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Cited by more than 300 scholars, Statistical Reasoning in the Behavioral Sciences continues to provide streamlined resources and easy-to-understand information on statistics in the behavioral sciences and related fields, including psychology, education, human resources management, and sociology.
The sixth edition includes new information about the use of computers in statistics and offers screenshots of IBM SPSS (formerly SPSS) menus, dialog boxes, and output in selected chapters without sacrificing any of the conceptual logic and the statistical formulas needed to facilitate understanding. The example problems have been updated to reflect more current topics (e.g., text messaging while driving, violence in the media). The latest research and new photos have been integrated throughout the text to make the material more accessible. With these changes, students and professionals in the behavioral sciences will develop an understanding of statistical logic and procedures, the properties of statistical devices, and the importance of the assumptions underlying statistical tools.
CHAPTER 1 Introduction.
1.1 Descriptive Statistics.
1.2 Inferential Statistics.
1.3 Our Concern: Applied Statistics.
1.4 Variables and Constants.
1.5 Scales of Measurement.
1.6 Scales of Measurement and Problems of Statistical Treatment.
1.7 Do Statistics Lie?
Point of Controversy: Are Statistical Procedures Necessary?
1.8 Some Tips on Studying Statistics.
1.9 Statistics and Computers.
1.10 Summary.
CHAPTER 2 Frequency Distributions, Percentiles, and Percentile Ranks.
2.1 Organizing Qualitative Data.
2.2 Grouped Scores.
2.3 How to Construct a Grouped Frequency Distribution.
2.4 Apparent versus Real Limits.
2.5 The Relative Frequency Distribution.
2.6 The Cumulative Frequency Distribution.
2.7 Percentiles and Percentile Ranks.
2.8 Computing Percentiles from Grouped Data.
2.9 Computation of Percentile Rank.
2.10 Summary.
CHAPTER 3 Graphic Representation of Frequency Distributions.
3.1 Basic Procedures.
3.2 The Histogram.
3.3 The Frequency Polygon.
3.4 Choosing between a Histogram and a Polygon.
3.5 The Bar Diagram and the Pie Chart.
3.6 The Cumulative Percentage Curve.
3.7 Factors Affecting the Shape of Graphs.
3.8 Shape of Frequency Distributions.
3.9 Summary.
CHAPTER 4 Central Tendency.
4.1 The Mode.
4.2 The Median.
4.3 The Mean.
4.4 Properties of the Mode.
4.5 Properties of the Mean.
Point of Controversy: Is It Permissible to Calculate the Mean for Tests in the Behavioral Sciences?
4.6 Properties of the Median.
4.7 Measures of Central Tendency in Symmetrical and Asymmetrical Distributions.
4.8 The Effects of Score Transformations.
4.9 Summary.
CHAPTER 5 Variability and Standard (z) Scores.
5.1 The Range and Semi-Interquartile Range.
5.2 Deviation Scores.
5.3 Deviational Measures: The Variance.
5.4 Deviational Measures: The Standard Deviation.
5.5 Calculation of the Variance and Standard Deviation: Raw-Score Method.
5.6 Calculation of the Standard Deviation with IBM SPSS (formerly SPSS).
Point of Controversy: Calculating the Sample Variance: Should We Divide by n or (n - 1)?
5.7 Properties of the Range and Semi-Interquartile Range.
5.8 Properties of the Standard Deviation.
5.9 How Big Is a Standard Deviation?
5.10 Score Transformations and Measures of Variability.
5.11 Standard Scores (z Scores).
5.12 A Comparison of z Scores and Percentile Ranks.
5.13 Summary.
CHAPTER 6 Standard Scores and the Normal Curve.
6.1 Historical Aspects of the Normal Curve.
6.2 The Nature of the Normal Curve.
6.3 Standard Scores and the Normal Curve.
6.4 The Standard Normal Curve: Finding Areas When the Score Is Known.
6.5 The Standard Normal Curve: Finding Scores When the Area Is Known.
6.6 The Normal Curve as a Model for Real Variables.
6.7 The Normal Curve as a Model for Sampling Distributions.
6.8 Summary. Point of Controversy: How Normal Is the Normal Curve?
CHAPTER 7 Correlation.
7.1 Some History.
7.2 Graphing Bivariate Distributions: The Scatter Diagram.
7.3 Correlation: A Matter of Direction.
7.4 Correlation: A Matter of Degree.
7.5 Understanding the Meaning of Degree of Correlation.
7.6 Formulas for Pearson's Coefficient of Correlation.
7.7 Calculating r from Raw Scores.
7.8 Calculating r with IBM SPSS.
7.9 Spearman's Rank-Order Correlation Coefficient.
7.10 Correlation Does Not Prove Causation.
7.11 The Effects of Score Transformations.
7.12 Cautions Concerning Correlation Coefficients.
7.13 Summary.
CHAPTER 8 Prediction.
8.1 The Problem of Prediction.
8.2 The Criterion of Best Fit.
Point of Controversy: Least-Squares Regression versus the Resistant Line.
8.3 The Regression Equation: Standard-Score Form.
8.4 The Regression Equation: Raw-Score Form.
8.5 Error of Prediction: The Standard Error of Estimate.
8.6 An Alternative (and Preferred) Formula for SYX.
8.7 Calculating the “Raw-Score” Regression Equation and Standard Error of Estimate with IBM SPSS.
8.8 Error in Estimating Y from X.
8.9 Cautions Concerning Estimation of Predictive Error.
8.10 Prediction Does Not Prove Causation.
8.11 Summary.
CHAPTER 9 Interpretive Aspects of Correlation and Regression.
9.1 Factors Influencing r : Degree of Variability in Each Variable.
9.2 Interpretation of r : The Regression Equation I.
9.3 Interpretation of r : The Regression Equation II.
9.4 Interpretation of r : Proportion of Variation in Y Not Associated with Variation in X.
9.5 Interpretation of r : Proportion of Variation in Y Associated with Variation in X.
9.6 Interpretation of r : Proportion of Correct Placements.
9.7 Summary.
CHAPTER 10 Probability.
10.1 Defining Probability.
10.2 A Mathematical Model of Probability.
10.3 Two Theorems in Probability.
10.4 An Example of a Probability Distribution: The Binomial.
10.5 Applying the Binomial.
10.6 Probability and Odds.
10.7 Are Amazing Coincidences Really That Amazing?
10.8 Summary.
CHAPTER 11 Random Sampling and Sampling Distributions.
11.1 Random Sampling.
11.2 Using a Table of Random Numbers.
11.3 The Random Sampling Distribution of the Mean: An Introduction.
11.4 Characteristics of the Random Sampling Distribution of the Mean.
11.5 Using the Sampling Distribution of X to Determine the Probability for Different Ranges of Values of X.
11.6 Random Sampling Without Replacement.
11.7 Summary.
CHAPTER 12 Introduction to Statistical Inference: Testing Hypotheses about Single Means (z and t).
12.1 Testing a Hypothesis about a Single Mean.
12.2 The Null and Alternative Hypotheses.
12.3 When Do We Retain and When Do We Reject the Null Hypothesis?
