University of Wisconsin-Whitewater
Curriculum Proposal Form #3

New Course

Effective Term:		

Subject Area - Course Number:	SOC 295 	Cross-listing:	MATH 295
(See Note #1 below)

Course Title: (Limited to 65 characters)	Basic Social Statistics
25-Character Abbreviation:  	     	
Sponsor(s):  	Leda Nath and Akiko Yoshida / William Mickelson
Department(s):	Sociology, Anthropology & Criminal Justice / Math & Computer Science
College(s):		
Consultation took place:		NA 		Yes  (list departments and attach consultation sheet)
Departments:  Mathematics, Psychology, Economics	
Programs Affected:	Mathematics; Psychology; Economics
Is paperwork complete for those programs? (Use "Form 2" for Catalog & Academic Report updates)
	NA 		Yes		will be at future meeting

Prerequisites:	SOC 240 Sociological Principles; grade of C or better in MATH 141 Intermediate Algebra  or MATH 140 Mathematical Ideas (Unrequisite: MATH 230 Introductory Statistics, MATH 231 Understanding Probability and Statistics,  PSYCH 215 Basic Statistical Methods, ECON 245 Business Statistics I)

Grade Basis:		Conventional Letter		S/NC or Pass/Fail	 

Course will be offered:		Part of Load		 Above Load 
		On Campus		Off Campus - Location       
College:		Dept/Area(s):	Sociology, Anthropology & Criminal Justice
Instructor:	William Mickelson and one new faculty member who begins August 2012
	Note: If the course is dual-listed, instructor must be a member of Grad Faculty.

Check if the Course is to Meet Any of the Following:
  Technological Literacy Requirement	  Writing Requirement	
  Diversity 		  General Education Option:     
Note:  For the Gen Ed option, the proposal should address how this course relates to specific core courses, meets the goals of General Education in providing breadth, and incorporates scholarship in the appropriate field relating to women and gender.

Credit/Contact Hours: (per semester)
Total lab hours:	0	Total lecture hours: 	2400	 
Number of credits:	        3	Total contact hours:	2400 

Can course be taken more than once for credit?  (Repeatability)  
 No    Yes          If "Yes", answer the following questions:
No of times in major:	     	No of credits in major: 	     	 
No of times in degree:	     	No of credits in degree:	      

Proposal Information: (Procedures for form #3)
Course justification: Statistical methods are the most commonly used research methods in Sociology. After a review of comparable undergraduate Sociology degree programs in the region, it was noted that we are one of the only programs that does not formally prepare our students in understanding how to conduct and interpret various statistics related to social science. Currently, we squeeze a statistics component into our required SOC 476 Research Methods class. Doing so compromises the time spent on teaching and learning research methods, though it allows us to introduce students to some basic statistics.  During Spring 2009 and Spring 2010, some quantitative and qualitative assessment was conducted on the SOC 476 course to determine the level of learning in the class.  In relation to the statistical component of the course, students indicated that not only did they appreciate the statistics section of the course, but they also felt the time spent on that subject was not enough, was rushed, and that more lab time and evaluation of statistical tables in actual research and texts would be helpful. Adding this course provides the ideal platform to meet those needs and extend time and attention to statistics for our students.  This course also provides relevant education for students to be better prepared for a professional job and/or for graduate studies in Sociology or other related programs. In addition, this type of quantitative literacy is in demand as noted in the LEAP initiative as an essential learning outcome. Statistics learned within our department allows us to use applied and basic social research examples relevant to our students’ immediate post-college future while also addressing the need to increase the value of our program.  Finally, this course helps fulfill the technological literacy requirement as it requires students use computers to work on raw and existing (e.g., General Social Survey) datasets, and to run and interpret several statistical models using the well-known SPSS statistical software program.  This course will be primarily taught by new Assistant Professor whose starting date will be August 2012 (note that our interview candidates have been recommended and we intend to interview them in late January, 2012) and also by Dr. Mickelson from the Mathematics & CS.  There is no current plan to “team teach.” 

Relationship to program assessment objectives: This course aligns directly with several Sociology program student learning outcomes in quotes that follow. For example, through understanding of different multivariate models which may include various demographic variables, students will begin to “recognize the complex intersections between race, class, gender and other social categories.” Through in-class group assignments, students will “engage in collaboration and team work.”  By gaining knowledge in quantitative research, specifically statistics, students begin to increase their “opportunities to prepare for advanced education beyond the bachelor's degree,” “have opportunities to participate in social science research,” and “demonstrate quantitative literacy.”  Through participation and practice in writing up statistical results, students will have opportunities to “demonstrate written communication.”
In relation to the Mathematical and Computer Science Department, cross-listing this course and allowing for Dr. Mickelson to teach the course fits with the department’s mission to promote collaboration while driving learning and expansion of mathematical knowledge within the university.  Collaboration is reflected in this course design in that both faculty in Sociology and in Mathematics contributed to its design. This joint effort in design and in teaching also reflects the Department mission to build a collaborative professional community of faculty and students.  Learning and expansion of mathematical knowledge within the university is reflected by the cross-listing of the course, allowing math and sociology students to take the class sitting side-by-side thereby creating an atmosphere for stimulating discussion and a climate that nurtures curiosity and inspires creativity, two other goals of the Mathematical and Computer Science Department.   
Budgetary impact: This new course will not affect the staffing budget as the Provost has approved budget for a new faculty line in our department. Candidates for this new line are likely to be scheduled for interview in January 2012. Dr. Mickelson from Mathematics will teach the course occasionally as well. The assigned text will be the primary text for the course, to be available via Textbook Rental.  Additional materials for the class include articles freely downloadable via our library EbscoHOST database which includes social science research. The course will include use of the VonNeuman computer lab. The lab is available for the time periods needed for this course without adding strain to other courses that may need the lab.  More specifically, this course is providing a more in-depth approach to a topic that is currently squeezed into an existing Methods course.  Upon its offering, the existing course will no longer squeeze in its statistical unit thereby no longer using the VonNeuman computer lab freeing it up for this new course.

Course description: (50 word limit)
Introduction to basic statistical methods and their utility in sociology including statistical concepts, frequency distribution, measures of central tendency and variability, correlation analysis, OLS regression analysis, and including the logic of hypothesis testing. In addition, introduction to basic operations of PASW (formally SPSS) statistical software in social data analysis.

If dual listed, list graduate level requirements for the following:
1. Content (e.g., What are additional presentation/project requirements?) 
2. Intensity (e.g., How are the processes and standards of evaluation different for graduates and undergraduates? ) 
3. Self-Directed (e.g., How are research expectations differ for graduates and undergraduates?) 

Course objectives and tentative course syllabus:		

Instructor: New Hire effective August 2012 (and occasionally Dr. Bill Mickelson)
Office: Carlson Hall TBD
Phone: 262-472-TBD
Hours: TBD

SOC 295/MATH 295 Basic Social Statistics

Learning Outcomes:
Students who complete this course will gain basic understanding of statistical methods and their utility within a sociological context and other related areas as a means of summarizing data, making estimates and testing hypotheses. Students will learn concepts and how to make inferences about populations from sample statistics. Students will gain quantitative literacy related to basic statistics and learn basic operations of SPSS statistical software.  Students will:
(1)	Understand patterns in social data by leaning to calculate (e.g., central tendencies, standard deviation, Pearson correlation) and interpret basic statistics (e.g., central tendencies, standard deviation, Pearson correlation, OLS regression).
(2)	learn to create and interpret graphs, tables, and charts displaying sociology-related statistical results.
(3)	critically evaluate others’ basic statistical reports in sociological journals and otherwise.
(4)	learn basic operations of SPSS to enter and analyze original data as well as secondary data.

Text: Jack Levin & James Alan Fox (2011). Elementary Statistics in Social Research The Essentials. Third Edition. Allyn & Bacon: Boston, MA

Homework: Weekly assignments practicing, understanding, identifying, conducting or interpreting basic statistics within the social science context and related applied fields.

In Class Work: Various individual and group exercises practicing various levels of statistical calculations, analysis and interpretation and within the social science context.

Attendance Policy: Students are expected to attend class to hear lecture, participate in discussion, tests, quizzing, and also the various in-class assignments. Students who miss a class must provide a formal document justifying their absence before requesting any opportunity to make up missed work. Make ups are allowed up to three times (for example, one quiz, one test, and one homework). Up to three unexcused undocumented make ups are also allowed, but with a 20% late penalty applied.  No make ups are allowed after three times total (for example, one excused quiz make up, two unexcused test make ups). 

Course Breakdown:
In Class Work:					10%
Homework:					15%
Writing Assignment (journal article critique): 	15%
Exam:					25%
Cumulative Final Exam: 				35%
Total:					100%

Tentative Calendar:
Week	Description	
1	Use of statistics in understanding the social world.	
2	Using General Social Survey (GSS) to illustrate variables and their levels of measurement; sampling.	
3	Using common demographic variables (e.g., sex, race, age) to understand the art of exploratory data analysis (e.g., descriptive statistics and graphing).	
4	Using social variables (e.g., crime, deviance) to demonstrate measures of variability (range, percentiles, standard deviation.), empirical rules and Z-scores	
5	Lab time for SPSS: introduction and practice (data entry; descriptive statistics; creating charts and tables.)	
6	Evaluating sociological journal articles and popular media to discuss the “ideal and the real”; descriptive statistics.	
7	Evaluating sociological journal articles and popular media to discuss the “ideal and the real”: sampling, sampling distribution, effect sizes, confidence intervals & margins of error. 	
8	Evaluating sociological journal articles and popular media to discuss the “ideal and the real”: Significance tests. Also, review and Exam.	
9	Understanding patterns in social data (e.g., years of education and status attainment) through techniques of correlation, linear regression and residual analysis.	
10	Understanding patterns in social data (e.g., population and crime incidents) through techniques of linear regression and correlation: measuring linear association (slope, Pearson correlation, R-squared).	
11	Understanding patterns in social data (e.g., natural resources and internal war while considering governance) through linear regression and correlation: multivariate model (causality; control variables) 	
12	Lab time for SPSS: Secondary data analysis with GSS: data cleaning, recoding, index, descriptive statistics	
13	Lab time for SPSS: Secondary data analysis using GSS: bivariate & multivariate models	
14	Social Science reporting: What to report in Methods, Findings, and Conclusion sections; making tables for descriptive & inferential statistics	
15	Evaluating sociological journal articles and popular media; interpreting statistics.	
16	Review & Final Exam (Cumulative—during finals week)	

The University of Wisconsin-Whitewater is dedicated to a safe, supportive and non-discriminatory learning environment.  It is the responsibility of all undergraduate and graduate students to familiarize themselves with University policies regarding Special Accommodations, Academic Misconduct, Religious Beliefs Accommodation, Discrimination and Absence for University Sponsored Events (for details, please refer to the Schedule of Classes, the “Rights and Responsibilities” section of the undergraduate catalog;  the Academic Requirements and Policies and the Facilities and Services sections of the graduate catalog;  and the “Student Academic Disciplinary Procedures (UWS Chapter 14);  and  the “Student Non-academic Disciplinary Procedures” (UWS Chapter 17).

The UW System standard for work required per credit is that students are expected to invest at least 3 hours of combined in-class and out-of-class work per week for each academic unit (credit) of coursework;  thus a 3-credit course will typically require a minimum of 9 hours of work per week (144 hours/semester).

Bibliography: (Key or essential references only.  Normally the bibliography should be no more than one or two pages in length.)  

*Blalock, H. M. (1987). “Some General Goals in Teaching Statistics,” Teaching Sociology 15(2):164-172.

Blalock, Hurbert (1994). Statistics, 5ed.  Wadsworth 

*DeCesare, Michael (2007). “’Statistics Anxiety’ Among Sociology Majors: A First Diagnosis and some Treatment Options,” Teaching Sociology, 35(4):360-367.

Hays, William (1979). Statistics for the Social Sciences, 2ed. McGraw Hill 

* Levin & James Alan Fox (2011). Elementary Statistics in Social Research The Essentials. Third Edition. Allyn & Bacon: Boston, MA

*Kinnear, Paul R. (2010). PASW Statistics 17 Made Simple. Psychology Press: New York, NY.

*Kirkpatrick, Lee A. (2009). A Simple Guide to SPSS. Wadsworth: London

Knoke, David, George W. Bohrnstedt and Alisa Potter Mee (2002). Statistics for Social Data Analysis, 2ed.  Wadsworth

Lewis-Beck, Michael (1995). Data Analysis: An Introduction. Sage Publications.

*Maier, Mark (1995). The Data Game: Controversies in Social Science Statistics. Sharpe: Armonk, NY

*Marson, Stephen M. (2007). “Three Empirical Strategies for Teaching Statistics,” Journal of Teaching Social Work, 27(3/4): 199-213.

*Nakagawa, Shinichi; Cuthill, Innes C (2007). “Effect size, confidence interval and statistical significance: a practical guide for biologists,” Biological Reviews Cambridge Philosophical Society, 82(4): 591–605.

*Paxton, Pamela (2006). “Dollars and Sense: Convincing Students That They Can Leanr and Want To Learn Statistics,” Teaching Sociology, 34(1):65-70.

*Potter, Alisa M. (1995). “Statistics for Sociologists: Teaching Techniques that Work,” Teaching Sociology, 23(3): 259-263.

*Reid, Howard and Susan Mason (2008). “Re-Thinking Statistics Education for Social Science Majors,” Education, 128(3):473-476.

Robinson, Daniel H. and Joel R. Levine (1997). “Reflections on Statistical and Substantive Significance, With a Slice of Replication,” Educational Researcher, 26(5):21-26.

Thompson, Bruce (1996). “AERA Editorial Policies Regarding Statistical Significance Testing: Three Suggested Reforms,” Educational Researcher, 25(2):26-30.

Thompson, Bruce (1997). “Editorial Policies Regarding Statistical Significance Tests: Further Comments,” Educational Researcher, 26(5):29-32.

*Wilkinson, Leland; APA Task Force on Statistical Inference (1999). “Statistical methods in psychology journals: Guidelines and explanations,” American Psychologist, 54:594–604

The University of Wisconsin-Whitewater is dedicated to a safe, supportive and    non-discriminatory learning environment.  It is the responsibility of all undergraduate and graduate students to familiarize themselves with University policies regarding Special Accommodations, Academic Misconduct, Religious Beliefs Accommodation, Discrimination and Absence for University Sponsored Events (for details please refer to the Schedule of Classes; the “Rights and Responsibilities” section of the Undergraduate Catalog; the Academic Requirements and Policies and the Facilities and Services sections of the Graduate Catalog; and the “Student Academic Disciplinary Procedures (UWS Chapter 14); and the “Student Nonacademic Disciplinary Procedures" (UWS Chapter 17).