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Master statistics with STATISTICS FOR THE BEHAVIORAL SCIENCES! With straightforward instruction, built-in learning aids, and real world examples, this psychology text provides you with the tools you need to succeed. You will have numerous opportunities to practice statistical techniques through learning checks, examples, demonstrations, and problems. Exam preparation is made easy with a student companion website that provides tutorials, crossword puzzles, flashcards, learning objectives, and more!

Tabla de contenidos provista por Syndetics

• Part I Introduction and Descriptive Statistics(p. 1)
• Chapter 1 Introduction to Statistics(p. 2)
• Preview(p. 3)
• 1.1 Statistics, Science, and Observations(p. 4)
• 1.2 Populations and Samples(p. 4)
• 1.3 Data Structures, Research Methods, and Statistics(p. 10)
• 1.4 Variables and Measurement(p. 18)
• 1.5 Statistical Notation(p. 24)
• Summary(p. 28)
• Focus on Problem Solving(p. 30)
• Demonstrations 1.1 and 1.2(p. 30)
• Problems(p. 33)
• Chapter 2 Frequency Distributions(p. 35)
• Preview(p. 36)
• 2.1 Overview(p. 37)
• 2.2 Frequency Distribution Tables(p. 37)
• 2.3 Frequency Distribution Graphs(p. 44)
• 2.4 The Shape of a Frequency Distribution(p. 50)
• 2.5 Percentiles, Percentile Ranks, and Interpolation(p. 52)
• 2.6 Stem and Leaf Displays(p. 58)
• Summary(p. 61)
• Focus on Problem Solving(p. 63)
• Demonstrations 2.1 and 2.2(p. 64)
• Problems(p. 66)
• Chapter 3 Central Tendency(p. 70)
• Preview(p. 71)
• 3.1 Overview(p. 71)
• 3.2 The Mean(p. 73)
• 3.3 The Median(p. 82)
• 3.4 The Mode(p. 87)
• 3.5 Selecting a Measure of Central Tendency(p. 89)
• 3.6 Central Tendency and the Shape of the Distribution(p. 95)
• Summary(p. 97)
• Focus on Problem Solving(p. 98)
• Demonstrations 3.1 and 3.2(p. 99)
• Problems(p. 101)
• Chapter 4 Variability(p. 104)
• Preview(p. 105)
• 4.1 Overview(p. 105)
• 4.2 The Range and Interquartile Range(p. 107)
• 4.3 Standard Deviation and Variance for a Population(p. 109)
• 4.4 Standard Deviation and Variance for Samples(p. 116)
• 4.5 More about Variance and Standard Deviation(p. 121)
• 4.6 Comparing Measures of Variability(p. 128)
• Summary(p. 129)
• Focus on Problem Solving(p. 131)
• Demonstration 4.1(p. 132)
• Problems(p. 133)
• Part II Foundations of Inferential Statistics(p. 136)
• Chapter 5 z-Scores: Location of Scores and Standardized Distributions(p. 137)
• Preview(p. 138)
• 5.1 Introduction to z-Scores(p. 138)
• 5.2 z-Scores and Location in a Distribution(p. 140)
• 5.3 Using z-Scores to Standardize a Distribution(p. 144)
• 5.4 Other Standardized Distributions Based on z-Scores(p. 149)
• 5.5 Computing z-Scores for Samples(p. 151)
• 5.6 Looking Ahead to Inferential Statistics(p. 152)
• Summary(p. 155)
• Focus on Problem Solving(p. 156)
• Demonstrations 5.1 and 5.2(p. 157)
• Problems(p. 158)
• Chapter 6 Probability(p. 161)
• Preview(p. 162)
• 6.1 Introduction to Probability(p. 162)
• 6.2 Probability and the Normal Distribution(p. 168)
• 6.3 Probabilities and Proportions for Scores from a Normal Distribution(p. 175)
• 6.4 Probability and the Binomial Distribution(p. 181)
• 6.5 Looking Ahead to Inferential Statistics(p. 186)
• Summary(p. 188)
• Focus on Problem Solving(p. 190)
• Demonstrations 6.1 and 6.2(p. 190)
• Problems(p. 193)
• Chapter 7 Probability and Samples: The Distribution of Sample Means(p. 195)
• Preview(p. 196)
• 7.1 Overview(p. 196)
• 7.2 The Distribution of Sample Means(p. 197)
• 7.3 Probability and the Distribution of Sample Means(p. 205)
• 7.4 More about Standard Error(p. 208)
• 7.5 Looking Ahead to Inferential Statistics(p. 213)
• Summary(p. 217)
• Focus on Problem Solving(p. 219)
• Demonstration 7.1(p. 219)
• Problems(p. 221)
• Part III Inferences about Means and Mean Differences(p. 224)
• Chapter 8 Introduction to Hypothesis Testing(p. 225)
• Preview(p. 226)
• 8.1 The Logic of Hypothesis Testing(p. 226)
• 8.2 Uncertainty and Errors in Hypothesis Testing(p. 237)
• 8.3 An Example of a Hypothesis Test(p. 241)
• 8.4 Directional (One-Tailed) Hypothesis Tests(p. 250)
• 8.5 The General Elements of Hypothesis Testing: A Review(p. 254)
• 8.6 Concerns about Hypothesis Testing: Measuring Effect Size(p. 256)
• 8.7 Statistical Power(p. 260)
• Summary(p. 265)
• Focus on Problem Solving(p. 267)
• Demonstrations 8.1 and 8.2(p. 268)
• Problems(p. 270)
• Chapter 9 Introduction to the t Statistic(p. 274)
• Preview(p. 275)
• 9.1 The t Statistic-An Alternative to z(p. 275)
• 9.2 Hypothesis Tests with the t Statistic(p. 281)
• 9.3 Measuring Effect Size for the t Statistic(p. 285)
• 9.4 Directional Hypotheses and One-Tailed Tests(p. 291)
• Summary(p. 293)
• Focus on Problem Solving(p. 295)
• Demonstrations 9.1 and 9.2(p. 295)
• Problems(p. 297)
• Chapter 10 The t Test for Two Independent Samples(p. 301)
• Preview(p. 302)
• 10.1 Overview(p. 302)
• 10.2 The t Statistic for an Independent-Measures Research Design(p. 304)
• 10.3 Hypothesis Tests and Effect Size with the Independent-Measures t Statistic(p. 311)
• 10.4 Assumptions Underlying the Independent-Measures t Formula(p. 320)
• Summary(p. 323)
• Focus on Problem Solving(p. 325)
• Demonstrations 10.1 and 10.2(p. 326)
• Problems(p. 328)
• Chapter 11 The t Test for Two Related Samples(p. 333)
• Preview(p. 334)
• 11.1 Overview(p. 334)
• 11.2 The t Statistic for Related Samples(p. 336)
• 11.3 Hypothesis Tests and Effect Size for the Repeated-Measures Design(p. 340)
• 11.4 Uses and Assumptions for Related-Samples t Tests(p. 346)
• Summary(p. 350)
• Focus on Problem Solving(p. 351)
• Demonstrations 11.1 and 11.2(p. 352)
• Problems(p. 354)
• Chapter 12 Estimation(p. 359)
• Preview(p. 360)
• 12.1 An Overview of Estimation(p. 360)
• 12.2 Estimation with the t Statistic(p. 366)
• 12.3 A Final Look at Estimation(p. 375)
• Summary(p. 377)
• Focus on Problem Solving(p. 378)
• Demonstrations 12.1 and 12.2(p. 379)
• Problems(p. 382)
• Chapter 13 Introduction to Analysis of Variance(p. 387)
• Preview(p. 388)
• 13.1 Introduction(p. 389)
• 13.2 The Logic of Analysis of Variance(p. 393)
• 13.3 ANOVA Notation and Formulas(p. 397)
• 13.4 The Distribution of F-Ratios(p. 406)
• 13.5 Examples of Hypothesis Testing and Effect Size with ANOVA(p. 408)
• 13.6 Post Hoc Tests(p. 418)
• 13.7 The Relationship Between ANOVA and t Tests(p. 423)
• Summary(p. 425)
• Focus on Problem Solving(p. 428)
• Demonstrations 13.1 and 13.2(p. 428)
• Problems(p. 430)
• Chapter 14 Repeated-Measures Analysis of Variance (ANOVA)(p. 435)
• Preview(p. 436)
• 14.1 Overview(p. 436)
• 14.2 Testing Hypotheses with the Repeated-Measures ANOVA(p. 441)
• 14.3 Advantages of the Repeated-Measures Design(p. 449)
• 14.4 Individual Differences and the Consistency of the Treatment Effects(p. 451)
• Summary(p. 453)
• Focus on Problem Solving(p. 456)
• Demonstrations 14.1 and 14.2(p. 456)
• Problems(p. 460)
• Chapter 15 Two-Factor Analysis of Variance (Independent Measures)(p. 465)
• Preview(p. 466)
• 15.1 Overview(p. 466)
• 15.2 Main Effects and Interactions(p. 468)
• 15.3 Notation and Formulas(p. 476)
• 15.4 Interpreting the Results from a Two-Factor ANOVA(p. 485)
• 15.5 Assumptions for the Two-Factor ANOVA(p. 489)
• Summary(p. 490)
• Focus on Problem Solving(p. 492)
• Demonstrations 15.1 and 15.2(p. 493)
• Problems(p. 499)
• Part IV Correlations and Nonparametric Tests(p. 504)
• Chapter 16 Correlation(p. 505)
• Preview(p. 506)
• 16.1 Overview(p. 506)
• 16.2 The Pearson Correlation(p. 511)
• 16.3 Understanding and Interpreting the Pearson Correlation(p. 516)
• 16.4 Hypothesis Tests with the Pearson Correlation(p. 521)
• 16.5 The Spearman Correlation(p. 525)
• 16.6 Other Measures of Relationship(p. 532)
• Summary(p. 536)
• Focus on Problem Solving(p. 539)
• Demonstrations 16.1 and 16.2(p. 540)
• Problems(p. 543)
• Chapter 17 Introduction to Regression(p. 548)
• Preview(p. 549)
• 17.1 Introduction to Linear Equations and Regression(p. 549)
• 17.2 Testing the Significance of the Regression Equation: Analysis of Regression(p. 562)
• 17.3 Introduction to Multiple Regression with Two Predictor Variables(p. 564)
• 17.4 Evaluating the Contribution of Each Predictor Variable(p. 569)
• Summary(p. 571)
• Focus on Problem Solving(p. 573)
• Demonstrations 17.1 and 17.2(p. 574)
• Problems(p. 577)
• Chapter 18 The Chi-Square Statistic: Tests for Goodness of Fit and Independence(p. 580)
• Preview(p. 581)
• 18.1 Parametric and Nonparametric Statistical Tests(p. 581)
• 18.2 The Chi-Square Test for Goodness of Fit(p. 582)
• 18.3 The Chi-Square Test for Independence(p. 592)
• 18.4 Measuring Effect Size for the Chi-Square Test for Independence(p. 602)
• 18.5 Assumptions and Restrictions for Chi-Square Tests(p. 604)
• 18.6 Special Applications of the Chi-Square Tests(p. 605)
• Summary(p. 608)
• Focus on Problem Solving(p. 611)
• Demonstration 18.1(p. 612)
• Problems(p. 614)
• Chapter 19 The Binomial Test(p. 619)
• Preview(p. 620)
• 19.1 Overview(p. 620)
• 19.2 The Binomial Test(p. 624)
• 19.3 The Relationship Between Chi-Square and the Binomial Test(p. 626)
• 19.4 The Sign Test(p. 627)
• Summary(p. 631)
• Focus on Problem Solving(p. 632)
• Demonstration 19.1(p. 633)
• Problems(p. 633)
• Chapter 20 Statistical Techniques for Ordinal Data: Mann-Whitney, Wilcoxon, Kruskal Wallis, and Friedman Tests(p. 637)
• Preview(p. 638)
• 20.1 Data from an Ordinal Scale(p. 638)
• 20.2 The Mann-Whitney U-Test: An Alternative to the Independent-Measures t Test(p. 641)
• 20.3 The Wilcoxon Signed-Ranks Test: An Alternative to the Repeated-Measures t Test(p. 649)
• 20.4 The Kruskal-Wallis Test: An Alternative to the Independent-Measures ANOVA(p. 655)
• 20.5 The Friedman Test: An Alternative to Repeated-Measures ANOVA(p. 659)
• Summary(p. 662)
• Focus on Problem Solving(p. 666)
• Demonstrations 20.1, 20.2, 20.3, and 20.4(p. 666)
• Problems(p. 671)
• Appendix A Basic Mathematics Review(p. 677)
• A.1 Symbols and Notation(p. 679)
• A.2 Proportions: Fractions, Decimals, and Percentages(p. 681)
• A.3 Negative Numbers(p. 687)
• A.4 Basic Algebra: Solving Equations(p. 689)
• A.5 Exponents and Square Roots(p. 692)
• Appendix B Statistical Tables(p. 699)
• Appendix C Solutions for Odd-Numbered Problems(p. 71)
• Appendix D General Instructions for Using SPSS(p. 735)
• Statistics Organizer(p. 738)
• References(p. 750)
• Index(p. 753)