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Behavioral Research and Analysis: An Introduction to Statistics within the Context of Experimental Design, Fourth Edition 4th edition [Mīkstie vāki]

(Hawaii Academy, Honolulu, USA), (Castle Pines Village, Colorado, USA)
  • Formāts: Paperback / softback, 299 pages, height x width: 254x178 mm, weight: 560 g, 61 Tables, black and white; 54 Illustrations, black and white
  • Izdošanas datums: 05-Feb-2018
  • Izdevniecība: CRC Press
  • ISBN-10: 1138073180
  • ISBN-13: 9781138073180
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  • Mīkstie vāki
  • Cena: 78,11 €
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  • Formāts: Paperback / softback, 299 pages, height x width: 254x178 mm, weight: 560 g, 61 Tables, black and white; 54 Illustrations, black and white
  • Izdošanas datums: 05-Feb-2018
  • Izdevniecība: CRC Press
  • ISBN-10: 1138073180
  • ISBN-13: 9781138073180
Citas grāmatas par šo tēmu:
Now in its fourth edition, Behavioral Research and Analysis: An Introduction to Statistics within the Context of Experimental Design presents an overview of statistical methods within the context of experimental design. It covers fundamental topics such as data collection, data analysis, interpretation of results, and communication of findings.

New in the Fourth Edition:











Extensive improvements based on suggestions from those using this book in the classroom





Statistical procedures that have been developed and validated since the previous edition





Each chapter in the body now contains relevant key words, chapter summaries, key word definitions, and end of chapter exercises (with answers)





Revisions to include recent changes in the APA Style Manual

When looking for a book for their own use, the authors found none that were totally suitable. They found books that either reviewed the basics of behavioral research and experimental design but provided only cursory coverage of statistical methods or they provided coverage of statistical methods with very little coverage of the research context within which these methods are used. No single resource provided coverage of methodology, statistics, and communication skills. In a classic example of necessity being the mother of invention, the authors created their own.

This text is ideal for a single course that reviews research methods, essential statistics through multi-factor analysis of variance, and thesis (or major project) preparation without discussion of derivation of equations, probability theory, or mathematic proofs. It focuses on essential information for getting a research project completed without prerequisite math or statistics training. It has been revised many times to help students at a variety of academic levels (exceptional high school students, undergraduate honors students, masters students, doctoral students, and post-doctoral fellows) across varied academic disciplines (e.g., human factors and ergonomics, behavioral and social sciences, natural sciences, engineering, exercise and sport sciences, business and management, industrial hygiene and safety science, health and medical sciences, and more).

Illustrating how to plan, prepare, conduct, and analyze an experimental or research report, the book emphasizes explaining statistical procedures and interpreting obtained results without discussing the derivation of equations or history of the method. Destined to spend more time on your desk than on the shelf, the book will become the single resource you reach for again and again when conducting scientific research and reporting it to the scientific community.











Illustrates how to plan, conduct, analyze, and prepare an experimental or research report





Includes new statistical procedures that have been developed and validated since the previous edition





Incorporates SAS in the exercises at the end of each chapter





Takes into account the changes in the APA guidebook





Provides new examples in exercise and sport science, public health, gerontology, and biomedical areas

Solutions manual available upon qualifying course adoption
Preface xv
Acknowledgments xvii
About the Authors xix
Chapter 1 Overview of Scientific Research
1(22)
Keywords
1(1)
What Is Science?
1(1)
Scientific Method
2(4)
Identify Problem
2(1)
Formulate Hypothesis
2(1)
Conduct Pilot Study
3(1)
Collect Data
3(1)
Participant (Subject) Sampling
3(1)
Experimental and Control Groups
4(1)
Independent and Dependent Variables
5(1)
Describing Collected Data
5(1)
Test Hypothesis
5(1)
Generalize Results
5(1)
Replicate Experiment
6(1)
Goals, Principles, and Assumptions of Science
6(3)
Goals of Science
6(1)
Description
6(1)
Explanation
6(1)
Principles of Science
7(1)
Empirical Verification
7(1)
Assumptions of Science
8(1)
Determinism
8(1)
Limited Causality
8(1)
Contiguity of Events
8(1)
Five Basic Approaches to Scientific Research
9(9)
Correlation Approach
9(1)
Establishing Validity
9(1)
Using Multiple Predictors
10(1)
Establishing Test Reliability
11(1)
Developing Homogeneous Subgroups
11(1)
Case History Approach
12(1)
Solving Personal Problems
12(1)
Predicting and Subgrouping
12(1)
Field Study Approach
13(1)
Experimental Approach
14(1)
Advantages of Experimental Approach
14(1)
Disadvantages of Experimental Approach
14(1)
Purposes of Experimentation
15(1)
Experimentation Versus Demonstration
16(1)
Manipulation Versus Selection of Independent Values
16(1)
Quasi-Experimental Approach
16(1)
Time Series Design
17(1)
Nonequivalent Control Group Design
17(1)
Summary
18(1)
Keyword Definitions
19(1)
Exercises
20(1)
Exercise Answers
21(1)
References
21(2)
Chapter 2 Methods of Describing Data
23(30)
Keywords
23(1)
Samples and Populations
24(1)
Consideration of Numbers in Statistics
24(3)
Continuous Versus Discrete Data
25(1)
Four General Scales of Measurement
25(1)
Scaling Behavioral Dimensions
26(1)
Graphical Methods of Description
27(5)
Univariate Frequency Distribution
27(1)
Determine Range
27(1)
Determine Number and Size
27(1)
Set Up Frequency Distribution
27(1)
Tally Scores
27(1)
Post Tallies
28(1)
Add/Column
28(1)
Graphing Results
28(1)
Frequency Polygon
28(1)
Histogram
29(1)
Other Types of Graphs
30(1)
Cumulative Frequency Distribution
30(2)
Univariate Descriptive Statistics
32(12)
Measures of Central Tendency
32(1)
Mode
32(1)
Median
32(1)
Mean
33(1)
Averaging Means
33(1)
When to Use Different Measures of Central Tendency
34(1)
Centiles and Quartiles
34(1)
Measures of Dispersion, Variability, or Spread
34(1)
Range
34(1)
Semi-Interquartile Range
35(1)
Average Deviation
35(1)
Variance
36(1)
Standard Deviation
36(3)
Interpretation of Standard Deviation
39(1)
Standard Score
40(3)
Measures of Distribution Skewness
43(1)
Measures of Distribution Kurtosis
43(1)
Summary
44(1)
Keyword Definitions
45(1)
Exercises
46(2)
Exercise Answers
48(4)
References
52(1)
Chapter 3 Bivariate Descriptive Statistics
53(32)
Keywords
53(1)
Bivariate Frequency Distributions
53(4)
Graphing Relationship Between Two Variables
54(2)
Shapes of Bivariate Frequency Distributions
56(1)
Correlation: The Pearson r
57(5)
Nature of Correlation Coefficients
57(1)
Pearson Product-Moment Correlation (r)
57(1)
Computation of Pearson r
58(2)
Effect of Range on Value or Coefficient
60(1)
Interpretation of Correlation Coefficients
60(2)
Interpretation of r2 (Coefficient of Determination)
62(1)
Other Correlation Coefficients
62(8)
Point Biserial rpb
62(1)
Computation of Point Biserial
62(1)
Assumptions Underlying Point Biserial
63(1)
Biserial r
64(1)
Computation of Biserial rb
64(1)
Assumptions Underlying Biserial r
65(1)
Interpretation of Biserial r
65(1)
Spearman Rank Order Correlation Coefficient (Rho)
65(1)
Calculation of Spearman Rho
65(1)
Assumption Underlying Spearman Rho
66(1)
Use of Spearman Rho
66(1)
Kendall's Coefficient of Concordance (W)
66(1)
Computation of W
67(1)
Phi Coefficient (φ)
68(1)
Computation of Phi
68(1)
Assumptions Underlying Phi
68(1)
Special Uses of Phi Coefficient
68(1)
Correlation Ratio (Eta)
69(1)
Calculation of Correlation Ratio
69(1)
Prediction and Concept of Regression
70(4)
Concept of Regression
70(1)
Computation of Regression Lines
70(1)
Equation for Straight Line
71(1)
Computation of Linear Regression Line
71(2)
Relation of byx and bxy to r
73(1)
Standard Error of Estimate
73(1)
Computation of SEest
74(1)
Interpretation of SEesl
74(1)
Summary
74(2)
Keyword Definitions
76(1)
Exercises
77(3)
Exercise Answers
80(4)
References
84(1)
Chapter 4 Simple Experimental Designs
85(36)
Keywords
85(1)
Introduction to Inferential Statistics
86(2)
Sampling Distribution of Means
86(1)
Example
86(1)
Central Limit Theorem
86(1)
Relationship of Sample Size to σx
86(1)
Computing Standard Error of Mean σx
87(1)
Sampling Distribution of Difference Between Two Means σDx
87(1)
Example
87(1)
Computing σDx
87(1)
Statistical Hypothesis Testing
88(3)
Example
88(1)
One-Tailed Versus Two-Tailed Hypotheses
89(2)
Type I and Type II Errors
91(1)
Power of Statistical Testing
91(1)
Two Randomized Groups Designs: t-Test for Independent Samples
91(2)
Two Randomized Groups (Between Groups) Design
91(1)
t-Test for Independent Data
92(1)
Concept of Degrees of Freedom
92(1)
Use of t-Test in Statistical Hypothesis Testing
92(1)
Limitations of Randomized Groups Design
93(1)
Two Matched Groups and Repeated Measures Designs: t-Test for Correlated Data
93(7)
Two Matched Groups Design
94(1)
t-Test for Correlated Data
94(1)
Computation of t for Correlated Data
94(3)
Repeated Measures (Within Subjects) Design
97(1)
Advantages and Uses of Repeated Measures Designs
97(1)
Disadvantages of Repeated Measures Designs
98(1)
Counterbalancing in Repeated Measures Designs
98(1)
Using r-Test With Repeated Measures Design
99(1)
Nonparametric Analysis
100(8)
Mann-Whitney U-Test
100(1)
Assumptions of Mann-Whitney U-Test
100(1)
Computation of Mann-Whitney U-Test
101(1)
Explanation of U-Test
102(1)
Wilcoxon Matched-Pairs Signed-Ranks Test (T)
102(1)
Assumptions of Wilcoxon Test
102(1)
Computation of Wilcoxon Test
102(1)
Explanation of Wilcoxon Test
103(1)
Chi-Square
103(1)
Chi-Square Distribution
103(1)
Chi-Square Tests of Independence
104(1)
Computation of Degrees of Freedom for Chi-Square Tests
105(1)
Chi-Square Tests of Goodness of Fit
105(1)
Chi-Square Test for Goodness of Fit to Normal
106(1)
Computation of Chi-Square With Small Expected Frequencies
107(1)
Testing for Significance of Correlation
108(1)
Test for Significance of Phi (O)
108(1)
Testing for Significance of Pearson r and Spearman Rho
109(1)
Summary
109(2)
Keyword Definitions
111(2)
Exercises
113(3)
Exercise Answers
116(4)
References
120(1)
Chapter 5 Simple Analysis of Variance
121(26)
Keywords
121(1)
More Than Two Treatments Designs
121(3)
Reasons for Using More Than Two Treatments
121(1)
Using More Than Two Treatments May Yield a Different Answer
121(1)
To Obtain Fairly Precise Knowledge of the IV-DM Relationship
122(1)
To Study More Than Two Treatment Conditions
122(1)
Types of More Than Two Treatment Designs
123(1)
Single-Factor (Simple) Analysis of Variance
124(3)
Concept of Analysis of Variance (ANOVA)
124(2)
F-Test
126(1)
Rationale for F-Test
126(1)
Assumptions of F-Test
126(1)
Why Multiple t-Tests Should Not Be Used
127(1)
ANOVA for More Than Two Randomized Groups Design
127(4)
Computation of Sums of Squares
128(1)
Degrees of Freedom, Mean Squares, and F-Ratio
128(1)
Generalized ANOVA Summary Table
129(1)
Computational Example
130(1)
ANOVA for Repeated Measures Design
131(4)
Computation of Sums of Squares
131(1)
Degrees of Freedom, Mean Squares, and F-Ratio
132(1)
Generalized ANOVA Summary Table
132(1)
Computation Example
133(2)
Post Hoc Analyses: Multiple Comparisons Among Means
135(4)
Tukey's WSD (Wholly Significant Difference)
135(2)
Neuman-Keuls Test
137(1)
Bonferroni /-Test
137(1)
Scheffe Test for All Possible Comparisons
137(2)
Summary
139(1)
Keyword Definitions
140(1)
Exercises
141(1)
Exercise Answers
142(4)
References
146(1)
Chapter 6 Multifactor Analysis of Variance
147(34)
Keywords
147(1)
Rationale for Factorial Designs
147(2)
Factorial Designs
149(2)
Two-Factor Designs
150(1)
Three-Factor Designs
150(1)
Four-Factor Designs
150(1)
Nested Designs
151(1)
Fully Crossed Designs
151(1)
Nested Designs
151(1)
Limitation of Nested Designs
152(1)
Types of Analysis of Variance Designs
152(2)
Between-Groups Designs
152(1)
Completely Within-Subjects (Repeated Measures) Designs
153(1)
Mixed Designs
153(1)
Between-Groups (Random Blocks) Two-Factor ANOVA Designs
154(4)
Rationale for Between-Groups ANOVA Designs
154(1)
Computational Example
155(3)
Within-Subjects (Repeated Measures) Two-Factor ANOVA Designs
158(3)
Rationale for Within-Subjects ANOVA Designs
158(1)
Computational Example
159(2)
Mixed Two-Factor ANOVA Designs
161(3)
Rationale for Mixed ANOVA Designs
161(2)
Computational Example
163(1)
Summary
164(1)
Keyword Definitions
165(1)
Exercises
165(3)
Exercise Answers
168(11)
References
179(2)
Chapter 7 Planning, Conducting, and Reporting Research
181(22)
Keywords
181(1)
Planning and Conducting Study
181(3)
Surveying Literature
181(1)
Stating Problem and Hypothesis
182(1)
Defining Variables
182(1)
Selecting Design
183(1)
Developing Experimental Procedure
183(1)
Analyzing Results
183(1)
Techniques for Controlling Extraneous Variables
184(3)
Eliminating Conditions
185(1)
Holding Conditions Constant
185(1)
Balancing Conditions
185(1)
Counterbalancing Conditions
186(1)
Randomizing Conditions
187(1)
Conducting an Experiment
187(1)
Ethics
187(1)
Informed Consent
187(1)
Etiquette
188(1)
Writing Research Report
188(4)
General Comments
188(2)
General Format
190(1)
Abstract
190(1)
Introduction
190(1)
Purpose
190(1)
Crediting Sources
190(2)
Method
192(1)
Participants
192(1)
Apparatus
192(1)
Procedure
192(1)
Treatment of Data
193(1)
Results
193(1)
Discussion
193(1)
Summary
193(1)
References
193(4)
Book References
194(1)
Journal References
194(1)
Proceedings References
194(1)
Dissertation References
194(1)
Technical Report References
195(1)
Motion Picture References
195(1)
Newspaper and Magazine References
195(1)
Other References
195(1)
Tables and Figures
195(1)
Tables
196(1)
Figures
196(1)
Additional Comments About Writing Scientific Reports
197(1)
Summary
197(1)
Keyword Definitions
198(2)
Exercises
200(1)
Exercise Answers
201(1)
References
201(2)
Appendix A Statistical Tables 203(24)
Appendix B Glossary of Statistical Terms, Equations, and Symbols 227(36)
Appendix C Statistical Equations 263(10)
Index 273
Max Vercruyssen, PhD, is the director of Hawaii Academy, a private school for lifetime fitness, gymnastics, and human sciences, where he also serves as chair of the research department and as head coach of the school's elitelevel trampoline gymnastics teams. For the past 15 years, Dr. Vercruyssen has reduced his research activities so that he and his wife, Dr. Donna Mah, could coach their four daughters in national and international championships.

He holds a bachelor's degree in experimental psychology; master's degrees in experimental and physiological psychology, exercise and sport sciences, and public health; a PhD in neuromuscular control, and pursued postdoctoral training to earn advanced certificates in ergonomics and gerontology. Most of his advanced statistics training was under Paul A. Games (Pennsylvania State University) and as required for experimental psychology, biostatistics, and longitudinal studies of aging.At the University of Southern California (USC), Dr. Vercruyssen served as assistant professor of human factors and ergonomics, director of the Human Factors Laboratory, and codirector of the Laboratory of Attention and Motor Performance in the Andrus Gerontology Center. He also helped develop the university's ergonomics graduate degree programs and mentored the first ergonomics majors in safety science. At the University of Hawaii, he was an associate professor in psychology, gerontology, and geriatric medicine.During the 1990s, Dr. Vercruyssen was also a research associate at the University of Minnesota's Center for Transportation Research, Institute of Intelligent Transportation Systems, and a distinguished fellow of gerontechnology at the Technical University of Eindhoven, Netherlands. Dr. Vercruyssen authored or coauthored over 200 refereed publications and presented papers at international scientific and technical conferences. Nine years of his university appointments involved teaching experimenta