Amazon no longer offers textbook rentals. We do!

Elements of Statistical Reasoning, 2nd Edition

by: Edward W. Minium (San Jose State Univ.); Robert C. Clarke (San Jose State Univ.); Theodore Coladarci (Univ. of Maine)

Elements of Statistical Reasoning, 2nd Edition

by: Edward W. Minium (San Jose State Univ.); Robert C. Clarke (San Jose State Univ.); Theodore Coladarci (Univ. of Maine)

ISBN 13:
9780471192770
ISBN 10:
0471192775
Edition: 2nd
Format: Paperback
Copyright: 11/01/1998
Publisher: Wiley

Rent

Sorry, this item is currently unavailable.

Note: Supplemental materials are not guaranteed with Rental or Used book purchases.

Extend or Purchase Your Rental at Any Time

Need to keep your rental past your due date? At any time before your due date you can extend or purchase your rental through your account.

Summary

This text stresses conceptual development and the logic of statistics for use in the Behavioral Sciences. It is designed for those who need to evaluate statistical findings. Coverage includes estimation procedures, independent samples t-test, Steven's scales of measurements, Pearson correlation coefficient. Updated to include Turkey's HSD test on factorial analysis of variance and Spearman's rho on assumption-free tests and all chapter have been edited to enhance clarity and flow, to simplify numbers in the worked problems, and to align all examples with the behavioral sciences.

Chapter 1 Introduction

(16)

1.1 Why Statistics?

(1)

1.2 Descriptive Statistics

(1)

1.3 Inferential Statistics

(1)

1.4 The Role of Statistics in Behavioral Research

(1)

1.5 Variables and Their Measurement

(4)

1.6 Approximate Numbers, Computational Accuracy, and Rounding

(1)

1.7 Computers, Statistical Analyses, and the Novice

(1)

1.8 Some Tips on Studying Statistics

(6)

PART I DESCRIPTIVE STATISTICS

(142)

Chapter 2 Frequency Distributions

(18)

2.1 Why Organize Data?

(1)

2.2 Frequency Distributions for Quantitative Variables

(2)

2.3 Grouped Scores

(1)

2.4 Some Guidelines for Forming Class Intervals

(1)

2.5 Constructing a Grouped-Data Frequency Distribution

(2)

2.6 The Relative Frequency Distribution

(1)

2.7 Exact Limits

(2)

2.8 The Cumulative Percentage Frequency Distribution

(1)

2.9 Percentile Scores and Percentile Ranks

(1)

2.10 Frequency Distributions for Qualitative Variables

(1)

2.11 Summary

(6)

Chapter 3 Graphic Representation

(16)

3.1 Why Graph Data?

(1)

3.2 Graphing Qualitative Data: The Bar Chart

(1)

3.3 Graphing Quantitative Data: The Histogram

(4)

3.4 The Frequency Polygon

(1)

3.5 Comparing Different Distributions

(1)

3.6 Relative Frequency and Proportional Area

(2)

3.7 Characteristics of Frequency Distributions

(3)

3.8 Summary

(4)

Chapter 4 Central Tendency

(12)

4.1 The Concept of Central Tendency

(1)

4.2 The Mode

(1)

4.3 The Median

(1)

4.4 The Arithmetic Mean

(3)

4.5 Central Tendency and Distribution Symmetry

(2)

4.6 Which Measure of Central Tendency to Use?

(1)

4.7 Summary

(4)

Chapter 5 Variability

(16)

5.1 Central Tendency Is Not Enough: The Importance of Variability

(1)

5.2 The Range

(2)

5.3 Variability and Deviations from the Mean

(1)

5.4 The Variance

(1)

5.5 The Standard Deviation

(3)

5.6 The Predominance of the Variance and Standard Deviation

(1)

5.7 The Standard Deviation and the Normal Distribution

(1)

5.8 Comparing Means of Two Distributions: The Relevance of Variability

(3)

5.9 Summary

(4)

Chapter 6 Normal Distributions and Standard Scores

(24)

6.1 A Little History: Sir Francis Galton and the Normal Curve

(1)

6.2 Properties of the Normal Curve

(2)

6.3 More on the Standard Deviation and the Normal Distribution

(1)

6.4 z Scores

(3)

6.5 The Normal Curve Table

(1)

6.6 Finding Area When the Score Is Known

(3)

6.7 Reversing the Process: Finding Scores When the Area Is Known

(3)

6.8 Comparing Scores from Different Distributions

(1)

6.9 Other Standard Scores

(1)

6.10 Interpreting Effect Size

(2)

6.11 Percentile Ranks and the Normal Distribution

(1)

6.12 The Normal Curve and Probability

(1)

6.13 Summary

(5)

Chapter 7 Correlation

103

(28)

7.1 The Concept of Association

103

(1)

7.2 Bivariate Distributions and Scatterplots

103

(5)

7.3 The Covariance

108

(7)

7.4 The Pearson r

115

(3)

7.5 Computation of r: The Calculating Formula

118

(1)

7.6 Correlation and Causation

119

(1)

7.7 Factors Influencing the Pearson r

120

(4)

7.8 Judging the Strength of Association

124

(2)

7.9 Other Correlation Coefficients

126

(1)

7.10 Summary

126

(5)

Chapter 8 Regression and Prediction

131

(28)

8.1 Correlation Versus Prediction

131

(1)

8.2 Determining the Line of Best Fit

132

(3)

8.3 The Regression Equation in Terms of Raw Scores

135

(3)

8.4 Interpreting the Raw-Score Slope

138

(1)

8.5 The Regression Equation in Terms of z Scores

139

(1)

8.6 Some Insights Regarding Correlation and Prediction

140

(3)

8.7 Regression and Sums of Squares

143

(2)

8.8 Measuring the Margin of Prediction Error: The Standard Error of Estimate

145

(5)

8.9 Correlation and Causality (Revisited)

150

(1)

8.10 Summary

151

(8)

PART 2 INFERENTIAL STATISTICS

159

(266)

Chapter 9 Probability and Probability Distributions

159

(18)

9.1 Statistical Inference: Accounting for Chance in Sample Results

159

(1)

9.2 Probability: The Study of Chance

160

(1)

9.3 Definition of Probability

161

(2)

9.4 Probability Distributions

163

(2)

9.5 The OR/addition Rule

165

(2)

9.6 The AND/multiplication Rule

167

(1)

9.7 The Normal Curve as a Probability Distribution

168

(2)

9.8 "So What?": Probability Distributions as the Basis for Statistical Inference

170

(1)

9.9 Summary

171

(6)

Chapter 10 Sampling Distributions

177

(22)

10.1 From Coins to Means

177

(1)

10.2 Samples and Populations

178

(1)

10.3 Statistics and Parameters

179

(1)

10.4 Random Sampling Model

180

(1)

10.5 Random Sampling in Practice

181

(1)

10.6 Sampling Distributions of Means

182

(2)

10.7 Characteristics of a Sampling Distribution of Means

184

(3)

10.8 Using a Sampling Distribution of Means to Determine Probabilities

187

(4)

10.9 The Importance of Sample Size (n)

191

(1)

10.10 Generality of the Concept of a Sampling Distribution

192

(1)

10.11 Summary

193

(6)

Chapter 11 Testing Statistical Hypotheses About Mu When XXX Is Known: The One-Sample z Test

199

(22)

11.1 Testing a Hypothesis About Mu: Does "Home Schooling" Make a Difference?

199

(1)

11.2 Dr. Meyer's Problem in a Nutshell

200

(1)

11.3 The Statistical Hypotheses: H(0) and H(1)

201

(2)

11.4 The Test Statistic: z

203

(1)

11.5 The Probability of the Test Statistic: The p Value

204

(1)

11.6 The Decision Criterion: Level of Significance (Alpha)

205

(2)

11.7 The Level of Significance and Decision Error

207

(2)

11.8 The Nature and Role of H(0) and H(1)

209

(1)

11.9 Rejection Versus Retention of H(0)

210

(1)

11.10 Statistical Significance Versus Importance

211

(2)

11.11 Directional and Nondirectional Alternative Hypotheses

213

(2)

11.12 Prologue: The Substantive Versus the Statistical

215

(1)

11.13 Summary

216

(5)

Chapter 12 Estimation

221

(12)

12.1 Hypothesis Testing Versus Estimation

221

(1)

12.2 Point Estimation Versus Interval Estimation

222

(1)

12.3 Constructing an Interval Estimate of Mu

223

(3)

12.4 Interval Width and Level of Confidence

226

(1)

12.5 Interval Width and Sample Size

227

(1)

12.6 Interval Estimation and Hypothesis Testing

227

(2)

12.7 Advantages of Interval Estimation

229

(1)

12.8 Summary

230

(3)

Chapter 13 Testing Statistical Hypotheses About Mu When XXX Is Not Known: The One-Sample t-Test

233

(18)

13.1 Reality: XXX Often Is Unknown

233

(1)

13.2 Estimating the Standard Error of the Mean

234

(2)

13.3 The Test Statistic, t

236

(1)

13.4 Degrees of Freedom

237

(1)

13.5 The Sampling Distribution of Student's t

238

(3)

13.6 An Application of Student's t

241

(2)

13.7 Assumption of Population Normality

243

(1)

13.8 Levels of Significance Versus p Values

243

(2)

13.9 Constructing a Confidence Interval for Mu When XXX Is Not Known

245

(1)

13.10 Summary

246

(5)

Chapter 14 Comparing the Means of Two Populations: Independent Samples

251

(24)

14.1 From One Mu to Two

251

(1)

14.2 Statistical Hypotheses

252

(1)

14.3 The Sampling Distribution of Differences Between Means

253

(2)

14.4 Estimating XXX X(1) - X(2)

255

(3)

14.5 The t Test for Two Independent Samples

258

(1)

14.6 Testing Hypotheses About Two Independent Population Means: An Example

259

(1)

14.7 Interval Estimation of Mu(1) - Mu(2)

260

(3)

14.8 How Meaningful is the Difference? Appraising the Magnitude of X(1) - X(2)

263

(3)

14.9 How Were Groups Formed? The Role of Randomization

266

(2)

14.10 Statistical Inferences and Nonstatistical Generalizations

268

(1)

14.11 Summary

269

(6)

Chapter 15 Comparing the Means of Dependent Samples

275

(18)

15.1 The Meaning of "Dependent"

275

(1)

15.2 Standard Error of the Difference Between Dependent Means

276

(2)

15.3 Degrees of Freedom

278

(1)

15.4 The t Test for Two Dependent Samples

278

(3)

15.5 Testing Hypotheses About Two Dependent Means: An Example

281

(4)

15.6 Interval Estimation Mu(D)

285

(1)

15.7 Summary

286

(7)

Chapter 16 Inferences About the Pearson Correlation Coefficient

293

(16)

16.1 From Mu to p

293

(1)

16.2 The Sampling Distribution of r when p = 0

293

(2)

16.3 Testing the Statistical Hypothesis That p = 0

295

(1)

16.4 An Example

295

(2)

16.5 Table C

297

(2)

16.6 The Role of n in the Statistical Significance of r

299

(1)

16.7 Statistical Significance Versus Importance (Again)

300

(1)

16.8 Testing Hypotheses Other Than p = 0

301

(1)

16.9 Interval Estimation of p

301

(3)

16.10 Summary

304

(5)

Chapter 17 Statistical "Power" (And How to Increase It)

309

(14)

17.1 The Power of a Statistical Test

309

(1)

17.2 Power and Type 2 Error

310

(1)

17.3 Effect Size (Revisited)

311

(1)

17.4 Factors Affecting Power: The Effect Size

312

(1)

17.5 Factors Affecting Power: Sample Size

313

(1)

17.6 Additional Factors Affecting Power

314

(2)

17.7 Significance Versus Importance

316

(1)

17.8 Selecting an Appropriate Sample Size

316

(4)

17.9 Summary

320

(3)

Chapter 18 Comparing the Means of Three or More Independent Samples: One-Way Analysis of Variance

323

(30)

18.1 Comparing More Than Two Groups: Way Not Multiple t Tests?

323

(1)

18.2 The Statistical Hypotheses in One-Way ANOVA

324

(1)

18.3 The Logic of One-Way ANOVA: An Overview

325

(3)

18.4 Alison's Reply of Gregory

328

(1)

18.5 Partition of Sums of Squares

329

(4)

18.6 Within-Groups and Between-Groups Variance Estimates

333

(1)

18.7 The F Test

334

(2)

18.8 Raw Score Formulas for One-Way ANOVA

336

(3)

18.9 Tukey's "HSD" Test

339

(3)

18.10 Interval Estimation of Mu(i) - Mu(j)

342

(1)

18.11 One-Way ANOVA: Summarizing the Steps

343

(2)

18.12 ANOVA Assumptions (and Other Considerations)

345

(1)

18.13 Summary

346

(7)

Chapter 19 Factorial Analysis of Variance: Two-Way ANOVA

353

(30)

19.1 "Factorial" Designs

353

(1)

19.2 The Logic of Two-Way ANOVA: An Overview

354

(1)

19.3 An Example of a "2 X 3" Design

355

(2)

19.4 Main Effects

357

(1)

19.5 Interaction

358

(3)

19.6 The Importance of Interaction

361

(1)

19.7 Assumptions (and Other Considerations)

362

(1)

19.8 Partitioning the Total Sum of Squares and Degrees of Freedom

363

(5)

19.9 Variance Estimates and F Tests

368

(2)

19.10 Two-Way ANOVA: Summarizing the Steps

370

(3)

19.11 Summary

373

(10)

Chapter 20 Chi-Square and Frequency Data

383

(24)

20.1 Frequency Data Versus Score Data

383

(1)

20.2 A Problem Involving Frequencies: The One-Variable Case

384

(1)

20.3 X(2): A Measure of Discrepancy Between Expected and Observed Frequencies

385

(2)

20.4 The Sampling Distribution of X(2)

387

(1)

20.5 Completion of the Voter Survey Problem: The X(2) Test

388

(1)

20.6 The Case of Two Categories: The X(2) Test of a Single Proportion

389

(2)

20.7 The Two-Variable Case and the Test of Independence: Contingency Tables

391

(1)

20.8 The Null Hypothesis of Independence

392

(2)

20.9 Finding Expected Frequencies in a Contingency Table and Calculating X(2)

394

(2)

20.10 The X(2) Test of Independence: Summarizing the Steps

396

(1)

20.11 The 2 X 2 Contingency Table

397

(2)

20.12 The Independence of Observations

399

(1)

20.13 X(2) and Quantitative Variables

399

(1)

20.14 Other Considerations

400

(1)

20.15 Summary

401

(6)

Chapter 21 Some (Almost) Assumption-Free Tests

407

(18)

21.1 Parametric Versus Nonparametric Tests

407

(1)

21.2 Placing Scores in Rank Order

408

(1)

21.3 Test of Location for Two Independent Groups: The Mann-Whitney Test

409

(3)

21.4 Test of Location Among Several Independent Groups: The Kruskal-Wallis Test

412

(3)

21.5 Test of Location for Two Dependent Groups: The Sign Test

415

(2)

21.6 Test of Association: The Spearman Rank Correlation

417

(2)

21.7 Summary

419

(6)

References

425

(2)

Appendices

427

(50)

Appendix A Review of Basic Mathematics

427

(6)

Appendix B Answers to Selected End-of-Chapter Problems

433

(26)

Appendix C Statistical Tables

459

(1)

Table A Areas Under the Normal Curve

460

(4)

Table B Student's t Distribution

464

(2)

Table C Critical Values of r

466

(1)

Table D The F Distribution

467

(3)

Table E The Studentized Range Statistic

470

(2)

Table F The X(2) Statistic

472

(2)

Table G Critical Values of SigmaR(1) for the Mann-Whitney Test

474

(2)

Table H Critical Values for the Spearman Rank Correlation

476

(1)

Index

477

Amazon no longer offers textbook rentals. We do!

Elements of Statistical Reasoning, 2nd Edition

Elements of Statistical Reasoning, 2nd Edition

9780471192770

0471192775

Summary

Author Biography

Table of Contents

Supplemental Materials

Write a Review