9780471509820

Statistical Applications for the Behavioral Sciences

by
  • ISBN13:

    9780471509820

  • ISBN10:

    0471509825

  • Format: Hardcover
  • Copyright: 1993-02-01
  • Publisher: Wiley

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Supplemental Materials

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Summary

Explains the conceptual foundations of statistical analyses (particularly inferential statistics), emphasizing the research context. Teaches students how to ''work the formulas'' by presenting cleary defined steps for dealing with statistical problems. Covers chi-square and other nonparametric tests, one/two-way and repeated-measures ANOVA, data analytic procedures such as linear correlation and regression. Boxed features highlight the topics under discussion using published and unpublished research and contain biographical sketches of luminaries in the field of statistics. Each chapter concludes with an extended summary, the relevant formulas and numerous work problems.

Table of Contents

PART 1: BASIC CONCEPTS OF RESEARCH AND DESCRIPTIVE STATISTICS 1(120)
Basic Concepts in Research
3(21)
Why Study Research Design in Conjunction with Statistics?
3(2)
An Overview of the Research Process
5(2)
Basic Concepts in the Conduct of Research
7(17)
Feeling Good and Helping Others A Study with a Confound
16(4)
Summary
20(1)
Work Problems
20(4)
Scales of Measurement and Data Display
24(23)
Measurement and Its Scales
24(3)
Discrete Variables, Continuous Variables, and the Real Limits of Numbers
27(4)
Some Notes on the History of Statistics
30(1)
Using Tables to Organize Data
31(4)
Using Graphs to Display Data
35(6)
Using a Graph to Provide a Visual Display of Data
40(1)
The Shape of Things to Come
41(6)
Summary
44(1)
Work Problems
45(2)
Measures of Central Tendency
47(22)
Describing a Distribution of Scores
47(1)
Populations and Samples
48(1)
The Rounding Rule
49(1)
The Mean
49(5)
The Median
54(4)
The Mode
58(1)
How the Shape of Distributions Affects Measures of Central Tendency
59(2)
When to Use the Mean, Median, and Mode
61(1)
Experimental Research and the Mean: A Glimpse of Things to Come
62(7)
Learning to Control Your Heart Rate
63(2)
Summary
65(1)
Key Formulas for Chapter 3
66(1)
Work Problems
66(3)
Measures of Variability
69(23)
The Importance of Measures of Variability
69(1)
Range
70(3)
Mean Deviation
73(2)
The Variance
75(6)
The Substantive Importance of the Variance
81(1)
The Standard Deviation
81(2)
The Origins of the Standard Deviation
83(1)
Simple Transformations and Their Effect on the Mean and Variance
83(1)
Deciding Which Measure of Variability to Use
84(8)
Summary
86(1)
Key Formulas for Chapter 4
87(1)
Work Problems
88(4)
The Normal Curve and Transformation: Percentiles, z Scores and T Scores
92(29)
Percentile Rank
91(8)
The Normal Distributions
99(4)
Abraham De Moivre
101(2)
Standard Scores: z Scores and T Scores
103(18)
With z Scores You Can Compare Apples and Oranges
115(1)
Summary
115(1)
Key Formulas for Chapter 5
116(1)
Work Problems
116(5)
PART 2: INFERENTIAL STATISTICS: PARAMETRIC TESTS 121(306)
Hypothesis Testing and Sampling Distributions
123(17)
Inferential Statistics
123(1)
Two Kinds of Inferential Procedures
124(6)
Sampling Distributions
130(10)
Population and Sample Distributions A Sampling Distribution of Means Summary
137(1)
Key Formulas for Chapter 6
138(1)
Work Problems
138(2)
Testing the Significance of a Single Mean: The Single-Sample z and t Tests
140(27)
The Research Context
140(2)
Using the Sampling Distribution of Means for the Single-Sample z Test
142(8)
The Implications of Rejecting the Null Hypothesis
150(1)
Type I and Type II Errors
151(2)
Is a Significant Finding ``Significant''?
153(1)
The Statistical Test for the Mean of a Population When is Unknown: The t Distributions
154(13)
Visual Illusions and Immaculate Perception
160(2)
Summary
162(1)
Key Formulas for Chapter 7
162(1)
Work Problems
163(4)
Directional and Nondirectional Testing of the Difference Between Two Means: The Independent-Samples t Test
167(31)
The Research Context
167(3)
William Gossett
169(1)
The Independent-Samples t test
170(14)
Can Epileptic Seizures Be Controlled by Relaxation Training?
183(1)
Directional Tests of Significance
184(14)
Summary
189(1)
Key Formulas for Chapter 8
190(1)
Work Problems
191(7)
Testing the Difference Between Two Means: The Dependent-Samples t Test
198(19)
The Research Context
198(3)
The Sampling Distribution for the Dependent-Samples t Test
201(4)
The t Distribution for Dependent-Samples
205(3)
Comparing the Independent- and Dependent-Samples t Tests
208(1)
The One-Tailed Test Revisited
209(1)
An Example of the Questionable Use of a One-Tailed Dependent-Samples t Test
210(1)
Assumptions of the Dependent-Samples t Test
210(2)
The First Applications of the t Test
211(1)
Summary
212(1)
Key Formulas for Chapter 9
212(1)
Work Problems
212(5)
Confidence Intervals and Hypothesis Testing
217(24)
The Research Context
217(2)
Confidence Intervals When Q is Known
219(2)
Sampling Distributions and Confidence Intervals
221(2)
The Probabilistic Nature of Confidence Intervals
223(1)
Confidence Intervals When Q is Unknown
224(3)
Using Confidence Intervals for Hypothesis Testing
227(14)
Where Did the Passion Go?
228(4)
Premarital Cohabitation: A New America Institution
232(2)
Summary
234(1)
Key Formulas for Chapter 10
235(1)
Work Problems
236(5)
Power Analysis and Hypothesis Testing
241(16)
Decision Making While Hypothesis Testing
242(1)
Why Study Power?
243(1)
The Size of the Treatment Effect and Power
244(3)
Using the Power Table
247(2)
The Bigger the Treatment Effect the Greater the Power
249(1)
Determining the Effect Size: The Achilles Heel of the Power Analysis
250(1)
Determining Sample Size for a Single-Sample Test
251(2)
Failing to Reject the Null Hypothesis: Can a Power Analysis Help?
253(4)
Psychopathy and Frontal Lobe Damage
254(1)
Summary
255(1)
Key Formulas for Chapter 11
255(1)
Work Problems
256(1)
One-Way Analysis of Variance
257(34)
The Research Context
258(1)
Sir Ronald Fisher
259(1)
The Conceptual Basis of ANOVA: Sources of Variation
259(10)
Computing the F Ratio in ANOVA
269(6)
Testing for Statistical Significance
275(2)
The ANOVA Summary Table
277(1)
Reporting the Results of the ANOVA
278(2)
Initiation Rites and Club Loyalty
278(2)
An Example of ANOVA with Unequal Numbers of Subjects
280(1)
Strength of Association
280(3)
Locating the Source of Significance: The Protected t Test
283(8)
Summary
285(1)
Key Formulas for Chapter 12
285(1)
Work Problems
286(5)
Two-Way Analysis of Variance
291(45)
The Research Context
291(12)
The Logic of the Two-Way ANOVA
303(2)
Definitional and Computational Formulas for the Two-Way ANOVA
305(12)
Testing the Statistical Significance of the F Ratios
317(4)
Do Firearms Create Aggression?
319(2)
Strength of Association
321(1)
Multiple Comparisons
321(4)
Interpreting the Factors in a Two-Way ANOVA
325(11)
Summary
325(1)
Key Formulas for Chapter 13
326(2)
Work Problems
328(8)
Repeated-Measures Analysis of Variance
336(25)
The Research Context
337(2)
The Logic of the Repeated-Measures ANOVA
339(3)
The Formulas for the Repeated-Measures ANOVA
342(8)
Testing the Statistical Significance of the F Ratio
350(1)
Interpreting the Findings
350(1)
The ANOVA Summary Table
351(1)
Multiple Comparisons
351(3)
The Inverted U Relation Between Arousal and Task Performance
353(1)
Strength of Association
354(1)
Assumptions of the Repeated-Measures ANOVA
354(7)
Summary
355(1)
Key Formulas for Chapter 14
356(1)
Work Problems
357(4)
Linear Correlation
361(35)
The Research Context
361(1)
The Correlation Coefficient and Scatter Diagrams
362(15)
Karl Pearson
364(13)
Testing the Significance of the Pearson r
377(5)
Maternal Cognitions and Aggressive Children
381(1)
Factors That Can Create Misleading Correlation Coefficients
382(14)
Summary
387(2)
Key Formulas for Chapter 15
389(1)
Work Problems
389(7)
Linear Regression
396(31)
The Research Context
396(1)
Overview of Regression
397(3)
Establishing the Regression Line
400(13)
Sir Francis Galton
411(2)
Putting It All Together: A Worked Problem
413(5)
Why is a Prediction Equation Called a Regression Equation?
417(1)
The Coefficient of Determination in the Context of Prediction
418(1)
The Pitfalls of Linear Regression
419(8)
Summary
421(1)
Key Formulas for Chapter 16
422(2)
Work Problems
424(3)
PART 3: INFERENTIAL STATISTICS: NONPARAMETRIC TESTS 427(64)
The Chi-Square Test
429(27)
The Research Context
430(1)
The Chi-Square Test for One-Way Designs: The Goodness-of Fit Test
431(6)
The Chi-Square Distribution and Degrees of Freedom
437(3)
Two-Way Designs: The Chi-Square Test for Independence
440(7)
The Chi-Square Test for a 2 x 2 Contingency Table
447(1)
What is Beautiful is Good
447(1)
Which Cells Are Major Contributors to a Significant Chi-Square Test?
448(2)
Using the Chi-Square Test with Quantitative Variables
450(1)
Assumptions of the Chi-Square Test
450(6)
Summary
451(1)
Key Formulas for Chapter 17
451(1)
Work Problems
452(4)
Other Nonparametric Tests
456(35)
The Research Context
456(1)
The Use of Ranked Data in Research
457(1)
The Spearman Rank Correlation Coefficient
458(7)
The Point-Biserial Correlation Coefficient
465(5)
The Mann-Whitney U Test
470(6)
The Wilcoxon Signed-Ranks Test
476(5)
Do Infants Notice the Difference Between Lip Movements and Speech Sounds?
480(1)
Using Nonparametric Tests
481(10)
Summary
482(1)
Key Formulas for Chapter 18
483(1)
Work Problems
484(7)
Appendixes 491(80)
A. Some Basic Concepts of Probability
491(11)
B. Statistical Tables
502(15)
1. z Table
502(4)
2. t Table
506(1)
3. Power Table (Finding Power)
507(1)
4. Power Table (Finding Delta)
508(1)
5. F Table
508(3)
6. Pearson r Table
511(1)
7. Spearman rs Table
512(1)
8. Chi-Square Table
513(1)
9. Mann-Whitney U Table
514(2)
10. Wilcoxon Signed-Ranks Table
516(1)
C. Answers to Problems
517(54)
Glossary 571(6)
References 577(9)
Index 586

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