Research Methods, Statistics, and Applications

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.

• PrefaceAbout the AuthorsChapter 1 • Thinking Like a ResearcherLearning ObjectivesCritical ThinkingThe Scientific ApproachOverview of the Research Process (a.k.a. The Scientific Method)Thinking Critically About EthicsThe Big Picture: Proof and Progress in ScienceChapter ResourcesChapter 2 • Build a Solid Foundation for Your Study Based on Past ResearchLearning ObjectivesTypes of SourcesStrategies to Identify and Find Past ResearchReading and Evaluating Primary Research ArticlesCrediting SourcesThe Big Picture: Use the Past to Inform the PresentChapter ResourcesChapter 3 • Measuring Your VariablesLearning ObjectivesConstructs and Operational DefinitionsFour Scales of MeasurementSelf-Report MeasuresBehavioral and Physiological MeasuresArchival ResearchThe Big Picture: How to Choose Measures For Your StudyChapter ResourcesChapter 4 • The Cornerstones of Good Research: Reliability and ValidityLearning ObjectivesReliability and Validity DefinedReliability and Validity of MeasurementAssessing Reliability of MeasuresAssessing Validity of MeasuresReliability and Validity at the Study LevelThe Big Picture: Consistency and AccuracyChapter ResourcesChapter 5 • Basics of Research Design: Description and SamplingLearning ObjectivesWhen is a Descriptive Study Appropriate?Validity in Descriptive StudiesDefining the Population and Obtaining a SampleProbability SamplingNonprobability SamplingThe Big Picture: Choosing a Sampling MethodChapter ResourcesChapter 6 • Describing Your SampleLearning ObjectivesEthical and Practical Issues in Describing Your SampleDescriptive StatisticsChoosing the Appropriate Descriptive StatisticsComparing Interval/Ratio Scores with z Scores and PercentilesThe Big Picture: Know Your Data and Your SampleChapter ResourcesChapter 7 • Beyond Descriptives: Making Inferences Based On Your SampleLearning ObjectivesInferential StatisticsHypothesis TestingErrors in Hypothesis TestingEffect Size, Confidence Intervals, and Practical SignificanceThe Big Picture: Making Sense of ResultsChapter ResourcesChapter 8 • Comparing Your Sample to a Known or Expected ScoreLearning ObjectivesChoosing the Appropriate TestOne-Sample t TestsCalculating an Effect SizeCalculating a Confidence IntervalThe Big Picture: Examining One Variable at a TimeChapter ResourcesChapter 9 • Examining Relationships Among Your Variables: Correlational DesignLearning ObjectivesCorrelational DesignBasic Statistics to Evaluate Correlational ResearchRegressionThe Big Picture: Correlational Designs Versus Correlational AnalysesChapter ResourcesChapter 10 • Examining CausalityLearning ObjectivesTesting Cause and EffectEight Key Threats to Internal ValidityBasic Issues in Designing an ExperimentValidity in ExperimentsThe Big Picture: Benefits and Limits of Experimental DesignChapter ResourcesChapter 11 • Independent-Groups DesignsLearning ObjectivesDesigns with Independent GroupsDesigning a Simple ExperimentAnalysis of Two Independent-Groups DesignsFORMULAS and CALCULATIONS: Independent-Samples t TestUsing a Data Analysis Program to Calculate Independent Samples t TestsDesigns with More than Two Independent GroupsAnalysis of Multiple Independent-Groups DesignsFormulas and Calculations: One-Way Independent-Samples ANOVAUsing a Data Analysis Program to Calculate One-Way Independent-Samples ANOVAsThe Big Picture: Identifying and Analyzing Independent-Groups DesignsChapter ResourcesChapter 12 • Dependent-Groups DesignsLearning ObjectivesDesigns with Dependent GroupsDesigns with More than two Dependent GroupsAnalysis of Dependent Multiple-Groups DesignsFormulas and Calculations: Within-Subjects ANOVAThe Big Picture: Selecting Analyses and Interpreting Results for Dependent-Groups DesignsChapter ResourcesChapter 13 • Factorial DesignsLearning ObjectivesBasic Concepts in Factorial DesignRationale for Factorial Designs2 × 2 DESIGNSTwo-Way Between Subjects ANOVABeyond the 2 × 2 Independent-Groups DesignThe Big Picture: Embracing ComplexityChapter ResourcesChapter 14 • Nonparametric StatisticsLearning ObjectivesParametric Versus Nonparametric StatisticsChi-Square Goodness of FitChi-Square Test For IndependenceDependent-Groups Designs with Nominal Outcome MeasuresSpearman’s Rho to Examine Relationships Between Ordinal (Ranked) DataNonparametric Statistics for Independent- and Dependent-Groups Designs with Ordinal DataThe Big Picture: Selecting Parametric Versus Nonparametric TestsChapter ResourcesChapter 15 • Focusing on the Individual: Case Studies and Single N DesignsLearning ObjectivesSamples Versus IndividualsThe Case StudyQualitative AnalysesSingle N DesignsThe Big Picture: Choosing Between a Sample, Case Study, or Single N DesignChapter ResourcesChapter 16 • How to Decide? Choosing a Research Design and Selecting the Correct AnalysisLearning ObjectivesChoosing a Research DesignSelecting Your Statistical AnalysesThe Big Picture: Beyond This ClassChapter ResourcesAppendix A • Answers to Practice QuestionsAppendix B • APA Style and Format GuidelinesAppendix C • Statistical TablesAppendix D • Statistical FormulasGlossaryReferencesIndex