Statistics For The Behavioral Sciences

by Gravetter, Frederick J.

Edition:

7th

ISBN13:

9780495095200

ISBN10:

0495095206

Format:

Paperback

Pub. Date:

5/12/2006

Publisher(s):

Wadsworth Publishing

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Summary

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!

Introduction and Descriptive Statistics	p. 1
Introduction to Statistics	p. 2
Preview	p. 3
Statistics, Science, and Observations	p. 4
Populations and Samples	p. 4
Data Structures, Research Methods, and Statistics	p. 10
Variables and Measurement	p. 18
Statistical Notation	p. 24
Summary	p. 28
Focus on Problem Solving	p. 30
Demonstrations 1.1 and 1.2	p. 30
Problems	p. 33
Frequency Distributions	p. 35
Preview	p. 36
Overview	p. 37
Frequency Distribution Tables	p. 37
Frequency Distribution Graphs	p. 44
The Shape of a Frequency Distribution	p. 50
Percentiles, Percentile Ranks, and Interpolation	p. 52
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
Central Tendency	p. 70
Preview	p. 71
Overview	p. 71
The Mean	p. 73
The Median	p. 82
The Mode	p. 87
Selecting a Measure of Central Tendency	p. 89
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
Variability	p. 104
Preview	p. 105
Overview	p. 105
The Range and Interquartile Range	p. 107
Standard Deviation and Variance for a Population	p. 109
Standard Deviation and Variance for Samples	p. 116
More about Variance and Standard Deviation	p. 121
Comparing Measures of Variability	p. 128
Summary	p. 129
Focus on Problem Solving	p. 131
Demonstration 4.1	p. 132
Problems	p. 133
Foundations of Inferential Statistics	p. 136
z-Scores: Location of Scores and Standardized Distributions	p. 137
Preview	p. 138
Introduction to z-Scores	p. 138
z-Scores and Location in a Distribution	p. 140
Using z-Scores to Standardize a Distribution	p. 144
Other Standardized Distributions Based on z-Scores	p. 149
Computing z-Scores for Samples	p. 151
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
Probability	p. 161
Preview	p. 162
Introduction to Probability	p. 162
Probability and the Normal Distribution	p. 168
Probabilities and Proportions for Scores from a Normal Distribution	p. 175
Probability and the Binomial Distribution	p. 181
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
Probability and Samples: The Distribution of Sample Means	p. 195
Preview	p. 196
Overview	p. 196
The Distribution of Sample Means	p. 197
Probability and the Distribution of Sample Means	p. 205
More about Standard Error	p. 208
Looking Ahead to Inferential Statistics	p. 213
Summary	p. 217
Focus on Problem Solving	p. 219
Demonstration 7.1	p. 219
Problems	p. 221
Inferences about Means and Mean Differences	p. 224
Introduction to Hypothesis Testing	p. 225
Preview	p. 226
The Logic of Hypothesis Testing	p. 226
Uncertainty and Errors in Hypothesis Testing	p. 237
An Example of a Hypothesis Test	p. 241
Directional (One-Tailed) Hypothesis Tests	p. 250
The General Elements of Hypothesis Testing: A Review	p. 254
Concerns about Hypothesis Testing: Measuring Effect Size	p. 256
Statistical Power	p. 260
Summary	p. 265
Focus on Problem Solving	p. 267
Demonstrations 8.1 and 8.2	p. 268
Problems	p. 270
Introduction to the t Statistic	p. 274
Preview	p. 275
The t Statistic-An Alternative to z	p. 275
Hypothesis Tests with the t Statistic	p. 281
Measuring Effect Size for the t Statistic	p. 285
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
The t Test for Two Independent Samples	p. 301
Preview	p. 302
Overview	p. 302
The t Statistic for an Independent-Measures Research Design	p. 304
Hypothesis Tests and Effect Size with the Independent-Measures t Statistic	p. 311
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
The t Test for Two Related Samples	p. 333
Preview	p. 334
Overview	p. 334
The t Statistic for Related Samples	p. 336
Hypothesis Tests and Effect Size for the Repeated-Measures Design	p. 340
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
Estimation	p. 359
Preview	p. 360
An Overview of Estimation	p. 360
Estimation with the t Statistic	p. 366
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
Introduction to Analysis of Variance	p. 387
Preview	p. 388
Introduction	p. 389
The Logic of Analysis of Variance	p. 393
ANOVA Notation and Formulas	p. 397
The Distribution of F-Ratios	p. 406
Examples of Hypothesis Testing and Effect Size with ANOVA	p. 408
Post Hoc Tests	p. 418
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
Repeated-Measures Analysis of Variance (ANOVA)	p. 435
Preview	p. 436
Overview	p. 436
Testing Hypotheses with the Repeated-Measures ANOVA	p. 441
Advantages of the Repeated-Measures Design	p. 449
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
Two-Factor Analysis of Variance (Independent Measures)	p. 465
Preview	p. 466
Overview	p. 466
Main Effects and Interactions	p. 468
Notation and Formulas	p. 476
Interpreting the Results from a Two-Factor ANOVA	p. 485
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
Correlations and Nonparametric Tests	p. 504
Correlation	p. 505
Preview	p. 506
Overview	p. 506
The Pearson Correlation	p. 511
Understanding and Interpreting the Pearson Correlation	p. 516
Hypothesis Tests with the Pearson Correlation	p. 521
The Spearman Correlation	p. 525
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
Introduction to Regression	p. 548
Preview	p. 549
Introduction to Linear Equations and Regression	p. 549
Testing the Significance of the Regression Equation: Analysis of Regression	p. 562
Introduction to Multiple Regression with Two Predictor Variables	p. 564
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
The Chi-Square Statistic: Tests for Goodness of Fit and Independence	p. 580
Preview	p. 581
Parametric and Nonparametric Statistical Tests	p. 581
The Chi-Square Test for Goodness of Fit	p. 582
The Chi-Square Test for Independence	p. 592
Measuring Effect Size for the Chi-Square Test for Independence	p. 602
Assumptions and Restrictions for Chi-Square Tests	p. 604
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
The Binomial Test	p. 619
Preview	p. 620
Overview	p. 620
The Binomial Test	p. 624
The Relationship Between Chi-Square and the Binomial Test	p. 626
The Sign Test	p. 627
Summary	p. 631
Focus on Problem Solving	p. 632
Demonstration 19.1	p. 633
Problems	p. 633
Statistical Techniques for Ordinal Data: Mann-Whitney, Wilcoxon, Kruskal Wallis, and Friedman Tests	p. 637
Preview	p. 638
Data from an Ordinal Scale	p. 638
The Mann-Whitney U-Test: An Alternative to the Independent-Measures t Test	p. 641
The Wilcoxon Signed-Ranks Test: An Alternative to the Repeated-Measures t Test	p. 649
The Kruskal-Wallis Test: An Alternative to the Independent-Measures ANOVA	p. 655
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
Basic Mathematics Review	p. 677
Symbols and Notation	p. 679
Proportions: Fractions, Decimals, and Percentages	p. 681
Negative Numbers	p. 687
Basic Algebra: Solving Equations	p. 689
Exponents and Square Roots	p. 692
Statistical Tables	p. 699
Solutions for Odd-Numbered Problems	p. 71
General Instructions for Using SPSS	p. 735
Statistics Organizer	p. 738
References	p. 750
Index	p. 753
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