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Simple Statistics

Applications in Social Research

Terance D. Miethe and Jane Florence Gauthier

Publication Date - January 2008

ISBN: 9780195332544

352 pages

In Stock

Retail Price to Students: $89.99

A lively introduction to a complex subject, Simple Statistics is a vital resource for understanding the fascinating world of social statistics.


The efficient use of statistics can transform excellent research into dynamic, persuasive scholarship. To demystify the process of calculating data, Simple Statistics: Applications in Social Research provides a concise introduction to basic social statistics.

In this innovative text, authors Terance D. Miethe and Jane Florence Gauthier illustrate how verbal statements and other types of material are converted into statistical codes, measures, and variables. To give students a sense of the "big picture," they clearly explain the relationship between research and statistics. Moreover, they focus on essential techniques rather than attempting to provide an intimidating, encyclopedic sweep of statistical procedures.

Written in a conversational tone, this invaluable resource does not talk down to students; instead, the authors clearly demonstrate the value of statistical thinking and reasoning in specific contexts. While most statistics texts focus primarily on how to do statistical procedures, they neglect to explain why we do them. This unique book covers both the how and why of statistics, preparing students to be better-informed, conscientious researchers. At the end of each chapter, a set of problems provides a rich context for social inquiry, challenging students to directly apply--and think critically--about what they've learned.

Throughout, the authors use hand computation methods to demonstrate how to apply various statistical procedures, and each procedure is illustrated by several helpful examples. In addition, each book is packaged with a user-friendly CD-ROM, which provides a step-by-step guide to using SPSS to perform the analyses described in the text. Detailed summaries, lists of key terms, and major formulas are included at the end of each chapter, and a comprehensive Instructor's Manual is also available.

A lively introduction to a complex subject, Simple Statistics is a vital resource for understanding the fascinating world of social statistics.

About the Author(s)

Terance D. Miethe is Professor of Criminal Justice at the University of Nevada, Las Vegas. He is author of Simple Statistics: Applications in Criminology and Criminal Justice (OUP, 2006) and coauthor of many books, including Crime Profiles: The Anatomy of Dangerous Persons, Places, and Situations (OUP, 2005).

Jane Florence Gauthier is Assistant Professor of Criminal Justice at the University of Nevada, Las Vegas. Her current research interests focus on gender differences in criminal offending and issues surrounding community structure and crime.


"The authors' approach, tone, and structure are nearly flawless. They use excellent and thought-provoking examples, and a good distribution of sample problems. Students need to do the basic hand calculations in order to master the substantive meaning of statistical results, and this text puts them into that task. I would definitely adopt this text."--Richard Fancy, Wayne State University

"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 at 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
    2. Garbage In, Garbage Out
    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 Variables
    Ratios and Rates
    Maps of Qualitative and Quantitative Variables
    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, ? Known
    Confidence Intervals for Population Means
    Confidence Intervals for Population
    Confidence Intervals: Small Samples and Unknown ?
    Properties of the t-Distribution
    Confidence Intervals for Population Means for Unknown ?
    Confidence Intervals for Population
    Proportion for Unknown ?
    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 Tests 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
    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 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/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 Analysi
    14. Introduction to Multivariate Analysis
    Why Do Multivariate Analysis?
    Exploring Multiple Causes
    Statistical Control
    Types of Multivariate Analysis
    Contingency Table Analysis
    Partial Correlation Coefficients
    Multiple Regression Analysis

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