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Simple Statistics

Applications in Criminology and Criminal Justice

Terance D. Miethe

Publication Date - 15 September 2006

ISBN: 9780195330717

336 pages
6-7/8 x 9-3/16 inches

A concise and compelling introduction to basic statistics for students of criminology and criminal justice.


Simple Statistics 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; instead, it demonstrates the value of statistical 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 illustrates how verbal statements and other types of information are converted into statistical codes, measures, and variables.

While most statistics texts emphasize how to do statistical procedures, they often neglect to explain why we do them. This unique book covers both areas, and the problems at the end of each chapter focus on applications, offering even more context for "why we do" these procedures.

Simple Statistics uses hand computation methods to demonstrate how to apply the various statistical procedures, and most chapters include an optional section on how to do these procedures in SPSS and/or Microsoft Excel spreadsheets. Helpful examples illustrate each statistical procedure, and specific problems, detailed summaries, key terms, and major formulas are provided at the end of each chapter to further highlight major points. A comprehensive Instructor's Manual is also available.


"What makes Simple Statistics distinctive is its remarkable balance between extremely technical statistics texts that are not written in a student-friendly fashion and oversimplified texts. Miethe writes in an exceptionally readable style, challenging students without intimidating them. Another key strength is the book's use of actual crime data, demonstrating the real-world applications of major statistical concepts."--Kent Kerley, University of Alabama, Birmingham

"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 what students are expected to learn."--Wayne J. Pitts, University of Memphis

Table of Contents

    1. Introduction to Statistical Thinking
    Some Definitions and Basic Ideas
    Math Phobia, Panic, and Terror in Social Statistics
    The Practical Value of Social Statistics and Statistical Reasoning
    Types of Statistical Methods
    Pedagogical (Teaching) Approaches
    2. Garbage In, Garbage Out (GIGO)
    Measurement Invalidity
    Sampling Problems
    Faulty Causal Inferences
    Political Influences
    Human Fallibility
    3. Issues in Data Preparation
    Why Is Data Preparation Important?
    Operationalization and Measurement
    Nominal Measurement of Qualitative Variables
    Measurement of Quantitative Variables
    Issues in Levels of Measurement
    Coding and Inputting Statistical Data
    Available Computer Software for Basic Data Analysis
    4. Displaying Data in Tables and Graphic Forms
    The Importance of Data Tables and Graphs
    Types of Tabular and Visual Presentations
    Tables and Graphs for Qualitative Variables
    Tables and Graphs for Quantitative
    Ratios and Rates
    Maps of Qualitative and Quantitative
    Hazards and Distortions in Visual Displays and Collapsing Categories
    5. Modes, Medians, Means, and More
    Modes and Modal Categories
    The Median and Other Measures of Location
    The Mean and Its Meaning
    Weighted Means
    Strengths and Limitations of Mean Ratings
    Choice of Measure of Central Tendency and Position
    6. Measures of Variation and Dispersion
    The Range of Scores
    The Variance and Standard Deviation
    Variances and Standard Deviations for Binary Variables
    Population Versus Sample Variances & Standard Deviations
    7. The Normal Curve and Sampling Distributions
    The Normal Curve
    Z-Scores as Standard Scores
    Reading a Normal Curve Table
    Other Sampling Distributions
    Binomial Distribution
    Chi-Square Distribution
    8. Parameter Estimation and Confidence Intervals
    Sampling Distributions and the Logic of Parameter Estimation
    Inferences from Sampling Distributions to One Real Sample
    Confidence Intervals: Large Samples
    Confidence Intervals for Population Means
    Confidence Intervals for Population Proportions
    Confidence Intervals: Small Samples
    Properties of the t-Distribution
    Confidence Intervals for Population Means
    Confidence Intervals for Population Proportions
    9. Introduction to Hypothesis Testing
    Confidence Intervals Versus Hypothesis Testing
    Basic Terminology and Symbols
    Types of Hypotheses
    Zone of Rejection and Critical Values
    Significance Levels and Errors in Decision Making
    10. Hypothesis Testing for Means and Proportions
    Types of Hypothesis Testing
    One-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 Hypotheses
    11. Statistical Association in Contingency Tables
    The Importance of Statistical Association and Contingency Tables
    The Structure of a Contingency Table
    Developing Tables of Total, Row, and Column Percentages
    The Rules for Interpreting a Contingency Table
    Specifying Causal Relations in Contingency Tables
    Assessing the Magnitude of Bivariate Associations in Contingency Tables
    Visual and Intuitive Approach
    The Chi-Square Test of Statistical Independence
    Issues in Contingency Table Analysis
    How 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
    12. The Analysis of Variance (ANOVA)
    Overview of ANOVA and When It Is Used
    Partitioning Variation into Between- and Within-Group Differences
    Calculating the Total Variation in a Dependent Variable
    Calculating the Between-Group Variation
    Calculating the Within-Group Variation
    Hypothesis Testing and Measures of Association in ANOVA
    Testing the Hypothesis of Equality of Group Means
    Measures of Association in ANOVA
    Issues in the Analysis of Variance
    13. Correlation and Regression
    The Scatterplot of Two Interval or Ratio Variables
    The Correlation Coefficient Regression Analysis
    The Computation of the Regression
    Coefficient & 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 Analysis
    14. Introduction to Multivariate Analysis
    Why Do Multivariate Analysis?
    Exploring Multiple Causes
    Statistical Control
    Types of Multivariate Analysis
    Multivariate Contingency Table Analysis
    Partial Correlation Coefficients
    Multiple Regression Analysis

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