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Essential first steps to data analysis : scenario-based examples using SPSS / Carol S. Parke.

By: Material type: TextTextPublication details: Los Angeles: SAGE Publications, Inc. [2013]Description: xxi, 265 p. : ill. ; 24 cmISBN:
  • 9781412997515 (pbk. : acid-free paper)
Subject(s): LOC classification:
  • HA32  .P37 2013
Contents:
Section 1. The Sample Module 1. Checking the Representativeness of a Sample Module 2. Splitting a File, Selecting Cases, Creating Standardized Values and Ranks Section 2. Nature and Distribution of Variables Module 3. Recoding, Counting, and Computing Variables Module 4. Determining the Scale of a Variable Module 5. Identifying and Addressing Outliers Section 3. Model Assumptions Module 6. Evaluating Model Assumptions for Testing Mean Differences Module 7. Evaluating Model Assumptions for Multiple Regression Analysis Section 4. Missing Data Module 8. Determining the Quantity and Nature of Missing Data Module 9. Quantifying Missing Data and Diagnosing its Patterns Section 5. Working with Multiple Data Files Module 10. Merging Files Module 11. Aggregating Data and Restructuring Files Module 12. Identifying a Cohort of Students
Item type: Research Collection
Holdings
Current library Collection Call number Vol info Status Date due Barcode
Judith Thomas Library Judith Thomas Library Research Section JTL Research Collection HA 32 .P37 2013 (Browse shelf(Opens below)) AUA017510 Available AUA017510

Includes bibliographical references (page 261) and index.

Section 1. The Sample Module 1. Checking the Representativeness of a Sample Module 2. Splitting a File, Selecting Cases, Creating Standardized Values and Ranks Section 2. Nature and Distribution of Variables Module 3. Recoding, Counting, and Computing Variables Module 4. Determining the Scale of a Variable Module 5. Identifying and Addressing Outliers Section 3. Model Assumptions Module 6. Evaluating Model Assumptions for Testing Mean Differences Module 7. Evaluating Model Assumptions for Multiple Regression Analysis Section 4. Missing Data Module 8. Determining the Quantity and Nature of Missing Data Module 9. Quantifying Missing Data and Diagnosing its Patterns Section 5. Working with Multiple Data Files Module 10. Merging Files Module 11. Aggregating Data and Restructuring Files Module 12. Identifying a Cohort of Students