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Statistics for the Behavioral Sciences, Second Edition, engages students in an ongoing spirit of discovery by illustrating how statistics apply to modern-day research problems. By integrating robust pedagogy as well as screenshots and practical examples for using IBM SPSS® Statistics software, award-winning author Gregory J. Privitera balances statistical theory, computation, and application with the technical instruction needed for students to succeed in the modern era of data collection, analysis, and statistical interpretation. Fully updated with current research and a full-color design, this new edition features even more real-world examples and an updated Student Study Guide with SPSS Workbook.

“Privitera does an EXCELLENT job of balancing clarity with depth.” —Ronald W. Stoffey, Kutztown University of Pennsylvania

“The writing style and the presentation of material are not only ENJOYABLE TO READ but are EASY TO FOLLOW and understand.” —Joshua J. Dobias, Rutgers University

“Privitera ties research methods, SPSS, and statistics together in a SEAMLESS fashion.” —Walter M. Yamada, Azusa Pacific University

“I like the objectives, the readability of the text, the straightforwardness of the presentations of concepts, the problems that are quite APPROPRIATE ON MANY LEVELS (computation, theory, etc.), and the emphasis on SPSS.” —Ted R. Bitner, DePauw University

E-Book Information

Year: 2,014
Edition: Second Edition
Pages: C, XL, 768, B
Language: English
Topic: 198
Identifier: 1452286906,9781452286907
Color: 1
Paginated: 1
Org File Size: 89,688,960
Extension: pdf
Tags: Психологические дисциплины Матметоды и моделирование в психологии
Toc: Part I: Introduction and Descriptive Statistics
Chapter 1: Introduction to Statistics
1.1 The Use of Statistics in Science
1.2 Descriptive and Inferential Statistics
1.3 Research Methods and Statistics
1.4 Scales of Measurement
1.5 Types of Data
1.6 Research in Focus: Types of Data and Scales of Measurement
1.7 SPSS in Focus: Entering and Defining Variables
Chapter 2: Summarizing Data: Frequency Distributions in Tables and Graphs
2.1 Why Summarize Data?
2.2 Frequency Distributions for Grouped Data
2.3 Identifying Percentile Points and Percentile Ranks
2.4 SPSS in Focus: Frequency Distributions for Quantitative Data
2.5 Frequency Distributions for Ungrouped Data
2.6 Research in Focus: Summarizing Demographic Information
2.7 SPSS in Focus: Frequency Distributions for Categorical Data
2.8 Pictorial Frequency Distributions
2.9 Graphing Distributions: Continuous Data
2.10 Graphing Distributions: Discrete and Categorical Data
2.11 Research in Focus: Frequencies and Percents
2.12 SPSS in Focus: Histograms, Bar Charts, and Pie Charts
Chapter 3: Summarizing Data: Central Tendency
3.1 Introduction to Central Tendency
3.2 Measures of Central Tendency
3.3 Characteristics of the Mean
3.4 Choosing an Appropriate Measure of Central Tendency
3.5 Research in Focus: Describing Central Tendency
3.6 SPSS in Focus: Mean, Median, and Mode
Chapter 4: Summarizing Data: Variability
4.1 Measuring Variability
4.2 The Range
4.3 Research in Focus: Reporting the Range
4.4 Quartiles and Intequartiles
4.5 The Variance
4.6 Explaining Variance for Populations and Samples
4.7 The Computational Formula for Variance
4.8 The Standard Deviation
4.9 What Does the Standard Deviation Tell Us?
4.10 Characteristics of the Standard Deviation
4.11 SPSS in Focus: Range, Variance, and Standard Deviation
Part II: Probability and the Foundations of Inferential Statistics
Chapter 5: Probability
5.1 Introduction to Probability
5.2 Calculating Probability
5.3 Probability and Relative Frequency
5.4 The Relationship Between Multiple Outcomes
5.5 Conditional Probabilities and Bayes’ Theorem
5.6 SPSS in Focus: Probability Tables
5.7 Probability Distributions
5.8 The Mean of a Probability Distribution and Expected Value
5.9 Research in Focus: When Are Risks Worth Taking?
5.10 The Variance and Standard Deviation of a Probability Distribution
5.11 Expected Value and the Binomial Distribution
5.12 A Final Thought on the Likelihood of Random Behavioral Outcomes
Chapter 6: Probability, Normal Distributions, and z Scores
6.1 The Normal Distribution in Behavioral Science
6.2 Characteristics of the Normal Distribution
6.3 Research in Focus: The Statistical Norm
6.4 The Standard Normal Distribution
6.5 The Unit Normal Table: A Brief Introduction
6.6 Locating Proportions
6.7 Locating Scores
6.8 SPSS in Focus: Converting Raw Scores to Standard z Scores
6.9 Going From Binomial to Normal
6.10 The Normal Approximation to the Binomial Distribution
Chapter 7: Probability and Sampling Distributions
7.1 Selecting Samples From Populations
7.2 Selecting a Sample: Who’s in and Who’s out?
7.3 Sampling Distributions: The Mean
7.4 Sampling Distributions: The Variance
7.5 The Standard Error of the Mean
7.6 Factors that Decrease Standard Error
7.7 SPSS in Focus: Estimating the Standard Error of the Mean
7.8 APA in Focus: Reporting the Standard Error
7.9 Standard Normal Transformations With Sampling Distributions
Part III: Probability and the Foundations of Inferential Statistics
Chapter 8: Hypothesis Testing: Significance, Effect Size, and Power
8.1 Inferential Statistics and Hypothesis Testing
8.2 Four Steps to Hypothesis Testing
8.3 Hypothesis Testing and Sampling Distributions
8.4 Making a Decision: Types of Error
8.5 Testing for Significance: Examples Using the z Test
8.6 Research in Focus: Directional Versus Nondirectional Tests
8.7 Measuring the Size of an Effect: Cohen’s d
8.8 Effect Size, Power, and Sample Size
8.9 Additional Factors That Increase Power
8.10 SPSS in Focus: A Preview for Chapters 9 to 18
8.11 APA in Focus: Reporting the Test Statistic and Effect Size
Chapter 9: Testing Means: One-Sample and Two-Independent Sample t Tests
9.1 Going From z to t
9.2 The Degrees of Freedom
9.3 Reading the t Table
9.4 One Sample t Test
9.5 Effect Size for the One Sample t Test
9.6 SPSS in Focus: One Sample t Test
9.7 Two–Independent Sample t Test
9.8 Effect Size for the Two–Independent Sample t Test
9.9 SPSS in Focus: Two–Independent Sample t Test
9.10 APA in Focus: Reporting the t Statistic and Effect Size
Chapter 10: Testing Means: Related Samples t Test
10.1 Related and Independent Samples
10.2 Introduction to the Related Samples t Test
10.3 Related Samples t Test: Repeated-Measures Design
10.4 SPSS in Focus: The Related Samples t Test
10.5 Related Samples t Test: Matched-Pairs Design
10.6 Measuring Effect Size for the Related Samples t Test
10.7 Advantages for Selecting Related Samples
10.8 APA in Focus: Reporting the t Statistic and Effect Size for Related Samples
Chapter 11: Estimation and Confidence Intervals
11.1 Point Estimation and Interval Estimation
11.2 The Process of Estimation
11.3 Estimation for the One–Sample z Test
11.4 Estimation for the One–Sample t Test
11.5 SPSS in Focus: Confidence Intervals for the One–Sample t Test
11.6 Estimation for the Two–Independent Sample t Test
11.7 SPSS in Focus: Confidence Intervals for the Two–Independent Sample t Test
11.8 Estimation for the Related Samples t Test
11.9 SPSS in Focus: Confidence Intervals for the Related Samples t Test
11.10 Characteristics of Estimation: Precisions and Certainty
11.11: APA in Focus: Reporting Confidence Intervals
Part IV: Making Inferences About the Variability of Two or More Means
Chapter 12. Analysis of Variance: One-Way Between-Subjects Design
12.1 Increasing k: A Shift to Analyzing Variance
12.2 An Introduction to Analysis of Variance
12.3 Sources of Variation and the Test Statistic
12.4 Degrees of Freedom
12.5 The One-Way Between-Subjects ANOVA
12.6 What Is the Next Step?
12.7 Post Hoc Comparisons
12.8 SPSS in Focus: The One-Way Between-Subjects ANOVA
12.9 Measuring Effect Size
12.10 APA in Focus: Reporting the F Statistic, Significance, and Effect Size
Chapter 13: Analysis of Variance: One-Way Within-Subjects (Repeated Measures) Design
13.1 Observing the Same Participants Across Groups
13.2 Sources of Variation and the Test Statistic
13.3 Degrees of Freedom
13.4 The One-Way Within-Subjects ANOVA
13.5 Post Hoc Comparison: Bonferroni Procedure
13.6 SPSS in Focus: The One-Way Within-Subjects ANOVA
13.7 Measuring Effect Size
13.8 The Within-Subjects Design: Consistency and Power
13.9 APA in Focus: Reporting the F Statistic, Significance, and Effect Size
Chapter 14. Analysis of Variance: Two-Way Between-Subjects Factorial Design
14.1 Observing Two Factors at the Same Time
14.2 New Terminology and Notation
14.3 Designs for the Two-Way ANOVA
14.4 Describing Variability: Main Effects and Interactions
14.5 The Two-Way Between-Subjects ANOVA
14.6 Analyzing Main Effects and Interactions
14.7 Measuring Effect Size
14.8 SPSS in Focus: The Two-Way Between-Subjects ANOVA
14.9 APA in Focus: Reporting Main Effects, Interactions, and Effect Size
Part V: Making Inferences About Patterns, Frequencies, and Ordinal Data
Chapter 15. Correlation
15.1 The Structure of a Correlational Design
15.2 Describing a Correlation
15.3 Pearson Correlation Coefficient
15.4 SPSS in Focus: Pearson Correlation Coefficient
15.5 Assumptions of Tests for Linear Correlations
15.6 Limitations in Interpretation: Causality, Outliers, and Restrictions of Range
15.7 Alternative to Pearson r: Spearman Correlation Coefficient
15.8 SPSS in Focus: Spearman Correlation Coefficient
15.9 Alternative to Pearson r: Point-Biserial Correlation Coefficient
15.10 SPSS in Focus: Point-Biserial Correlation Coefficient
15.11 Alternative to Pearson r: Phi Correlation Coefficient
15.12 SPSS in Focus: Phi Correlation Coefficient
15.13 APA in Focus: Reporting Correlations
Chapter 16: Linear Regression and Multiple Regression
16.1 From Relationships to Predictions
16.2 Fundamentals of Linear Regression
16.3 What Makes the Regression Line the Best-Fitting Line?
16.4 The Slope and y Intercept of a Straight Line
16.5 Using the Method of Least Squares to Find the Best Fit
16.6 Using Analysis of Regression to Measure Significance
16.7 SPSS in Focus: Analysis of Regression
16.8 Using the Standard Error of Estimate to Measure Accuracy
16.9 Introduction to Multiple Regression
16.10 Computing and Evaluating Significance for Multiple Regression
16.11 The Beta Coefficient for Multiple Regression
16.12 Evaluating Significance for the Relative Contribution of Each Predictor Variable
16.13 SPSS in Focus: Multiple Regression Analysis
16.14 APA in Focus: Reporting Regression Analysis
Chapter 17: Nonparametric Tests: Chi-Square Tests
17.1 Tests for Nominal Data
17.2 The Chi-Square Goodness-of-Fit Test
17.3 SPSS in Focus: The Chi-Square Goodness-of-Fit Test
17.4 Interpreting the Chi-Square Goodness-of-Fit Test
17.5 Independent Observations and Expected Frequency Size
17.6 The Chi-Square Test for Independence
17.7 The Relationship Between Chi-Square and the Phi Coefficient
17.8 Measures of Effect Size
17.9 SPSS in Focus: The Chi-Square Test for Independence
17.10 APA in Focus: Reporting the Chi-Square Test
Chapter 18: Nonparametric Tests: Tests for Ordinal Data
18.1 Tests for Ordinal Data
18.2 The Sign Test
18.3 SPSS in Focus: The Related Samples Sign Test
18.4. The Wilcoxon Signed-Ranks T Test
18.5 SPSS in Focus: The Wilcoxon Signed-Ranks T Test
18.6 The Mann-Whitney U Test
18.7 SPSS in Focus: The Mann-Whitney U Test
18.8 The Kruskal-Wallis H Test
18.9 SPSS in Focus: The Kruskal-Wallis H Test
18.10 The Friedman Test
18.11 SPSS in Focus: The Friedman Test
18.12 APA in Focus: Reporting Nonparametric Tests
Appendix A: Basic Math Review and Summation Notation
A.1 Positive and Negative Numbers
A.2 Addition
A.3 Subtraction
A.4 Multiplication
A.5 Division
A.6 Fractions
A.7 Decimals and Percents
A.8 Exponents and Roots
A.9 Order of Computation
A.10 Equations: Solving for x
A.11 Summation Notation
Appendix B: Statistical Tables
Table B.1 The Unit Normal Table
Table B.2 The t Distribution
Table B.3 Critical Values for F Distribution
Table B.4 The Studentized Range Statistic (q)
Table B.5 Critical Values for the Pearson Correlation
Table B.6 Critical Values for the Spearman Correlation
Table B.7 Critical Values of Chi-Square
Table B.8 Distribution of Binomial Probabilities
Table B.9 Wilcoxon Signed-Rank T Critical Values
Table B.10 Critical Values of the Mann-Whitney U
Appendix C: Chapter Solutions for Even-Numbered Problems