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SAGE Publications
Research Methods, Statistics, and Applications / Edition 1

Research Methods, Statistics, and Applications / Edition 1

by Kathrynn A. Adams
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  • Product Details

    ISBN-13: 2901452220184
    Publisher: SAGE Publications
    Publication date: 02/04/2014
    Pages: 656
    Product dimensions: 7.30(w) x 9.00(h) x 1.00(d)

    About the Author

    Kathrynn (Kathy) A. Adams earned her PhD in general experimental psychology from the University of Alabama in 1977. She is a Charles A. Dana professor of psychology at Guilford College, where she has taught since 1980. Her professional interests include gender issues, relationships, and teaching pedagogy. She has worked with the Preparing Future Faculty Program since the mid-1990s and helped establish the Early College at Guilford, a nationally ranked high school. In her spare time, she spends as much time as possible outdoors, jogs, practices yoga, and bakes chocolate desserts.

    Eva K. Lawrence, now Eva K. McGuire, earned her PhD in clinical psychology from Virginia Commonwealth University in 2002. She is a professor at Guilford College, where she has taught since 2003. Her research interests include environmental psychology, computer-mediated communication, and teaching. In her spare time, she enjoys walking and bike riding; she also loves to listen to live music.

    Table of Contents

    About The Authors
    Chapter 1: Thinking Like A Researcher
    Critical Thinking
    Thinking Critically About Ethics
    The Scientific Approach
    Overview of the Research Process (a.k.a. the Scientific Method)
    The Big Picture: Proof and Progress in Science
    Chapter 2: Build a Solid Foundation for Your Study Based On Past Research
    Types of Sources
    Types of Scholarly Works
    Strategies to Identify and Find Past Research
    Reading and Evaluating Primary Research Articles
    Develop Study Ideas Based on Past Research
    APA Format for References
    The Big Picture: Use the Past to Inform the Present
    Chapter 3: The Cornerstones of Good Research: Reliability and Validity
    Using Data Analysis Programs: Measurement Reliability
    Reliability and Validity Broadly Defined
    Reliability and Validity of Measurement
    Constructs and Operational Definitions
    Types of Measures
    Assessing Reliability of Measures
    Assessing Validity of Measures
    Reliability and Validity at the Study Level
    The Big Picture: Consistency and Accuracy
    Chapter 4: Basics of Research Design: Description, Measurement, and Sampling
    When Is a Descriptive Study Appropriate?
    Validity in Descriptive Studies
    Measurement Methods
    Defining the Population and Obtaining a Sample
    The Big Picture: Beyond Description
    Chapter 5: Describing Your Sample
    Ethical Issues in Describing Your Sample
    Practical Issues in Describing Your Sample
    Descriptive Statistics
    Choosing the Appropriate Descriptive Statistics
    Using Data Analysis Programs: Descriptive Statistics
    Comparing Interval/Ratio Scores with z Scores and Percentiles
    The Big Picture: Know Your Data and Your Sample
    Chapter 6: Beyond Descriptives: Making Inferences Based on Your Sample
    Inferential Statistics
    Hypothesis Testing
    Errors in Hypothesis Testing
    Effect Size, Confidence Intervals, and Practical Significance
    Determining the Effect Size, Confidence Interval, and Practical Significance in a Study
    The Big Picture: Making Sense of Results
    Chapter 7: Comparing Your Sample to a Known or Expected Score
    Choosing the Appropriate Test
    One-Sample t Tests
    Formulas and Calculations: One-Sample t Test
    Using Data Analysis Programs: One-Sample t Test
    The Big Picture: Examining One Variable at a Time
    Chapter 8: Examining Relationships among Your Variables: Correlational Design
    Correlational Design
    Basic Statistics to Evaluate Correlational Research
    Using Data Analysis Programs: Pearson's r and Point-Biserial r
    Formulas and Calculations: Simple Linear Regression
    Using Data Analysis Programs: Regression
    The Big Picture: Correlational Designs Versus Correlational Analyses
    Chapter 9: Examining Causality
    Testing Cause and Effect
    Threats to Internal Validity
    Basic Issues in Designing an Experiment
    Other Threats to Internal Validity
    Balancing Internal and External Validity
    The Big Picture: Benefits and Limits of Experimental Design
    Chapter 10: Independent-Groups Designs
    Designs with Independent Groups
    Designing a Simple Experiment
    Independent-Samples t Tests
    Formulas and calculations: independent-samples t test
    Using data analysis programs: independent-samples t test
    Designs With More Than Two Independent Groups
    Formulas and calculations: one-way independent-samples anova
    Using data analysis programs: one-way independent-samples anova
    The big picture: identifying and analyzing independent-groups designs
    Chapter 11: Dependent-Groups Designs
    Designs with dependent groups
    Formulas and Calculations: Dependent-Samples t Test
    Using data analysis programs: dependent-samples t test
    Designs with more than two dependent groups
    Formulas and calculations: within-subjects ANOVA
    Using data analysis programs: within-subjects ANOVA
    The big picture: selecting analyses and interpreting results for dependent-groups designs
    Chapter 12: Factorial Designs
    Basic Concepts in Factorial Design
    Rationale for Factorial Designs
    2 x 2 Designs
    Analyzing Factorial Designs
    Analyzing Independent-Groups Factorial Designs
    Formulas and Calculations: Two-Way Between-Subjects ANOVA
    Using Data Analysis Programs: Two-Way Between-Subjects ANOVA
    Reporting and Interpreting Results of a Two-Way ANOVA
    Dependent-Groups Factorial Designs
    Mixed Designs
    The Big Picture: Embracing Complexity
    Chapter 13: Nonparametric Statistics
    Parametric Versus Nonparametric Statistics
    Nonparametric Tests for Nominal Data
    Formulas and Calculations: Chi-Square Goodness of Fit
    Using Data Analysis Programs: Chi-Square Goodness of Fit
    Formulas and calculations: chi-square test for independence
    Using data analysis programs: chi-square test for independence
    Nonparametric statistics for ordinal (ranked) data
    Formulas and calculations: spearman’s rho
    Using data analysis programs: spearman’s rho
    The big picture: selecting parametric versus nonparametric tests
    Chapter 14: Focusing on the Individual Case Studies and Single N Designs
    Samples Versus Individuals
    The Case Study
    Single N Designs
    The Big Picture: Choosing Between a Sample, Case Study, or Single N Design
    Chapter 15: How to Decide? Choosing a Research Design and Selecting the Correct Analysis
    First and Throughout: Base Your Study on Past Research
    Choosing a Research Design
    Selecting Your Statistical Analyses
    The Big Picture: Beyond This Class
    Appendix A: Answers to Practice Questions
    Appendix B: APA Style and Format Guidelines
    Appendix C: Statistical Tables
    Appendix D: Statistical Formulas
    Author index
    Subject index

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