Simple Statistics Applications in Social Research

by Miethe, Terance D.; Gauthier, Jane Florence

• ISBN13: 9780195332544
• ISBN10: 0195332547
• Edition: CD
• Format: Paperback
• Copyright: 2008-01-23
• Publisher: Oxford University Press

Summary

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.

Author Biography

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.

Table of Contents

Measurement Invalidity

Sampling Problems

Faulty Causal Inferences

Political Influences

Human Fallibility

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

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

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

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 and Standard Deviations

The Normal Curve and Sampling Distributions

The Normal CurveZ-Scores as Standard Scores

Reading a Normal Curve Table

Other Sampling Distributions

Binomial Distributiont-Distribution

Chi-Square Distribution

F-Distribution

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 PopulationProportion for Unknown

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

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

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

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

Correlation and Regression

The Scatterplot of Two Interval/Ratio Variables

The Correlation Coefficient

Regression Analysis

The 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 Analysi

Intro

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