Essential Statistics for Public Managers and Policy Analysts

Evan M. Berman is Professor of Public Management at Fundação Getúlio Vargas, EAESP (São Paulo, Brazil), specializing in human resource management, public performance, and comparative public administration. Previously, he was Huey McElveen Distinguished Professor at Louisiana State University. Dr. Berman is ranked among the very top scholars in public administration world-wide. He has received the Outstanding Scholarship Award from the American Society for Public Administration (Section on Personnel and Labor Relations) for contributions to  research, teaching and service in public sector HRM, and many other awards, including from the Network of Schools of Public Policy, Affairs, and Administration (NASPAA), the accrediting body for public administration programs. He has authored 15 books and over 175 publications, in all of the leading journals of the discipline. Dr Berman has chaired ASPA’s Section on Personnel and Labor Relations, he is founding editor of ASPA’s Book Series in Public Administration and Public Policy, senior editor of Public Performance and Management Review, and a Distinguished Fulbright Scholar. His 2023 book, Performance and Innovation in the Public Sector (with I. Hijal-Moghrabi), won the 2024 ASPA/SPALR Outstanding Book Award. Evan is a Fellow of the National Academy of Public Administration and Visiting Professor at Fudan University (Shanghai) and University of Indonesia (Jakarta).

• Tables, Figures, and BoxesPrefaceAcknowledgmentsStatistics RoadmapSection I: IntroductionChapter 1 Why Statistics for Public Managers and Policy Analysts?Chapter ObjectivesRole of Data in Public ManagementCompetency and ProficiencyEthics in Data Analysis and ResearchSummaryKey TermsSection II: Research MethodsChapter 2 Research DesignChapter ObjectivesIntroducing Variables and Their RelationshipsProgram EvaluationA Bit More: Extending through Quasi-experimental DesignSummaryKey TermsChapter 3 Conceptualization and MeasurementChapter ObjectivesMeasurement Levels and ScalesConceptualizationOperationalizationIndex VariablesMeasurement ValiditySummaryKey TermsChapter 4 Measuring and Managing Performance: Present and FutureChapter ObjectivesPerformance MeasurementManaging PerformanceEfficiency, Effectiveness, and a Bit MorePeering Into the Future: ForecastingSummaryKey TermsChapter 5 Data CollectionChapter ObjectivesSources of DataSamplingData InputPutting It TogetherSummaryKey TermsSection III: Descriptive StatisticsChapter 6 Central TendencyChapter ObjectivesThe MeanThe MedianThe ModeSummaryKey TermsAppendix 6.1: Using Grouped DataChapter 7 Measures of DispersionChapter ObjectivesFrequency DistributionsStandard DeviationSummaryKey TermsAppendix 7.1: BoxplotsChapter 8 Contingency TablesChapter ObjectivesContingency TablesRelationship and DirectionPivot TablesSummaryKey TermsChapter 9 Getting ResultsChapter ObjectivesAnalysis of Outputs and OutcomesAnalysis of Efficiency and EffectivenessAnalysis of EquityQuality-of-Life AnalysisA Bit of Forecasting, TooSome Cautions in Analysis and PresentationSummaryKey TermsAppendix 9.1: Forecasting with Periodic EffectsSection IV: Inferential StatisticsChapter 10 Introducing Inference: Estimation from SamplesChapter ObjectivesFrom Sample to PopulationStatistical Estimation of Population ParametersSummaryKey TermsChapter 11 Hypothesis Testing with Chi-SquareChapter ObjectivesWhat Is Chi-Square?Hypothesis TestingThe Goodness-of-Fit TestA Nonparametric AlternativeSummaryKey TermsAppendix 11.1: Rival Hypotheses: Adding a Control VariableAppendix 11.2: Some Nonparametric Tests for Specific SituationsChapter 12 The T-TestChapter ObjectivesT-Tests for Independent SamplesTwo T-Test VariationsNonparametric Alternatives to T-TestsSummaryKey TermsChapter 13 Analysis of Variance (ANOVA)Chapter ObjectivesAnalysis of VarianceA Nonparametric AlternativeSummaryKey TermsChapter 14 Simple RegressionChapter ObjectivesSimple RegressionPearson’s Correlation CoefficientSpearman’s Rank Correlation CoefficientSummaryKey TermsChapter 15 Multiple RegressionChapter ObjectivesModel SpecificationA Working ExampleFurther StatisticsUse of Nominal VariablesTesting AssumptionsSummaryKey TermsSection V: Further StatisticsChapter 16 Logistic and Time Series RegressionChapter ObjectivesThe Logistic ModelA Working ExampleTime Series in Multiple RegressionSummaryKey TermsChapter 17 Survey of Other TechniquesChapter ObjectivesPath AnalysisStatistical ForecastingSurvival AnalysisFactor AnalysisSummaryKey TermsAppendixesA: Normal DistributionB: Chi-Square (c2) DistributionC: T-Test DistributionD: Durbin-Watson DistributionE: F-Test DistributionGlossaryIndexAbout the Authors