Simple Statistics: Applications in Social Research / Edition 1

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Overview


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.

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Editorial Reviews

From the Publisher

"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

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Product Details

  • ISBN-13: 9780195332544
  • Publisher: Oxford University Press, USA
  • Publication date: 1/23/2008
  • Edition description: New Edition
  • Edition number: 1
  • Pages: 352
  • Sales rank: 144,236
  • Product dimensions: 9.20 (w) x 6.90 (h) x 0.80 (d)

Meet the Author

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.

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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 t-Distribution
Chi-Square Distribution
F-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
Proportions
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
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 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
Multivariate Contingency Table Analysis
Partial Correlation Coefficients
Multiple Regression Analysis

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Sort by: Showing 1 Customer Reviews
  • Anonymous

    Posted April 22, 2009

    Would not recommend even to high school students!

    If anyone has ever taken statistics regardless of undergraduate or graduate, I would NOT recommend this book. The formulas are great IF the author has shown STEPS on how he got the answer. There are way too many shortcuts mathematically speaking and the questions at the end of the chapter are extremely vague. Paris Hilton could have put this book together with the same level of effort. Recommendation: to students who are not familiar with statistics, I would HIGHLY recommend statistics by Joseph E. Healey instead. The author has also wrote another exact book (blue cover) with same title and content. The only difference is that the tan book has a cd in it.

    1 out of 1 people found this review helpful.

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