Fundamental Statistics for the Behavioral Sciences,9780534358211

Fundamental Statistics for the Behavioral Sciences

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Edition: CD
ISBN13: 9780534358211
ISBN10: 0534358217
Format: Hardcover
Pub. Date: 1998-08-01
Publisher(s): Wadsworth Publishing
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Summary

With an emphasis on looking at data before jumping into a test, author David C. Howell's practical approach focuses on the context of statistics in behavioral research. This approach promotes an understanding of the logic behind the statistics: why and how certain methods are used, rather than just doing techniques by rote. Howell takes students beyond crunching numbers to discover the meaning of statistical results and how they relate to the research questions being asked. Howell uses real data and research studies as a basis and moves through an analysis of data. As FUNDAMENTAL STATISTICS FOR THE BEHAVIORAL SCIENCES, Fourth Edition clearly demonstrates, it is not only important to concentrate on whether there is a difference between two groups but also to understand what the difference means.

Table of Contents

Introduction
1(14)
The Importance of Context
3(2)
Basic Terminology
5(3)
Selection Among Statistical Procedures
8(2)
Using Computers
10(2)
Summary
12(1)
Exercises
12(3)
Basic Concepts
15(15)
Scales of Measurement
16(4)
Variables
20(1)
Random Sampling
21(1)
Notation
22(2)
Summary
24(1)
Exercises
25(5)
Displaying Data
30(24)
A First Look at the Data
31(3)
Stem-and-Leaf Displays
34(3)
Histograms
37(3)
Alternative Methods of Plotting Data
40(3)
Describing Distributions
43(3)
Using Computer Programs to Display Data
46(1)
Summary
47(1)
Exercises
48(6)
Measures of Central Tendency
54(9)
The Mode
55(1)
The Median
55(1)
The Mean
56(1)
Advantages and Disadvantages of the Mode, the Median, and the Mean
57(2)
Obtaining Measures of Central Tendency Using Minitab
59(1)
Summary
60(1)
Exercises
61(2)
Measures of Variability
63(23)
Range
66(1)
Interquartile Range and Other Range Statistics
67(1)
The Average Deviation
67(1)
The Variance
68(1)
The Standard Deviation
69(1)
Computational Formulae for the Variance and the Standard Deviation
70(2)
The Mean and the Variance as Estimators
72(2)
Boxplots: Graphical Representations of Dispersions and Extreme Scores
74(4)
Obtaining Measures of Dispersion Using JMP
78(2)
A Final Worked Example
80(2)
Summary
82(1)
Exercises
83(3)
The Normal Distribution
86(18)
The Normal Distribution
88(4)
The Standard Normal Distribution
92(6)
Setting Probable Limits on an Observation
98(1)
Measures Related to z
99(1)
Summary
100(1)
Exercises
100(4)
Basic Concepts of Probability
104(14)
Probability
105(2)
Basic Terminology and Rules
107(4)
Discrete versus Continuous Variables
111(1)
Probability Distributions for Discrete Variables
112(1)
Probability Distributions for Continuous Variables
113(2)
Summary
115(1)
Exercises
116(2)
Sampling Distributions and Hypothesis Testing
118(23)
Two Simple Examples Involving Course Evaluations and Rude Motorists
120(2)
Sampling Distributions
122(1)
Hypothesis Testing
123(2)
The Null Hypothesis
125(1)
Test Statistics and Their Sampling Distributions
126(1)
Using the Normal Distribution to Test Hypotheses
127(3)
Type I and Type II Errors
130(3)
One- and Two-Tailed Tests
133(3)
A Final Worked Example
136(1)
Back to Course Evaluations and Rude Motorists
137(1)
Summary
138(1)
Exercises
139(2)
Correlation
141(31)
Scatter Diagrams
142(6)
An Example: The Relationship Between Speed and Accuracy
148(3)
The Covariance
151(1)
The Pearson Product-Moment Correlation Coefficient (r)
152(2)
Correlations with Ranked Data
154(1)
Factors That Affect the Correlation
155(3)
If Something Looks Too Good to Be True, Perhaps It Is
158(1)
Testing the Significance of a Correlation Coefficient
159(2)
Intercorrelation Matrices
161(2)
Other Correlation Coefficients
163(1)
Using Minitab and SPSS to Obtain Correlation Coefficients
164(2)
A Final Worked Example
166(2)
Summary
168(1)
Exercises
169(3)
Regression
172(25)
The Relationship Between Stress and Health
173(2)
The Basic Data
175(1)
The Regression Line
176(4)
The Accuracy of Prediction
180(6)
Hypothesis Testing in Regression
186(1)
Computer Solution Using SPSS
187(2)
A Final Worked Example
189(2)
Summary
191(1)
Exercises
192(5)
Multiple Regression
197(26)
Course Evaluations Again
200(5)
Residuals
205(2)
The Visual Representation of Multiple Regression
207(1)
Hypothesis Testing
208(2)
Refining the Regression Equation
210(1)
A Second Example: Height and Weight
211(3)
A Third Example: Psychological Symptoms in Cancer Patients
214(4)
Summary
218(1)
Exercises
219(4)
Hypothesis Tests Applied to Means: One Sample
223(24)
Sampling Distribution of the Mean
225(2)
Testing Hypotheses About Means When σ is Known
227(3)
Testing a Sample Mean When σ Is Unknown (One-Sample t Test)
230(6)
Factors That Affect the Magnitude of t and the Decision About H0
236(1)
A Second Example: The Moon Illusion
237(1)
Confidence Limits on the Mean
238(3)
Using a Computer Program to Run One-Sample t Tests
241(1)
A Final Worked Example
242(1)
Summary
243(1)
Exercises
244(3)
Hypothesis Tests Applied to Means: Two Related Samples
247(12)
Related Samples
248(1)
Student's t Applied to Difference Scores
249(3)
A Second Example: The Moon Illusion Again
252(1)
Advantages and Disadvantages of Using Related Samples
253(1)
Using Computer Software for t Tests on Related Samples
254(1)
Summary
255(1)
Exercises
256(3)
Hypothesis Tests Applied to Means: Two Independent Samples
259(20)
Distribution of Differences Between Means
260(7)
Heterogeneity of Variance
267(1)
Nonnormality of Distributions
268(1)
A Second Example with Two Independent Samples
269(1)
Confidence Limits on μ1 -- μ2
270(1)
Use of Computer Programs for Analysis of Two Independent Sample Means
271(3)
A Final Worked Example
274(2)
Summary
276(1)
Exercises
276(3)
Power
279(20)
The Basic Concept
281(1)
Factors That Affect the Powr of a Test
282(2)
Effect Size
284(2)
Power Calculations for the One-Sample t Test
286(3)
Power Calculations for Differences Between Two Independent Means
289(3)
Power Calculations for the t Test for Related Samples
292(2)
Power Considerations in Terms of Sample Size
294(1)
You Don't Have to Do It by Hand
295(1)
Summary
296(1)
Exercises
296(3)
One-way Analysis of Variance
299(36)
The General Approach
300(3)
The Logic of the Analysis of Variance
303(5)
Calculations for the Analysis of Variance
308(7)
Unequal Sample Sizes
315(1)
Multiple Comparison Procedures
316(8)
Violations of Assumptions
324(1)
Magnitude of Effect
325(1)
Using JMP for a One-Way Analysis of Variance
326(1)
A Final Worked Example
326(4)
Summary
330(1)
Exercises
331(4)
Factorial Analysis of Variance
335(22)
Factorial Designs
336(2)
The Extension of the Eysenck Study
338(5)
Interactions
343(2)
Simple Effects
345(3)
Unequal Sample Sizes
348(1)
Magnitude of Effect
348(1)
A Final Example: Maternal Adaptation Revisited
349(2)
Using SPSS for Factorial Analysis of Variance
351(1)
Summary
352(1)
Exercises
353(4)
Repeated-Measures Analysis of Variance
357(14)
An Example: The Treatment of Migraine Headaches
358(2)
Multiple Comparisons
360(2)
Assumptions Involved in Repeated-Measures Designs
362(1)
Advantages and Diadvantages of Repeated-Measures Designs
362(1)
Using BMDP to Analyze Data in a Repeated-Measures Design
363(3)
A Final Worked Example
366(2)
Summary
368(1)
Exercises
368(3)
Chi-Square
371(22)
One Classification Variable: The Chi-Square Goodness-of-Fit Test
373(5)
Two Classification Variables: Contingency Table Analysis
378(2)
Correction for Continuity
380(1)
Chi-Square for Larger Contingency Tables
381(1)
The Problem of Small Expected Frequencies
382(1)
The Use of Chi-Square as a Test on Proportions
383(2)
Nonindependent Observations
385(1)
Minitab Analysis of Contingency Tables
386(1)
A Final Worked Example
386(2)
Summary
388(1)
Exercises
389(4)
Nonparametric and Distribution-Free Statistical Tests
393(22)
The Mann--Whitney Test
395(6)
Wilcoxon's Matched-Pairs Signed-Ranks Test
401(4)
Kruskal--Wallis One-Way Analysis of Variance
405(1)
Friedman's Rank Test for k Correlated Samples
406(3)
Summary
409(1)
Exercises
409(6)
Choosing the Appropriate Analysis
415(8)
Exercises and Examples
417(6)
Appendix A Arithmetic Review 423(6)
Appendix B Symbols and Notation 429(3)
Appendix C Basic Statistical Formulae 432(4)
Appendix D Dataset 436(2)
Appendix E Statistical Tables 438(19)
Glossary 457(8)
References 465(6)
Answers to Selected Exercises 471(16)
Index 487

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