DPLS 722sp07 - Quantitative Data Analysis

DPLS 722 - Quantitative Data Analysis
Spring 2007                              3 Credits

Room 202 Shoenberg Center

Professor: Sandra M. Wilson; wilson@gonzaga.edu  (509-323-3517)

Course Overview
Quantitative data analyses require the use of statistics (descriptive and inferential) to summarize data collected, to make comparisons of data sets, and to generalize results obtained for a sample back to the population from which the sample was drawn.  Knowledge about data analyses can help the researcher interpret data for the purpose of providing meaningful insights about the problem being investigated. 

This course approaches statistics from a problem-solving perspective as emphasis is placed on selecting appropriate statistical techniques for various research designs and on interpreting and reporting data analyses results.  Computer data analysis (using the windows 13.0 version of the Statistical Package for the Social Sciences (SPSS)) will be a primary focus of the course to further illustrate the use and interpretation of statistics in research.

The course content is organized into seven modules.  Although the course will progress through the modules on a scheduled basis, if you find yourself confused and frustrated, we suggest you take an incomplete for the course and finish the work at a later time.  Please call to schedule an appointment with either of us if you need some assistance.  We would be most willing to take whatever time is needed to help you learn and hopefully enjoy statistics.

Course Objectives
This course is designed to enable students to:

1. understand the basic conceptual framework for parametric and nonparametric inferential statistics as presented in the seven modules;
2. learn how to use SPSS for data entry and analysis;
3. read and interpret results obtained from various statistical analyses and computer printouts; and
4. clearly present and discuss results in writing.

Note: each module includes objectives specific to that module.

Course Evaluation
Grades for the course will be based on:

1. Self-assessment scores obtained for the seven modules (5%)
2. SPSS lab exercises (25%)
3. Final exam (individual and in small groups) (25%)
4. Statistics project (35%)
5. Attendance and participation (10%)

Course Texts
Required:

Norusis, M.J. (2005).  SPSS 13.0 guide to data analysis. Englewood Cliffs, NJ: Prentice Hall.

Module Packages designed by Sandra Wilson

Optional (but strongly recommended):

Hinkle, D., Wiersma, W., & Jurs, S. (1998). Applied statistics for the behavioral sciences.  Boston: Houghton Mifflin.

Outline of Sessions

Session 1 (January 13)
a. Course overview
b. Overview of statistics (Module 1)
c. Introduction to SPSS

Session 2 (January 20)
a. Inferential statistics and sampling distributions (Module 2)
b. The standard normal distribution and hypothesis testing (Module 3)
c. SPSS lab

Session 3 (February 3)
a. The t-distribution and hypothesis testing (Module 4)
b. SPSS lab

Session 4 (February 17)
a. Module 4 (cont.)
b. Hypothesis testing for ANOVA (Module 5)
c. SPSS lab

Session 5 (March 3)
a. Module 5 (cont.)
b. SPSS lab

Session 6 (March 10)
a. Hypothesis testing for correlation and regression (Module 6)
b. SPSS lab

Session 7 (March 31)
a. Hypothesis testing for nonparametric data (Module 7)
b. SPSS lab

Session 8 (April 14)
a. Final exam
b. Lab assignments, self-evaluations, and statistics project due