Simple Statistics Applications in Social Research

ISBN-10: 0195332547

ISBN-13: 9780195332544

Edition: 2008

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Simple Statistics provides a concise, compelling, and reasonably priced introduction to basic statistics for students of criminology and criminal justice. Written in a conversational tone, this text does not "dumb down" the material. Rather, it demonstrates the value of statistical thinking and reasoning in context. For example, Chapter 2 illustrates the various ways that "garbage in, garbage out" affects the substantive conclusions drawn from statistical analyses. This book covers essential statistical techniques. It does not attempt 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 don't have a clue as to how research methods and statistics are related or how to conduct statistical analysis from the bottom up. This textbook provides answers to both "how to do..." and "why we do..." statistical procedures. Most statistics texts emphasize the "how to do..."--to the neglect of the "why we do..." questions. The problems at the end of each chapter focus on applications to provide more context for "why we do..." these procedures. The term informed consumer is frequently used to convey the importance of understanding social statistics for becoming a better student, employee, and citizen. The book 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 understanding the statistical methods described in this book. Several examples are used to illustrate each statistical procedure to help students understand and apply them. However, rather than overwhelming students with too many examples, the book offers a balance between computation methods and examples of how to do them. 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, written by Miethe, is available.
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Book details

List price: $59.95
Copyright year: 2008
Publisher: Oxford University Press, Incorporated
Publication date: 1/23/2008
Binding: Paperback
Pages: 352
Size: 6.75" wide x 8.75" long x 1.00" tall
Weight: 1.188
Language: English

Terance Miethe received his Ph.D. in Sociology from Washington State University, and is currently a Professor of Criminal Justice at the University of Nevada-Las Vegas. He is the author of several books and research articles in the areas of criminal victimization, theories of crime, and criminal processing.

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
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
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