Statistics and Data Analysis

Instructor: Ting Shih-Chan (Eric Ting)

Office: Room729                 Tel.: (02)24622192 ext.7050

Cell phone: 0928813517     Email: ericting@mail.ntou.edu.tw      

Personal web page: Eric Ting Ocean

Office hour: meetings by appointments

Learning Objectives:

Students completing the course should gain the following knowledge and skills:

l      An understanding of basic descriptive statistics and ability to calculate these statistics and to generate them using SPSS (Statistical Package for the Social Sciences) software; an understanding of when each may be appropriate for descriptive purposes.

l      An ability to determine appropriate tests of statistical significance for differences in means, differences in percentage distributions and cross-tabulations, correlation coefficients and partial coefficients, and to generate the relevant statistics using SPSS software.

l      An ability to structure a multiple regression analysis, to generate regression results using SPSS software, and to interpret these results for statistical and theoretical significance.

Course Overview:

This course introduces students to basic statistical methods and their application, and aims to familiarize students with the basics of research design and statistical analysis. If you don't remember much from your previous statistics courses -- which is typical, actually -- don't panic. We'll be starting more or less at the beginning. In many ways this semester will be much like an advanced version of an introductory postgraduate statistics course, covering the same topics -- e.g., descriptive statistics, t-tests and chi-square tests, correlation and simple/multiple regression, and analysis of variance -- but in somewhat greater depth.

Proficiency with algebra is helpful; yet, no mathematics beyond algebra will be taught in this course. The purpose of this course is to prepare students to analyze real data from real research, and to understand these analyses at a conceptual level. Toward this end, we will focus more on concepts and computer analyses, and less on hand calculations and mathematics. Students will practice working with and analyzing an actual dataset using SPSS software for Windows and should be able to distinguish between theories and hypotheses; analyze and interpret statistical results; present data in graphical and tabular form; and perform basic statistical analysis using SPSS.

Texts and Software:

Textbooks

1, Morgan, G. A., Leech, N. L., Gloeckner, G. W. and Barrett, K. C., SPSS for Introductory Statistics: Use and Interpretation, Lawrence Erlbaum Associates, Inc., New Jersey, 2004.

2, Leech, N. L., Barrett, K. C. and Morgan, G. A., SPSS for Intermediate Statistics: Use and Interpretation, Lawrence Erlbaum Associates, Inc., New Jersey, 2004.

SPSS

This course will use Statistical Package for the Social Sciences (SPSS) computer software. Students will be expected to have access to SPSS and be able to print SPSS output to complete homework assignments.

Tentative Course Schedule:

Week 1

Introduction to statistics and data analysis

Week 2

Chapter 1 Variables, Research Problems and Questions. (Textbook 1)

Chapter 2 Data Coding, Entry, and Checking. (Textbook 1)

Week 3

Chapter 3 Measurement and Descriptive Statistics. (Textbook 1)

Chapter 4 Understanding Your Data and Checking Assumptions. (Textbook 1)

Week 4

Chapter 5 Data File Measurement. (Textbook 1)

Chapter 6 Selecting and Interpreting Inferential Statistics. (Textbook 1)

Week 5

Chapter 7 Cross Tabulation, Chi-Square, and Nonparametric Measures of Association. (Textbook 1)

Week 6

Chapter 8 Correlation and Regression. (Textbook 1)

Week 7

Chapter 9 Comparing Groups with t Tests and Similar Nonparametric Tests.  (Textbook 1)

Week 8

Chapter 10 Analysis of Variance ANOVA.  (Textbook 1)

Week 9

Mid-term exam.

Week 11

Chapter 4 Several Measures of Reliability.  (Textbook 2)

Week 12

Chapter 5 Exploratory Factor Analysis and Principal Components Analysis.  (Textbook 2)

Week 13

Chapter 6 Multiple Regression.  (Textbook 2)

Week 14

Chapter 7 Logistic Regression and Discriminant Analysis .  (Textbook 2)

Week 15

Chapter 8 Factorial ANOVA and ANCOVA.  (Textbook 2)

Week 16

Term paper presentation.

Week 17

Term paper presentation.

Week 18

Final exam.

Course  Requirements and Grades:

The course grade will be based on the following:

1. out of class, open-book mid-term exam (20%);

2. out of class, open-book final exam (20%);

3. term paper report and presentation (40%); and

4. assigned homework and contribution to class discussions (20%).