Description
SAGE PUBLISHING Research Methods Statistics And Applications 2018 Edition by Adams
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. PrefaceAbout The AuthorsChapter 1: Thinking Like A ResearcherCritical ThinkingThinking Critically About EthicsThe Scientific ApproachOverview 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 ResearchTypes of SourcesTypes of Scholarly WorksStrategies to Identify and Find Past ResearchReading and Evaluating Primary Research ArticlesDevelop Study Ideas Based on Past ResearchAPA Format for ReferencesThe Big Picture: Use the Past to Inform the PresentChapter 3: The Cornerstones of Good Research: Reliability and ValidityUsing Data Analysis Programs: Measurement ReliabilityReliability and Validity Broadly DefinedReliability and Validity of MeasurementConstructs and Operational DefinitionsTypes of MeasuresAssessing Reliability of MeasuresAssessing Validity of MeasuresReliability and Validity at the Study LevelThe Big Picture: Consistency and AccuracyChapter 4: Basics of Research Design: Description, Measurement, and SamplingWhen Is a Descriptive Study Appropriate?Validity in Descriptive StudiesMeasurement MethodsDefining the Population and Obtaining a SampleThe Big Picture: Beyond DescriptionChapter 5: Describing Your SampleEthical Issues in Describing Your SamplePractical Issues in Describing Your SampleDescriptive StatisticsChoosing the Appropriate Descriptive StatisticsUsing Data Analysis Programs: Descriptive StatisticsComparing Interval/Ratio Scores with z Scores and PercentilesThe Big Picture: Know Your Data and Your SampleChapter 6: Beyond Descriptives: Making Inferences Based on Your SampleInferential StatisticsHypothesis TestingErrors in Hypothesis TestingEffect Size, Confidence Intervals, and Practical SignificanceDetermining the Effect Size, Confidence Interval, and Practical Significance in a StudyThe Big Picture: Making Sense of ResultsChapter 7: Comparing Your Sample to a Known or Expected ScoreChoosing the Appropriate TestOne-Sample t TestsFormulas and Calculations: One-Sample t TestUsing Data Analysis Programs: One-Sample t TestResultsDiscussionThe Big Picture: Examining One Variable at a TimeChapter 8: Examining Relationships among Your Variables: Correlational DesignCorrelational DesignBasic Statistics to Evaluate Correlational ResearchUsing Data Analysis Programs: Pearson's r and Point-Biserial rRegressionFormulas and Calculations: Simple Linear RegressionUsing Data Analysis Programs: RegressionThe Big Picture: Correlational Designs Versus Correlational AnalysesChapter 9: Examining CausalityTesting Cause and EffectThreats to Internal ValidityBasic Issues in Designing an ExperimentOther Threats to Internal ValidityBalancing Internal and External ValidityThe Big Picture: Benefits and Limits of Experimental DesignChapter 10: Independent-Groups DesignsDesigns with Independent GroupsDesigning a Simple ExperimentIndependent-Samples t TestsFormulas and calculations: independent-samples t testUsing data analysis programs: independent-samples t testDesigns With More Than Two Independent GroupsFormulas and calculations: one-way independent-samples anovaUsing data analysis programs: one-way independent-samples anovaThe big picture: identifying and analyzing independent-groups designsChapter 11: Dependent-Groups DesignsDesigns with dependent groupsFormulas and Calculations: Dependent-Samples t TestUsing data analysis programs: dependent-samples t testDesigns with more than two dependent groupsFormulas and calculations: within-subjects ANOVAUsing data analysis programs: within-subjects ANOVAThe big picture: selecting analyses and interpreting results for dependent-groups designsChapter 12: Factorial DesignsBasic Concepts in Factorial DesignRationale for Factorial Designs2 x 2 DesignsAnalyzing Factorial DesignsAnalyzing Independent-Groups Factorial DesignsFormulas and Calculations: Two-Way Between-Subjects ANOVAUsing Data Analysis Programs: Two-Way Between-Subjects ANOVAReporting and Interpreting Results of a Two-Way ANOVADependent-Groups Factorial DesignsMixed DesignsThe Big Picture: Embracing ComplexityChapter 13: Nonparametric StatisticsParametric Versus Nonparametric StatisticsNonparametric Tests for Nominal DataFormulas and Calculations: Chi-Square Goodness of FitUsing Data Analysis Programs: Chi-Square Goodness of FitFormulas and calculations: chi-square test for independenceUsing data analysis programs: chi-square test for independenceNonparametric statistics for ordinal (ranked) dataFormulas and calculations: spearman's rhoUsing data analysis programs: spearman's rhoThe big picture: selecting parametric versus nonparametric testsChapter 14: Focusing on the Individual Case Studies and Single N DesignsSamples Versus IndividualsThe Case StudySingle N DesignsThe Big Picture: Choosing Between a Sample, Case Study, or Single N DesignChapter 15: How to Decide? Choosing a Research Design and Selecting the Correct AnalysisFirst and Throughout: Base Your Study on Past ResearchChoosing a Research DesignSelecting Your Statistical AnalysesThe Big Picture: Beyond This ClassAppendix A: Answers to Practice QuestionsAppendix B: APA Style and Format GuidelinesAppendix C: Statistical TablesAppendix D: Statistical FormulasGlossaryReferencesAuthor indexSubject index