Research Design Explained

, by Mitchell, Mark L.; Jolley, Janina M.

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ISBN: 9780155075054 | 0155075055
Cover: Hardcover
Copyright: 8/1/2000

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Celebrated for its easy-to-grasp, student-oriented approach, this book puts research design into a practical context that teaches students how to use theory and gives novices the practical advice they need. The book encourages students to value, read, and conduct ethical research. The authors introduce ethical issues in Chapter 1 and discuss ethics throughout the book. It includes an entire chapter on how to read research.

Preface

iii

Psychology and Science

(46)

Overview

(1)

Why Psychology Uses the Scientific Approach

(15)

The Characteristics of Science

(6)

The Characteristics of Psychology

(7)

The Importance of Science to Psychology

(2)

Questions About Applying Techniques From Physical Sciences to Psychology

(16)

Internal Validity Questions: Did the Treatment Cause a Change in Behavior?

(3)

Construct Validity Questions: Making the Leap From the Physical World to the Mental World?

(5)

External Validity Questions: Can the Results Be Generalized?

(1)

Ethical Questions: Should the Study Be Conducted?

(5)

Conclusions About the Questions That Researchers Face

(1)

Why You Should Understand Research Design

(5)

To Understand Psychology

(1)

To Read Research

(1)

To Evaluate Research

(1)

To Protect Yourself From ``Quacks''

(1)

To Be a Better Thinker

(1)

To Be Scientifically Literate

(1)

To Increase Your Marketability

(1)

To Do Your Own Research

(1)

Concluding Remarks

(1)

Summary

(2)

Key Terms

(1)

Exercises

(5)

Generating and Refining Research Hypotheses

(24)

Overview

(1)

Generating Research Ideas From Common Sense

(2)

Generating Research Ideas From Previous Research

(4)

Specific Strategies

(3)

Conclusions About Generating Research Ideas From Previous Research

(1)

Converting an Idea Into a Research Hypothesis

(10)

Make It Testable

(1)

Make It Supportable

(1)

Be Sure to Have a Rationale: How Theory Can Help

(1)

Demonstrate Its Relevance: Theory Versus Trivia

(1)

Refine It: 10 Time-Tested Tips

(5)

Make Sure That Testing the Hypothesis Is Both Practical and Ethical

(1)

Changing Unethical and Impractical Ideas Into Research Hypotheses

(3)

Make Variables More General

(1)

Use Smaller Scale Models of the Situation

(1)

Carefully Screen Potential Participants

(1)

Use ``Moderate'' Manipulations

(1)

Do Not Manipulate Variables

(1)

Concluding Remarks

(1)

Summary

(1)

Key Terms

(1)

Exercises

(2)

Measuring and Manipulating Variables: Reliability And Validity

(49)

Overview

(1)

Choosing a Behavior to Measure

(1)

Errors in Measuring Behavior

(13)

Overview of Types of Errors

(2)

Two Types of Observer Errors

(4)

Errors in Administering the Measure

(1)

Errors Due to the Participant

(4)

Summary of Types of Measurement Error

(1)

Reliability: The (Relative) Absence of Random Error

(11)

The Importance of Being Reliable: Reliability as a Prerequisite to Validity

(1)

Using Test-Retest Reliability to Assess Overall Reliability: To What Degree Is a Measure ``Random Error Free''?

(2)

Identifying (and Then Dealing With) the Main Source of a Measure's Reliability Problems

(8)

Conclusions About Reliability

(1)

Beyond Reliability: Establishing Construct Validity

(10)

Content Validity: Does Your Test Have the Right Stuff?

(1)

Internal Consistency Revisited: Evidence That You Are Measuring One Characteristic

(2)

Convergent Validation Strategies: Statistical Evidence That You Are Measuring the Right Construct

101

(2)

Discriminant Validation Strategies: Showing That You Are Not Measuring the Wrong Construct

103

(3)

Summary of Construct Validity

106

(1)

Manipulating Variables

107

(7)

Common Threats to a Manipulation's Validity

108

(1)

Evidence Used to Argue for a Manipulation's Construct Validity

109

(1)

Tradeoffs Among Three Common Types of Manipulations

110

(3)

Manipulating Variables: Conclusions

113

(1)

Concluding Remarks

114

(1)

Summary

114

(1)

Key Terms

115

(2)

Exercises

117

(3)

Beyond Reliability and Validity: Choosing the Best Measure for Your Study

120

(23)

Overview

121

(1)

Sensitivity: Will the Measure Be Able to Detect the Differences You Need to Detect?

122

(6)

Achieving The Necessary Level of Sensitivity

122

(5)

Sensitivity: Conclusions

127

(1)

Scales of Measurement: Will the Measure Allow You to Make the Kinds of Comparisons You Need to Make?

128

(10)

The Different Scales of Measurement

128

(4)

Why Our Numbers Do Not Always Measure Up

132

(1)

Which Level of Measurement Do You Need?

133

(4)

Conclusions About Scales of Measurement

137

(1)

Ethical and Practical Considerations

138

(1)

Concluding Remarks

139

(1)

Summary

139

(1)

Key Terms

140

(1)

Exercises

141

(2)

Internal Validity

143

(32)

Overview

144

(1)

Two-Group Designs

145

(12)

Why We Never Have Identical Groups

145

(11)

Conclusions About Two-Group Designs

156

(1)

Problems With the Pretest-Posttest Design

157

(7)

Three Reasons Participants May Change Between Pretest and Posttest

158

(3)

How Measurement Changes May Cause Scores to Change Between Pretest and Pretest

161

(3)

Conclusions About Trying to Keep Everything Except the Treatment Constant

164

(1)

Ruling out Extraneous Variables

165

(3)

Accounting for Extraneous Variables

166

(1)

Identifying Extraneous Variables

167

(1)

The Relationship Between Internal and External Validity

168

(2)

Concluding Remarks

170

(1)

Summary

170

(1)

Key Terms

171

(1)

Exercises

172

(3)

The Simple Experiment

175

(48)

Overview

176

(1)

Basic Logic and Terminology

177

(13)

Experimental Hypothesis: The Treatment Has an Effect

177

(3)

Null Hypothesis: The Treatment Does Not Have an Effect

180

(1)

Conclusions About Experimental and Null Hypotheses

181

(1)

Manipulating the Independent Variable

181

(1)

Experimental and Control Groups: Similar, but Treated Differently

182

(1)

The Value of Independence: Why Control and Experimental Groups Shouldn't Really Be ``Groups''

182

(2)

The Value of Assignment (Manipulating the Treatment)

184

(2)

Collecting the Dependent Variable

186

(1)

The Statistical Significance Decision: Deciding Whether to Declare That a Difference Is Not a Coincidence

186

(1)

Statistically Significant Results: Declaring That the Treatment Has a Reliable Effect

186

(2)

Null Results: Why We Can't Draw Conclusions From Nonsignificant Results

188

(2)

Summary of the ``Ideal'' Simple Experiment

190

(1)

Errors in Determining Whether Results Are Statistically Significant

190

(3)

Type 1 Errors: ``Crying Wolf''

190

(2)

Type 2 Errors: ``Failing to Announce the Wolf''

192

(1)

The Need to Prevent Type 2 Errors: Why You Want the Power to Find Significant Differences

193

(1)

Statistics and the Design of the Simple Experiment

193

(5)

Power and the Design of the Simple Experiment

194

(3)

Conclusions About How Statistical Considerations Impact Design Decisions

197

(1)

Nonstatistical Considerations and the Design of the Simple Experiment

198

(3)

External Validity Versus Power

198

(1)

Construct Validity Versus Power

199

(2)

Ethics Versus Power

201

(1)

Analyzing Data From the Simple Experiment: Basic Logic

201

(10)

Estimating What You Want to Know: Your Means Are Sample Means

202

(2)

Why We Must Do More Than Subtract the Means From Each Other

204

(1)

How Random Error Affects Data From the Simple Experiment

205

(2)

When Is a Difference Too Big to Be Due to Random Error?

207

(4)

Analyzing the Results of the Simple Experiment: The t-Test

211

(2)

Using the r-Table

211

(1)

Assumptions of the t-Test

212

(1)

Questions Raised by Results

213

(2)

Questions Raised by Nonsignificant Results

214

(1)

Questions Raised by Significant Results

214

(1)

Concluding Remarks

215

(1)

Summary

215

(2)

Key Terms

217

(3)

Exercises

220

(3)

Expanding the Simple Experiment: The Multiple-Group Experiment

223

(36)

Overview

124

(100)

The Advantages of Using More Than Two Values of an Independent Variable

224

(15)

Comparing More Than Two Kinds of Treatments

224

(2)

Comparing Two Kinds of Treatments With No Treatment

226

(1)

Comparing More Than Two Levels (Amounts) of an Independent Variable to Increase External Validity

226

(7)

Using Multiple Levels to Improve Construct Validity

233

(6)

Analysis of Multiple-Group Experiments

239

(13)

Analyzing the Multiple-Group Experiment: An Intuitive Overview

240

(2)

A Closer Look ac the Analysis of a Multiple-Group Experiment

242

(10)

Concluding Remarks

252

(1)

Summary

253

(1)

Key Terms

254

(2)

Exercises

256

(3)

Expanding the Simple Experiment: Factorial Designs

259

(56)

Overview

260

(1)

The 2 x 2 Factorial Design

260

(12)

How One Experiment Can Do as Much as Two

261

(1)

How One Experiment Can Do More Than Two

262

(8)

Example of Questions Answered by the 2 x 2 Factorial Experiment

270

(2)

Potential Results of a 2 x 2 Factorial Experiment

272

(14)

A Main Effect and No Interaction

273

(5)

Two Main Effects and No Interaction

278

(2)

Two Main Effects and an Interaction

280

(1)

Interaction Without Main Effects

281

(2)

One Main Effect and an Interaction

283

(2)

No Main Affects and No Interaction

285

(1)

Analyzing the Results From a 2 x 2 Experiment

286

(14)

What Degrees of Freedom Tell You

286

(2)

Interpreting the Results of an ANOVA Table

288

(12)

Putting the 2 x 2 to Work

300

(5)

Adding a Replication Factor to Increase Generalizability

301

(1)

Using an Interaction to Find an Exception to the Rule: Looking at a Potential Moderating Factor

302

(1)

Using Interactions to Create New Rules

303

(2)

The Hybrid Design: A Factorial Design That Allows You to Study Nonexperimental Variables

305

(4)

Increasing Generalizability

307

(1)

Studying Effects of Similarity

308

(1)

Finding an Exception to the Rule

308

(1)

Concluding Remarks

309

(1)

Summary

309

(2)

Key Terms

311

(1)

Exercises

312

(3)

Within-Subjects Designs

315

(40)

Overview

316

(1)

The Matched-Pairs Design

317

(6)

Procedure

317

(1)

Considerations in Using Matched-Pairs Designs

317

(5)

Analysis of Data

322

(1)

Summary of the Matched-Pairs Design

322

(1)

Within-Subjects (Repeated Measures) Designs

323

(8)

Considerations in Using Within-Subjects Designs

323

(5)

Dealing With Order Effects

328

(3)

Randomized Within-Subjects Designs

331

(2)

Procedure

331

(1)

Analysis of Data

332

(1)

Summary of Randomized Within-Subjects Designs

333

(1)

Counterbalanced Within-Subjects Designs

333

(12)

Procedure

334

(1)

Advantages and Disadvantages of Counterbalancing

334

(10)

Conclusions About Counterbalanced Within-Subjects Designs

344

(1)

Choosing Designs

345

(5)

Choosing Designs: The Two-Conditions Case

345

(1)

Choosing Designs: When You Have More Than One Independent Variable

346

(4)

Concluding Remarks

350

(1)

Summary

350

(1)

Key Terms

351

(2)

Exercises

353

(2)

Reading and Evaluating Research

355

(28)

Overview

356

(1)

Reading for Understanding

356

(12)

Choosing an Article

356

(1)

Reading the Abstract

357

(1)

Reading the Introduction

357

(4)

Reading the Method Section

361

(2)

Reading the Results Section

363

(4)

Reading the Discussion

367

(1)

Developing Research Ideas From Existing Research

368

(12)

The Direct Replication

368

(5)

The Systematic Replication

373

(4)

The Conceptual Replication

377

(1)

The Value of Replications

378

(1)

Extending Research

379

(1)

Concluding Remarks

380

(1)

Summary

380

(1)

Key Terms

381

(1)

Exercises

382

(1)

Single-N Designs And Quasi-Experiments

383

(43)

Overview

384

(1)

Inferring Causality in Randomized Experiments

384

(2)

Establishing Covariation

384

(1)

Establishing Temporal Precedence

385

(1)

Battling Spuriousness

385

(1)

Single-n Designs

386

(14)

Keeping Nontreatment Factors Constant: The A-B Design

387

(4)

Variations on the A-B Design

391

(5)

Evaluation of Single-n Designs

396

(3)

Conclusions About Single-n Designs

399

(1)

Quasi-Experiments

400

(20)

The Problem: Accounting for Nontreatment Factors

401

(5)

Time-Series Designs

406

(8)

The Nonequivalent Control-Group Design

414

(5)

Conclusions About Quasi-Experimental Designs

419

(1)

Concluding Remarks

420

(1)

Summary

420

(2)

Key Terms

422

(3)

Exercises

425

(1)

Introduction to Descriptive Methods

426

(42)

Overview

427

(1)

Uses and Limitations of Descriptive Methods

427

(5)

Descriptive Research and Causality

428

(2)

Description for Description's Sake

430

(1)

Description for Prediction's Sake

431

(1)

Why We Need Science to Describe Behavior

432

(3)

We Need Scientific Measurement

432

(1)

We Need Systematic, Scientific Record-Keeping

433

(1)

We Need Objective Ways to Determine If Variables Are Related

433

(1)

We Need Scientific Methods to Generalize From Experience

434

(1)

Conclusions About the Need for Descriptive Research

435

(1)

Sources of Data

435

(8)

Data You Previously Collected

435

(2)

Archival Data

437

(3)

Observation

440

(2)

Tests

442

(1)

Describing Data From Correlational Studies

443

(10)

Graphing Data

444

(3)

Correlation Coefficients

447

(6)

Summary of Describing Correlational Data

453

(1)

Making Inferences From Correlational Data

453

(10)

Analyses Based on Correlation Coefficients

454

(2)

Analyses Not Involving Correlation Coefficients

456

(4)

Interpreting Significant Results

460

(2)

Interpreting Null Results

462

(1)

Concluding Remarks

463

(1)

Summary

463

(2)

Key Terms

465

(1)

Exercises

466

(2)

Survey Research

468

(43)

Overview

469

(1)

Questions to Ask Before Doing Survey Research

470

(7)

What Is Your Hypothesis?

470

(4)

Can Self Report Provide Accurate Answers?

474

(2)

To Whom Will Your Results Apply?

476

(1)

Conclusions About the Advantages and Disadvantages of Survey Research

477

(1)

The Advantages and Disadvantages of Different Survey Instruments

477

(6)

Written Instruments

477

(3)

Interviews

480

(3)

Planning a Survey

483

(16)

Deciding on a Research Question

483

(1)

Choosing the Format of Your Questions

484

(4)

Choosing the Format of Your Survey

488

(1)

Editing Questions: Nine Mistakes to Avoid

489

(3)

Sequencing Questions

492

(2)

Putting the Final Touches on Your Survey Instrument

494

(1)

Choosing a Sampling Strategy

495

(4)

Administering the Survey

499

(1)

Analyzing Survey Data

500

(6)

Summarizing Data

500

(3)

Using Inferential Statistics

503

(3)

Concluding Remarks

506

(1)

Summary

506

(1)

Key Terms

507

(3)

Exercises

510

(1)

Putting It All Together: Writing Research Proposals and Reports

511

Overview

512

(1)

Aids to Developing Your Idea

512

(2)

The Research Journal

512

(1)

The Research Proposal

513

(1)

Writing the Research Proposal

514

(21)

General Strategies for Writing the Introduction

514

(4)

Specific Strategies for Writing Introduction Sections for Different Types of Studies

518

(6)

Writing the Method Section

524

(3)

Writing the Results Section

527

(1)

Writing the Discussion Section

528

(2)

Putting on the Front and Back

530

(5)

Writing the Research Report

535

(5)

What Stays the Same of Changes Very Little

535

(1)

Writing the Results Section

535

(4)

Writing the Discussion Section

539

(1)

Concluding Remarks

540

(1)

Summary

540

(1)

Key Terms

541

Appendix A Ethics

A-2

Appendix B Searching the Literature (Electronically and the Old-Fashioned Way)

B-1

Appendix C Conducting a Study

C-1

Appendix D Sample Research Paper

D-1

Appendix E Statistics and Random Numbers Tables

E-1

Appendix F Introduction to Statistics

F-1

Glossary

G-1

References

R-1

Credits

CR-1

Index

I-1

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Research Design Explained

Summary

Table of Contents

Supplemental Materials