Research Methods, Statistics, and Applications

One of the greatest strengths of this text is the consistent integration of research methods and statistics so that students can better understand how the research process requires the combination of these elements. The end goal is to spark students' interest in conducting research and to increase their ability to critically analyze it. In the new second edition of the text, Katherine Adams and Eva Lawrence have integrated additional information on online data collection and research methods, additional coverage of regression and ANOVA, and new examples to engage students. 

Table of Contents:
Preface 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 Results Discussion 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 Regression 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 Glossary References Author index Subject index

About the Author :
Kathrynn (Kathy) A. Adams earned her PhD in general experimental psychology from the University of Alabama in 1977. She was a Charles A. Dana Professor of Psychology at Guilford College when she retired in 2017 after 37 years of teaching. Her professional interests include gender issues, relationships, and teaching pedagogy. She worked with the Preparing Future Faculty Program for 20 years 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, practices yoga, and bakes chocolate desserts. Eva K. McGuire earned her PhD in clinical psychology from Virginia Commonwealth University in 2002. She is a Charles A. Dana Professor of Psychology at Guilford College, where she has taught since 2003. Her research interests include environmental psychology and computer-mediated communication. Eva enjoys walking, yoga, and bike riding, and she loves to listen to live music.

Review :
"The authors have constructed a manuscript that utilizes real life research questions and takes the reader through a detailed process of how a researcher would construct a study to answer the question, select the appropriate statistics to answer the question, and disseminate the results in how to write up the results/discussion." "It outlines the bare necessities of research. It’s not full of definitions, it more so teaches students about the application of materials. The book comes across as task-oriented." "In our sections of research methods & statistics, students are asked to buy two books (methods + stats). I appreciate that this textbook is able to unite the two domains in such a clear way. This book really stands out as a detailed "field manual" for psychological research, and it's the kind of book that students might be more likely to hang on to for future reference. The integration of SPSS & APA style conventions throughout was nice to see. Also nice to see effect sizes and power analysis show up so students don't have a simple view of null hypothesis statistical tests." "I like the combination of the methods and stats. I believe that this book would be beneficial for an advanced research methods/stats class at the undergraduate level. I like that the book provides adequate coverage to case studies and single case research design." "Some good thorough discussion of aspects of each kind of research and statistical approach. Workbook pretty thorough." "The simplicity and relevance of the supplemental materials built in to illustrate the concepts are the key strengths of this text."