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Understanding Data Management and Reduction in SPSS: A Comprehensive Workshop Guide

This workshop offers an in-depth exploration of data management and reduction techniques in SPSS. Participants will learn how to organize data, code both closed-ended and open-ended responses, and effectively categorize raw research materials for analysis. The session will cover essential concepts like grouping respondents and understanding the implications of data reduction on research outcomes. With a focus on reliable coding practices and hands-on exercises, attendees will gain practical skills needed for effective data analysis and ensure data integrity throughout the research process.

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Understanding Data Management and Reduction in SPSS: A Comprehensive Workshop Guide

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Presentation Transcript

  1. Agenda • Organizing Data • Coding • Data reduction • Exercise: Managing data in SPSS

  2. Coding • Moving from questions posed to respondents to data for analysis • Assigning codes (usually numbers) to raw research materials • Questionnaires • Closed-ended responses • Open-ended responses • Archival material

  3. Each column is one variable Each row is one case (e.g., respondent)

  4. Data reduction • Reducing information available in raw data by grouping respondents into fewer categories • Grouping age into ranges (under 18, 19-29, 30-39, etc.) • Reduced data do not convey as much information • Knowing someone is “under 18” does not convey actual age

  5. Why reduce data? • May be easier to analyze • May be appropriate to given research question • Values above or below some threshold (e.g., a poverty line) • Type concepts (e.g., young, middle, older age) • May be only reasonable approach • Content analysis • Distribution strongly suggests categories

  6. Reliable coding • Use considerable care • Minimize number of “translation” steps • Double check all entered data • Random record checks • Double-entry • Close scrutiny of distributions, missing data • Remember: Noisy data will increase Type II error

  7. Managing data • Most use specialized computing packages (SPSS, SAS, BMDP, etc.) • Data entered into matrix • Variables and values fully labeled • Data re-coded as necessary for analysis • Data carefully inspected for univariate properties • Introduction to SPSS • Workshops: Organizing data, descriptive statistics, measuring relationships • Sample data available on course website

  8. For Thursday • Descriptive statistics • Norusis • Review Ch. 4-5 • Read Ch. 6 • Workshop schedule • Today (organizing data, descriptives) • None Thursday • Tuesday, Nov. 29 (descriptives, relationships) • Thursday, Dec. 1 (“third variables”)

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