proper Tables
Week 17- Even More Statistics? April 29- Advanced Statistical Methods: An Introduction
May 1- FINAL PROJECT DUE/ DISCUSSIONS OF FINAL PROJECTS
Lab # 14- Discussion of Final Project
Lab #15- If you have done all of your other labs, you will automatically receive the 3 points for this lab. If you have missed any you must complete a supplementary assignment. Details to be announced.