You are previewing Understanding and Applying Research Design.
O'Reilly logo
Understanding and Applying Research Design

Book Description

A fresh approach to bridging research design with statistical analysis

While good social science requires both research design and statistical analysis, most books treat these two areas separately. Understanding and Applying Research Design introduces an accessible approach to integrating design and statistics, focusing on the processes of posing, testing, and interpreting research questions in the social sciences.

The authors analyze real-world data using SPSS software, guiding readers on the overall process of science, focusing on premises, procedures, and designs of social scientific research. Three clearly organized sections move seamlessly from theoretical topics to statistical techniques at the heart of research procedures, and finally, to practical application of research design:

  • Premises of Research introduces the research process and the capabilities of SPSS, with coverage of ethics, Empirical Generalization, and Chi Square and Contingency Table Analysis

  • Procedures of Research explores key quantitative methods in research design including measurement, correlation, regression, and causation

  • Designs of Research outlines various design frameworks, with discussion of survey research, aggregate research, and experiments

Throughout the book, SPSS software is used to showcase the discussed techniques, and detailed appendices provide guidance on key statistical procedures and tips for data management. Numerous exercises allow readers to test their comprehension of the presented material, and a related website features additional data sets and SPSS code.

Understanding and Applying Research Design is an excellent book for social sciences and education courses on research methods at the upper-undergraduate level. The book is also an insightful reference for professionals who would like to learn how to pose, test, and interpret research questions with confidence.

Table of Contents

  1. COVER
  2. TITLE PAGE
  3. COPYRIGHT PAGE
  4. DEDICATION
  5. PREFACE
  6. ACKNOWLEDGMENTS
  7. PART I: WHEEL OF SCIENCE: PREMISES OF RESEARCH
    1. 1 “DUH” SCIENCE VERSUS “HUH” SCIENCE
      1. HOW DO WE KNOW WHAT WE KNOW?
      2. “DUH” SCIENCE
      3. “HUH SCIENCE”
      4. HOW DOES SOCIAL SCIENCE RESEARCH ACTUALLY WORK?
    2. 2 THEORIES AND HYPOTHESES
      1. WHAT ARE THEORIES?
      2. WHAT ARE HYPOTHESES?
      3. OPERATIONALIZING VARIABLES
      4. INDEPENDENT AND DEPENDENT VARIABLES
    3. 3 OBSERVATION AND EMPIRICAL GENERALIZATION
      1. QUANTITATIVE DESIGNS
      2. QUALITATIVE DESIGNS
      3. RELIABILITY AND VALIDITY
      4. EMPIRICAL GENERALIZATIONS
      5. CORRELATIONAL VERSUS CAUSAL RELATIONSHIPS
      6. TYPES OF RESEARCH
    4. 4 ETHICS
      1. HUMAN SUBJECTS ABUSES
      2. PROTECTION OF HUMANS IN RESEARCH
      3. PROFESSIONAL ETHICAL STANDARDS
  8. PART II: WHEEL OF SCIENCE: PROCEDURES OF RESEARCH
    1. 5 MEASUREMENT
      1. VARIABLES AND CONSTANTS
      2. OPERATIONALIZATION
      3. VARIATION
      4. CONSTANTS
      5. LEVELS OF MEASUREMENT
      6. UNITS OF ANALYSIS
      7. RELIABILITY AND VALIDITY OF MEASURES
    2. 6 USING SPSS IN RESEARCH
      1. REAL-WORLD DATA
      2. COVERAGE OF STATISTICAL PROCEDURES
      3. SPSS BASICS
      4. GENERAL FEATURES
      5. USING SPSS WITH GENERAL SOCIAL SURVEY DATA
    3. 7 CHI-SQUARE AND CONTINGENCY TABLE ANALYSIS
      1. CONTINGENCY TABLES
      2. USING CHI SQUARE TO DETERMINE THE SIGNIFICANCE OF RESEARCH FINDINGS
      3. USING SPSS FOR THE CHI-SQUARE TEST OF INDEPENDENCE
      4. THE CROSSTABS PROCEDURE
      5. EFFECT SIZE: CONTINGENCY COEFFICIENT
      6. EFFECT SIZE: PHI COEFFICIENT
      7. EFFECT SIZE: CRAMER’S V
      8. CREATING AND ANALYZING THE CONTINGENCY TABLE DATA DIRECTLY
      9. CONCLUDING COMMENTS
    4. 8 LEARNING FROM POPULATIONS: CENSUSES AND SAMPLES
      1. CENSUSES
      2. SAMPLES
      3. PROBABILITY SAMPLING
      4. TYPES OF PROBABILITY SAMPLES
      5. SAMPLING AND STATISTICS
      6. POTENTIAL BIASES IN PROBABILITY SAMPLES
      7. NONPROBABILITY “SAMPLES”
    5. 9 CORRELATION
      1. THE NATURE OF CORRELATION: EXPLORE AND PREDICT
      2. DIFFERENT MEASUREMENT VALUES
      3. CORRELATION MEASURES
      4. INTERPRETING THE PEARSON’S CORRELATION
      5. ASSUMPTIONS FOR CORRELATION
      6. PLOTTING THE CORRELATION: THE SCATTERGRAM
      7. PATTERNS OF CORRELATIONS
      8. STRENGTH OF CORRELATIONS IN SCATTERGRAMS
      9. EVALUATING PEARSON’S r
      10. CORRELATION USING SPSS
      11. INTERPRETING r: EFFECT SIZE
      12. CORRELATION INFLUENCES
      13. CORRELATION IS NOT CAUSATION
      14. AN EXAMPLE OF CORRELATION USING SPSS
      15. NONPARAMETRIC CORRELATION
    6. 10 REGRESSION
      1. UNDERSTANDING REGRESSION THROUGH CORRELATION
      2. REGRESSION MODELS
      3. USING SPSS TO UNDERSTAND REGRESSION
      4. INTERPRETING MULTIPLE REGRESSION: THE COMBINED, OMNIBUS FINDINGS
      5. INTERPRETING MULTIPLE REGRESSION: THE INDIVIDUAL PREDICTOR FINDINGS
      6. USING MLR TO ESTABLISH CAUSALITY
      7. USING MLR WITH CATEGORICAL DATA
    7. 11 CAUSATION
      1. CRITERIA FOR CAUSATION
      2. REGRESSION ANALYSIS AND TESTING FOR SPURIOUSNESS
  9. PART III: WHEEL OF SCIENCE: DESIGNS OF RESEARCH
    1. 12 SURVEY RESEARCH
      1. NATURE OF THE SURVEY
      2. THREE TYPES OF SURVEYS
      3. ONLINE SURVEY METHODS
      4. ONLINE FORUMS
      5. SURVEY ITEM CONSTRUCTION
      6. RELIABILITY AND VALIDITY
      7. BIAS IN SURVEYS
      8. STUDYING CHANGE WITH SURVEYS
      9. USING TIME IN SURVEY STUDIES
    2. 13 AGGREGATE RESEARCH
      1. NATURE OF AGGREGATE DATA
    3. 14 EXPERIMENTS
      1. EXPERIMENTAL DESIGNS
      2. PRE-EXPERIMENTAL DESIGNS
      3. TRUE EXPERIMENTAL DESIGNS
      4. QUASI-EXPERIMENTAL DESIGNS
      5. FIDELITY OF EXPERIMENTAL DESIGN
      6. EXPERIMENTAL SETTINGS
      7. ETHICS
      8. RELIABILITY AND VALIDITY
    4. 15 STATISTICAL METHODS OF DIFFERENCE: T TEST
      1. INDEPENDENT AND DEPENDENT SAMPLES
      2. INDEPENDENT T TEST
      3. INDEPENDENT T TEST: THE PROCEDURE
      4. INDEPENDENT T TEST EXAMPLE
    5. 16 ANALYSIS OF VARIANCE
      1. THE NATURE OF THE ANOVA DESIGN
      2. THE COMPONENTS OF VARIANCE
      3. THE PROCESS OF ANOVA
      4. CALCULATING ANOVA
      5. EFFECT SIZE
      6. POST HOC ANALYSES
      7. ASSUMPTIONS OF ANOVA
      8. ADDITIONAL CONSIDERATIONS WITH ANOVA
      9. A REAL-WORLD EXAMPLE OF ANOVA
      10. USING SPSS FOR ANOVA PROCEDURES
      11. SPSS PROCEDURES WITH ONE-WAY ANOVA
      12. SPSS ANOVA RESULTS FOR THE EXAMPLE STUDY
    6. 17 FIELD RESEARCH
      1. SELECTING A TOPIC
      2. ENTERING THE FIELD
      3. TAKING DATA IN THE FIELD
      4. RELIABILITY AND VALIDITY
      5. ETHICS
    7. 18 CONTENT ANALYSIS
      1. DEFINING THE POPULATION
      2. CENSUS OR SAMPLE?
      3. CODING IN CONTENT ANALYSIS
      4. CODING ROLLING STONE
      5. RELIABILITY AND VALIDITY
  10. PART IV: STATISTICS AND DATA MANAGEMENT
    1. STATISTICAL PROCEDURES UNIT A: WRITING THE STATISTICAL RESEARCH SUMMARY
    2. STATISTICAL PROCEDURES UNIT B: THE NATURE OF INFERENTIAL STATISTICS
      1. PROBABILITY
      2. PROBABILITY, THE NORMAL CURVE, AND P VALUES
      3. POPULATIONS (PARAMETERS) AND SAMPLES (STATISTICS)
      4. THE HYPOTHESIS TEST
      5. STATISTICAL SIGNIFICANCE
      6. PRACTICAL SIGNIFICANCE: EFFECT SIZE
    3. DATA MANAGEMENT UNIT A: USE AND FUNCTIONS OF SPSS
      1. MANAGEMENT FUNCTIONS
      2. ADDITIONAL MANAGEMENT FUNCTIONS
      3. ANALYSIS FUNCTIONS
      4. DATA MANAGEMENT UNIT A: USES AND FUNCTIONS
    4. DATA MANAGEMENT UNIT B: USING SPSS TO RECODE FOR T TEST
      1. USING SPSS TO RECODE QUESTIONNAIRE ITEMS
      2. DATA MANAGEMENT UNIT B: USES AND FUNCTIONS
    5. DATA MANAGEMENT UNIT C: DESCRIPTIVE STATISTICS
      1. DESCRIPTIVE AND INFERENTIAL STATISTICS
      2. DESCRIPTIVE STATISTICS
      3. DESCRIPTIVE PROCEDURES FOR NOMINAL AND ORDINAL DATA
      4. DESCRIPTIVE PROCEDURES FOR INTERVAL DATA
      5. OBTAINING DESCRIPTIVE (NUMERICAL) STATISTICS FROM SPSS
      6. OBTAINING DESCRIPTIVE (VISUAL) STATISTICS FROM SPSS
      7. DATA MANAGEMENT UNIT C: USES AND FUNCTIONS
    6. STATISTICAL PROCEDURES UNIT C: Z SCORES
      1. THE NATURE OF THE NORMAL CURVE
      2. THE STANDARD NORMAL SCORE: Z SCORE
      3. CALCULATING Z SCORES
      4. USING SPSS TO CREATE Z SCORES
      5. STATISTICAL PROCEDURES UNIT C: USES AND FUNCTIONS
  11. GLOSSARY
  12. BIBLIOGRAPHY
  13. INDEX