Oct 2:  Introduction of the Course and Data Processing

Oct. 4:  A Data Processing System - from observation to knowledge

Week 2: Data Acquisition

Oct. 9:  The Data Collection Process - from observation to data

[ Recommended Reading: Linda B. Bourgue & Virginia A. Clark 1992 Processing Data: The Survey Example. Newbury Park, CA: Sage ]

Oct. 11:  Data Achieves & Internet 

[ Recommended Reading: Glen H. Elder Jr., Eliza K. Paralko and Elizabeth C. Clipp 1993 Working with Archival Data: Studying Lives. Newbury Park, CA: Sage ]

Week 3: Data Types

Oct 16:  Qualitative vs. Quantitative Data

Oct. 18:  Cross-sectional vs. Longitudinal & Measurement Levels

Week 4: Data Processing Tools

Oct. 23:  Software Tools & SPSS Introduction I

[ Play with these software tools and read the HELP and TUTORIALS ]

Oct. 25:  SPSS Introduction II

Week 5: Data Processing Methods I

Oct. 30:  Missing Values & Outliners / Midterm Review 

Nov. 1:  Summarizing and Visualizing Data

Week 6: Data Processing Methods II

Nov. 6:  Statistically Analyzing Data I: Correlation and others

Nov. 8:  Statistically Analyzing Data II: Hypothesis Testing 

Week 7: Data Processing Methods III

Nov. 13:  Geometric Data Model & Dimensional Analysis I

Nov. 15:  Hypothesis Testing II and Introduction to Regression

Week 8: Data Processing Methods IV

Nov. 20:  Causal Modeling & Regression

Nov. 22:  Holiday No Class

Week 9: Presenting Data Processing Results

Nov. 27:  Research Presentation I - Organizing Your Evidence

Nov. 29:  Research Presentation II - PowerPoint & Web Pages

[ Play with these software tools and read the HELP and TUTORIALS ]

Week 10: Summary

Dec. 4:  Summarizing DP & Research Presentation

Dec. 6:  Conclusion (and about the final)

Click here for the PowerPoint slides of the lectures