Course Syllabus
SO 301: Quantitative Research Methods
Block 1, Fall 2014
Professor Wade Roberts
Office: Palmer 130E
wroberts@ColoradoCollege.edu
Phone: 227-8220
Office Hours: M & W, 1:30-3:00 and by appointment
COURSE DESCRIPTION
This course is designed to develop, broaden, and advance your understanding of and facility with quantitative methods. The material assumes your familiarity with the basics of quantitative research design (SO229 Research Design), including sampling, measurement, survey design, and basic quantitative tests. I do not assume, however, that you now own these skills and ideas. Thus, we will be revisiting those elements while adding many more to your repertoire.
The course is not particularly math-intensive. To be sure, we are aiming to develop a solid understanding of the mathematics (i.e., probability theory) behind key statistics and techniques (see Urdan book), but the primary emphasis throughout will be on applied statistics and learning key tricks of the trade.
By the end of the course, you should be proficient in:
- Downloading and preparing existing/secondary datasets; Constructing your own dataset
- Using core functions of Geographic Information System (GIS) software to conduct spatial analyses and prepare professionally-formatted maps
- Using STATA statistical software to manipulate and analyze data
- Analyzing and presenting descriptive statistics through a variety of graphs and tables
- Understanding the principles behind inferential statistics and how they apply to particular techniques (i.e., probability theory)
- Manipulating variables—constructing scale/composite measures out of multiple variables; collapsing categories; reverse/reordering values; etc.
- Conducting comparison of means tests (e.g., t-tests and ANOVA) and analyzing results
- Conducting cross-tabulations and analyzing associations (e.g., Chi-square)
- Conducting bivariate analyses, including correlation, scatterplots, and simple regression
- Conducting various types of multivariate regression analyses (e.g., OLS, Logistic)
- Dealing with issues such as influential cases (outliers), multicollinearity, heteroskedasticity, and non-normal data
- Nonparametric alternatives to the above techniques (e.g., Mann-Whitney; Kruskal-Wallis)
A basic assumption of this course is that proficiency in these elements cannot come about solely through reading. Ultimately, it is arrived at through application, practice, discussion, and play. In that regard, developing quantitative analysis skills is akin to learning a musical instrument or a foreign language. There is no substitute for immersion, repetition, and application. Quite simply—always be working. Perhaps more than in any other area of sociology, attention to detail is absolutely essential.
Attention to Detail
Working with software such as ArcMap (GIS) and STATA requires that you pay unfailing attention to detail. There is simply no room for error. Perfection is a requirement when working with software. Do not take directions lightly. Additionally, it helps profoundly if you think and plan before you act. Racing through steps will only lead to frustration—on your part as well as mine. A good rule to follow is to make sure you understand what a command does before you do it. The same applies to your write-up of your results. Words have very particular meanings in this area of sociology. Be precise and deliberate in your choice of words.
COURSE MATERIALS
There is one book required for this course. Students may check out copies from the department. Please keep your copy in good shape. Students will be asked to replace a damaged copy.
- Statistics in Plain English (3rd ed) (SIPE). 2010. Urdan, Timothy C.
Additional readings are available in the course Canvas site.
We will be using the following network drives and folders in this class. Be sure to map them on your computer: \\GISDataserv\students\AAAso301 [contains student folders]
We will be working with two primary software programs—ArcMap 10.1 (GIS) and STATA IC/13. We will be accessing both programs through the use of a “remote desktop” (terminal server) called VLab1. This allows you to use your own laptop to access and use software on a remote server from both on and off campus. If you have a PC or a Mac with Boot Camp (GIS can only run on Windows), you can choose to install a copy of GIS on your own computer (1-year license; see Matt Gottfried in the GIS lab).
COURSE REQUIREMENTS
Participation and Applications
You will be expected to have read any assigned or recommended material by the start of every day and be prepared to raise questions, address issues, and apply the material to course data or your own imagined projects. As important, you should come with questions about your analyses and be prepared to share your questions and findings with the class. You’ll be working with a lot of data; it’s important that you be ready to present your findings or problems/questions to the class. Discussion will also relate to issues of measurement, sampling, research design, data preparation, and other relevant tricks of the trade. I also intend for the class to serve as a brain trust for students. As such, you are invited to present your research ideas throughout the course and seek feedback from your peers.
Your daily routine should entail a journal that summarizes key insights and concepts. It should also include an appendix where you keep notes on the “tricks of the trade.” These might include notes on key steps, processes, or STATA commands.
Applications
Think and comprehend as you do assignments. Do NOT just go through the motions. If you don’t understand something, ask. You will apply the techniques learned to a variety of data sets (e.g., General Social Survey, World Values Survey, Social Capital Community Benchmark Survey). I will also task you on occasion with developing a particular GIS project. This work will become part of a portfolio of work.
More information on each of these assignments will be provided as we go along. Additional activities may be required and are considered part of class participation.
Projects and Exam
There is a midterm project, an individual research project, and a multi-day final exam. The final exam will consist of assigned project(s) and a more standard written portion.
ACADEMIC INTEGRITY
Students are required to abide by the Honor Code. Copying others’ work is obviously prohibited. Additionally, it is a violation of this code to use another’s data set or analyses. You must arrive at your results independently. Discussion and requests for assistance from others is allowed. Any violations of the code will be reported and will result in either the loss of the grade on the exam or assignment at issue, or in a failing grade for the course.
GRADING
Applications/participation: 10%
Individual project: 30%
Midterm Project: 25%
Final Exam/Projects: 35%
A = Excellent work that reflects superior understanding, creativity, or skill. Responses
are well articulated and coherent, showing complete “ownership” of the technique
and interpretation of results.
B = Good work that reflects a high level of understanding, creativity, or skill.
C = Adequate work that indicates a modest readiness to continue study in the field.
Indicates a need to boost one’s commitment to the course.
D = Marginal work, only minimally adequate, raising serious question about one’s
commitment to the class and readiness to continue in the field.
NC = Failing work, clearly inadequate, and unworthy of credit.
Note: A student cannot earn a passing grade for the course unless they receive a passing grade on the final exam. This rule applies even where the student’s combined points would normally earn them a passing grade.
Topics
Comparison of Means Tests (t-test)
Comparison of Means Tests (ANOVA)
Cross-tabulation and Chi-Squared Test of Independence
ONLINE RESOURCES
STATISTICS TEXTS
-Online Stat Book (alternative)
STATA RESOURCES
-UCLA Stat Computing Portal (STATA) (SPSS)
-UCLA ATS Resources to Help You Learn and Use STATA
-UCLA ATS STATA Learning Modules
-UCLA ATS STATA Seminars and Classes
-UCLA ATS What Statistical Analysis Should I Use?
DATA RESOURCES
Data Analysis Learning Sites
-Data-Driven Learning Guides (ICPSR)
Data Archives
-ICPSR (contains most everything)
-DSDR (top downloads)
-Substance Abuse & Mental Health Data Archive (SAMHDA)
-The Association of Religion Data Archives
-National Archive of Criminal Justice Data
Data Sets (Survey-based)
General (contain a lot of stuff)
-International Social Survey (ISS) or here
Health, Aging, and Health-Related Behaviors
-Health Behavior in School-Aged Children (HBSC), 2001-2002
-National Health Interview Survey Series
-Behavioral Risk Factor Surveillance System (CDC)
-Harvard School of Public Health College Alcohol Study
-National Longitudinal Study of Adolescent Health (Add Health)
-National Social Life, Health, and Aging Project (NSHAP)
-National Survey on Drug Use and Health, 2007
-National Health and Social Life Survey, 1992
-Midlife Development in the United States (MIDUS) Series
-National Health and Nutrition Examination Survey Series
-National Survey of Children’s Health
-Mother Jones data on mass shootings
Families
-The Fragile Families and Child Wellbeing Study
-National Survey of America’s Families (NSAF), 2002
Political
-American National Election Studies
Race/Ethnicity
-Latino National Survey (LNS), 2006
Social Capital
-Social Capital Community Benchmark Survey, 2000 and 2006
Urban Communities
-Project on Human Development in Chicago Neighborhoods (PHDCN)
-Multi-City Study of Urban Inequality, 1992-1994
Corrections/Inmates
-Survey of Inmates in State and Federal Correctional Facilities, 2004
Cross-National Data
-The UC Atlas of Global Inequality
-World Development Indicators (WDI)
-Nations, Development, and Democracy, 1800-2005
-James McGuire cross-national data sets
State-Level Data
-Annie E Casey Foundation Kids Count
-CDC State and Territorial Data
-National Center for Education Statistics
-National Institute for Early Education
County-Level Data
GIS Data
-PolicyMap (great resource for data sources)
-MIT State Geospatial Data Resources
-Site for translating addresses into coordinates
-Los Angeles County GIS Data Portal
-New York City Coalition against Hunger
-NYC GIS example (financial justice research)
Course Summary:
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