PrefaceAcknowledgmentsAbout the AuthorsChapter 1: The Foundations of Social ResearchIntroductionWhat Is Social Science Research?Ethics and Social ScienceThe Language and Logic of Social ResearchVariables: The Joy of MeasurementConcepts and MeasurementConceptual and Operational DefinitionsLevels of MeasurementValidity, Reliability, Accuracy, and PrecisionIs My Measure Any Good? Determining ValidityThe Problem with ValidityThe Bottom LineKey Concepts In This ChapterSummaryExercisesFurther ReadingChapter 2: Preparing For ResearchSetting Things UpEthics of Social ResearchTheory--Explanation and PredictionA Guide to Finding Research Questions, AnywayGenerating Types of StudiesThe Literature SearchMeta-AnalysisKey Concepts in This ChapterSummaryExercisesFurther ReadingChapter 3: Research DesignIntroduction: What Is Research Design?About Numbers and Words: The Qualitative/Quantitative SplitCause and EffectUnits of AnalysisThree Decisions in Research DesignThe Eight Types of Research DesignMixed-methods Research DesignsParticipatory and Action ResearchThe Components of a Research DesignThe Art of Proposal WritingHow to Develop Your Proposal with Mentors and PeersKey Concepts in This ChapterSummaryExercisesFurther ReadingChapter 4: Experiments In Social ScienceThe Logic Of The Experimental MethodInternal and External ValidityControlling for Threats to ValidityFactorial Designs: Main Effects and Interaction EffectsField ExperimentsAre Field Experiments Ethical?Thought ExperimentsKey Concepts in This Chapter Summary ExercisesFurther ReadingChapter 5: Scales And ScalingIntroductionSingle-question ScalesSingle-indicator Graphic Representational ScalesComposite (or Complex) Scales: Multiple IndicatorsIndexesGuttman ScalesLikert ScalesItem Analysis Testing for Unidimensionality with Factor AnalysisThe Semantic DifferentialAnd Finally .Key Concepts in This Chapter Summary ExercisesFurther ReadingChapter 6: Probability SamplingWhat Are Samples and Why Do We Need Them?Why Samples Can Be More Accurate than CountsSampling FramesStratified SamplingCluster SamplingProbability Proportionate to SizeHow Big Should a Sample Be?Probability DistributionsThe Normal Curve and the Standard DeviationThe Central Limit TheoremThe Standard Error and Confidence IntervalsSmall Samples: The t-DistributionEstimating ProportionsKey Concepts in This ChapterSummaryExercisesFurther ReadingChapter 7: Nonprobability SamplingIntroductionReasons to Use a Non-Probability SampleFour Common and Two Uncommon Types of Non-Probability SamplesMinimum Sizes for Different Types of Nonprobability SamplesDeciding on a nonprobability sampling method and sample sizeAnd Finally .Key Concepts in This ChapterSummaryExercisesFurther ReadingChapter 8: Interviewing and Focus GroupsThe Big PictureInterview ControlUnstructured InterviewingProbingLearning to InterviewPositionality and Presentation of SelfUsing a Voice RecorderUsing Visual Cues, Like Photos in InterviewsFocus GroupsResponse EffectsRespondent/Informant AccuracyKey concepts in this ChapterSummaryExercisesFurther ReadingChapter 9: Survey ResearchIntroductionMethods for Collecting Questionnaire DataWhen to Use whatWorking with InterviewersClosed- Versus Open-ended QuestionsFourteen Rules for Question Wording and FormatPretesting and Learning from MistakesTranslation and Back TranslationThe Response Rate ProblemImproving the Response Rate: Dillman''s Total Design MethodCross-sectional and Longitudinal StudiesSome Specialized Survey MethodsKey concepts in this ChapterSummaryExercisesFurther ReadingChapter 10: Collecting Social Network DataSocial NetworksTwo Kinds of Social NetworksDoing Network AnalysisCollecting Whole (Sociocentric) Network DataCollecting Personal (Egocentric) Network DataKey concepts in this ChapterSummaryExercisesFurther ReadingChapter 11: Fieldwork: Direct and Participant ObservationIntroductionSome History: Observing Behavior in the LabDirect Observation in the WildReactive Observation: Continuous Monitoring and Spot SamplingSpot SamplingA Few Final Words on Reactive ObservationUnobtrusive ObservationDisguised Field ObservationIndirect ObservationParticipant ObservationDifferent Roles in Participant ObservationDoing Participant ObservationThe Skills of a Participant ObserverHanging Out, Gaining RapportObjectivityInsider Research: Studying Your Own CultureGender, Parenting, and Other Personal CharacteristicsSex and FieldworkSurviving FieldworkLeaving the FieldKey concepts in this ChapterSummaryExercisesFurther ReadingChapter 12: Analyzing Text: Grounded Theory and Content AnalysisIntroductionOverview of Grounded TheoryContent AnalysisDoing Classical Content AnalysisIntercoder ReliabilityAutomated Content Analysis: Content DictionariesAI and Text AnalysisKey Concepts in this ChapterSummaryExercisesFurther ReadingChapter 13: Discourse AnalysisIntroductionConversation AnalysisTaking Turns in a JuryNarrative AnalysisPhenomenological Analysis of NarrativesLanguage in UseCritical Discourse Analysis: Language and PowerKey Concepts in This ChapterSummaryExercisesFurther ReadingChapter 14: Univariate and Bivariate AnalysisIntroductionUnivariate Analysis: Raw DataFrequency DistributionsMeasures of Central TendencyOutliers and SkewnessVisualizing DataMeasures of Dispersion: Variance and the Standard DeviationThe Logic of Hypothesis TestingTesting the Means of Large Samples: Using z-ScoresThe Univariate Chi-square TestTesting Relations: Bivariate AnalysisThe t test: Comparing Two MeansANOVA--Analysis of VarianceVisualizing the Direction and Shape of CovariationsCrosstabs of Nominal VariablesCorrelation and Cause: Antecedent and Intervening VariablesChi-Square for Bivariate ComparisonsTesting the Association between Ordinal VariablesWhat to Use for Nominal and Ordinal VariablesCorrelation: The Powerhouse Statistic for CovariationRegressionAdvantages and disadvantages of r and r^2Nonlinear RelationsStatistical Significance, the Shotgun Approach, and Other IssuesKey Concepts in This Chapter Summary Exercises Further Reading Chapter 15: Multivariate AnalysisIntroductionElaboration: Controlling for Independent VariablesCar Wrecks and Teenage BirthsThe Multiple Regression EquationUsing Multiple Regression to Solve the MVD-TEENBIRTH PuzzlePath AnalysisFactor AnalysisDiscriminant Function Analysis (DFA)And Finally .Key Concepts in This ChapterSummary ExercisesFurther ReadingChapter 16: Analyzing Network DataIntroduction: About MatricesAnalyzing Relational Data: MDS and Cluster AnalysisAnalyzing Social Network DataAnalyzing Whole (Sociocentric) Network DataAnalyzing Personal (Egocentric) Network Data"It''s Not what You Know, It''s Who You Know"Adding Network Data to the Classic RecipeAffiliation MatricesSemantic NetworksKey Concepts in This ChapterSummaryExercisesFurther ReadingChapter 17: On Writing UpIntroductionGetting Your Article PublishedBibliography.