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E-grāmata: Correlation and Regression: Applications for Industrial Organizational Psychology and Management

3.00/5 (10 ratings by Goodreads)
  • Formāts: PDF+DRM
  • Sērija : Organizational Research Methods
  • Izdošanas datums: 10-Apr-2001
  • Izdevniecība: SAGE Publications Inc
  • Valoda: eng
  • ISBN-13: 9781452212173
  • Formāts - PDF+DRM
  • Cena: 96,36 €*
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  • Formāts: PDF+DRM
  • Sērija : Organizational Research Methods
  • Izdošanas datums: 10-Apr-2001
  • Izdevniecība: SAGE Publications Inc
  • Valoda: eng
  • ISBN-13: 9781452212173

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"This book provides one of the clearest treatments of correlations and regression of any statistics book I have seen. . . . Bobko has achieved his objective of making the topics of correlation and regression accessible to students. . . . For someone looking for a very clearly written treatment of applied correlation and regression, this book would be an excellent choice."

--Paul E. Spector, University of South Florida

 "As a quantitative methods instructor, I have reviewed and used many statistical textbooks. This textbook and approach is one of the very best when it comes to user-friendliness, approachability, clarity, and practical utility."

--Steven G. Rogelberg, Bowling Green State University



". . . the writing makes this book interesting to all levels of students. Bobko tackles tough issues in an easy way but provides references for more complex and complete treatment of the subject. . . . there is a familiarity and love of the material that radiates through the words."

--Malcolm James Ree, ORGANIZATIONAL RESEARCH METHODS, April 2002

"This book provides one of the clearest treatments of correlations and regression of any statistics book I have seen. . . . Bobko has achieved his objective of making the topics of correlation and regression accessible to students. . . . For someone looking for a very clearly written treatment of applied correlation and regression, this book would be an excellent choice."

--Paul E. Spector, University of South Florida

"As a quantitative methods instructor, I have reviewed and used many statistical textbooks. This textbook and approach is one of the very best when it comes to user-friendliness, approachability, clarity, and practical utility."

--Steven G. Rogelberg, Bowling Green State University

Building on the classical examples in the first edition, this updated edition provides students with an accessible textbook on statistical theories in correlation and regression. Taking an applied approach, the author uses concrete examples to help the student thoroughly understand how statistical techniques work and how to creatively apply them based on specific circumstances they face in the "real world."

The author uses a layered approach in each chapter, first offering the student an intuitive understanding of the problems or examples and progressing through to the underlying statistics. This layered approach and the applied examples provide students with the foundation and reasoning behind each technique, so they will be able to use their own judgement to effectively choose from the alternative data analytic options.

Recenzijas

". . . the writing makes this book interesting to all levels of students. Bobko tackles tough issues in an easy way but provides references for more complex and complete treatment of the subject. . . . there is a familiarity and love of the material that radiates through the words." -- Malcolm James Ree "The greatest strength of this book is the way in which the material is presented.  I particularly liked the clarity in the way that formulae were presented and that they are explained in terms of what each part contributes and the effect it has." -- Rebecca Walwyn * Statistical Methods in Medical Research *

Preface xiii
An Introduction, an Overview, and Some Reminders
1(11)
An Introduction and Overview
1(1)
The Structure of This Book
2(4)
An Outline
2(4)
A Brief Review and Reminder
6(6)
Notation
6(1)
Standardization
7(1)
Hypothesis Testing
7(5)
A Review of the Correlation Coefficient and Its Properties
12(31)
Chapter Objectives
12(2)
Good Old r
14(4)
Taking a Break: A Numerical Example
17(1)
Properties of (and Comments on) the Correlation Coefficient
18(12)
Linearity and r
18(1)
Scale Transformations and r
19(2)
Extreme Values on X or Y (Outliers)
21(2)
Causation
23(1)
Range Restriction and r
24(1)
Levels of Analysis and r
25(2)
Interpreting the Size of r
27(3)
Other Correlation Coefficients (``Yes, Virginia, there are Many Others'')
30(9)
Spearman's Rho, rs
31(2)
Phi Coefficient
33(3)
Point-Biserial and Biserial Correlations
36(3)
Problems
39(2)
References
41(2)
Testing Correlations for Statistical Significance
43(24)
Chapter Objectives
43(1)
Testing a Single Correlation Against Zero
44(3)
A Look at the Test for H0: ρ = 0
45(2)
Testing a Single Correlation vs. any specified value (the use of Fisher's z)
47(6)
The Problem, the Solution, and the Test
48(4)
An Important Related Result: Averaging Correlations
52(1)
Testing the Equality of Two Independent Correlations
53(3)
An Important Statistical Aside about Differential Validity
54(2)
Testing the Equality of Two Dependent Correlations
56(2)
Thoughts and Facts about Equation III.E
57(1)
Tests of Other Correlation Coefficients
58(2)
Point-Biserial
58(1)
Phi Coefficient
58(2)
Spearman's Rho, rs
60(1)
Some Comments on ``Harvesting'' Correlations
60(3)
The Problem
60(2)
Some Solutions
62(1)
Problems
63(2)
References
65(2)
Applications of Pearson Correlation to Measurement Theory
67(30)
Chapter Objectives
67(1)
Reliability
68(6)
Test-Retest Reliability
69(1)
Alternate Forms Reliability
70(1)
Split-Half Reliability
71(3)
Validity
74(4)
Face Validity
74(1)
Content Validity
74(1)
Criterion-related Validity
75(2)
Construct Validity
77(1)
Some Results in the Theory of Measurement
78(8)
Reliability as a Proportion of Variance
79(1)
Standard Error of Measurement
79(1)
Corrections for Unreliability
80(2)
Some Validity Implications
82(1)
Test Length
83(1)
What the Field's Been Doing Lately
84(2)
Some Optional, but Easy, Proofs
86(6)
Exploiting the Covariance
87(2)
Some Classical Test Theory Results
89(3)
Problems
92(1)
References
93(4)
Range Restriction
97(21)
Chapter Objectives
97(1)
Range Restriction Issues
98(17)
A More Typical Example
100(4)
Some Handy Formulas
104(2)
Statistical Proof and Assumptions
106(2)
An Extended Break: More Examples
108(2)
More Statistical Stuff
110(2)
Two Asides
112(1)
Reverse Range Restriction
113(2)
Problems
115(1)
References
116(2)
``Simple,'' Two-Variable Regression
118(40)
Chapter Objectives
118(1)
The ``Best''-Fitting Regression Line
119(5)
An Example
123(1)
Some Thoughts About Regression Lines
124(7)
Ah, the Choices We Make
124(3)
An Alternate Formula for b1
127(1)
There are Really Two Regression Lines
128(3)
Regression Lines and r
131(5)
A Quick Example
134(1)
The Analysis of Variance
134(2)
The Underlying Regression Model
136(3)
The Regression Model
136(1)
Error Variance
137(2)
Distribution Theory
139(4)
An Example
140(1)
Some Thoughts on Distribution Theory Results
141(1)
An Application of Bivariate Regression
142(1)
Prediction Intervals
143(2)
Some Thoughts about Equation VI.N
143(1)
A Quick Example
144(1)
Some Regression Leftovers
145(10)
Going Beyond the Data
145(1)
Standardization
145(1)
Fixed-X Versus Random-X Models
146(1)
Constrained Regression
147(1)
Residual Analysis
148(6)
Transformations
154(1)
Problems
155(2)
References
157(1)
Three Applications of Bivariate Regression: Utility Analysis, Regression to the Mean, Partial Correlation
158(19)
Chapter Objectives
158(1)
Utility Analysis
159(3)
Completing the Derivation of Equation VII.A
161(1)
Regression to the Mean
162(6)
Some Equations, Some History
163(1)
What's Going on Here?
164(1)
Practical Implications
165(3)
Partial Correlation
168(6)
Some Motivations
168(1)
Two Amazing Facts
168(2)
Further Examples, Spurious Correlations, and Mediators
170(2)
Important Leftover 1: Semipartial r
172(2)
Important Leftover 2: Multiple Partial r
174(1)
Problems
174(1)
References
175(2)
Multiple (Mostly Trivariate) Regression
177(30)
Chapter Objectives
177(1)
Trivariate Regression
178(11)
Three Examples
178(2)
The Underlying Model and the Prediction Equation
180(2)
Interpreting b1 and b2
182(2)
An Example
184(1)
The Analysis of Variance
185(1)
Multiple R2
185(1)
Interpreting the Magnitude of R2y.12
186(1)
``Unpacking'' the Value of R2y.12
187(2)
An Aside: Why Always Square the Multiple Correlation?
189(1)
Hypothesis Testing
189(7)
The Squared Multiple Correlation
189(3)
The Regression Weights, Bi
192(2)
Two Examples
194(1)
Collinearity
195(1)
Multiple Regression in General
196(8)
Regression Is Popular
196(1)
This is Really a Review
197(2)
Two Asides About Tests of Bi = 0
199(2)
An Example of Multiple Regression
201(3)
Problems
204(2)
References
206(1)
Expanding the Regression Repertoire: Polynomial and Interaction Terms
207(32)
Chapter Objectives
207(2)
Polynomial Regression
209(9)
An Example (and Some Additional Ideas)
211(4)
Polynomials and Power
215(1)
Two Additional Comments and Examples
216(2)
Interaction Terms
218(11)
The Interactive Model
219(1)
Interpreting B3: An Extended Example
219(6)
The Applied Literature Is Chock Full of Interactions
225(1)
My Goodness, What Have We Done?
226(3)
Five ``Leftovers'' About Interactions
229(4)
Yes, Interactions Can Contain More Than Two X's
229(1)
Measurement Suggestions
230(1)
The Chow Test
231(1)
Moderators and Mediators
231(1)
Experimental Design
232(1)
Problems
233(2)
References
235(4)
More about Regression, and Beyond
239(30)
Chapter Objectives
239(1)
Validity Shrinkage
240(7)
Shrinkage Formulas
243(2)
Some Comments About Shrinkage
245(2)
Sample Size
247(1)
Ridge Regression
248(2)
Other Weighting Schemes
250(2)
How Did We Do?
251(1)
Residual Analysis
252(1)
Suppressor Variables
252(3)
Indicator Variables
255(4)
Multiple Categories
256(3)
Analysis of Variance
259(3)
Adding Other Factors
261(1)
Cell Size (and Collinearity)
261(1)
Coda
262(2)
Problems
264(1)
References
265(4)
Appendix Tables of Critical Values 269(2)
ANSWERS TO SELECTED PROBLEMS 271(16)
INDEXES
Name Index
275(6)
Subject Index
281(6)
About the Author 287


Dr. Bobko has held academic appointments in management and psychology at four state universities. He has won or been nominated for teaching awards at three universities. He is the author of over 60 articles and book chapters in selection, test fairness, statistics, goal setting, managerial decision making, measurement, standard setting, and so forth. He has authored two books, including a recent text on correlation and regression (Sage Press). He serves on numerous editorial boards and has served as Editor of the Journal of Applied Psychology.