Simple Statistics

Description

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.

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
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-Distributions
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