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Explaining Psychological Statistics 4th edition [Hardback]

3.29/5 (71 ratings by Goodreads)
(New York University, New York, NY)
  • Formāts: Hardback, 848 pages, height x width x depth: 257x185x38 mm, weight: 1452 g
  • Izdošanas datums: 21-Jan-2014
  • Izdevniecība: John Wiley & Sons Inc
  • ISBN-10: 1118436601
  • ISBN-13: 9781118436608
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Explaining Psychological Statistics 4th editionPalielināt
  • Formāts: Hardback, 848 pages, height x width x depth: 257x185x38 mm, weight: 1452 g
  • Izdošanas datums: 21-Jan-2014
  • Izdevniecība: John Wiley & Sons Inc
  • ISBN-10: 1118436601
  • ISBN-13: 9781118436608
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9781118436608.html
Keywords:
"Now in its 4th edition, this popular and comprehensive graduate-level statistics text offers students an easy to grasp and non-intimidating approach to statistics for the non-mathematician. The text provides practical coverage of SPSS in every chapter, including screen shots, procedures, exercises, and direction on how to interpret SPSS output. The use of common data sets throughout the book aid in student comprehension. Now with a new chapter showing students how to apply the right test in the right way to come out with the most accurate and true result, the new edition continues to offer students a lively and engaging introduction to the field"--

Praise for the previous edition of Explaining Psychological Statistics

"I teach a master's level, one-semester statistics course, and it is a challenge to find a textbook that is at the right level. Barry Cohen's book is the best one I have found. . . . I like the fact that the chapters have different sections that allow the professor to decide how much depth of coverage to include in his/her course. . . . This is a strong and improved edition of an already good book."

?Karen Caplovitz Barrett, PhD, Professor, and Assistant Department Head of Human Development and Family Studies, Colorado State University

"The quality is uniformly good. . . . This is not the first statistics text I have read but it is one of the best."

?Michael Dosch, PhD, MS, CRNA, Associate Professor and Chair, Nurse Anesthesia, University of Detroit Mercy

A clear and accessible statistics text? now fully updated and revised

Now with a new chapter showing students how to apply the right test in the right way to yield the most accurate and true result, Explaining Psychological Statistics, Fourth Edition offers students an engaging introduction to the field. Presenting the material in a logically flowing, non-intimidating way, this comprehensive text covers both introductory and advanced topics in statistics, from the basic concepts (and limitations) of null hypothesis testing to mixed-design ANOVA and multiple regression.

The Fourth Edition covers:

  • Basic statistical procedures
  • Frequency tables, graphs, and distributions
  • Measures of central tendency and variability
  • One- and two-sample hypothesis tests
  • Hypothesis testing
  • Interval estimation and the t distribution
Preface to the Fourth Edition xxiii
Acknowledgments xxix
Part One Descriptive Statistics
1(134)
Chapter One Introduction to Psychological Statistics
1(26)
A Conceptual Foundation
1(13)
What Is (Are) Statistics?
1(1)
Statistics and Research
2(1)
Variables and Constants
2(1)
Scales of Measurement
3(3)
Parametric Versus Nonparametric Statistics
6(1)
Likert Scales and the Measurement Controversy
7(1)
Continuous Versus Discrete Variables
8(1)
Scales Versus Variables Versus Underlying Constructs
8(1)
Independent Versus Dependent Variables
9(1)
Experimental Versus Observational Research
10(1)
Populations Versus Samples
11(1)
Statistical Formulas
12(1)
Summary
12(1)
Exercises
13(1)
B Basic Statistical Procedures
14(7)
Variables With Subscripts
14(1)
The Summation Sign
15(1)
Properties of the Summation Sign
16(2)
Rounding Off Numbers
18(1)
Summary
19(1)
Exercises
20(1)
C Analysis by SPSS
21(6)
Ihno's Data
21(1)
Variable View
22(1)
Data Coding
23(1)
Missing Values
23(1)
Computing New Variables
24(1)
Reading Excel Files Into SPSS
24(1)
Exercises
25(2)
Chapter 2 Frequency Tables, Graphs, and Distributions
27(30)
A Conceptual Foundation
27(11)
Frequency Distributions
27(1)
The Cumulative Frequency Distribution
28(1)
The Relative Frequency and Cumulative Relative Frequency Distributions
29(1)
The Cumulative Percentage Distribution
29(1)
Percentiles
30(1)
Graphs
30(4)
Real Versus Theoretical Distributions
34(1)
Summary
35(2)
Exercises
37(1)
B Basic Statistical Procedures
38(10)
Grouped Frequency Distributions
38(1)
Apparent Versus Real Limits
39(1)
Constructing Class Intervals
39(1)
Choosing the Class Interval Width
39(1)
Choosing the Limits of the Lowest Interval
40(1)
Relative and Cumulative Frequency Distributions
41(1)
Cumulative Percentage Distribution
41(1)
Estimating Percentiles and Percentile Ranks by Linear Interpolation
42(1)
Graphing a Grouped Frequency Distribution
43(1)
Guidelines for Drawing Graphs of Frequency Distributions
44(2)
Summary
46(1)
Exercises
47(1)
C Analysis by SPSS
48(9)
Creating Frequency Distributions
48(2)
Percentile Ranks and Missing Values
50(1)
Graphing Your Distribution
50(2)
Obtaining Percentiles
52(1)
The Split File Function
52(1)
Stem-and-Leaf Plots
53(2)
Exercises
55(2)
Chapter 3 Measures of Central Tendency and Variability
57(42)
A Conceptual Foundation
57(19)
Measures of Central Tendency
57(4)
Measures of Variability
61(8)
Skewed Distributions
69(4)
Summary
73(2)
Exercises
75(1)
B Basic Statistical Procedures
76(13)
Formulas for the Mean
76(1)
Computational Formulas for the Variance and Standard Deviation
77(3)
Obtaining the Standard Deviation Directly From Your Calculator
80(1)
Properties of the Mean
81(2)
Properties of the Standard Deviation
83(1)
Measuring Skewness
84(1)
Measuring Kurtosis
85(2)
Summary
87(1)
Exercises
88(1)
C Analysis by SPSS
89(10)
Summary Statistics
89(1)
Using Explore to Obtain Additional Statistics
90(1)
Boxplots
91(3)
Selecting Cases
94(2)
Exercises
96(1)
Key Formulas
96(3)
Chapter 4 Standardized Scores and the Normal Distribution
99(36)
A Conceptual Foundation
99(16)
z Scores
99(2)
Finding a Raw Score From a z Score
101(1)
Sets of z Scores
101(1)
Properties of z Scores
102(1)
SAT, T, and IQ Scores
103(1)
The Normal Distribution
104(2)
Introducing Probability: Smooth Distributions Versus Discrete Events
106(1)
Real Distributions Versus the Normal Distribution
107(1)
z Scores as a Research Tool
108(1)
Sampling Distribution of the Mean
109(1)
Standard Error of the Mean
110(1)
Sampling Distribution Versus Population Distribution
111(1)
Summary
112(1)
Exercises
113(2)
B Basic Statistical Procedures
115(15)
Finding Percentile Ranks
115(1)
Finding the Area Between Two z Scores
116(2)
Finding the Raw Scores Corresponding to a Given Area
118(1)
Areas in the Middle of a Distribution
119(1)
From Score to Proportion and Proportion to Score
119(1)
Describing Groups
120(2)
Probability Rules
122(3)
Summary
125(2)
Advanced Material: The Mathematics of the Normal Distribution
127(1)
Exercises
128(2)
C Analysis by SPSS
130(5)
Creating z Scores
130(1)
Obtaining Standard Errors
130(1)
Obtaining Areas of the Normal Distribution
131(1)
Data Transformations
131(1)
Exercises
132(1)
Key Formulas
132(3)
Part Two One- and Two-Sample Hypothesis Tests
135(136)
Chapter 5 Introduction to Hypothesis Testing: The One-Sample z Test
135(38)
A Conceptual Foundation
135(13)
Selecting a Group of Subjects
135(1)
The Need for Hypothesis Testing
136(1)
The Logic of Null Hypothesis Testing
137(1)
The Null Hypothesis Distribution
137(1)
The Null Hypothesis Distribution for the One-Sample Case
138(1)
Z Scores and the Null Hypothesis Distribution
139(1)
Statistical Decisions
140(1)
The z Score as Test Statistic
141(1)
Type I and Type II Errors
142(1)
The Trade-Off Between Type I and Type II Errors
143(1)
One-Tailed Versus Two-Tailed Tests
144(3)
Summary
147(1)
Exercises
147(1)
B Basic Statistical Procedures
148(21)
Step 1 State the Hypothesis
149(1)
Step 2 Select the Statistical Test and the Significance Level
150(1)
Step 3 Select the Sample and Collect the Data
150(1)
Step 4 Find the Region of Rejection
151(1)
Step 5 Calculate the Test Statistic
152(1)
Step 6 Make the Statistical Decision
153(1)
Interpreting the Results
154(1)
Assumptions Underlying the One-Sample z Test
155(2)
Varieties of the One-Sample Test
157(1)
Why the One-Sample Test Is Rarely Performed
158(1)
Publishing the Results of One-Sample Tests
159(1)
Summary
160(2)
Exercises
162(1)
Advanced Material: Correcting Null Hypothesis Testing Fallacies
163(5)
Advanced Exercises
168(1)
C Analysis by SPSS
169(4)
The One-Sample z Test
169(1)
Testing the Normality Assumption
170(1)
Exercises
171(1)
Key Formulas
172(1)
Chapter 6 Interval Estimation and the t Distribution
173(30)
A Conceptual Foundation
173(12)
The Mean of the Null Hypothesis Distribution
174(1)
When the Population Standard Deviation Is Not Known
174(1)
Calculating a Simple Example
175(1)
The t Distribution
175(2)
Degrees of Freedom and the t Distribution
177(1)
Critical Values of the t Distribution
178(1)
Calculating the One-Sample t Test
179(1)
Sample Size and the One-Sample t Test
179(1)
Uses for the One-Sample t Test
180(1)
Cautions Concerning the One-Sample t Test
180(2)
Estimating the Population Mean
182(1)
Summary
183(1)
Exercises
184(1)
Advanced Material: A Note About Estimators
185(1)
B Basic Statistical Procedures
185(11)
Step 1 Select the Sample Size
186(1)
Step 2 Select the Level of Confidence
186(1)
Step 3 Select the Random Sample and Collect the Data
186(1)
Step 4 Calculate the Limits of the Interval
186(4)
Relationship Between Interval Estimation and Null Hypothesis Testing
190(1)
Assumptions Underlying the One-Sample t Test and the Confidence Interval for the Population Mean
191(2)
Use of the Confidence Interval for the Population Mean
193(1)
Publishing the Results of One-Sample t Tests
194(1)
Summary
194(1)
Exercises
195(1)
C Analysis by SPSS
196(7)
Performing a One-Sample t Test
196(2)
Confidence Intervals for the Population Mean
198(1)
Bootstrapping
198(2)
Exercises
200(1)
Key Formulas
200(3)
Chapter 7 The t Test for Two Independent Sample Means
203(34)
A Conceptual Foundation
203(12)
Null Hypothesis Distribution for the Differences of Two Sample Means
204(1)
Standard Error of the Difference
205(1)
Formula for Comparing the Means of Two Samples
206(1)
Null Hypothesis for the Two-Sample Case
207(1)
The z Test for Two Large Samples
208(1)
Separate-Variances t Test
209(1)
The Pooled-Variances Estimate
209(1)
The Pooled-Variances t Test
210(1)
Formula for Equal Sample Sizes
211(1)
Calculating the Two-Sample t Test
211(1)
Interpreting the Calculated t
212(1)
Limitations of Statistical Conclusions
213(1)
Summary
213(1)
Exercises
214(1)
B Basic Statistical Procedures
215(17)
Step 1 State the Hypotheses
215(1)
Step 2 Select the Statistical Test and the Significance Level
216(1)
Step 3 Select the Samples and Collect the Data
216(1)
Step 4 Find the Region of Rejection
217(1)
Step 5 Calculate the Test Statistic
217(1)
Step 6 Make the Statistical Decision
218(1)
Interpreting the Results
218(1)
Confidence Intervals for the Difference Between Two Population Means
219(2)
Assumptions of the t Test for Two Independent Samples
221(2)
HOV Tests and the Separate-Variances t Test
223(1)
Random Assignment and the Separate-Variances t Test
224(1)
When to Use the Two-Sample t Test
225(1)
When to Construct Confidence Intervals
226(1)
Heterogeneity of Variance as an Experimental Result
226(1)
Publishing the Results of the Two-Sample t Test
226(1)
Summary
227(1)
Exercises
228(2)
Advanced Material: Finding the Degrees of Freedom for the Separate-Variances t Test
230(1)
Advanced Exercises
231(1)
C Analysis by SPSS
232(5)
Performing the Two-Independent-Samples t Test
232(1)
Confidence Interval for the Difference of Two Population Means
233(1)
Bootstrapping
233(1)
Exercises
233(1)
Key Formulas
234(3)
Chapter 8 Statistical Power and Effect Size
237(34)
A Conceptual Foundation
237(11)
The Alternative Hypothesis Distribution
237(2)
The Expected t Value (Delta)
239(2)
The Effect Size
241(1)
Power Analysis
242(1)
The Interpretation of t Values
243(1)
Estimating Effect Size
244(2)
Manipulating Power
246(1)
Summary
246(1)
Exercises
247(1)
B Basic Statistical Procedures
248(17)
Using Power Tables
248(1)
The Relationship Between Alpha and Power
249(1)
Power Analysis With Fixed Sample Sizes
250(1)
Sample Size Determination
251(1)
The Case of Unequal Sample Sizes
252(1)
The Power of a One-Sample Test
253(1)
Constructing Confidence Intervals for Effect Sizes
254(1)
Calculating Power Retrospectively
255(1)
Meta-Analysis
256(1)
Summary
257(1)
Exercises
258(1)
Advanced Material: When Is Null Hypothesis Testing Useful?
259(6)
C Analysis by SPSS
265(6)
Power Calculations in SPSS
265(2)
G*Power 3
267(1)
Exercises
268(1)
Key Formulas
269(2)
Part Three Hypothesis Tests Involving Two Measures ON Each Subject
271(94)
Chapter 9 Linear Correlation
271(32)
A Conceptual Foundation
271(12)
Perfect Correlation
271(1)
Negative Correlation
272(1)
The Correlation Coefficient
272(2)
Linear Transformations
274(1)
Graphing the Correlation
274(1)
Dealing With Curvilinear Relationships
275(2)
Problems in Generalizing From Sample Correlations
277(2)
Correlation Does Not Imply Causation
279(1)
True Experiments Involving Correlation
280(1)
Summary
280(1)
Exercises
281(2)
B Basic Statistical Procedures
283(13)
The Covariance
283(1)
The Unbiased Covariance
284(1)
An Example of Calculating Pearson's r
284(1)
Which Formula to Use
285(1)
Testing Pearson's r for Significance
285(2)
Understanding the Degrees of Freedom
287(1)
Assumptions Associated With Pearson's r
288(1)
Uses of the Pearson Correlation Coefficient
289(1)
Publishing the Results of Correlational Studies
290(1)
The Power Associated With Correlational Tests
291(2)
Summary
293(1)
Exercises
294(2)
C Analysis by SPSS
296(7)
Creating Scatterplots
296(1)
Computing Pearson's r
296(2)
The Listwise Option
298(1)
Using the Syntax Window for More Options
298(1)
Using the Keyword "With" to Reduce the Size of Your Correlation Matrix
299(1)
Bootstrapping
300(1)
Exercises
301(1)
Key Formulas
302(1)
Chapter 10 Linear Regression
303(34)
A Conceptual Foundation
303(11)
Perfect Predictions
303(1)
Predicting With z Scores
304(1)
Calculating an Example
304(1)
Regression Toward the Mean
305(1)
Graphing Regression in Terms of z Scores
305(1)
The Raw-Score Regression Formula
306(1)
The Slope and the Y Intercept
307(1)
Predictions Based on Raw Scores
308(1)
Interpreting the Y Intercept
309(1)
Quantifying the Errors Around the Regression Line
309(1)
The Variance of the Estimate
310(1)
Explained and Unexplained Variance
311(1)
The Coefficient of Determination
312(1)
The Coefficient of Nondetermination
312(1)
Calculating the Variance of the Estimate
312(1)
Summary
313(1)
Exercises
313(1)
B Basic Statistical Procedures
314(16)
Life Insurance Rates
314(1)
Regression in Terms of Sample Statistics
315(1)
Finding the Regression Equation
315(1)
Making Predictions
316(1)
Using Sample Statistics to Estimate the Variance of the Estimate
316(1)
Standard Error of the Estimate
317(1)
Testing the Regression Slope for Significance
318(1)
Assumptions Underlying Linear Regression
319(1)
Regressing X on Y
319(1)
Alternative Formula for the Regression Slope
320(1)
When to Use Linear Regression
320(2)
The Point-Biserial Correlation Coefficient
322(1)
Calculating rpb
323(1)
Deriving rpb From a t Value
324(1)
Interpreting rpb
324(1)
Strength of Association in the Population (Omega Squared)
325(2)
Biserial r
327(1)
Summary
327(1)
Exercises
328(2)
C Analysis by SPSS
330(7)
Computing a Linear Regression Analysis
330(3)
Bootstrapping
333(1)
Point-Biserial Correlations
333(1)
Exercises
333(1)
Key Formulas
334(3)
Chapter 11 The Matched t Test
337(28)
A Conceptual Foundation
337(9)
Before-After Design
337(1)
The Direct-Difference Method
338(1)
The Matched t Test as a Function of Linear Correlation
339(2)
Reduction in Degrees of Freedom
341(1)
Drawback of the Before-After Design
341(1)
Other Repeated-Measures Designs
341(1)
Matched-Pairs Design
342(1)
Correlated or Dependent Samples
343(1)
When Not to Use the Matched t Test
343(1)
Summary
344(1)
Exercises
345(1)
B Basic Statistical Procedures
346(14)
Step 1 State the Hypotheses
346(1)
Step 2 Select the Statistical Test and the Significance Level
346(1)
Step 3 Select the Samples and Collect the Data
346(1)
Step 4 Find the Region of Rejection
347(1)
Step 5 Calculate the Test Statistic
347(1)
Step 6 Make the Statistical Decision
348(1)
Using the Correlation Formula for the Matched t Test
348(1)
The Confidence Interval for the Difference of Two Population Means
349(1)
Effect Size for the Matched t Test
350(2)
Power of the Matched t Test
352(1)
Assumptions of the Matched t Test
353(1)
The Varieties of Designs Calling for the Matched f Test
353(2)
Publishing the Results of a Matched t Test
355(1)
Summary
356(1)
Exercises
357(2)
Advanced Material: Displaying the Results From a Matched t Test
359(1)
C Analysis by SPSS
360(5)
Performing a Matched-Pairs t Test
360(2)
Bootstrapping
362(1)
Exercises
362(1)
Key Formulas
362(3)
Part Four Analysis of Variance Without Repeated Measures
365(136)
Chapter 12 One-Way Independent ANOVA
365(42)
A Conceptual Foundation
365(12)
Transforming the t Test Into ANOVA
366(1)
Expanding the Denominator
367(1)
Expanding the Numerator
368(1)
The F Ratio
368(1)
The F Ratio as a Ratio of Two Population Variance Estimates
368(1)
Degrees of Freedom and the F Distribution
369(1)
The Shape of the F Distribution
370(1)
ANOVA as a One-Tailed Test
371(1)
Using Tables of F Values
371(1)
An Example With Three Equal-Sized Groups
371(1)
Calculating a Simple ANOVA
372(1)
Interpreting the F Ratio
373(2)
Advantages of the One-Way ANOVA
375(1)
Summary
375(1)
Exercises
376(1)
B Basic Statistical Procedures
377(24)
An ANOVA Example With Unequal Sample Sizes
377(1)
Step 1 State the Hypotheses
377(1)
Step 2 Select the Statistical Test and the Significance Level
378(1)
Step 3 Select the Samples and Collect the Data
378(1)
Step 4 Find the Region of Rejection
378(1)
Step 5 Calculate the Test Statistic
379(1)
Step 6 Make the Statistical Decision
380(1)
Interpreting Significant Results
381(1)
The Sums of Squares Approach
381(2)
The Proportion of Variance Accounted for in an ANOVA
383(2)
Assumptions of the One-Way ANOVA for Independent Groups
385(1)
Testing Homogeneity of Variance
386(2)
The Brown-Forsythe and Welch Tests
388(1)
Power and Effect Size for ANOVA
388(4)
Varieties of the One-Way ANOVA
392(2)
Publishing the Results of a One-Way ANOVA
394(2)
Summary
396(2)
Exercises
398(3)
C Analysis by SPSS
401(6)
Performing a One-Way ANOVA
401(1)
Reporting Effect Size for a One-Way ANOVA
402(1)
Exercises
403(1)
Key Formulas
403(4)
Chapter 13 Multiple Comparisons
407(44)
A Conceptual Foundation
407(12)
The Number of Possible t Tests
407(1)
Experimentwise Alpha
408(1)
Complex and Planned Comparisons
409(1)
Fisher's Protected t Tests
409(2)
Complete Versus Partial Null Hypotheses
411(1)
Tukey's HSD Test
412(1)
The Studentized Range Statistic
412(1)
Advantages and Disadvantages of Tukey's Test
413(1)
Other Procedures for Post Hoc Pairwise Comparisons
414(2)
The Advantage of Planning Ahead
416(1)
Bonferroni t, or Dunn's Test
416(1)
Summary
417(1)
Exercises
418(1)
B Basic Statistical Procedures
419(25)
Calculating Protected t Tests
419(1)
Calculating Fisher's LSD
420(1)
Calculating Tukey's HSD
421(1)
The Harmonic Mean Revisited
422(1)
Interpreting the Results of Post Hoc Pairwise Comparisons
422(1)
Confidence Intervals for Post Hoc Pairwise Comparisons
423(1)
Tukey's HSD Versus ANOVA
424(1)
The Modified LSD (Fisher-Hayter) Test
424(1)
Which Pairwise Comparison Procedure Should You Use?
425(1)
Complex Comparisons
425(4)
Scheffe's Test
429(1)
Orthogonal Contrasts
430(2)
Modified Bonferroni Tests
432(1)
The Analysis of Trend Components
433(7)
Summary
440(2)
Exercises
442(2)
C Analysis by SPSS
444(7)
Multiple Comparisons
444(2)
Contrasts
446(2)
Exercises
448(1)
Key Formulas
448(3)
Chapter 14 Two-Way ANOVA
451(50)
A Conceptual Foundation
451(16)
Calculating a Simple One-Way ANOVA
451(1)
Adding a Second Factor
452(1)
Regrouping the Sums of Squares
453(1)
New Terminology
453(1)
Calculating the Two-Way ANOVA
454(1)
Calculating MSW
455(1)
Calculating the Main Effect of the Drug Treatment Factor
455(1)
Calculating the Main Effect of the Gender Factor
455(1)
Graphing the Cell Means
456(1)
The General Linear Model
457(1)
Calculating the Variability Due to Interaction
458(1)
Types of Interactions
459(3)
Separating Interactions From Cell Means
462(1)
The F Ratio in a Two-Way ANOVA
463(1)
Advantages of the Two-Way Design
463(2)
Summary
465(1)
Exercises
466(1)
B Basic Statistical Procedures
467(26)
Step 1 State the Null Hypothesis
467(1)
Step 2 Select the Statistical Test and the Significance Level
467(1)
Step 3 Select the Samples and Collect the Data
468(1)
Step 4 Find the Regions of Rejection
468(1)
Step 5 Calculate the Test Statistics
469(3)
Step 6 Make the Statistical Decisions
472(1)
The Summary Table for a Two-Way ANOVA
472(1)
Interpreting the Results
473(1)
Post Hoc Comparisons for the Significant Main Effects
474(1)
Effect Sizes in the Two-Way ANOVA
475(2)
Post Hoc Comparisons for a Significant Interaction
477(4)
Interaction of Trend Components
481(1)
Assumptions of the Two-Way ANOVA
481(1)
Advantages of the Two-Way ANOVA With Two Experimental Factors
482(1)
Advantages of the Two-Way ANOVA With One Grouping Factor
483(1)
Advantages of the Two-Way ANOVA With Two Grouping Factors
483(1)
Publishing the Results of a Two-Way ANOVA
484(1)
The Two-Way ANOVA for Unbalanced Designs
485(2)
Summary
487(2)
Exercises
489(4)
C Analysis by SPSS
493(8)
Performing a Two-Way ANOVA
493(2)
Options for Univariate ANOVA
495(1)
Simple Main Effects
496(2)
Exercises
498(1)
Key Formulas
498(3)
Part Five Analysis of Variance With Repeated Measures
501(84)
Chapter 15 Repeated Measures ANOVA
501(44)
A Conceptual Foundation
501(11)
Calculation of an Independent-Groups ANOVA
501(1)
The One-Way RM ANOVA as a Two-Way Independent ANOVA
502(1)
Calculating the SS Components of the RM ANOVA
503(1)
Comparing the Independent ANOVA With the RM ANOVA
504(1)
The Advantage of the RM ANOVA
505(1)
Picturing the Subject by Treatment Interaction
506(1)
Comparing the RM ANOVA to a Matched t Test
506(2)
Dealing With Order Effects
508(1)
Differential Carryover Effects
509(1)
The Randomized-Blocks Design
509(1)
Summary
510(1)
Exercises
511(1)
B Basic Statistical Procedures
512(24)
Step 1 State the Hypotheses
513(1)
Step 2 Select the Statistical Test and the Significance Level
513(1)
Step 3 Select the Samples and Collect the Data
513(1)
Step 4 Find the Region of Rejection
513(1)
Step 5 Calculate the Test Statistic
514(1)
Step 6 Make the Statistical Decision
515(1)
Interpreting the Results
515(1)
The Residual Component
516(1)
The Effect Size of an RM ANOVA
517(2)
Power of the RM ANOVA
519(1)
Assumptions of the RM ANOVA
520(2)
Dealing With a Lack of Sphericity
522(1)
Post Hoc Comparisons
523(1)
Varieties of Repeated-Measures and Randomized-Blocks Designs
524(2)
Counterbalancing
526(2)
Trend Analysis With Repeated Measures
528(1)
Publishing the Results of an RM ANOVA
529(2)
Summary
531(1)
Exercises
532(3)
Advanced Material: Using MANOVA to Test Repeated Measures
535(1)
C Analysis by SPSS
536(9)
Performing a One-Way RM ANOVA
536(4)
Plots and Contrasts
540(1)
Options
540(2)
Exercises
542(1)
Key Formulas
542(3)
Chapter 16 Two-Way Mixed-Design ANOVA
545(40)
A Conceptual Foundation
545(10)
The One-Way RM ANOVA Revisited
546(1)
Converting the One-Way RM ANOVA to a Mixed-Design ANOVA
547(3)
Two-Way Interaction in the Mixed-Design ANOVA
550(1)
Summarizing the Mixed-Design ANOVA
551(1)
Interpreting the Results
552(1)
The Varieties of Mixed Designs
552(2)
Summary
554(1)
Exercises
555(1)
B Basic Statistical Procedures
555(23)
Step 1 State the Hypotheses
556(1)
Step 2 Select the Statistical Test and the Significance Level
556(1)
Step 3 Select the Samples and Collect the Data
556(1)
Step 4 Find the Regions of Rejection
557(1)
Step 5 Calculate the Test Statistics
558(3)
Step 6 Make the Statistical Decisions
561(1)
Interpreting the Results
561(1)
Alternative Breakdown of the SS Components of a Mixed-Design ANOVA
562(1)
Estimating Effect Sizes for a Mixed Design
563(1)
Publishing the Results of a Mixed ANOVA
563(1)
Assumptions of the Mixed-Design ANOVA
564(1)
A Special Case: The Before-After Mixed Design
565(1)
Post Hoc Comparisons
566(3)
An Excerpt From the Psychological Literature
569(1)
Interactions Involving Trends
570(1)
Removing Error Variance From Counterbalanced Designs
571(1)
Summary
572(2)
Exercises
574(4)
C Analysis by SPSS
578(7)
Performing a Two-Way Mixed-Design ANOVA
578(1)
Plots
579(1)
Post Hoc Tests
580(1)
Options: Homogeneity Tests
580(1)
Simple Main Effects
581(1)
Exercises
582(1)
Key Formulas
582(3)
Part Six Multiple Regression and Its Connection to ANOVA
585(100)
Chapter 17 Multiple Regression
585(54)
A Conceptual Foundation
585(20)
Uncorrelated Predictors
586(1)
The Standardized Regression Equation
587(1)
More Than Two Mutually Uncorrelated Predictors
587(1)
The Sign of Correlations
588(1)
Two Correlated Predictors
588(1)
The Beta Weights
589(2)
Completely Redundant Predictors
591(1)
Partial Regression Slopes
591(2)
Degrees of Freedom
593(1)
Semipartial Correlations
593(1)
Calculating the Semipartial Correlation
594(1)
Suppressor Variables
595(1)
Complementary Variables
596(1)
The Raw-Score Prediction Formula
597(1)
Partial Correlation
598(2)
Finding the Best Prediction Equation
600(1)
Hierarchical (Theory-Based) Regression
601(1)
Summary
602(1)
Exercises
603(2)
B Basic Statistical Procedures
605(27)
The Significance Test for Multiple R
605(1)
Tests for the Significance of Individual Predictors
606(1)
Methods for Variable Selection
607(4)
Problems Associated With Having Many Predictors
611(4)
Too Few Predictors
615(1)
Minimal Sample Size
615(1)
Basic Assumptions of Multiple Regression
616(2)
Regression With Dichotomous Predictors
618(1)
Multiple Regression as a Research Tool: Variable Ordering
619(2)
Publishing the Results of Multiple Regression
621(1)
Summary
622(1)
Exercises
623(3)
Optional Exercise
626(1)
Advanced Material
626(6)
C Analysis by SPSS
632(7)
Performing a Multiple Regression Analysis
632(2)
Statistics, Plots, Save, and Options
634(1)
Stepwise Regression
635(1)
Hierarchical Regression
636(1)
Exercises
636(1)
Key Formulas
637(2)
Chapter 18 The Regression Approach to ANOVA
639(46)
A Conceptual Foundation
639(14)
Dummy Coding
640(1)
The Regression Plane
640(1)
Effect Coding
641(1)
The General Linear Model
642(1)
Equivalence of Testing ANOVA and R2
642(1)
Two-Way ANOVA as Regression
643(2)
The GLM for Higher-Order ANOVA
645(1)
Analyzing Unbalanced Designs
646(3)
Methods for Controlling Error Variance
649(1)
Summary
650(2)
Exercises
652(1)
B Basic Statistical Procedures
653(22)
Simple ANCOVA as Multiple Regression
653(3)
The Linear Regression Approach to ANCOVA
656(7)
Post Hoc Comparisons
663(1)
Performing ANCOVA by Multiple Regression
664(1)
Power and Effect Size
665(1)
The Assumptions of ANCOVA
665(1)
Additional Considerations
666(1)
Factorial ANCOVA
667(1)
Using Two or More Covariates
668(1)
Alternatives to ANCOVA
668(2)
Using ANCOVA With Intact Groups
670(1)
Summary
671(2)
Exercises
673(2)
C Analysis by SPSS
675(10)
Dummy Coding
675(2)
Effect Coding
677(1)
Two-Way ANOVA by Regression
677(1)
Analysis of Covariance
678(3)
Analysis of Covariance by Multiple Regression
681(1)
Exercises
682(1)
Key Formulas
682(3)
Part Seven Nonparametric Statistics
685(96)
Chapter 19 The Binomial Distribution
685(28)
A Conceptual Foundation
685(12)
The Origin of the Binomial Distribution
686(1)
The Binomial Distribution With N = 4
687(1)
The Binomial Distribution With N = 12
688(1)
When the Binomial Distribution Is Not Symmetrical
689(2)
The z Test for Proportions
691(1)
The Classical Approach to Probability
692(1)
The Rules of Probability Applied to Discrete Variables
693(1)
The Empirical Approach to Probability
694(1)
Summary
695(1)
Exercises
696(1)
B Basic Statistical Procedures
697(9)
Step 1 State the Hypotheses
697(1)
Step 2 Select the Statistical Test and the Significance Level
697(1)
Step 3 Select the Samples and Collect the Data
698(1)
Step 4 Find the Region of Rejection
698(1)
Step 5 Calculate the Test Statistic
698(1)
Step 6 Make the Statistical Decision
699(1)
Interpreting the Results
699(1)
Assumptions of the Sign Test
699(1)
The Gambler's Fallacy
700(1)
When to Use the Binomial Distribution for Null Hypothesis Testing
700(2)
Summary
702(1)
Exercises
703(1)
Advanced Material: Permutations and Combinations
704(1)
Constructing the Binomial Distribution
705(1)
C Analysis by SPSS
706(7)
Performing a Binomial Test
706(2)
Options for the Binomial Test
708(1)
The Sign Test
709(1)
Exercises
710(1)
Key Formulas
711(2)
Chapter 20 Chi-Square Tests
713(68)
A Conceptual Foundation
713(8)
The Multinomial Distribution
713(1)
The Chi-Square Distribution
714(1)
Expected and Observed Frequencies
714(1)
The Chi-Square Statistic
715(1)
Critical Values of Chi-Square
715(1)
Tails of the Chi-Square Distribution
716(1)
Expected Frequencies Based on No Preference
717(1)
The Varieties of One-Way Chi-Square Tests
718(2)
Summary
720(1)
Exercises
720(1)
B Basic Statistical Procedures
721(16)
Two-Variable Contingency Tables
721(1)
Pearson's Chi-Square Test of Association
722(1)
An Example of Hypothesis Testing With Categorical Data
722(4)
The Simplest Case: 2 × 2 Tables
726(1)
Measuring Strength of Association
726(3)
Assumptions of the Chi-Square Test
729(1)
Some Uses for the Chi-Square Test for Independence
730(1)
Publishing the Results of a Chi-Square Test
731(1)
Summary
732(1)
Exercises
733(2)
Advanced Material
735(2)
C Analysis by SPSS
737(6)
Performing a One-Way Chi-Square Test
737(2)
Performing a Two-Way Chi-Square Test
739(2)
Exercises
741(1)
Key Formulas
741(2)
Appendix A Statistical Tables
743(16)
A.1 Areas Under the Standard Normal Distribution
743(3)
A.2 Critical Values of the t Distribution
746(1)
A.3 Power as a Function of δ and Significance Criterion (α)
747(1)
A.4 δ as a Function of Significance Criterion (α) and Power
748(1)
A.5 Critical Values of Pearson's r (df = N - 2)
749(1)
A.6 Table of Fisher's Transformation of r to Z
750(1)
A.7 Critical Values of the F Distribution for α = .05
751(1)
A.8 Critical Values of the F Distribution for α = .025
752(1)
A.9 Critical Values of the F Distribution for α = .01
753(1)
A.10 Power of ANOVA (α = .05)
754(1)
A.11 Critical Values of the Studentized Range Statistic (q) for α = .05
755(1)
A.12 Orthogonal Polynomial Trend Coefficients
756(1)
A.13 Probabilities of the Binomial Distribution for P = .5
757(1)
A.14 Critical Values of the Χx2 Distribution
758(1)
Appendix B Answers to Selected Exercises in Sections A and B
759(18)
Appendix C Data From Ihno's Experiment
777(4)
References 781(6)
Index 787
BARRY H. COHEN, PhD, is a clinical associate professor in the department of psychology at New York University, where he has been teaching statistics for more than twenty-five years. He is the coauthor of two other successful statistics books from Wiley: Introductory Statistics for the Behavioral Sciences, Seventh Edition and Essentials of Statistics for the Social and Behavioral Sciences.