行为研究纲要

第一编 概念与伦理基础第1章 行为研究的精神第2章 探索与辩护的环境第3章 伦理问题、道德两难问题与指导方针第二编 因变量的可操作化和可测量化第4章 测量信度和效度第5章 观察、判断和复合变量第6章 问卷法、访谈法和日志法第三编 研究设计的逻辑第7章 随机控制的实验和因果推论第8章 非随机化研究与函数关系第9章 随机与非随机抽取样本第四编 数据分析的基本原理第10章 描述、展示以及探究数据第11章 相关关系第12章 统计检验力与效应值第五编 单因素设计第13章 用t 检验比较均值第14章 方差分析与F 检验第15章 单因素对比分析第六编 因素设计第16章 多因素方差分析第17章 方差分析中的交互作用第18章 重复测量方差分析第七编 数据分析的补充问题第19章 显著性检验和列联表分析第20章 多元数据分析第21章 元分析：研究结果的比较和综合第八编 附录详细目录PART I CONCEPTUAL AND ETHICAL　FOUNDATIONSChapter The Spirit of Behavioral ResearchScience and the Search for KnowledgeWhat Do Behavioral Researchers Really KnowSocial ConstructionismContextualism/PerspectivismEvolutionary EpistemologyPeirce’s Four Ways of KnowingRhetoric, Perceptibility, and AestheticsLimitations of the Four Supports of ConvictionBehavioral Research Defi nedThree Broad Research OrientationsThe Descriptive Research OrientationThe Relational Research OrientationThe Experimental Research OrientationEmpirical Principles as Probabilistic AssertionsOrienting Habits of Good Scientifi c PracticeChapter Contexts of Discovery and Justifi cationInspiration and ExplanationTheories and HypothesesSources of Inspiration and InsightSerendipity in Behavioral ResearchMolding Ideas Into Working HypothesesPositivism, Falsifi cationism, and ConventionalismType I and Type II Decision ErrorsStatistical Signifi cance and the Effect SizeTwo Families of Effect SizesInterval Estimates Around Effect SizesSumming UpChapter Ethical Considerations, Dilemmas,and GuidelinesPuzzles and ProblemsA Delicate Balancing ActHistorical Context of the American Psychological Association CodeThe Belmont Report, Federal Regulations, and the InstitutionalReview BoardPrinciple I； Respect for Persons and Their AutonomyPrinciple II； Benefi cence and Nonmalefi cencePrinciple III； JusticePrinciple IV； TrustPrinciple V； Fidelity and Scientifi c IntegrityCosts, Utilities, and Institutional Review BoardsScientifi c and Societal ResponsibilitiesPART II OPERATIONALIZATION AND MEASUREMENT　OF DEPENDENT VARIABLESChapter Reliability and Validity of MeasurementsRandom and Systematic ErrorAssessing Stability and EquivalenceInternal-Consistency Reliability and Spearman-BrownKR20 and Cronbach’s AlphaEffective Reliability of JudgesEffective Cost of JudgesEffective Cost of ItemsInterrater Agreement and ReliabilityCohen’s KappaReplication in ResearchValidity Criteria in AssessmentConvergent and Discriminant ValidityTest Validity, Practical Utility, and the Taylor-Russell TablesRelationship of Validity to ReliabilityChapter Observations, Judgments, and　Composite VariablesObserving, Classifying, and EvaluatingObserving While ParticipatingMaximizing Credibility and SerendipityOrganizing and Sense-Making in Ethnographic ResearchInterpreter and Observer BiasesUnobtrusive Observations and Nonreactive MeasurementsSelecting the Most Appropriate JudgesChoosing the Number of Response AlternativesEffects of Guessing and Omissions on AccuracyIntrinsic Factors and the Level of AccuracyApplications of Categorical JudgmentsCategory Scales and Rating ScalesNumerical, Graphic, and Magnitude RatingsRating Biases and Their ControlBipolar Versus Unipolar ScalesForming Composite VariablesForming Multiple CompositesQuantifying the Clarity of CompositesChapter Questionnaires, Interviews, and DiariesConcerns About Self-Report DataOpen-Ended Versus Structured ItemsCritical Incident TechniqueStages in Developing Interview ProtocolsResearch Interviews by TelephoneDeveloping Research QuestionnairesDefensiveness, Inconsistency, and Yea-SayingCross-Cultural Questionnaire and Interview ResearchOne-Dimensional and Multidimensional Attitude ScalesSemantic Differentials for Attitudinal MeaningQ-Sorts for Subjectivity RatingsLikert Method of Item AnalysisThurstone Equal-Appearing Intervals MethodMemory and the Use of Self-Recorded DiariesPART III THE LOGIC OF RESEARCH DESIGNSChapter Randomized Controlled Experiments and　Causal InferenceExperimentation in ScienceRandomized Experimental DesignsCharacteristics of RandomizationThe Philosophical Puzzle of CausalityContiguity, Priority, and Constant ConjunctionFour Types of Experimental ControlMill’s Methods of Agreement and DifferenceBetween-Group Designs and Mill’s Joint MethodIndependent, Dependent, and Moderator VariablesSolomon’s Extended Control Group DesignThreats to Internal ValidityThreats to External ValidityStatistical Conclusion and Construct ValiditySubject and Experimenter ArtifactsDemand Characteristics and Their ControlInteractional Experimenter EffectsExperimenter Expectancy Effects andTheir ControlConcluding CommentaryChapter Nonrandomized Research and Functional　RelationshipsNonrandomized and Quasi-Experimental StudiesNonequivalent Groups and Historical ControlsInterrupted Time Series and the Autoregressive Integrated　Moving AverageSingle-Case Experimental DesignsCross-Lagged Correlational DesignsInvisible Variables and the Mediation ProblemPath Analysis and Causal InferenceThe Cohort in Longitudinal ResearchDifferent Forms of Cohort StudiesSubclassifi cation on Propensity ScoresMultiple Confounding CovariatesChapter Randomly and Nonrandomly Selected　Sampling UnitsSampling a Small Part of the WholeBias and Instability in SurveysSimple Random-Sampling PlansImproving Accuracy in Random SamplingConfi dence Intervals for Population EstimatesSpeaking of Confi dence IntervalsOther Selection ProceduresNonresponse Bias and Its ControlStudying the Volunteer SubjectCharacteristics of the Volunteer SubjectImplications for the Interpretation ofResearch FindingsSituational Correlates and the Reduction ofVolunteer BiasThe Problem of Missing DataProcedures for Dealing With Missing DataPART IV FUNDAMENTALS OF DATA ANALYSISChapter Describing, Displaying, and Exploring DataDescriptions of Sampling UnitsFrequency Diagrams and Stem-and-Leaf DisplaysBox PlotsComparing Distributions Back to BackMeasures of Central TendencyMeasures of SpreadThe Normal DistributionStandard ScoresData Not Distributed NormallyPrecision of Estimating Population MeansDefi ning OutliersCoping With OutliersExploring the DataChapter CorrelationPearson rProportion of Variance Interpretation of CorrelationBinomial Effect-Size DisplayConfi dence Intervals for Effect-Size CorrelationsSmall Correlations, But Important EffectsCounternull Values of Effect SizesSpearman Rank CorrelationRanks as a TransformationObservations of Disproportionate Infl uencePoint-Biserial CorrelationExact Tests for RhoPhi Coeffi cientCurvilinear (Quadratic) CorrelationFive Product-Moment CorrelationsComparing CorrelationsConsidering Third VariablesEffects of Variability on CorrelationsChapter Statistical Power and Effect Size RevisitedWhy Assess Statistical PowerThe Neglect of Statistical PowerThe requivalent StatisticCohen’s Multipurpose Power TablesThe t Test for Comparing Two MeansThe Signifi cance of a Product-Moment rDifferences Between Correlation Coeffi cientsThe Test That a Proportion is .50The Difference Between ProportionsThe Focused Chi-Square TestF Tests for Focused ComparisonsAdditional Strategies for Improving PowerPART V ONE-WAY DESIGNSChapter Comparing Means by Standard t TestsGosset and the t TestTwo Components of t TestsMaximizing tEffect Sizes and Adjustments for Unequal Sample SizesInterpreting the Independent Sample tComputing the Independent Sample tReporting the Resultst Tests for Nonindependent SamplesEffect Size and Study Size Components of Nonindependent Sample tAssumptions Underlying t TestsNonparametric ProceduresThe Bootstrap, the Jackknife, and Permutation TestsChapter Analysis of Variance and the F TestThe F Test and the t TestThe Analysis of “Variances”Illustration of an Omnibus FDividing Up the Total VarianceANOVA Summary TablesDistributions of FAfter the Omnibus FProtecting Against “Too Many t Tests”Bonferroni ProceduresBonferroni Tolerance ValueComparing Two Independent VariabilitiesIllustration Using TransformationsComparing Two Correlated VariabilitiesComparing Three or More Independent VariabilitiesComparing Three or More Correlated VariabilitiesSummary of Procedures for Comparing VariabilitiesChapter One-Way Contrast AnalysesFocusing Our Questions and Statistical TestsContrast F Tests on Original DataContrast t Tests on Original Data Carving Contrasts Out of Published DataOrthogonal ContrastsNonorthogonal ContrastsEffect Size Indices for ContrastsThe BESD and the Binomial Effect-Size CorrelationOverview of the Four Effect-Size IndicesComparing Competing ContrastsCombining Competing ContrastsOptimal Design and the Allocation of Sample SizesPART VI FACTORIAL DESIGNSChapter Factorial Analysis of VarianceConfronting Persistent MisunderstandingsAn Economy of DesignEffects and the Structure of ANOVAIndividual Differences as ErrorThe Table of VarianceTesting the Grand Mean by t and FUnweighted Means Analysis for Equal or Unequal Sample SizesEffects on F of Unequal Sample SizesUnequal Sample Sizes and ContrastsTranscending Factorial Structure Using ContrastsHigher Order Factorial DesignsBlocking and the Increase of PowerBlocking and the Analysis of CovarianceTransforming Data to Remove InteractionsChapter Interaction Effects in Analysis of VarianceThe Interpretation of InteractionCrossed and Uncrossed Combinations of Group MeansIllustration of Mean PolishingConstructing Tables of Predicted MeansStudying Interactions in Two-Way TablesThree-Way Factorial DesignsDefi ning Three-Way InteractionsFurther Notes on InterpretationSimplifying Complex Tables of ResidualsIllustration of a Five-Way InteractionA Note on ComplexityChapter Repeated Measures in Analysis of VarianceUse of Repeated ObservationsBasic ComputationsFixed and Random Effects Error Terms in the Four Basic CombinationsLatin Squares and CounterbalancingAnalysis of Latin SquaresSome Latin Squares May Be Better Than OthersOther Counterbalancing DesignsThree or More FactorsTwo Within-Subjects FactorsAggregating Error TermsThree Within-Subjects FactorsFixed or Random FactorsDid Repeated Measures HelpThe Intraclass r RevisitedComposite Variables and Additional AssumptionsContrasts in Repeated MeasuresHierarchically Nested DesignsThe Choice of Error TermsPART VII ADDITIONAL TOPICS IN DATA ANALYSISChapter Significance Testing and Association in　Tables of CountsTable Analysis and Chi-SquareThe - df Chi-Square TestLarger Tables of CountsDistributions of Chi-SquareProcedures for Larger Contingency TablesSubdividing Tables to Test Specifi c HypothesesFisher Exact Probability TestStrengthening the Fisher Exact TestAdjustments for in _ TablesComplete Partitioning of Larger TablesThe Corner-Cells Test SubtableContrasts in ProportionsAlternative Analyses for Smaller Sample StudiesStandardizing Row and Column TotalsOdds Ratio, Relative Risk, Risk Difference, and PhiOne-Sample Tables of CountsMultiple-Choice-Type Data and the Proportion IndexChapter Multivariate Data AnalysisBackgroundRedescribing Relationships Within Sets of VariablesCluster Analysis for RedescriptionPrincipal Components Analysis for Redescriptionros31960_ Improving Interpretation by RotationPsychometric Applications of Principal Components AnalysisAlternatives for the Redescription of VariablesMultidimensional Scaling IllustrationRelationships Among Sets of VariablesChapter Meta-Analysis； Comparing and Combining　Research ResultsStatistical Analysis of Statistical AnalysesCriticisms of Meta-AnalysisInterpreting Two or More StudiesComparing Two Signifi cance LevelsComparing Two Effect-Size CorrelationsCombining Two Signifi cance LevelsCombining Two Effect-Size CorrelationsOverall Comparisons of Three or More StudiesFocused Comparisons of Three or More StudiesCombining Three or More p LevelsCombining Three or More Effect-Size CorrelationsResults That Are Not IndependentThe File Drawer ProblemAn Eye to VariabilityPART VIII APPENDIXESA List of Numbered EquationsB Statistical TablesGlossaryReferencesSubject IndexName Index

这是一本经典的高级研究方法教材。两位作者分别用几十年的时间从事心理学教学和研究工作，其研究重心又集中在心理学研究方法和数据分析方面。他们在这方面进行了深入钻研和分析，并结出了丰硕的成果，共同合作出版了多本关于研究方法和数据分析的著作。　　《行为研究纲要：方法与数据分析（英文注释版）（第3版）》这本著作的前半部分着重于研究方法的论述，涉及基础概念和基本伦理、因变量的操作和测量以及研究设计的逻辑；后半部分着重于描述高级统计过程，包括四个部分：数据分析基础、单因素设计、因素设计以及数据分析的其他问题。附录中还包括书中用到的公式、常用统计图表、术语及参考文献。　　《行为研究纲要：方法与数据分析（英文注释版）（第3版）》是心理学、社会学、人类学、传播学、教育学、商学、统计学、市场营销学等多个学科领域的教师、学者和学生从事教学和研究工作所必备的一本工具书。【作者简介】Robert Rosenthal is Distinguished　Professor at the University of California　at Riverside and Edgar Pierce Professor　of Psychology, Emeritus, Harvard University.　His research has centered for some　50 years on the role of the self-fulfi lling　prophecy in everyday life and in laboratory　situations. Special interests include the　effects of teachers’ expectations on students’performance, the effects of experimenters’expectations on the results of　their research, and the effects of clinicians’expectations on their patients’mental and physical health. He also has　strong interests in sources of artifact in　behavioral research and in various quantitative procedures. In the realm of data analysis,　his special interests are in experimental design and analysis, contrast analysis,　and meta-analysis. His most recent books and articles are about these areas of data　analysis and about the nature of nonverbal communication in teacher-student, doctorpatient,　manager-employee, judge-jury, and psychotherapist-client interaction. He has　been Co-Chair of the Task Force on Statistical Inference of the American Psychological　Association and has served as Chair of the Research Committee of the Bayer　Institute for Health Care Communication. He was a co-recipient of two behavioral　science awards of the American Association for the Advancement of Science (1960,1993) and recipient of the James McKeen Cattell Award of the American Psychological　Society, the Distinguished Scientist Award of the Society of Experimental　Social Psychology, the Samuel J. Messick Distinguished Scientifi c Contributions　Award of the APA’s Division 5—Evaluation, Measurement, and Statistics, and the　APA’s Distinguished Scientifi c Award for Applications of Psychology.Ralph L. Rosnow is Thaddeus Bolton Professor Emeritus at Temple University, where he taught for 34 years and directed the graduate program in social and organizational psychology. He has also taught research methods at Boston University and Harvard University and does consulting on research and data analysis. The overarching theme of his scholarly work concerns how people make sense of, and impose meaning on,their experiential world, called the “will to meaning” by Viktor Frankl. Rosnow has explored aspects of this construct in research and theory within the framework of contextualism, the psychology of rumor and gossip, attitude and social cognition, the structure of interpersonal acumen, artifacts and ethical dilemmas in human research,and the statistical justifi cation of scientifi c conclusions. He has authored and coauthored many articles and books on these topics and, with Mimi Rosnow, coauthored Writing Papers in Psychology, a popular writing manual now in its seventh edition (published by Thomson Wadsworth, 2006). He has served on the editorial boards of journals and encyclopedias, was coeditor (with R. E. Lana) of the Reconstruction of Society Series published by Oxford University Press, and chaired the APA’s Committee on Standards in Research. He is a fellow of the American Association for the Advancement of Science, the APA, and the Association for Psychological Science, received the Society of General Psychology’s George A. Miller Award, and was recently honored with a Festschrift book edited by D. A. Hantula, Advances in Social and Organizational PsychologyRosenthal and Rosnow have also collaborated on other books on research methods and data analysis, including Artifact in Behavioral Research (Academic Press, 1969)；The Volunteer Subject (Wiley, 1975)； Primer of Methods for the Behavioral Sciences(Wiley, 1975)； Understanding Behavioral Science Research Methods for Research Consumers (McGraw-Hill, 1984)； Contrast Analysis Focused Comparisons in the Analysis of Variance (Cambridge University Press, 1985)； People Studying People Artifacts and Ethics in Behavioral Research (W. H. Freeman, 1997)； (with D. B. Rubin)Contrasts and Effect Sizes in Behavioral Research A Correlational Approach (Cambridge University Press, 2000)； and Beginning Behavioral Research A ConceptualPrimer (6 th ed., Pearson/PrenticeHall, 2008).