Morton 314
Time: 10:45 to 11:40 Monday, Wednesday
Course Numbers: 4500: 10714, 5500: 10717
Develop your excitement for learning how to use data analysis tools to make sociological insights
Develop aptitude in managing and processing data; organizing your analysis through well documented code; analysis of sociological questions.
Encourage engaged student directed learning. Assignments are designed to provide the basic skills, with plenty of instruction, and then encourage further exploration and development of skills.
Increase your sociological imagination: gain first hand experience analyzing how individuals act in social contexts and also create the social context for the actions of others. Find evidence of ways that social attributes shape social action, and in turn create social context for the actions of others.
Meta level goal: maximize the breadth and depth of learning in data analysis skills for all members of learning community through collaborative construction of learning materials and projects.
Students will become self sufficient contributors to research projects. They should be able to use existing data to answer sociological questions using descriptive statistics, inferential statistics and visualization to learn and convey insights about the social questions represented in the data. Students should understand how to use survey items to create indicators, how to combine or recode indicators in order to construct new variables, and how to use variables to represent concepts that allow testing of theoretical questions.
Undergraduate and graduate students are expected to work on their projects as though they are working as summer interns on a project in the research department of a major business. Because of their higher levels of experience graduate students are expected to demonstrate leadership in the completion of assignments and taking leadership roles in their projects. The assignments for graduate and undergraduate students are the same, however the graduate level will be held to a higher standard.
There are three types of evaluations for a total of 500 points:
Projects = 200
White paper = 100
Learning modules = 100
Participation = 50
Note: Why you need a Gmail / Google account. You need a google account to create and use google documents, and google sheets.
Projects will focus on the following stages, each stage including the previous stages. There will be 3 projects, all of which are organized on the student resource page.
Project 1 Data management, preliminary analysis and reporting: (data set 1, 100 points)
Tasks include: use Excel and Sheets, construct a codebook, work with data sets, clean data, construct indices, and perform descriptive analysis
Deliverables: Individual report, individual data set, collaborative codebook.
Project 2: Using R, work with syntax, re-code variables, perform exploratory analysis (dataset 1) (100 pts)
Write up results. Deliverable items include report, and professional syntax file.
individual report, individual data set, individual syntax file
White paper: Performing and interpreting regression analysis, testing hypotheses, (data set 1 or alternative)
Write up results. Connect to research literature.
Deliverables: Group or individual report, dataset, syntax file.
Online learning
A small set of online learning modules are included to help students review and reinforce key concepts.
Participation
Contribute and interact in class
Make contributions to the function and syntax exchange
We use the 4th edition of Singleton and Straits because it is a good reference text about general research methods. The fourth edition is much less expensive, do not get the current edition.
Singleton Jr, R. A., Straits, B. C., & Straits, M. M. (2005). Approaches to social research. Oxford University Press.
Order a used, 4th edition version of our textbook for cheap: link to ~20 copies under $20 shipping include
Stowell, S. (2014). Using R for statistics. Apress. This introduces many base R procedures that are relevant for statistical analysis
A PDF of this book is available free to OU students via the OU Library.
Google sheets 101: Zapier team. Online and free.
Google sheets is like Excel, but it is easier to use online data sources and to work in a group. Additional optional books on using google sheets.
R for Data Science: Garrett Grolemund and Hadley Wickham. Online and free.
Start learning how to work more effectively with both quantitative and qualitative data.
DO NOT buy the following books (not yet). These are helpful resources for reviewing ideas standard in statistical analysis.
Lewis-Beck, C., & Lewis-Beck, M. (2015). Applied regression: An introduction (Vol. 22). Sage publications.
This is super helpful as an intro and a reference.
Pampel, F. C. (2000). Logistic regression: A primer (Vol. 132). Sage.
Great explanation of the logit transformation and intro to logistic regression.
Message me on Slack. I am at my computer many more hours than the office hours,
Howard T. Welser, Professor Welser
Office: Bentley Annex 123
Email: h.t.welser@gmail.com; welser@ohio.edu
Attention: Treat class time like your job, but treat learning like your favorite game. Try to discover the spark of interest that makes you want to learn more about something and dive into it.
Attendance: Be in class everyday, and be ready to do the work that we have planned of that day. If you know you will miss a day it is your job to get your work done ahead of time. You need to inform me well in advance of class if you will not be attending.
Have fun: This is an upper level / graduate level elective. We are all here to learn, and the best way to learn is to have fun by getting involved in the work.
Polite electronic communication: Use subjects for your emails like “Soc 101 Project Question” etc. Be brief, courteous and considerate. I will be brief, to the point, and to the best of my abilities, prompt. Send a follow up if you don’t hear back after 24 hours (during the school week).
Take credit for your work only: I should not need to mention this in this course, but I will include it from my 100 level syllabus: You should, with pride, lay claim to all of your unique contributions. When you work with others on assignments you should take pains to assure that you know, appreciate, and clearly identify the contributions of each of your colleagues. Deliberate attempts to claim the work of others as your own without clear acknowledgement will be seen as plagiarism and will be severely punished: a grade of F will be assigned to the plagiarized assignment.
Students with disabilities: I will gladly provide reasonable accommodations for students with disabilities, with the recommendation of Disability Services, at the Office for Institutional Equity (740-593-2620). Please show me the letter from that office indicating accommodations that you may need for this class.