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E-grāmata: Understanding Statistics in the Behavioral Sciences [Taylor & Francis e-book]

  • Formāts: 378 pages
  • Izdošanas datums: 11-Jan-2005
  • Izdevniecība: Psychology Press
  • ISBN-13: 9781410612625
Citas grāmatas par šo tēmu:
  • Taylor & Francis e-book
  • Cena: 155,64 €*
  • * this price gives unlimited concurrent access for unlimited time
  • Standarta cena: 222,34 €
  • Ietaupiet 30%
  • Formāts: 378 pages
  • Izdošanas datums: 11-Jan-2005
  • Izdevniecība: Psychology Press
  • ISBN-13: 9781410612625
Citas grāmatas par šo tēmu:
Understanding Statistics in the Behavioral Sciences is designed to help readers understand research reports, analyze data, and familiarize themselves with the conceptual underpinnings of statistical analyses used in behavioral science literature. The authors review statistics in a way that is intended to reduce anxiety for students who feel intimidated by statistics. Conceptual underpinnings and practical applications are stressed, whereas algebraic derivations and complex formulas are reduced. New ideas are presented in the context of a few recurring examples, which allows readers to focus more on the new statistical concepts than on the details of different studies.

The authors' selection and organization of topics is slightly different from the ordinary introductory textbook. It is motivated by the needs of a behavioral science student, or someone in clinical practice, rather than by formal, mathematical properties. The book begins with hypothesis testing and then considers how hypothesis testing is used in conjunction with statistical designs and tests to answer research questions. In addition, this book treats analysis of variance as another application of multiple regression. With this integrated, unified approach, students simultaneously learn about multiple regression and how to analyze data associated with basic analysis of variance and covariance designs. Students confront fewer topics but those they do encounter possess considerable more power, generality, and practical importance. This integrated approach helps to simplify topics that often cause confusion.

Understanding Statistics in the Behavioral Sciences features:*Computer-based exercises, many of which rely on spreadsheets, help the reader perform statistical analyses and compare and verify the results using either SPSS or SAS. These exercises also provide an opportunity to explore definitional formulas by altering raw data or terms within a formula and immediately see the consequences thus providing a deeper understanding of the basic concepts. *Key terms and symbols are boxed when first introduced and repeated in a glossary to make them easier to find at review time. *Numerous tables and graphs, including spreadsheet printouts and figures, help students visualize the most critical concepts.

This book is intended as a text for introductory behavioral science statistics. It will appeal to instructors who want a relatively brief text. The book's active approach to learning, works well both in the classroom and for individual self-study.
Preface xi
Preliminaries: How to Use This Book
1(16)
Statistics and the Behavioral Sciences
1(2)
Computing Statistics by Hand and Computer
3(9)
An Integrated Approach to Learning Statistics
12(5)
Getting Started: The Logic of Hypothesis Testing
17(14)
Statistics, Samples, and Populations
17(3)
Hypothesis Testing: An Introduction
20(4)
False Claims, Real Effects, and Power
24(6)
Why Discuss Inferential Before Descriptive Statistics?
30(1)
Inferring From a Sample: The Binomial Distribution
31(14)
The Binomial Distribution
31(8)
The Sign Test
39(6)
Measuring Variables: Some Basic Vocabulary
45(8)
Scales of Measurement
45(3)
Designing a Study: Independent and Dependent Variables
48(1)
Matching Study Designs with Statistical Procedures
49(4)
Describing a Sample: Basic Descriptive Statistics
53(18)
The Mean
54(6)
The Variance
60(3)
The Standard Deviation
63(3)
Standard Scores
66(5)
Describing a Sample: Graphical Techniques
71(12)
Principles of good design
72(1)
Graphical Techniques Explained
73(10)
Inferring From a Sample: The Normal and t Distributions
83(20)
The Normal Approximation for the Binomial
84(3)
The Normal Distribution
87(4)
The Central Limit Theorem
91(1)
The t Distribution
92(1)
Single-Sample Tests
93(5)
Ninety-Five Percent Confidence Intervals
98(5)
Accounting for Variance: A Single Predictor
103(14)
Simple Regression and Correlation
103(10)
What Accounting for Variance Means
113(4)
Bivariate Relations: The Regression and Correlation Coefficients
117(20)
Computing the Slope and the Y Intercept
119(5)
Computing the Correlation Coefficient
124(3)
Detecting Group Differences with a Binary Predictor
127(5)
Graphing the Regression Line
132(5)
Inferring From a Sample: The F Distribution
137(18)
Estimating Population Variance
137(3)
The F Distribution
140(2)
The F Test
142(7)
The Analysis of Variance: Two Independent Groups
149(3)
Assumptions of the F test
152(3)
Accounting for Variance: Multiple Predictors
155(26)
Multiple Regression and Correlation
156(10)
Significance Testing with Multiple Predictors
166(2)
Accounting for Unique Additional Variance
168(3)
Hierarchie MRC and the Analysis of Covariance
171(7)
More Than Two Predictors
178(3)
Single-Factor Between-Subjects Studies
181(20)
Coding Categorical Predictor Variables
182(12)
One-Way Analysis of Variance
194(3)
Trend Analysis
197(4)
Planned Comparisons, Post Hoc Tests, and Adjusted Means
201(22)
Organizing Stepwise Statistics
203(2)
Planned Comparisons
205(1)
Post Hoc Tests
206(4)
Unequal Numbers of Subjects Per Group
210(2)
Adjusted Means and the Analysis of Covariance
212(11)
Studies With Multiple Between-Subjects Factors
223(22)
Between-Subjects Factorial Studies
224(9)
Significance Testing for Main Effects and Interactions
233(2)
Interpreting Significant Main Effects and Interactions
235(3)
Magnitude of Effects and Partial Eta Squared
238(7)
Single-Factor Within-Subjects Studies
245(24)
Within-Subjects or Repeated-Measures Factors
245(5)
Controlling Between-Subjects Variability
250(8)
Modifying the Source Table for Repeated Measures
258(8)
Assumptions of the Repeated Measure ANOVA
266(3)
Two-Factor Studies With Repeated Measures
269(20)
One Between- and One Within-Subjects Factor
269(9)
Two Within-Subjects Factors
278(6)
Explicating Interactions with Repeated Measures
284(2)
Generalizing to More Complex Designs
286(3)
Power, Pitfalls, and Practical Matters
289(12)
Pretest, Posttest: Repeated Measure Or Covariate?
289(6)
Power Analysis: How Many Subjects Are Enough?
295(6)
References
301(2)
Glossary of Symbols and Key Terms
303(6)
Appendix A: SAS exercises
309(16)
Appendix B: Answers to Selected Exercises
325(32)
Appendix C: Statistical Tables
A. Critical Values for the Binomial Distribution, P = 0.5
345(2)
B. Areas Under the Normal Curve
347(3)
C. Critical Values for the t Distribution
350(1)
D.1 Critical Values for the F Distribution, α = .05
351(1)
D.2 Critical Values for the F Distribution, α = .01
352(1)
E.1 Distribution of the Studentized Range Statistic, α = .05
353(1)
E.2 Distribution of the Studentized Range Statistic, α = .01
354(1)
F.1 L Values for α = .05
355(1)
F.2 L Values for α = .01
356(1)
Author Index 357(2)
Subject Index 359


Roger Bakeman, Byron F. Robinson