Research Methods, Statistics, and Applications / Edition 1 available in Paperback
This updated Second Edition of Research Methods, Statistics, and Applications consistently integrates methods and statistics to prepare students for both graduate work and critical analysis of research as professionals and informed citizens. Maintaining the conversational writing style, multiple examples, and hands-on applications of key concepts that made the first edition so accessible, Kathrynn A. Adams and Eva K. Lawrence enhance the new edition with additional coverage of online data collection, inferential statistics, and regression and ANOVA, as well as a wide range of diverse examples. In every chapter, the authors develop and apply research topics and examples from the current research literature across all aspects of the research process.
New to this Edition
- New diverse examples from current research literature in criminal justice, politics, education, and counseling expose students to different research designs in the social sciences and demonstrate commonalities.
- New chapter-ending The Big Picture sections with appropriate charts and tables encourage students to consider decisions about specific statistical analyses.
- Two separate chapters (Inferential Statistics and Comparing Your Sample to a Known or Expected Score) now allow instructors to focus on the theoretical concepts associated with inferential statistics before introducing each specific inferential statistic to enhance student understanding.
- Expanded coverage of inferential statistics includes more discussion of APA guidelines for appropriate statistics and more focus on effect sizes and confidence intervals.
- New consistent headings make it easy for students to quickly locate information and for instructors to identify sections they may wish to focus on, skip, or present in a different order.
|Product dimensions:||7.30(w) x 9.00(h) x 1.00(d)|
About the Author
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 ContentsPrefaceAbout The AuthorsChapter 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 ScienceChapter 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 PresentChapter 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 AccuracyChapter 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 DescriptionChapter 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 SampleChapter 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 ResultsChapter 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 Results Discussion The Big Picture: Examining One Variable at a TimeChapter 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 Regression Formulas and Calculations: Simple Linear Regression Using Data Analysis Programs: Regression The Big Picture: Correlational Designs Versus Correlational AnalysesChapter 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 DesignChapter 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 designsChapter 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 designsChapter 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 ComplexityChapter 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 testsChapter 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 DesignChapter 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 ClassAppendix A: Answers to Practice QuestionsAppendix B: APA Style and Format GuidelinesAppendix C: Statistical TablesAppendix D: Statistical FormulasGlossaryReferencesAuthor indexSubject index