Introductory Statistics for the Behavioral Sciences	3
	Contents	9
	Preface	17
	Acknowledgments	21
	Glossary of Symbols	23
	Part I: Descriptive Statistics	29
	Chapter 1 Introduction	31
	Why Study Statistics?	32
	Descriptive and Inferential Statistics	33
	Populations, Samples, Parameters, and Statistics	34
	Measurement Scales	35
	Independent and Dependent Variables	38
	Summation Notation	40
	Ihno’s Study	44
	Summary	46
	Exercises	47
	Thought Questions	51
	Computer Exercises	51
	Bridge to SPSS	52
	Chapter 2 Frequency Distributions and Graphs	54
	The Purpose of Descriptive Statistics	55
	Regular Frequency Distributions	56
	Cumulative Frequency Distributions	58
	Grouped Frequency Distributions	59
	Real and Apparent Limits	61
	Interpreting a Raw Score	62
	Definition of Percentile Rank and Percentile	62
	Computational Procedures	63
	Deciles, Quartiles, and the Median	66
	Graphic Representations	67
	Shapes of Frequency Distributions	71
	Summary	73
	Exercises	75
	Thought Questions	77
	Computer Exercises	77
	Bridge to SPSS	78
	Chapter 3 Measures of Central Tendency and Variability	81
	Introduction	82
	The Mode	84
	The Median	84
	The Mean	86
	The Concept of Variability	90
	The Range	93
	The Standard Deviation and Variance	94
	Summary	101
	Exercises	103
	Thought Questions	104
	Computer Exercises	105
	Bridge to SPSS	106
	Chapter 4 Standardized Scores and the Normal Distribution	109
	Interpreting a Raw Score Revisited	110
	Rules for Changing ? and ?	112
	Standard Scores (z Scores)	113
	T Scores, SAT Scores, and IQ Scores	116
	The Normal Distribution	118
	Table of the Standard Normal Distribution	121
	Illustrative Examples	123
	Summary	129
	Exercises	131
	Thought Questions	133
	Computer Exercises	134
	Bridge to SPSS	134
	Part II: Basic Inferential Statistics	137
	Chapter 5 Introduction to Statistical Inference	139
	Introduction	141
	The Goals of Inferential Statistics	142
	Sampling Distributions	142
	The Standard Error of the Mean	147
	The z Score for Sample Means	150
	Null Hypothesis Testing	152
	Assumptions Required by the Statistical Test for the Mean of a Single Population	160
	Summary	161
	Exercises	163
	Thought Questions	165
	Computer Exercises	166
	Bridge to SPSS	166
	Appendix: The Null Hypothesis Testing Controversy	167
	Chapter 6 The One-Sample t Test and Interval Estimation	170
	Introduction	171
	The Statistical Test for the Mean of a Single Population When ? Is Not Known: The t Distributions	172
	Interval Estimation	176
	The Standard Error of a Proportion	180
	Summary	183
	Exercises	184
	Thought Questions	185
	Computer Exercises	186
	Bridge to SPSS	186
	Chapter 7 Testing Hypotheses About the Difference Between the Means of Two Populations	188
	The Standard Error of the Difference	190
	Estimating the Standard Error of the Difference	194
	The t Test for Two Sample Means	195
	Confidence Intervals for ?1 ? ?2	200
	The Assumptions Underlying the Proper Use of the t Test for Two Sample Means	203
	Measuring the Size of an Effect	204
	The t Test for Matched Samples	206
	Summary	213
	Exercises	215
	Thought Questions	218
	Computer Exercises	219
	Bridge to SPSS	219
	Chapter 8 Nonparametric Tests for the Difference Between Two Means	222
	Introduction	223
	The Difference Between the Locations of Two Independent Samples: The Rank-Sum Test	227
	The Difference Between the Locations of Two Matched Samples: The Wilcoxon Test	233
	Summary	238
	Exercises	240
	Thought Questions	243
	Computer Exercises	244
	Bridge to SPSS	244
	Chapter 9 Linear Correlation	246
	Introduction	247
	Describing the Linear Relationship Between Two Variables	250
	Interpreting the Magnitude of a Pearson r	257
	When Is It Important That Pearson’s r Be Large?	262
	Testing the Significance of the Correlation Coefficient	264
	The Relationship Between Two Ranked Variables: The Spearman Rank-Order Correlation Coefficient	267
	Summary	270
	Exercises	272
	Thought Questions	275
	Computer Exercises	276
	Bridge to SPSS	276
	Appendix: Equivalence of the Various Formulas for r	279
	Chapter 10 Prediction and Linear Regression	281
	Introduction	282
	Using Linear Regression to Make Predictions	282
	Measuring Prediction Error: The Standard Error of Estimate	291
	The Connection Between Correlation and the t Test	293
	Estimating the Proportion of Variance Accounted for in the Population	299
	Summary	301
	Exercises	303
	Thought Questions	305
	Computer Exercises	305
	Bridge to SPSS	306
	Chapter 11 Introduction to Power Analysis	309
	Introduction	310
	Concepts of Power Analysis	311
	The Significance Test of the Mean of a Single Population	313
	The Significance Test of the Proportion of a Single Population	318
	The Significance Test of a Pearson r	320
	Testing the Difference Between Independent Means	321
	Testing the Difference Between the Means of Two Matched Populations	325
	Choosing a Value for d for a Power Analysis Involving Independent Means	327
	Using Power Analysis Concepts to Interpret the Results of Null Hypothesis Tests	329
	Summary	332
	Exercises	334
	Thought Questions	336
	Computer Exercises	337
	Bridge to SPSS	338
	Part III: Analysis of Variance Methods	341
	Chapter 12 One-Way Analysis of Variance	343
	Introduction	345
	The General Logic of ANOVA	346
	Computational Procedures	349
	Testing the F Ratio for Statistical Significance	354
	Calculating the One-Way ANOVA From Means and Standard Deviations	356
	Comparing the One-Way ANOVA With the t Test	357
	A Simplified ANOVA Formula for Equal Sample Sizes	358
	Effect Size for the One-Way ANOVA	359
	Some Comments on the Use of ANOVA	361
	A Nonparametric Alternative to the One-Way ANOVA: The Kruskal-Wallis H Test	364
	Summary	367
	Exercises	371
	Thought Questions	374
	Computer Exercises	374
	Bridge to SPSS	374
	Appendix: Proof That the Total Sum of Squares Is Equal to the Sum of the Between-Group and the Within-Group Sum of Squares	376
	Chapter 13 Multiple Comparisons	377
	Introduction	378
	Fisher’s Protected t Tests and the Least Significant Difference (LSD)	379
	Tukey’s Honestly Significant Difference (HSD)	383
	Other Multiple Comparison Procedures	388
	Planned and Complex Comparisons	390
	Nonparametric Multiple Comparisons: The Protected Rank-Sum Test	393
	Summary	394
	Exercises	396
	Thought Questions	397
	Computer Exercises	398
	Bridge to SPSS	398
	Chapter 14 Introduction to Factorial Design: Two-Way Analysis of Variance	400
	Introduction	401
	Computational Procedures	402
	The Meaning of Interaction	412
	Following Up a Significant Interaction	415
	Measuring Effect Size in a Factorial ANOVA	418
	Summary	420
	Exercises	423
	Thought Questions	426
	Computer Exercises	427
	Bridge to SPSS	427
	Chapter 15 Repeated-Measures ANOVA	430
	Introduction	431
	Calculating the One-Way RM ANOVA	431
	Rationale for the RM ANOVA Error Term	436
	Assumptions and Other Considerations Involving the RM ANOVA	436
	The RM Versus RB Design: An Introduction to the Issues of Experimental Design	439
	The Two-Way Mixed Design	443
	Summary	451
	Exercises	456
	Thought Questions	458
	Computer Exercises	458
	Bridge to SPSS	459
	Part IV: Nonparametric Statistics for Categorical Data	463
	Chapter 16 Probability of Discrete Events and the Binomial Distribution	465
	Introduction	466
	Probability	467
	The Binomial Distribution	470
	The Sign Test for Matched Samples	476
	Summary	478
	Exercises	479
	Thought Questions	481
	Computer Exercises	481
	Bridge to SPSS	482
	Chapter 17 Chi-Square Tests	485
	Chi Square and the Goodness of Fit: One-Variable Problems	486
	Chi Square as a Test of Independence: Two-Variable Problems	492
	Measures of Strength of Association in Two-Variable Tables	498
	Summary	500
	Exercises	502
	Thought Questions	504
	Computer Exercises	505
	Bridge to SPSS	506
	Appendix	509
	Statistical Tables	511
	Answers to Odd-Numbered Exercises	527
	Data From Ihno’s Experiment	539
	Glossary of Terms	543
	References	553
	Index	555