Classroom: Administration 204B

Class Time: Monday/Wednesday 1:45-3:30 P.M.

Instructor: Devon Brewer, Ph.D.

This course provides an introduction to statistics for students in the social sciences. The primary emphasis is on descriptive, rather than inferential, statistics. In addition, the course stresses statistical reasoning over mechanical calculation. The course covers many of the common statistics used in the social sciences and highlights data analysis and research applications. This course also equips students with the essential skills for understanding and evaluating statistics encountered in daily life (such as those reported in the media). Classes involve lectures and computer lab sessions, both with active class participation. The only prerequisite is an understanding of basic algebra.

Course Objectives

This course has 4 main objectives. Students will:

• gain a basic understanding of common statistical methods used in the social sciences

• learn how to interpret and critique statistics encountered in daily life and apply statistical reasoning to real-world problems

• become familiar with statistical software for data analysis

• design and execute a small data analysis project

Textbooks, readings, and software

The following book is required for the course and is available at the PLU Bookstore:

Frankfort-Nachmias, Chava, & Leon-Guerrero, Anna. (2000). Social Statistics for a Diverse Society, 2^nd edition. Thousand Oaks, CA: Pine Forge Press.

There is also a small set of required readings (excerpts from other textbooks) on reserve at the library. You may copy these or access them for free via the library's eReserves (http://www.plu.edu/~ereserve). You should print out the readings to make sure you have a hard copy.

For this course you will need to have regular access to a computer that can access the Internet. The statistical software we will be using runs on the Web. The two main sites we'll be using are:

WebStat 2.0 (http://www.stat.sc.edu/webstat/version2.0/)

SDA: Survey Documentation and Analysis (http://csa.berkeley.edu:7502/)

The free programs at these web sites will allow you to do all the data analysis required for homework assignments and class projects. These sites also have many interesting data sets available for analysis (as well as the capability to import or enter other data), and are easier to use than standard statistical software packages.

If you do not already have access to the Internet on your home computer, you main obtain it free of charge from a number of services. One free service is Netzero. You can download the necessary software from http://www.netzero.net/ or order a CD. There are also many other free Internet service providers you can use as well.

Requirements and Grading

Attendance, notes, and reading assignments

I strongly urge you to attend class every scheduled session, although you will not be directly penalized for missing class. However, you are responsible for all material presented in class and handing in assignments on time. This means that if you miss class, you must get copies of notes from a classmate--I will not provide notes for anyone. I will make copies of my overheads available on the web at http://www.plu.edu/~brewerdd/stat231. If you need handouts given in a previous meeting of the class, you must make copies from a classmate or get them from me during office hours. In my previous classes, students' performance tended to drop about a full grade (on average) for every two to three class sessions missed. In effect, you penalize yourself if you miss class.

Make sure to read the material assigned for a particular day before coming to that class session.

Homework assignments

Homework assignments will consist of selected exercises at the end of assigned chapters, web-based data analysis exercises, and additional questions/problems. There are a total of 10 homework assignments, due at the beginning of class on the specified date. I will not accept any late homework assignments, but you may miss one homework assignment and still receive full course credit for homework (if you turn in all 10 assignments, I will take the 9 highest scores).

Exams and paper

Exams consist of primarily short answer questions with a few multiple choice, true-false, and fill-in-the-blank questions. Each student will also independently conduct a small data analysis project and report on it in a paper 5-7 pages long. We will discuss the paper assignment in greater detail in the 7th week of the course.

Grading components

Grades are based on the following components and weights:

quiz 10%

homework assignments 15%

midterm exam 25%

project paper 25%

final exam 25%

A = 93-100%  
A- = 90-92%  
B+ = 87-89%
B = 83-86%
B- = 80-82%
C+ = 77-79%
C = 73-76%
C- = 70-72%
D+ = 67-69
D = 63-66%
D- = 60-62%
E < 60%

Extra credit

Students can earn up to 10% additional course credit by completing optional extra assignments and attending class consistently. Two extra assignments, worth 3% each, involve reading additional materials and completing exercises relating to those materials. In addition, students may turn in up to 2 brief reports (worth 1% each) that describe and critique examples of the inappropriate use of statistics in the media or some other daily life context. Details on all of these extra credit assignments are available on the course web page. Finally, if you attend 24 or more of the 26 class sessions (I will take attendance), then you will earn 2% additional course credit.

Policies

• Come visit me during office hours if you have any questions about the requirements, assignments, exams, or any other aspect of the course. I cannot help you if I am not aware of your particular circumstances, concerns, or needs.

• Cheating and plagiarism (passing off someone else’s writing as your own) each will result in severe penalties, including a failing grade. Refer to your student handbook and the University Catalog for the University’s standards for academic honesty and integrity (a summary is available at http://www.plu.edu/acad/integ.shtml).

• Make-up exams will be given only to those who make special arrangements in advance or who have medical excuses or personal catastrophes that are documented in writing.

• If you disagree with the score I have given to your answer on an exam question or an assignment and you wish to challenge it, you may submit an appeal to me. The written appeal should be a paragraph that explains why you think you are correct and cites supporting materials presented in class or the readings.

• Late papers will be penalized.

• Please make an appointment with me as soon as possible if you need accommodations for or adaptations to the course because of a disability, if you have emergency medical information to share with me, or if you need special arrangements in case the building must be evacuated. If you have any questions about the services available for special needs at PLU, please call the Counseling/Testing office (7206).

• You are responsible for all deadlines and other information in this syllabus and any changes that might be announced in class.

Weekly Schedule (tentative)

Week 1
	Wednesday, Feb. 7
	Introduction
	Purposes of statistics
	Review of mathematical concepts,
	operations, and notation
Week 2
Monday, Feb. 12	Wednesday, Feb. 14
Overview of research design	Homework #1 due
Scales of measurement	Frequency distributions
Lab	Read: ch. 2
Read: ch. 1
Week 3
Monday, Feb. 19	Wednesday, Feb. 21
Presidents' Day Holiday - no class	Homework #2 due
	Graphic displays of univariate data
	Read: ch. 3
Week 4
Monday, Feb. 26	Wednesday, Feb. 28
Homework #3 due	Measures of dispersion
Measures of central tendency	Read: ch. 5
Distribution shapes
Lab
Read: Ch. 4
Week 5
Monday, Mar. 5	Wednesday, Mar. 7
Homework #4 due	Measures of bivariate association:
Displaying categorical bivariate data:	categorical data
crosstabulations	Read: ch. 7, Agresti & Finlay, Utts - ch. 12
Lab
Read: ch. 6
Week 6
Monday, Mar. 12	Wednesday, Mar. 14
Quiz	Homework #5 due
Measures of bivariate association:	Scatterplots
categorical data (cont.)	Regression
Lab	Read: ch. 8
Week 7
Monday, Mar. 19	Wednesday, Mar. 21
Regression (cont.)	Correlation (cont.)
Correlation	Project papers assigned
Lab
Week 8
Monday, Mar. 26	Wednesday, Mar. 28
Spring Break - no class	Spring Break - no class
Week 9
Monday, April 2	Wednesday, April 4
Review of descriptive data analysis	Normal distribution
Homework #6 due	Read: ch. 10
Week 10
Monday, April 9	Wednesday, April 11
Midterm Exam	Homework #7 due
Extra credit report #1 due	Probability, Sampling distributions
	Read: ch. 11
Week 11
Monday, April 16	Wednesday, April 18
Sampling distributions (cont.)	Homework #8 due
Lab	Estimation
	Read: ch. 12
Week 12
Monday, April 23	Wednesday, April 25
Estimation	Homework #9 due
Lab	Significance testing
	Read: Utts, ch. 21, 23
Week 13
Monday, April 30	Wednesday, May 2
Significance testing	Time series
	Read: Utts, ch. 14
Week 14
Monday, May 7	Wednesday, May 9
Time series	Homework #10 due
Meta-analysis	Meta-analysis (continued)

 
 

Week 15
Monday, May 13	Monday, May 15
Multivariate analysis	Review
	Course evaluations
	Paper Due

Readings on reserve

Agresti, A., & Finlay, B. (1986). Statistical methods for the social sciences. (pp. 212-219). San Francisco: Dellen Publishing Co.

Utts, J. M. (1999). Seeing through statistics, 2^nd ed. (Ch. 12, 14, 21, 23). Pacific Grove, CA: Duxbury Press.