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Explaining Psychological Statistics 3rd Revised edition [Hardback]

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  • Formāts: Hardback, 864 pages, height x width x depth: 256x188x35 mm, weight: 1482 g, Illustrations
  • Izdošanas datums: 06-Nov-2007
  • Izdevniecība: John Wiley & Sons Ltd
  • ISBN-10: 0470007184
  • ISBN-13: 9780470007181
Citas grāmatas par šo tēmu:
Explaining Psychological Statistics 3rd Revised editionPalielināt
  • Formāts: Hardback, 864 pages, height x width x depth: 256x188x35 mm, weight: 1482 g, Illustrations
  • Izdošanas datums: 06-Nov-2007
  • Izdevniecība: John Wiley & Sons Ltd
  • ISBN-10: 0470007184
  • ISBN-13: 9780470007181
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9780470007181.html
Keywords:
Cohen (psychology, New York U.) brings new sections on mixed-design analysis of variance (ANOVA) and robust statistics as well as expanded coverage of effect-sized measures and confidence intervals to this edition. Cohen takes an accessible, conversation approach when introducing the conceptual foundations and basic of statistic procedures, a practice he continues with material on frequency tables, graphs, distributions, measures of central tendency and variability, standardized scores and the normal distribution, hypothesis testing with one or two samples, internal estimation and the t distribution, the t test for two independent sample means, statistical power and effect size, linear correlation and regression, the matched t test, one-way independent ANOVA and two-way ANOVA, multiple regression and its connection to ANOVA, nonparametric statistics, chi-square tests and statistical tests for ordinal data. Cohen includes selected answers to the text's exercises. Annotation ©2008 Book News, Inc., Portland, OR (booknews.com)

This comprehensive graduate-level statistics text is aimed at students with a minimal background in the area or those who are wary of the subject matter. The new edition of this successful text will continue to offer students a lively and engaging introduction to the field, provide comprehensive coverage of the material, and will also include examples and exercises using common statistical software packages (SPSS).

Recenzijas

"In conclusion, Cohen's text provides a clear, comprehensive, and modern survey of the most commonly used staistical methods for psychological reseearch. After using the book to learn about these procedures in a statistics course, students and instructors alike will also find it a valuable reference as they progress through their research careers." (Comptes Rendues de Lecture Book Reviews)

Preface to the Third Edition xxv
Acknowledgments xxxi
Part One Descriptive Statistics
1(122)
Introduction to Psychological Statistics
1(20)
Conceptual Foundation
1(11)
What Is (Are) Statistics?
1(1)
Statistics and Research
2(1)
Variables and Constants
2(1)
Scales of Measurement
3(3)
Continuous versus Discrete Variables
6(1)
Scales versus Variables
7(1)
Parametric versus Nonparametric Statistics
7(1)
Independent versus Dependent Variables
7(1)
Experimental versus Correlational Research
8(1)
Populations versus Samples
9(1)
Statistical Formulas
10(1)
Summary
10(1)
Exercises
11(1)
Basic Statistical Procedures
12(6)
Variables with Subscripts
12(1)
The Summation Sign
12(1)
Properties of the Summation Sign
13(3)
Rounding Off Numbers
16(1)
Summary
17(1)
Exercises
18(1)
Optional Material
18(3)
Double Summations
18(1)
Random Variables and Mathematical Distributions
19(1)
Summary
20(1)
Exercises
20(1)
Frequency Tables, Graphs, and Distributions
21(25)
Conceptual Foundation
21(11)
Frequency Distributions
21(1)
The Cumulative Frequency Distribution
22(1)
The Relative Frequency and Cumulative Relative Frequency Distributions
23(1)
The Cumulative Percentage Distribution
23(1)
Percentiles
24(1)
Graphs
24(4)
Real versus Theoretical Distributions
28(1)
Summary
29(2)
Exercises
31(1)
Basic Statistical Procedures
32(10)
Grouped Frequency Distributions
32(1)
Apparent versus Real Limits
32(1)
Constructing Class Intervals
33(1)
Choosing the Class Interval Width
33(1)
Choosing the Limits of the Lowest Interval
34(1)
Relative and Cumulative Frequency Distributions
35(1)
Cumulative Percentage Distribution
35(1)
Estimating Percentiles and Percentile Ranks by Linear Interpolation
36(1)
Graphing a Grouped Frequency Distribution
37(1)
Guidelines for Drawing Graphs of Frequency Distributions
38(2)
Summary
40(1)
Exercises
41(1)
Optional Material
42(4)
Stem-and-Leaf Displays
42(3)
Summary
45(1)
Exercises
45(1)
Measures of Central Tendency and Variability
46(41)
Conceptual Foundation
46(18)
Measures of Central Tendency
46(4)
Measures of Variability
50(8)
Skewed Distributions
58(4)
Summary
62(1)
Exercises
63(1)
Basic Statistical Procedures
64(10)
Formulas for the Mean
64(2)
Computational Formulas for the Variance and Standard Deviation
66(2)
Obtaining the Standard Deviation Directly from Your Calculator
68(1)
Properties of the Mean
69(2)
Properties of the Standard Deviation
71(1)
Summary
72(1)
Exercises
73(1)
Optional Material
74(13)
Box-and-Whisker Plots
75(2)
Dealing with Outliers
77(2)
Measuring Skewness
79(1)
Measuring Kurtosis
80(2)
Summary
82(1)
Exercises
83(1)
Key Formulas
83(4)
Standardized Scores and the Normal Distribution
87(36)
Conceptual Foundation
87(15)
z Scores
87(2)
Finding a Raw Score from a z Score
89(1)
Sets of z Scores
89(1)
Properties of z Scores
90(1)
Sat, T, and IQ Scores
91(1)
The Normal Distribution
92(2)
Introducing Probability: Smooth Distributions versus Discrete Events
94(1)
Real Distributions versus the Normal Distribution
95(1)
z Scores as a Research Tool
96(1)
Sampling Distribution of the Mean
97(1)
Standard Error of the Mean
98(2)
Sampling Distribution versus Population Distribution
100(1)
Summary
100(1)
Exercises
101(1)
Basic Statistical Procedures
102(11)
Finding Percentile Ranks
103(1)
Finding the Area between Two z Scores
104(2)
Finding the Raw Scores Corresponding to a Given Area
106(1)
Areas in the Middle of a Distribution
106(1)
From Score to Proportion and Proportion to Score
107(1)
Describing Groups
107(4)
Summary
111(1)
Exercises
112(1)
Optional Material
113(10)
The Mathematics of the Normal Distribution
113(1)
The Central Limit Theorem
114(2)
Probability
116(4)
Summary
120(1)
Exercises
120(1)
Key Formulas
121(2)
Part Two One- and Two-Sample Hypothesis Tests
123(132)
Introduction to Hypothesis Testing: The One-Sample z Test
123(33)
Conceptual Foundation
123(13)
Selecting a Group of Subjects
123(1)
The Need for Hypothesis Testing
124(1)
The Logic of Null Hypothesis Testing
125(1)
The Null Hypothesis Distribution
125(1)
The Null Hypothesis Distribution for the One-Sample Case
126(1)
z Scores and the Null Hypothesis Distribution
127(1)
Statistical Decisions
128(1)
The z Score as Test Statistic
129(1)
Type I and Type II Errors
130(1)
The Trade-Off between Type I and Type II Errors
131(1)
One-Tailed versus Two-Tailed Tests
132(3)
Summary
135(1)
Exercises
135(1)
Basic Statistical Procedures
136(13)
Step
1. State the Hypotheses
136(2)
Step
2. Select the Statistical Test and the Significance Level
138(1)
Step
3. Select the Sample and Collect the Data
138(1)
Step
4. Find the Region of Rejection
139(1)
Step
5. Calculate the Test Statistic
140(1)
Step
6. Make the Statistical Decision
141(1)
Interpreting the Results
142(1)
Assumptions Underlying the One-Sample z Test
142(2)
Varieties of the One-Sample Test
144(1)
Why the One-Sample Test Is Rarely Performed
144(2)
Publishing the Results of One-Sample Tests
146(1)
Summary
146(2)
Exercises
148(1)
Optional Material
149(7)
NHT as a Spam Filter
150(3)
Is the Null Hypothesis Ever True in Psychological Research?
153(1)
Summary
154(1)
Exercises
154(1)
Key Formulas
155(1)
Interval Estimation and the t Distribution
156(32)
Conceptual Foundation
156(11)
The Mean of the Null Hypothesis Distribution
157(1)
When the Population Standard Deviation Is Not Known
157(1)
Calculating a Simple Example
158(1)
The t Distribution
158(2)
Degrees of Freedom and the t Distribution
160(1)
Critical Values of the t Distribution
161(1)
Calculating the One-Sample t Test
162(1)
Sample Size and the One-Sample t Test
162(1)
Uses for the One-Sample t Test
163(1)
Cautions Concerning the One-Sample t Test
163(1)
Estimating the Population Mean
164(1)
Summary
165(1)
Exercises
166(1)
Basic Statistical Procedures
167(10)
Step
1. Select the Sample Size
167(1)
Step
2. Select the Level of Confidence
167(1)
Step
3. Select the Random Sample and Collect the Data
167(1)
Step
4. Calculate the Limits of the Interval
168(4)
Relationship between Interval Estimation and Null Hypothesis Testing
172(1)
Assumptions Underlying the One-Sample t Test and the Confidence Interval for the Population Mean
173(1)
Use of the Confidence Interval for the Population Mean
174(1)
Publishing the Results of One-Sample t Tests
174(1)
Summary
175(1)
Exercises
176(1)
Optional Material
177(11)
Some Properties of Estimators
177(1)
A More Robust t Test
178(4)
Robust Confidence Intervals
182(2)
When Should You Use Robust Methods and Which Ones?
184(1)
Summary
185(1)
Exercises
186(1)
Key Formulas
186(2)
The t Test for Two Independent Sample Means
188(34)
Conceptual Foundation
188(11)
Null Hypothesis Distribution for the Differences of Two Sample Means
189(1)
Standard Error of the Difference
190(1)
Formula for Comparing the Means of Two Samples
191(1)
Null Hypothesis for the Two-Sample Case
192(1)
The z Test for Two Large Samples
193(1)
Separate-Variances t Test
193(1)
The Pooled-Variances Estimate
194(1)
The Pooled-Variances t Test
195(1)
Formula for Equal Sample Sizes
195(1)
Calculating the Two-Sample t Test
196(1)
Interpreting the Calculated t
196(1)
Limitations of Statistical Conclusions
197(1)
Summary
198(1)
Exercises
198(1)
Basic Statistical Procedures
199(15)
Step
1. State the Hypotheses
200(1)
Step
2. Select the Statistical Test and the Significance Level
200(1)
Step
3. Select the Samples and Collect the Data
201(1)
Step
4. Find the Region of Rejection
201(1)
Step
5. Calculate the Test Statistic
202(1)
Step
6. Make the Statistical Decision
203(1)
Interpreting the Results
203(1)
Confidence Intervals for the Difference between Two Population Means
204(2)
Assumptions of the t Test for Two Independent Samples
206(2)
HOV Tests and the Separate-Variances t Test
208(1)
When to Use the Two-Sample t Test
209(1)
When to Construct Confidence Intervals
209(1)
Heterogeneity of Variance as an Experimental Result
210(1)
Publishing the Results of the Two-Sample t Test
210(1)
Summary
211(1)
Exercises
212(2)
Optional Material
214(8)
Zero Differences between Sample Means
214(1)
Adding Variances to Find the Variance of the Difference
214(1)
The Critical Value for the Separate-Variances t Test
214(2)
Random Assignment and the Separate-Variances t Test
216(1)
The t Test for Two Trimmed Means
217(1)
Resampling Methods
218(1)
Summary
219(1)
Exercises
220(1)
Key Formulas
220(2)
Statistical Power and Effect Size
222(33)
Conceptual Foundation
222(11)
The Alternative Hypothesis Distribution
222(2)
The Expected t Value (Delta)
224(2)
The Effect Size
226(1)
Power Analysis
227(1)
The Interpretation of t Values
228(1)
Estimating Effect Size
229(1)
Manipulating Power
230(1)
Summary
231(1)
Exercises
232(1)
Basic Statistical Procedures
233(8)
Using Power Tables
233(1)
The Relationship between Alpha and Power
234(1)
Power Analysis with Fixed Sample Sizes
235(1)
Sample Size Determination
236(1)
The Case of Unequal Sample Sizes
237(1)
The Power of a One-Sample Test
238(1)
Summary
239(1)
Exercises
240(1)
Optional Material
241(14)
Calculating Power Retrospectively
241(1)
Constructing Confidence Intervals for Effect Sizes
242(1)
Robust Estimates of Effect Size
242(1)
Refining the Spam-Filter Analogy
243(6)
Another Advantage of NHT: Indicating the Probability of Replication
249(2)
Summary
251(1)
Exercises
252(1)
Key Formulas
252(3)
Part Three Hypothesis Tests Involving Two Measures on Each Subject
255(87)
Linear Correlation
255(31)
Conceptual Foundation
255(12)
Perfect Correlation
255(1)
Negative Correlation
256(1)
The Correlation Coefficient
256(1)
Linear Transformations
257(1)
Graphing the Correlation
258(1)
Dealing with Curvilinear Relationships
259(2)
Problems in Generalizing from Sample Correlations
261(2)
Correlation Does Not Imply Causation
263(1)
True Experiments Involving Correlation
264(1)
Summary
264(1)
Exercises
265(2)
Basic Statistical Procedures
267(11)
The Covariance
268(1)
The Unbiased Covariance
268(1)
An Example of Calculating Pearson's r
268(1)
Alternative Formulas
269(1)
Which Formula to Use
270(1)
Testing Pearson's r for Significance
270(2)
Understanding the Degrees of Freedom
272(1)
Assumptions Associated with Pearson's r
272(2)
Uses of the Pearson Correlation Coefficient
274(1)
Publishing the Results of Correlational Studies
275(1)
Summary
276(1)
Exercises
276(2)
Optional Material
278(8)
The Power Associated with Correlational Tests
278(2)
Fisher Z Transformation
280(1)
The Confidence Interval for ρ
280(1)
Testing a Null Hypothesis Other Than ρ = 0
281(1)
Testing the Difference of Two Independent Sample rs
282(1)
Summary
283(1)
Exercises
283(1)
Key Formulas
284(2)
Linear Regression
286(31)
Conceptual Foundation
286(11)
Perfect Predictions
286(1)
Predicting with z Scores
287(1)
Calculating an Example
287(1)
Regression toward the Mean
288(1)
Graphing Regression in Terms of z Scores
288(1)
The Raw-Score Regression Formula
289(1)
The Slope and the Y Intercept
290(1)
Predictions Based on Raw Scores
291(1)
Interpreting the Y Intercept
292(1)
Quantifying the Errors around the Regression Line
292(1)
The Variance of the Estimate
293(1)
Explained and Unexplained Variance
294(1)
The Coefficient of Determination
294(1)
The Coefficient of Nondetermination
295(1)
Calculating the Variance of the Estimate
295(1)
Summary
295(1)
Exercises
296(1)
Basic Statistical Procedures
297(11)
Life Insurance Rates
297(1)
Regression in Terms of Sample Statistics
298(1)
Finding the Regression Equation
298(1)
Making Predictions
298(1)
Using Sample Statistics to Estimate the Variance of the Estimate
299(1)
Standard Error of the Estimate
300(1)
Confidence Intervals for Predictions
301(1)
An Example of a Confidence Interval
301(1)
Assumptions Underlying Linear Regression
302(1)
Regressing X on Y
302(1)
Raw Score Formulas
303(1)
When to Use Linear Regression
303(2)
Summary
305(1)
Exercises
306(2)
Optional Material
308(9)
The Point-Biserial Correlation Coefficient
308(1)
Calculating rpb
309(1)
Deriving rpb from a t Value
310(1)
Interpreting rpb
310(1)
Strength of Association in the Population (Omega Squared)
311(1)
Biserial r
312(1)
Summary
313(1)
Exercises
313(1)
Key Formulas
314(3)
The Matched t Test
317(25)
Conceptual Foundation
317(9)
Before-After Design
317(1)
The Direct-Difference Method
318(1)
The Matched t Test as a Function of Linear Correlation
319(2)
Reduction in Degrees of Freedom
321(1)
Drawback of the Before-After Design
321(1)
Other Repeated-Measures Designs
321(1)
Matched-Pairs Design
322(1)
Correlated or Dependent Samples
323(1)
When Not to Use the Matched t Test
323(1)
Summary
324(1)
Exercises
325(1)
Basic Statistical Procedures
326(10)
Step
1. State the Hypotheses
326(1)
Step
2. Select the Statistical Test and the Significance Level
326(1)
Step
3. Select the Samples and Collect the Data
326(1)
Step
4. Find the Region of Rejection
327(1)
Step
5. Calculate the Test Statistic
328(1)
Step
6. Make the Statistical Decision
328(1)
Using the Correlation Formula for the Matched t Test
328(1)
Raw-Score Formula for the Matched t Test
329(1)
The Confidence Interval for the Difference of Two Population Means
330(1)
Assumptions of the Matched t Test
331(1)
The Varieties of Designs Calling for the Matched t Test
331(2)
Publishing the Results of a Matched t Test
333(1)
Summary
333(1)
Exercises
334(2)
Optional Material
336(6)
Power of the Matched t Test
337(1)
Effect Size for the Matched t Test
338(1)
Summary
339(1)
Exercises
340(1)
Key Formulas
340(2)
Part Four Analysis of Variance without Repeated Measures
342(145)
One-Way Independent ANOVA
342(44)
Conceptual Foundation
342(11)
Transforming the t Test into ANOVA
343(1)
Expanding the Denominator
344(1)
Expanding the Numerator
344(1)
The F Ratio
345(1)
The F Ratio As a Ratio of Two Population Variance Estimates
345(1)
Degrees of Freedom and the F Distribution
346(1)
The Shape of the F Distribution
347(1)
ANOVA As a One-Tailed Test
347(1)
Using Tables of F Values
348(1)
An Example with Three Equal-Sized Groups
348(1)
Calculating a Simple ANOVA
349(1)
Interpreting the F Ratio
350(1)
Advantages of the One-Way ANOVA
351(1)
Summary
352(1)
Exercises
352(1)
Basic Statistical Procedures
353(20)
An ANOVA Example with Unequal Sample Sizes
354(1)
Step
1. State the Hypotheses
354(1)
Step
2. Select the Statistical Test and the Significance Level
354(1)
Step
3. Select the Samples and Collect the Data
354(1)
Step
4. Find the Region of Rejection
355(1)
Step
5. Calculate the Test Statistic
355(2)
Step
6. Make the Statistical Decision
357(1)
Interpreting Significant Results
357(1)
The Sums of Squares Approach
358(1)
Raw-Score Formulas
358(2)
Assumptions of the One-Way ANOVA for Independent Groups
360(1)
Testing Homogeneity of Variance
361(1)
Power and Effect Size for ANOVA
362(3)
Varieties of the One-Way ANOVA
365(2)
Publishing the Results of a One-Way ANOVA
367(2)
Summary
369(2)
Exercises
371(2)
Optional Material
373(13)
Proportion of Variance Accounted for in ANOVA
373(3)
The Harmonic Mean Revisited
376(1)
The Analysis of Unweighted Means for One-Way ANOVA
377(1)
Adjusting the One-Way ANOVA for Heterogeneity of Variance
377(3)
Summary
380(2)
Exercises
382(1)
Key Formulas
382(4)
Multiple Comparisons
386(39)
Conceptual Foundation
386(12)
The Number of Possible t Tests
386(1)
Experimentwise Alpha
387(1)
Complex and Planned Comparisons
388(1)
Fisher's Protected t Tests
388(2)
Complete versus Partial Null Hypotheses
390(1)
Tukey's HSD Test
391(1)
The Studentized Range Statistic
391(1)
Advantages and Disadvantages of Tukey's Test
392(1)
Other Procedures for Post Hoc Pairwise Comparisons
393(2)
The Advantage of Planning Ahead
395(1)
Bonferroni t, or Dunn's Test
395(1)
Summary
396(1)
Exercises
397(1)
Basic Statistical Procedures
398(17)
Calculating Protected t Tests
398(1)
Calculating Fisher's LSD
399(1)
Calculating Tukey's HSD
400(1)
Interpreting the Results of Post Hoc Pairwise Comparisons
401(1)
Confidence Intervals for Post Hoc Pairwise Comparisons
401(1)
Tukey's HSD versus ANOVA
402(1)
The Modified LSD (Fisher-Hayter) Test
403(1)
Which Pairwise Comparison Procedure Should You Use?
403(1)
Complex Comparisons
403(4)
Scheffe's Test
407(1)
Orthogonal Contrasts
408(2)
Modified Bonferroni Tests
410(2)
Summary
412(2)
Exercises
414(1)
Optional Material
415(10)
The Analysis of Trend Components
415(6)
Summary
421(1)
Exercises
422(1)
Key Formulas
423(2)
Two-Way ANOVA
425(62)
Conceptual Foundation
425(16)
Calculating a Simple One-Way ANOVA
425(1)
Adding a Second Factor
426(1)
Regrouping the Sums of Squares
427(1)
New Terminology
427(1)
Calculating the Two-Way ANOVA
428(1)
Calculating MSW
429(1)
Calculating MSbet for the Drug Treatment Factor
429(1)
Calculating MSbet for the Gender Factor
429(1)
Graphing the Cell Means
430(1)
The Case of Zero Interaction
431(1)
General Linear Model
432(1)
Calculating the Variability Due to Interaction
433(1)
Types of Interactions
433(3)
Separating Interactions from Cell Means
436(1)
The F Ratio in a Two-Way ANOVA
437(1)
Advantages of the Two-Way Design
438(1)
Summary
439(1)
Exercises
440(1)
Basic Statistical Procedures
441(23)
Step
1. State the Null Hypothesis
441(1)
Step
2. Select the Statistical Test and the Significance Level
442(1)
Step
3. Select the Samples and Collect the Data
442(1)
Step
4. Find the Regions of Rejection
442(1)
Step
5. Calculate the Test Statistics
442(5)
Step
6. Make the Statistical Decisions
447(1)
The Summary Table for a Two-Way ANOVA
447(1)
Interpreting the Results
447(1)
Post Hoc Comparisons for Significant Main Effects
448(1)
Effect Sizes in the Two-Way ANOVA
449(3)
Post Hoc Comparisons for a Significant Interaction
452(4)
Assumptions of the Two-Way ANOVA
456(1)
Advantages of the Two-Way ANOVA with Two Experimental Factors
456(1)
Advantages of the Two-Way ANOVA with One Grouping Factor
457(1)
Advantages of the Two-Way ANOVA with Two Grouping Factors
458(1)
Publishing the Results of a Two-Way ANOVA
459(1)
Summary
460(1)
Exercises
461(3)
Optional Material
464(23)
Planned Comparisons for a Two-Way ANOVA
464(1)
Interaction of Trend Components
465(1)
The Two-Way ANOVA for Unbalanced Designs
466(2)
The Concepts of the Three-Way Factorial ANOVA
468(8)
Calculating the Three-Way ANOVA
476(2)
Follow-Up Tests for the Three-Way ANOVA
478(1)
2 X 2 X 2 Contrasts
479(1)
Types of Three-Way Designs
479(1)
Higher Order ANOVA
480(1)
Summary
480(1)
Exercises
481(3)
Key Formulas
484(3)
Part Five Analysis of Variance with Repeated Measures
487(84)
Repeated Measures ANOVA
487(41)
Conceptual Foundation
487(11)
Calculation of an Independent-Groups ANOVA
487(1)
The One-Way RM ANOVA as a Two-Way Independent ANOVA
488(1)
Calculating the SS Components of the RM ANOVA
489(1)
Comparing the Independent ANOVA with the RM ANOVA
490(1)
The Advantage of the RM ANOVA
491(1)
Picturing the Subject by Treatment Interaction
492(1)
Comparing the RM ANOVA to a Matched t Test
492(2)
Dealing with Order Effects
494(1)
Differential Carryover Effects
495(1)
The Randomized-Blocks Design
495(1)
Summary
496(1)
Exercises
497(1)
Basic Statistical Procedures
498(19)
Step
1. State the Hypotheses
499(1)
Step
2. Select the Statistical Test and the Significance Level
499(1)
Step
3. Select the Samples and Collect the Data
499(1)
Step
4. Find the Region of Rejection
499(1)
Step
5. Calculate the Test Statistic
500(1)
Step
6. Make the Statistical Decision
501(1)
Interpreting the Results
501(1)
The Residual Component
502(1)
The Effect Size of an RM ANOVA
503(1)
Power of the RM ANOVA
504(1)
Assumptions of the RM ANOVA
505(3)
Dealing with a Lack of Sphericity
508(1)
Post Hoc Comparisons
509(1)
Varieties of Repeated-Measures and Randomized-Blocks Designs
510(1)
Publishing the Results of an RM ANOVA
511(2)
Summary
513(1)
Exercises
514(3)
Optional Material
517(11)
Counterbalancing
517(2)
Trend Analysis with Repeated Measures
519(1)
The Two-Way RM ANOVA
520(4)
Summary
524(1)
Exercises
525(2)
Key Formulas
527(1)
Two-Way Mixed Design ANOVA
528(43)
Conceptual Foundation
528(10)
The One-Way RM ANOVA Revisited
529(1)
Converting the One-Way RM ANOVA to a Mixed Design ANOVA
530(3)
Two-Way Interaction in the Mixed Design ANOVA
533(1)
Summarizing the Mixed Design ANOVA
534(1)
Interpreting the Results
535(1)
The Varieties of Mixed Designs
535(2)
Summary
537(1)
Exercises
538(1)
Basic Statistical Procedures
538(20)
Step
1. State the Hypotheses
539(1)
Step
2. Select the Statistical Test and the Significance Level
539(1)
Step
3. Select the Samples and Collect the Data
539(1)
Step
4. Find the Regions of Rejection
540(1)
Step
5. Calculate the Test Statistics
541(3)
Step
6. Make the Statistical Decisions
544(1)
Interpreting the Results
544(1)
Publishing the Results of a Mixed ANOVA
545(1)
Assumptions of the Mixed Design ANOVA
546(1)
A Special Case: The Before-After Mixed Design
547(1)
Post Hoc Comparisons
548(3)
Effect Sizes for a Mixed Design
551(1)
An Excerpt from the Psychological Literature
552(1)
Summary
553(2)
Exercises
555(3)
Optional Material
558(13)
The Variance-Covariance Matrix for ANOVAs with an RM (or RB) Factor
558(3)
Planned Comparisons for a Mixed-Design ANOVA: Trend Interactions
561(2)
Removing Error Variance from Counterbalanced Designs
563(3)
Relative Efficiency
566(1)
Summary
566(2)
Exercises
568(1)
Key Formulas
569(2)
Part Six Multiple Regression and Its Connection to ANOVA
571(106)
Multiple Regression
571(56)
Conceptual Foundation
571(20)
Uncorrelated Predictors
572(1)
The Standardized Regression Equation
573(1)
More Than Two Mutually Uncorrelated Predictors
573(1)
The Sign of Correlations
574(1)
Two Correlated Predictors
574(1)
The Beta Weights
575(2)
Completely Redundant Predictors
577(1)
Partial Regression Slopes
577(2)
Degrees of Freedom
579(1)
Semipartial Correlations
579(1)
Calculating the Semipartial Correlation
580(1)
Suppressor Variables
581(1)
Complementary Variables
582(1)
The Raw-Score Prediction Formula
583(1)
Partial Correlation
584(2)
Finding the Best Prediction Equation
586(1)
Hierarchical (Theory-Based) Regression
587(1)
Summary
588(1)
Exercises
589(2)
Basic Statistical Procedures
591(22)
The Significance Test for Multiple R
591(1)
Tests for the Significance of Individual Predictors
592(1)
Forward Selection
593(2)
Backward Elimination
595(1)
Stepwise Regression
596(1)
The Misuse of Stepwise Regression
596(1)
Problems Associated with Having Many Predictors
597(4)
Too Few Predictors
601(1)
Minimal Sample Size
601(1)
Basic Assumptions of Multiple Regression
602(2)
Regression with Dichotomous Predictors
604(1)
Multiple Regression as a Research Tool
605(3)
Publishing the Results of Multiple Regression
608(1)
Summary
609(1)
Exercises
610(3)
Optional Material
613(14)
Dealing with Curvilinear Relationships
613(2)
Moderator Variables
615(2)
Multiple Regression with a Dichotomous Criterion
617(3)
Path Analysis
620(3)
Summary
623(1)
Exercises
624(1)
Key Formulas
625(2)
The Regression Approach to ANOVA
627(50)
Conceptual Foundation
627(14)
Dummy Coding
628(1)
The Regression Plane
628(1)
Effect Coding
629(1)
The General Linear Model
630(1)
Equivalence of Testing ANOVA and R2
630(1)
Two-Way ANOVA as Regression
631(2)
The GLM for Higher-Order ANOVA
633(1)
Analyzing Unbalanced Designs
633(4)
Methods for Controlling Variance
637(1)
Summary
638(2)
Exercises
640(1)
Basic Statistical Procedures
641(22)
Simple ANCOVA as Multiple Regression
641(3)
The Linear Regression Approach to ANCOVA
644(7)
Post Hoc Comparisons
651(1)
Performing ANCOVA by Multiple Regression
652(1)
Power and Effect Size
653(1)
The Assumptions of ANCOVA
653(1)
Additional Considerations
654(1)
Factorial ANCOVA
655(1)
Using Two or More Covariates
656(1)
Alternatives to ANCOVA
657(1)
Using ANCOVA with Intact Groups
658(1)
Summary
659(2)
Exercises
661(2)
Optional Material
663(14)
Multivariate Analysis of Variance
663(8)
Discriminant Analysis
671(1)
Using MANOVA to Test Repeated Measures
671(2)
Summary
673(1)
Exercises
674(1)
Key Formulas
675(2)
Part Seven Nonparametric Statistics
677(80)
The Binomial Distribution
677(23)
Conceptual Foundation
677(9)
The Origin of the Binomial Distribution
678(1)
The Binomial Distribution with N = 4
679(1)
The Binomial Distribution with N = 12
680(1)
When the Binomial Distribution Is Not Symmetrical
681(1)
The Normal Approximation to the Binomial Distribution
682(1)
The z Test for Proportions
683(1)
Summary
684(1)
Exercises
685(1)
Basic Statistical Procedures
686(6)
Step
1. State the Hypotheses
686(1)
Step
2. Select the Statistical Test and the Significance Level
686(1)
Step
3. Select the Samples and Collect the Data
686(1)
Step
4. Find the Region of Rejection
687(1)
Step
5. Calculate the Test Statistic
687(1)
Step
6. Make the Statistical Decision
687(1)
Interpreting the Results
688(1)
Assumptions of the Sign Test
688(1)
The Gambler's Fallacy
689(1)
When to Use the Binomial Distribution for Null Hypothesis Testing
689(2)
Summary
691(1)
Exercises
692(1)
Optional Material
692(8)
The Classical Approach to Probability
692(1)
The Rules of Probability Applied to Discrete Variables
693(1)
Permutations and Combinations
694(2)
Constructing the Binomial Distribution
696(1)
The Empirical Approach to Probability
696(1)
Summary
697(1)
Exercises
698(1)
Key Formulas
699(1)
Chi-Square Tests
700(28)
Conceptual Foundation
700(8)
The Multinomial Distribution
700(1)
The Chi-Square Distribution
701(1)
Expected and Observed Frequencies
701(1)
The Chi-Square Statistic
702(1)
Critical Values of Chi-Square
702(1)
Tails of the Chi-Square Distribution
703(1)
Expected Frequencies Based on No Preference
704(1)
The Varieties of One-Way Chi-Square Tests
704(2)
Summary
706(1)
Exercises
707(1)
Basic Statistical Procedures
708(10)
Two-Variable Contingency Tables
708(1)
Pearson's Chi-Square Test of Association
708(1)
An Example of Hypothesis Testing with Categorical Data
709(3)
The Simplest Case: 2 X 2 Tables
712(1)
Assumptions of the Chi-Square Test
713(1)
Some Uses for the Chi-Square Test for Independence
714(1)
Publishing the Results of a Chi-Square Test
715(1)
Summary
715(1)
Exercises
716(2)
Optional Material
718(10)
Measuring Strength of Association
718(3)
Measuring Interrater Agreement When Using Nominal Scales
721(2)
Fisher's Exact Test
723(1)
Contingency Tables Involving More Than Two Variables
724(1)
Summary
725(1)
Exercises
725(1)
Key Formulas
726(2)
Statistical Tests for Ordinal Data
728(29)
Conceptual Foundation
728(7)
Ranking Data
728(1)
Comparing the Ranks from Two Separate Groups
728(1)
The Sum of Ranks
729(1)
The U Statistic
729(1)
Dealing with Tied Scores
730(1)
When to Use the Mann-Whitney Test
731(2)
Repeated Measures or Matched Samples
733(1)
Summary
733(1)
Exercises
734(1)
Basic Statistical Procedures
735(13)
Testing for a Difference in Ranks between Two Independent Groups: The Mann-Whitney Test
735(4)
Ranking the Differences between Paired Scores: The Wilcoxon Signed-Ranks Test
739(4)
Correlation with Ordinal Data: The Spearman Correlation Coefficient
743(2)
Summary
745(2)
Exercises
747(1)
Optional Material
748(9)
Testing for Differences in Ranks among Several Groups: The Kruskal-Wallis Test
748(2)
Testing for Differences in Ranks among Matched Subjects: The Friedman Test
750(2)
Kendall's Coefficient of Concordance
752(1)
Summary
753(1)
Exercises
754(1)
Key Formulas
755(2)
Appendix A. Statistical Tables
757(20)
Areas under the Standard Normal Distribution
757(3)
Critical Values of the t Distribution
760(1)
Power as a Function of δ and α
761(1)
δ As a Function of α and Power
762(1)
Critical Values of Pearson's r
763(1)
Table of Fisher's Transformation from r to Z
764(1)
Critical Values of the F Distribution for α = .05
765(1)
Critical Values of the F Distribution for α = .025
766(1)
Critical Values of the F Distribution for α = .01
767(1)
Power of ANOVA for α = .05
768(1)
Critical Values of the Studentized Range Statistic for α = .05
769(1)
Orthogonal Polynomial Trend Coefficients
770(1)
Probabilities of the Binomial Distribution for P = .5
771(1)
Critical Values of the X2 Distribution
772(1)
Critical Values for the Mann-Whitney (Rank-Sum) Test
773(2)
Critical Values for the Wilcoxon Signed-Ranks Test
775(2)
Appendix B. Answers to Selected Exercises
777(22)
References 799(8)
Index 807


Barry H. Cohen, PhD, is the director of the master's program in psychology at New York University, where he has been teaching statistics for more than twenty years. He is the coauthor of two other successful statistics books from Wiley: Introductory Statistics for the Behavioral Sciences, Sixth Edition and Essentials of Statistics for the Social and Behavioral Sciences.