Simple Statistics: Applications in Criminology and Criminal Justice

Paperback | September 29, 2006

byTerance D. Miethe

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Simple Statistics: Applications in Criminology and Criminal Justice provides a concise and compelling introduction to basic statistics for students of criminology and criminal justice. Written in a conversational tone, it does not "dumb down" the material; rather, it demonstrates the value ofstatistical thinking and reasoning in context. The text covers essential techniques instead of attempting to provide an encyclopedic sweep of all statistical procedures. Author Terance D. Miethe shows how verbal statements and other types of information are converted into statistical codes,measures, and variables. Many texts don't cover this process of operationalization and measurement, so most students have no idea how research methods and statistics are related or how to conduct statistical analysis from the bottom up. While most statistics texts emphasize how to do statistical procedures and neglect why we do them, this unique book covers both areas. The problems at the end of each chapter focus on applications, offering more context for "why we do" these procedures. The term informed consumer is frequentlyused to convey the importance of understanding social statistics for becoming a better student, employee, and citizen. Simple Statistics uses hand computation methods to demonstrate how to apply the various statistical procedures. Most chapters also contain an optional section on how to do these procedures in SPSS and/or Microsoft Excel spreadsheets, but such applications are not necessary for understandingthe statistical methods described in this book. Several examples--but not an overwhelming amount--are used to illustrate each statistical procedure. Specific problems, detailed summaries, key terms, and major formulas are provided at the end of each chapter to further highlight major points. Acomprehensive Instructor's Manual is also available.

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From the Publisher

Simple Statistics: Applications in Criminology and Criminal Justice provides a concise and compelling introduction to basic statistics for students of criminology and criminal justice. Written in a conversational tone, it does not "dumb down" the material; rather, it demonstrates the value ofstatistical thinking and reasoning in contex...

Terance D. Miethe is at University of Nevada, Las Vegas.

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Format:PaperbackDimensions:336 pages, 6.89 × 9.09 × 0.71 inPublished:September 29, 2006Publisher:Oxford University PressLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0195330714

ISBN - 13:9780195330717

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Table of Contents

Chapter 1: Introduction to Statistical ThinkingSome Definitions and Basic IdeasMath Phobia, Panic, and Terror in Social StatisticsThe Practical Value of Social Statistics and Statistical ReasoningTypes of Statistical MethodsPedagogical (Teaching) ApproachesChapter 2: Garbage In, Garbage Out (GIGO)Measurement InvaliditySampling ProblemsFaulty Causal InferencesPolitical InfluencesHuman FallibilityChapter 3: Issues in Data PreparationWhy Is Data Preparation Important?Operationalization and MeasurementNominal Measurement of Qualitative Variables: Measurement of Quantitative Variables: Issues in Levels of Measurement: Coding and Inputting Statistical DataAvailable Computer Software for Basic Data AnalysisChapter 4: Displaying Data in Tables and Graphic FormsThe Importance of Data Tables and GraphsTypes of Tabular and Visual PresentationsTables and Graphs for Qualitative Variables: Tables and Graphs for Quantitative: Variables: Ratios and Rates: Maps of Qualitative and Quantitative: Variables: Hazards and Distortions in Visual Displays and Collapsing CategoriesChapter 5: Modes, Medians, Means, and MoreModes and Modal CategoriesThe Median and Other Measures of LocationThe Mean and Its MeaningWeighted Means: Strengths and Limitations of Mean Ratings: Choice of Measure of Central Tendency and PositionChapter 6: Measures of Variation and DispersionThe Range of ScoresThe Variance and Standard DeviationVariances and Standard Deviations for Binary Variables: Population Versus Sample Variances and Standard DeviationsChapter 7: The Normal Curve and Sampling DistributionsThe Normal CurveZ-Scores as Standard ScoresReading a Normal Curve TableOther Sampling DistributionsBinomial Distribution: t-Distribution: Chi-Square Distribution: F-Distributions: Chapter 8: Parameter Estimation and Confidence IntervalsSampling Distributions and the Logic of Parameter EstimationInferences from Sampling Distributions to One Real SampleConfidence Intervals: Large Samples, ? KnownConfidence Intervals for Population Means: Confidence Intervals for Population Proportions: Confidence Intervals: Small Samples and Unknown ?Properties of the t-Distribution: Confidence Intervals for Population Means for Unknown ?: Confidence Intervals for Population Proportions for Unknown ?: Chapter 9: Introduction to Hypothesis TestingConfidence Intervals Versus Hypothesis TestingBasic Terminology and SymbolsTypes of Hypotheses: Zone of Rejection and Critical Values: Significance Levels and Errors in Decision Making: Chapter 10: Hypothesis Testing for Means and ProportionsTypes of Hypothesis TestingOne-Sample Tests of the Population Mean: One-Sample Tests of a Population Proportion: Two Sample Test of Differences in Population Means: Two Sample Test of Differences in Population Proportions: Issues in Testing Statistical HypothesesChapter 11: Statistical Association in Contingency TablesThe Importance of Statistical Association and Contingency TablesThe Structure of a Contingency TableDeveloping Tables of Total, Row, and Column PercentagesThe Rules for Interpreting a Contingency TableSpecifying Causal Relations in Contingency TablesAssessing the Magnitude of Bivariate Associations in Contingency TablesVisual and Intuitive Approach: The Chi-Square Test of Statistical Independence: Issues in Contingency Table AnalysisHow Many Categories for Categorical Variables?: GIGO and the Value of Theory in Identifying Other Important Variables: Sample Size and Significance Tests: Other Measures of Association for Categorical Variables: Chapter 12: The Analysis of Variance (ANOVA)Overview of ANOVA and When It Is UsedPartitioning Variation into Between- and Within- Group DifferencesCalculating the Total Variation in a Dependent Variable: Calculating the Between-Group Variation: Calculating the Within-Group Variation: Hypothesis Testing and Measures of Association in ANOVATesting the Hypothesis of Equality of Group Means: Measures of Association in ANOVA: Issues in the Analysis of VarianceChapter 13: Correlation and RegressionThe Scatterplot of Two Interval or Ratio VariablesThe Correlation Coefficient Regression AnalysisThe Computation of the Regression: Coefficient and Y-Intercept: Goodness of Fit of a Regression Equation: Hypothesis Testing and Tests of Statistical Significance: Using Regression Analysis for Predicting Outcomes: Issues in Bivariate Regression and Correlation AnalysisChapter 14: Introduction to Multivariate AnalysisWhy Do Multivariate Analysis?Exploring Multiple Causes: Statistical Control: Types of Multivariate AnalysisMultivariate Contingency Table Analysis: Partial Correlation Coefficients: Multiple Regression Analysis:

Editorial Reviews

"Throughout this book, the author explains the relevance of statistical techniques--not just the mechanics. The conversational style is engaging, encouraging students to keep reading and realize that they can master statistics. The book distinguishes itself from other texts by paring down whatstudents are expected to learn."--Wayne J. Pitts, University of Memphis