Statistics for the Behavioral Sciences

by Jaccard, James; Becker, Michael A.

• ISBN13: 9780534103262
• ISBN10: 053410326X
• Edition: 2nd
• Format: Hardcover
• Copyright: 1989-09-01
• Publisher: Wadsworth Pub Co

Summary

Now your students can become intelligent consumers of scientific research, without being overwhelmed by the statistics! Jaccard and Becker's text teaches students the basic skills for analyzing data and helps them become intelligent consumers of scientific information. Praised for its real-life applications, the text tells students when to use a particular statistic, why they should use it, and how the statistic should be computed and interpreted. Because many students, given a set of data, cannot determine where to begin in answering relevant research questions, the authors explicate the issues involved in selecting a statistical test. Each statistical technique is introduced by giving instances where the test is most typically applied followed by an interesting research example (each example is taken from psychology literature).

Table of Contents

Preface	p. xv
To the Student	p. xxi
Statistical Preliminaries	p. 1
Introduction and Mathematical Preliminaries	p. 2
The Study of Statistics	p. 2
Research in the Behavioral Sciences	p. 4
Variables	p. 5
Measurement	p. 6
Discrete and Continuous Variables	p. 14
Populations and Samples	p. 16
Biased Sampling	p. 17
Descriptive and Inferential Statistics	p. 18
The Concept of Probability	p. 19
Mathematical Preliminaries: A Review	p. 20
Statistics and Computers	p. 28
Summary	p. 29
Frequency and Probability Distributions	p. 34
Frequency Distributions for Quantitative Variables: Ungrouped Scores	p. 34
Frequency Distributions for Quantitative Variables: Grouped Scores	p. 38
Frequency Distributions for Qualitative Variables	p. 41
Outliers	p. 42
Frequency Graphs	p. 43
Misleading Graphs	p. 51
Graphs of Relative Frequencies, Percentages, Cumulative Frequencies, and Cumulative Relative Frequencies	p. 52
Probability Distributions	p. 53
Empirical and Theoretical Distributions	p. 56
Method of Presentation	p. 57
Examples from the Literature	p. 59
Summary	p. 63
Measures of Central Tendency and Variability	p. 67
Measures of Central Tendency for Quantitative Variables	p. 68
Measures of Variability for Quantitative Variables	p. 78
Computational Formula for the Sum of Squares	p. 83
Relationship Between Central Tendency and Variability	p. 85
Graphs of Central Tendency and Variability	p. 86
Measures of Central Tendency and Variability for Qualitative Variables	p. 89
Skewness and Kurtosis	p. 90
Sample Versus Population Notation	p. 91
Method of Presentation	p. 91
Example from the Literature	p. 92
Summary	p. 95
Percentiles, Percentile Ranks, Standard Scores, and the Normal Distribution	p. 100
Percentiles and Percentile Ranks	p. 101
Standard Scores	p. 105
Standard Scores and the Normal Distribution	p. 109
Standard Scores and the Shape of the Distribution	p. 113
Method of Presentation	p. 113
Summary	p. 120
The Normal Distribution Formula	p. 121
Pearson Correlation and Regression: Descriptive Aspects	p. 125
Use of Pearson Correlation	p. 125
The Linear Model	p. 126
The Pearson Correlation Coefficient	p. 130
Correlation and Causation	p. 138
Interpreting the Magnitude of a Correlation Coefficient	p. 139
Regression	p. 140
Additional Issues Associated with the Use of Correlation and Regression	p. 145
Summary	p. 153
Probability	p. 157
Probabilities of Simple Events	p. 159
Conditional Probabilities	p. 160
Joint Probabilities	p. 161
Adding Probabilities	p. 162
Relationships Among Probabilities	p. 162
Sampling with Versus Without Replacement	p. 164
Beliefs and Probability Theory	p. 165
Counting Rules	p. 166
The Binomial Expression	p. 169
Summary	p. 176
Estimation and Sampling Distributions	p. 181
Finite Versus Infinite Populations	p. 181
Estimation of the Population Mean	p. 182
Estimation of the Population Variance and Standard Deviation	p. 184
Degrees of Freedom	p. 187
Sampling Distribution of the Mean and the Central Limit Theorem	p. 188
Polls and Random Samples	p. 191
Types of Sampling Distributions	p. 197
Summary	p. 202
Hypothesis Testing: Inferences About a Single Mean	p. 205
A Simple Analogy for Principles of Hypothesis Testing	p. 205
Statistical Inference and the Normal Distribution: The One-Sample z Test	p. 206
Defining Expected and Unexpected Results	p. 210
Failing to Reject Versus Accepting the Null Hypothesis	p. 211
Type I and Type II Errors	p. 212
Effects of Alpha and Sample Size on the Power of Statistical Tests	p. 214
Statistical and Real-World Significance	p. 216
Directional Versus Nondirectional Tests	p. 216
Statistical Inference Using Estimated Standard Errors: The One-Sample t Test	p. 219
Confidence Intervals	p. 225
Method of Presentation	p. 229
Examples from the Literature	p. 231
Summary	p. 233
The Analysis of Bivariate Relationships	p. 239
Research Design and Statistical Preliminaries for Analyzing Bivariate Relationships	p. 240
Principles of Research Design: Statistical Implications	p. 240
Confounding and Disturbance Variables	p. 247
Selecting the Appropriate Statistical Test to Analyze a Relationship: A Preview	p. 251
Summary	p. 255
Independent Groups t Test	p. 259
Use of the Independent Groups t Test	p. 259
Inference of a Relationship Using the Independent Groups t Test	p. 261
Strength of the Relationship	p. 271
Nature of the Relationship	p. 280
Methodological Considerations	p. 281
Numerical Example	p. 281
Planning an Investigation Using the Independent Groups t Test	p. 284
Method of Presentation	p. 286
Examples from the Literature	p. 287
Summary	p. 294
Correlated Groups t Test	p. 302
Use of the Correlated Groups t Test	p. 302
Inference of a Relationship Using the Correlated Groups t Test	p. 303
Strength of the Relationship	p. 308
Nature of the Relationship	p. 311
Methodological Considerations	p. 311
Power of Correlated Groups Versus Independent Groups t Tests	p. 312
Numerical Example	p. 314
Planning an Investigation Using the Correlated Groups t Test	p. 316
Method of Presentation	p. 317
Examples from the Literature	p. 318
Summary	p. 321
Computational Procedures for the Nullified Score Approach	p. 322
One-Way Between-Subjects Analysis of Variance	p. 329
Use of One-Way Between-Subjects Analysis of Variance	p. 329
Inference of a Relationship Using One-Way Between-Subjects Analysis of Variance	p. 330
Relationship of the F Test to the t Test	p. 344
Strength of the Relationship	p. 344
Nature of the Relationship	p. 345
Unstandardized Effect Sizes and Confidence Intervals	p. 349
Methodological Considerations	p. 350
Numerical Example	p. 350
Planning an Investigation Using One-Way Between-Subjects Analysis of Variance	p. 354
Method of Presentation	p. 354
Examples from the Literature	p. 356
Summary	p. 360
Rationale for the Degrees of Freedom	p. 361
One-Way Repeated Measures Analysis of Variance	p. 369
Use of One-Way Repeated Measures Analysis of Variance	p. 369
Inference of a Relationship Using One-Way Repeated Measures Analysis of Variance	p. 371
Strength of the Relationship	p. 380
Nature of the Relationship	p. 381
Unstandardized Effect Size and Confidence Intervals	p. 383
Methodological Considerations	p. 383
Numerical Example	p. 385
Planning an Investigation Using One-Way Repeated Measures Analysis of Variance	p. 388
Method of Presentation	p. 389
Examples from the Literature	p. 390
Summary	p. 395
Determining the Nature of the Relationship Under Sphericity Violations	p. 395
Pearson Correlation and Regression: Inferential Aspects	p. 402
Use of Pearson Correlation	p. 402
Inference of a Relationship Using Pearson Correlation	p. 403
Strength of the Relationship	p. 407
Confidence Intervals for the Correlation Coefficient	p. 407
Nature of the Relationship	p. 408
Planning an Investigation Using Pearson Correlation	p. 408
Method of Presentation for Pearson Correlation	p. 408
Examples from the Literature	p. 409
Regression	p. 411
Numerical Example	p. 414
Method of Presentation for Regression	p. 418
Summary	p. 423
Testing Null Hypotheses Other Than [rho] = 0	p. 423
Confidence Intervals for the Correlation Coefficient	p. 425
Chi-Square Test	p. 433
Use of the Chi-Square Test	p. 433
Two-Way Contingency Tables	p. 434
Chi-Square Tests of Independence and Homogeneity	p. 435
Inference of a Relationship Using the Chi-Square Test	p. 435
2 x 2 Tables	p. 441
Strength of the Relationship	p. 442
Nature of the Relationship	p. 443
Methodological Considerations	p. 444
Numerical Example	p. 445
Use of Quantitative Variables in the Chi-Square Test	p. 446
Planning an Investigation Using the Chi-Square Test	p. 447
Method of Presentation	p. 448
Examples from the Literature	p. 449
Chi-Square Goodness-of-Fit Test	p. 451
Summary	p. 455
Determining the Nature of the Relationship Using a Modified Bonferroni Procedure	p. 456
Nonparametric Statistics	p. 463
Rank Scores	p. 464
Nonparametric Statistics and Outliers	p. 466
Analysis of Ranked Data Using Parametric Formulas	p. 467
Rank Tests for Two Independent Groups	p. 467
Rank Test for Two Correlated Groups	p. 471
Rank Test for Three or More Independent Groups	p. 474
Rank Test for Three or More Correlated Groups	p. 477
Rank Test for Correlation	p. 480
Examples from the Literature	p. 483
Summary	p. 486
Corrections for Ties for Nonparametric Rank Tests	p. 486
Additional Topics	p. 495
Two-Way Between-Subjects Analysis of Variance	p. 496
Factorial Designs	p. 497
Use of Two-Way Between-Subjects Analysis of Variance	p. 498
The Concepts of Main Effects and Interactions	p. 499
Inference of Relationships Using Two-Way Between-Subjects Analysis of Variance	p. 506
Strength of the Relationships	p. 514
Nature of the Relationships	p. 515
Methodological Considerations	p. 518
Numerical Example	p. 518
Unequal Sample Sizes	p. 526
Planning an Investigation Using Two-Way Between-Subjects Analysis of Variance	p. 527
Method of Presentation	p. 529
Examples from the Literature	p. 531
Summary	p. 536
Overview and Extension: Selecting the Appropriate Statistical Test for Analyzing Bivariate Relationships and Procedures for More Complex Designs	p. 544
Selecting the Appropriate Statistical Test for Analyzing Bivariate Relationships	p. 544
Case I: The Relationship Between Two Qualitative Variables	p. 545
Case II: The Relationship Between a Qualitative Independent Variable and a Quantitative Dependent Variable	p. 545
Case III: The Relationship Between a Quantitative Independent Variable and a Qualitative Dependent Variable	p. 549
Case IV: The Relationship Between Two Quantitative Variables	p. 549
Procedures for More Complex Designs	p. 550
Alternative Approaches to Null Hypothesis Testing	p. 553
Summary	p. 554
Table of Random Numbers	p. 559
Proportions of Scores in a Normal Distribution	p. 562
Factorials	p. 572
Critical Values for the t Distribution	p. 573
Power and Sample Size	p. 575
Critical Values for the F Distribution	p. 599
Studentized Range Values (q)	p. 603
Critical Values for Pearson r	p. 606
Fisher's Transformation of Pearson r(r')	p. 608
Critical Values for the Chi-Square Distribution	p. 610
Critical Values for the Mann-Whitney U Test	p. 612
Critical Values for the Wilcoxon Signed-Rank Test	p. 615
Critical Values for Spearman r	p. 617
Formulas for Unbiased Estimators of Proportion of Explained Variance	p. 619
Answers to Selected Exercises	p. 620
Glossary of Major Symbols	p. 639
References	p. 644
Index	p. 651
Credits	p. 658
Table of Contents provided by Syndetics. All Rights Reserved.