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Produktbild: Using Statistics in the Social and Health Sciences with SPSS and Excel

Using Statistics in the Social and Health Sciences with SPSS and Excel

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Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

29.08.2016

Verlag

John Wiley & Sons

Seitenzahl

592

Maße (L/B/H)

24/16,1/3,6 cm

Gewicht

1035 g

Auflage

1. Auflage

Sprache

Englisch

ISBN

978-1-119-12104-6

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

29.08.2016

Verlag

John Wiley & Sons

Seitenzahl

592

Maße (L/B/H)

24/16,1/3,6 cm

Gewicht

1035 g

Auflage

1. Auflage

Sprache

Englisch

ISBN

978-1-119-12104-6

Herstelleradresse

Libri GmbH
Europaallee 1
36244 Bad Hersfeld
DE

Email: gpsr@libri.de

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  • Produktbild: Using Statistics in the Social and Health Sciences with SPSS and Excel
  • Preface xv

    Acknowledgments xix

    1 Introduction 1

    Big Data Analysis 1

    Visual Data Analysis 2

    Importance of Statistics for the Social and Health Sciences and Medicine 3

    Historical Notes: Early Use of Statistics 4

    Approach of the Book 6

    Cases from Current Research 7

    Research Design 9

    Focus on Interpretation 9

    2 Descriptive Statistics: Central Tendency 13

    What is the Whole Truth? Research Applications (Spuriousness) 13

    Descriptive and Inferential Statistics 16

    The Nature of Data: Scales of Measurement 16

    Descriptive Statistics: Central Tendency 23

    Using SPSS® and Excel to Understand Central Tendency 28

    Distributions 35

    Describing the Normal Distribution: Numerical Methods 37

    Descriptive Statistics: Using Graphical Methods 41

    Terms and Concepts 47

    Data Lab and Examples (with Solutions) 49

    Data Lab: Solutions 51

    3 Descriptive Statistics: Variability 55

    Range 55

    Percentile 56

    Scores Based on Percentiles 57

    Using SPSS® and Excel to Identify Percentiles 57

    Standard Deviation and Variance 60

    Calculating the Variance and Standard Deviation 61

    Population SD and Inferential SD 66

    Obtaining SD from Excel and SPSS® 67

    Terms and Concepts 70

    Data Lab and Examples (with Solutions) 71

    Data Lab: Solutions 73

    4 The Normal Distribution 77

    The Nature of the Normal Curve 77

    The Standard Normal Score: Z Score 79

    The Z Score Table of Values 80

    Navigating the Z Score Distribution 81

    Calculating Percentiles 83

    Creating Rules for Locating Z Scores 84

    Calculating Z Scores 87

    Working with Raw Score Distributions 90

    Using SPSS® to Create Z Scores and Percentiles 90

    Using Excel to Create Z Scores 94

    Using Excel and SPSS® for Distribution Descriptions 97

    Terms and Concepts 99

    Data Lab and Examples (with Solutions) 99

    Data Lab: Solutions 101

    5 Probability and the Z Distribution 105

    The Nature of Probability 106

    Elements of Probability 106

    Combinations and Permutations 109

    Conditional Probability: Using Bayes' Theorem 111

    Z Score Distribution and Probability 112

    Using SPSS® and Excel to Transform Scores 117

    Using the Attributes of the Normal Curve to Calculate Probability 119

    "Exact" Probability 123

    From Sample Values to Sample Distributions 126

    Terms and Concepts 127

    Data Lab and Examples (with Solutions) 128

    Data Lab: Solutions 129

    6 Research Design and Inferential Statistics 133

    Research Design 133

    Experiment 136

    Non-Experimental or Post Facto Research Designs 140

    Inferential Statistics 143

    Z Test 154

    The Hypothesis Test 154

    Statistical Significance 156

    Practical Significance: Effect Size 156

    Z Test Elements 156

    Using SPSS® and Excel for the Z Test 157

    Terms and Concepts 158

    Data Lab and Examples (with Solutions) 161

    Data Lab: Solutions 162

    7 The T Test for Single Samples 165

    Introduction 166

    Z Versus T: Making Accommodations 166

    Research Design 167

    Parameter Estimation 169

    The T Test 173

    The T Test: A Research Example 176

    Interpreting the Results of the T Test for a Single Mean 180

    The T Distribution 181

    The Hypothesis Test for the Single Sample T Test 182

    Type I and Type II Errors 183

    Effect Size 187

    Effect Size for the Single Sample T Test 187

    Power Effect Size and Beta 188

    One- and Two-Tailed Tests 189

    Point and Interval Estimates 192

    Using SPSS® and Excel with the Single Sample T Test 196

    Terms and Concepts 201

    Data Lab and Examples (with Solutions) 201

    Data Lab: Solutions 203

    8 Independent Sample T Test 207

    A Lot of "Ts" 207

    Research Design 208

    Experimental Designs and the Independent T Test 208

    Dependent Sample Designs 209

    Between and Within Research Designs 210

    Using Different T Tests 211

    Independent T Test: The Procedure 213

    Creating the Sampling Distribution of Differences 215

    The Nature of the Sampling Distribution of Differences 216

    Calculating the Estimated Standard Error of Difference with Equal Sample Size 218

    Using Unequal Sample Sizes 219

    The Independent T Ratio 221

    Independent T Test Example 222

    Hypothesis Test Elements for the Example 222

    Before-After Convention with the Independent T Test 226

    Confidence Intervals for the Independent T Test 227

    Effect Size 228

    The Assumptions for the Independent T Test 230

    SPSS® Explore for Checking the Normal Distribution Assumption 231

    Excel Procedures for Checking the Equal Variance Assumption 233

    SPSS® Procedure for Checking the Equal Variance Assumption 237

    Using SPSS® and Excel with the Independent T Test 239

    SPSS® Procedures for the Independent T Test 239

    Excel Procedures for the Independent T Test 243

    Effect Size for the Independent T Test Example 245

    Parting Comments 245

    Nonparametric Statistics: The Mann-Whitney U Test 246

    Terms and Concepts 249

    Data Lab and Examples (with Solutions) 249

    Data Lab: Solutions 251

    Graphics in the Data Summary 254

    9 Analysis of Variance 255

    A Hypothetical Example of ANOVA 255

    The Nature of ANOVA 257

    The Components of Variance 258

    The Process of ANOVA 259

    Calculating ANOVA 260

    Effect Size 268

    Post Hoc Analyses 269

    Assumptions of ANOVA 274

    Additional Considerations with ANOVA 275

    The Hypothesis Test: Interpreting ANOVA Results 276

    Are the Assumptions Met? 276

    Using SPSS® and Excel with One-Way ANOVA 282

    The Need for Diagnostics 289

    Non-Parametric ANOVA Tests: The Kruskal-Wallis Test 289

    Terms and Concepts 292

    Data Lab and Examples (with Solutions) 293

    Data Lab: Solutions 294

    10 Factorial ANOVA 297

    Extensions of ANOVA 297

    ANCOVA 298

    MANOVA 299

    MANCOVA 299

    Factorial ANOVA 299

    Interaction Effects 299

    Simple Effects 301

    2XANOVA: An Example 302

    Calculating Factorial ANOVA 303

    The Hypotheses Test: Interpreting Factorial ANOVA Results 306

    Effect Size for 2XANOVA: Partial ;;2 308

    Discussing the Results 309

    Using SPSS® to Analyze 2XANOVA 311

    Summary Chart for 2XANOVA Procedures 319

    Terms and Concepts 319

    Data Lab and Examples (with Solutions) 320

    Data Lab: Solutions 320

    11 Correlation 329

    The Nature of Correlation 330

    The Correlation Design 331

    Pearson's Correlation Coefficient 332

    Plotting the Correlation: The Scattergram 334

    Using SPSS® to Create Scattergrams 337

    Using Excel to Create Scattergrams 339

    Calculating Pearson's r 341

    The Z Score Method 342

    The Computation Method 344

    The Hypothesis Test for Pearson's r 345

    Effect Size: the Coefficient of Determination 347

    Diagnostics: Correlation Problems 349

    Correlation Using SPSS® and Excel 352

    Nonparametric Statistics: Spearman's Rank Order Correlation (rs) 358

    Terms and Concepts 363

    Data Lab and Examples (with Solutions) 364

    Data Lab: Solutions 365

    12 Bivariate Regression 371

    The Nature of Regression 372

    The Regression Line 374

    Calculating Regression 376

    Effect Size of Regression 379

    The Z Score Formula for Regression 380

    Testing the Regression Hypotheses 382

    The Standard Error of Estimate 383

    Confidence Interval 385

    Explaining Variance Through Regression 386

    A Numerical Example of Partitioning the Variation 389

    Using Excel and SPSS® with Bivariate Regression 390

    The SPSS® Regression Output 390

    The Excel Regression Output 396

    Complete Example of Bivariate Linear Regression 398

    Assumptions of Bivariate Regression 398

    The Omnibus Test Results 404

    Effect Size 404

    The Model Summary 405

    The Regression Equation and Individual Predictor Test of Significance 405

    Advanced Regression Procedures 406

    Detecting Problems in Bivariate Linear Regression 408

    Terms and Concepts 409

    Data Lab and Examples (with Solutions) 410

    Data Lab: Solutions 411

    13 Introduction to Multiple Linear Regression 417

    The Elements of Multiple Linear Regression 417

    Same Process as Bivariate Regression 418

    Some Differences between Bivariate Linear Regression and Multiple Linear Regression 419

    Stuff not Covered 420

    Assumptions of Multiple Linear Regression 421

    Analyzing Residuals to Check MLR Assumptions 422

    Diagnostics for MLR: Cleaning and Checking Data 423

    Extreme Scores 424

    Distance Statistics 428

    Influence Statistics 429

    MLR Extended Example Data 430

    Assumptions Met? 431

    Analyzing Residuals: Are Assumptions Met? 433

    Interpreting the SPSS® Findings for MLR 436

    Entering Predictors Together as a Block 437

    Entering Predictors Separately 442

    Additional Entry Methods for MLR Analyses 447

    Example Study Conclusion 448

    Terms and Concepts 448

    Data Lab and Example (with Solution) 450

    Data Lab: Solution 450

    14 Chi-Square and Contingency Table Analysis 455

    Contingency Tables 455

    The Chi-square Procedure and Research Design 456

    Chi-square Design One: Goodness of Fit 457

    A Hypothetical Example: Goodness of Fit 458

    Effect Size: Goodness of Fit 462

    Chi-square Design Two: The Test of Independence 463

    A Hypothetical Example: Test of Independence 464

    Special 2 × 2 Chi-square 468

    Effect Size in 2 × 2 Tables: PHI 470

    Cramer's V: Effect Size for the Chi-square Test of Independence 471

    Repeated Measures Chi-square: Mcnemar Test 472

    Using SPSS® and Excel with Chi-square 474

    Using SPSS® for the Chi-square Test of Independence 475

    Using Excel for Chi-square Analyses 481

    Terms and Concepts 483

    Data Lab and Examples (with Solutions) 483

    Data Lab: Solutions 484

    15 Repeated Measures Procedures: Tdep and ANOVAWS 489

    Independent and Dependent Samples in Research Designs 490

    Using Different T Tests 491

    The Dependent T Test Calculation: The "Long" Formula 491

    Example: The Long Formula 492

    The Dependent T Test Calculation: The "Difference" Formula 494

    Tdep and Power 496

    Conducting The Tdep Analysis Using SPSS® 496

    Conducting The Tdep Analysis Using Excel 498      

    Within-Subject ANOVA (ANOVAWS) 498

    Experimental Designs 499

    Post Facto Designs 500

    Within-Subject Example 501

    Using SPSS® for Within-Subject Data 501

    The SPSS® Procedure 502

    The SPSS® Output 504

    Nonparametric Statistics 508

    Terms and Concepts 508

    Appendices

    Appendix A SPSS® Basics 509

    Using SPSS® 509

    General Features 510

    Management Functions 513

    Additional Management Functions 517

    Appendix B Excel Basics 531

    Data Management 531

    The Excel Menus 533

    Using Statistical Functions 541

    Data Analysis Procedures 543

    Missing Values and "0" Values in Excel Analyses 544

    Using Excel with "Real Data" 544

    Appendix C Statistical Tables 545

    Table C.1: Z-Score Table (Values Shown are Percentages - %) 545

    Table C.2: Exclusion Values for the T-Distribution 547

    Table C.3: Critical (Exclusion) Values for the Distribution of F 548

    Table C.4: Tukey's Range Test (Upper 5% Points) 551

    Table C.5: Critical (Exclusion) Values for Pearson's Correlation Coefficient r 552

    Table C.6: Critical Values of the ;;2 (Chi-Square) Distribution 553

    References 555

    Index 557