Statistics using IBM SPSS : an integrative approach

목차 (Table of Contents)

• CONTENTS
• Preface = xv
• Acknowledgments = xix
• 1 Introduction = 1
• The Role of the Computer in Data Analysis = 1
• Statistics : Descriptive and Inferential = 2
• Variables and Constants = 3
• The Measurement of Variables = 3
• Discrete and Continuous Variables = 8
• Setting a Context with Real Data = 11
• Exercises = 12
• 2 Examining Univariate Distribu tions = 20
• Counting the Occurrence of Data Values = 20
• When Variables Are Measured at the Nominal Level = 20
• Frequency and Percent Distribution Tables = 20
• Bar Graphs = 21
• Pie Graphs = 23
• When Variables Are Measured at the Ordinal, Interval, or Ratio Level = 25
• Frequency and Percent Distribution Tables = 25
• Stem-and-Leaf Displays = 27
• Histograms = 30
• Line Graphs = 33
• Describing the Shape of a Distribution = 36
• Accumulating Data = 38
• Cumulative Percent Distributions = 38
• Ogive Curves = 38
• Percentile Ranks = 39
• Percentiles = 40
• Five-Number Summaries and Boxplots = 43
• Summary of Graphical Selection = 49
• Exercises = 49
• 3 Measures of Location, Spread, and Skewness = 65
• Characterizing the Location of a Distribution = 65
• The Mode = 65
• The Median = 69
• The Arithmetic Mean = 70
• Interpreting the Mean of a Dichotomous Variable = 72
• The Weighted Mean = 73
• Comparing the Mode, Median, and Mean = 74
• Characterizing the Spread of a Distribution = 76
• The Range and Interquartile Range = 79
• The Variance = 80
• The Standard Deviation = 83
• Characterizing the Skewness of a Distribution = 84
• Selecting Measures of Location and Spread = 86
• Applying What We Have Learned = 86
• Exercises = 90
• 4 Re -expressing Variables = 99
• Linear and Nonlinear Transformations = 99
• Linear Transformations : Addition, Subtraction, Multiplication, and Division = 100
• The Effect on the Shape of a Distribution = 101
• The Effect on Summary Statistics of a Distribution = 102
• Common Linear Transformations = 106
• Standard Scores = 107
• z-Scores = 109
• Using z-Scores to Detect Outliers = 111
• Using z-Scores to Compare Scores in Different Distributions = 112
• Relating z-Scores to Percentile Ranks = 115
• Nonlinear Transformations : Square Roots and Logarithms = 115
• Nonlinear Transformations : Ranking Variables = 123
• Other Transformations : Recoding and Combining Variables = 124
• Recoding Variables = 124
• Combining Variables = 126
• Data Management Fundamentals – The Syntax File = 126
• Exercises = 130
• 5 Exploring Relationships between Two Variables = 138
• When Both Variables Are at Least Interval-Leveled = 138
• Scatterplots = 139
• The Pearson Product Moment Correlation Coefficient = 147
• Interpreting the Pearson Correlation Coefficient = 152
• The Correlation Scale Itself Is Ordinal = 153
• Correlation Does Not Imply Causation = 153
• The Effect of Linear Transformations = 154
• Restriction of Range = 154
• The Shape of the Underlying Distributions = 155
• The Reliability of the Data = 155
• When at Least One Variable Is Ordinal and the Other Is at Least Ordinal : The Spearman Rank Correlation Coefficient = 155
• When at Least One Variable Is Dichotomous : Other Special Cases of the Pearson Correlation Coefficient = 157
• The Point Biserial Correlation Coefficient : The Case of One at Least Interval and One Dichotomous Variable = 157
• The Phi Coefficient : The Case of Two Dichotomous Variables = 162
• Other Visual Displays of Bivariate Relationships = 167
• Selection of Appropriate Statistic／Graph to Summarize a Relationship = 170
• Exercises = 171
• 6 Simple Linear Regression = 183
• The "Best-Fitting" Linear Equation = 183
• The Accuracy of Prediction Using the Linear Regression Model = 190
• The Standardized Regression Equation = 191
• R as a Measure of the Overall Fit of the Linear Regression Model = 191
• Simple Linear Regression When the Independent Variable Is Dichotomous = 196
• Using r and R as Measures of Effect Size = 199
• Emphasizing the Importance of the Scatterplot = 199
• Exercises = 201
• 7 Probability Fundamentals = 210
• The Discrete Case = 210
• The Complement Rule of Probability = 212
• The Additive Rules of Probability = 213
• First Additive Rule of Probability = 213
• Second Additive Rule of Probability = 214
• The Multiplicative Rule of Probability = 215
• The Relationship between Independence and Mutual Exclusivity = 218
• Conditional Probability = 218
• The Law of Large Numbers = 220
• Exercises = 220
• 8 Theoretical Probability Models = 223
• The Binomial Probability Model and Distribution = 223
• The Applicability of the Binomial Probability Model = 228
• The Normal Probability Model and Distribution = 232
• Using the Normal Distribution to Approximate the Binomial Distribution = 238
• Exercises = 239
• 9 The Role of Sampling in Inferential Statistics = 245
• Samples and Populations = 245
• Random Samples = 246
• Obtaining a Simple Random Sample = 247
• Sampling with and without Replacement = 249
• Sampling Distributions = 250
• Describing the Sampling Distribution of Means Empirically = 251
• Describing the Sampling Distribution of Means Theoretically : The Central Limit Theorem = 255
• Central Limit Theorem (CLT) = 255
• Estimators and Bias = 259
• Exercises = 260
• 10 Inferences Involving the Mean of a Single Population When σ Is Known = 264
• Estimating the Population Mean, μ, When the Population Standard Deviation,σ, Is Known = 264
• Interval Estimation = 266
• Relating the Length of a Confidence Interval = the Level of Confidence, and the Sample Size = 269
• Hypothesis Testing = 270
• The Relationship between Hypothesis Testing and Interval Estimation = 278
• Effect Size = 279
• Type Ⅱ Error and the Concept of Power = 280
• Increasing the Level of Significance, α = 284
• Increasing the Effect Size, δ = 284
• Decreasing the Standard Error of the Mean, σ$$His_{x}$$ = 284
• Closing Remarks = 285
• Exercises = 286
• 11 Inferences Involving the Mean When σ Is Not Known : One- and Two-Sample Designs = 290
• Single Sample Designs When the Parameter of Interest Is the Mean and σ Is Not Known = 290
• The t Distribution = 291
• Degrees of Freedom for the One-Sample t-Test = 292
• Violating the Assumption of a Normally Distributed Parent Population in the One-Sample t-Test = 293
• Confidence Intervals for the One-Sample t-Test = 294
• Hypothesis Tests : The One-Sample t-Test = 298
• Effect Size for the One-Sample t-Test = 300
• Two Sample Designs When the Parameter of Interest Is μ, and σ Is Not Known = 303
• Independent (or Unrelated) and Dependent (or Related) Samples = 304
• Independent Samples t-Test and Confidence Interval = 305
• The Assumptions of the Independent Samples t-Test = 307
• Effect Size for the Independent Samples t-Test = 315
• Paired Samples t-Test and Confidence Interval = 317
• The Assumptions of the Paired Samples t-Test = 318
• Effect Size for the Paired Samples t-Test = 323
• The Bootstrap = 323
• Summary = 327
• Exercises = 328
• 12 Rese arch Design : Introduction and Overview = 346
• Questions and Their Link to Descriptive, Relational, and Causal Research Studies = 346
• The Need for a Good Measure of Our Construct, Weight = 346
• The Descriptive Study = 347
• From Descriptive to Relational Studies = 348
• From Relational to Causal Studies = 348
• The Gold Standard of Causal Studies : The True Experiment and Random Assignment = 350
• Comparing Two Kidney Stone Treatments Using a Non-randomized Controlled Study = 351
• Including Blocking in a Research Design = 352
• Underscoring the Importance of Having a True Control Group Using Randomization = 353
• Analytic Methods for Bolstering Claims of Causality from Observational Data (Optional Reading) = 357
• Quasi-Experimental Designs = 359
• Threats to the Internal Validity of a Quasi-Experimental Design = 360
• Threats to the External Validity of a Quasi-Experimental Design = 361
• Threats to the Validity of a Study : Some Clarifications and Caveats = 361
• Threats to the Validity of a Study : Some Examples = 362
• Exercises = 363
• 13 One-Way Analysis of Variance = 367
• The Disadvantage of Multiple t-Tests = 367
• The One-Way Analysis of Variance = 369
• A Graphical Illustration of the Role of Variance in Tests on Means = 369
• ANOVA as an Extension of the Independent Samples t-Test = 370
• Developing an Index of Separation for the Analysis of Variance = 371
• Carrying Out the ANOVA Computation = 372
• The Between Group Variance (MSB) = 372
• The Within Group Variance (MSW) = 373
• The Assumptions of the One-Way ANOVA = 374
• Testing the Equality of Population Means : The F-Ratio = 375
• How to Read the Tables and to Use the SPSS Compute Statement for the F Distribution = 375
• ANOVA Summary Table = 380
• Measuring the Effect Size = 380
• Post-hoc Multiple Comparison Tests = 384
• The Bonferroni Adjustment : Testing Planned Comparisons = 394
• The Bonferroni Tests on Multiple Measures = 396
• Exercises = 399
• 14 Two-Way Analysis of Variance = 404
• The Two-Factor Design = 404
• The Concept of Interaction = 407
• The Hypotheses That Are Tested by a Two-Way Analysis of Variance = 412
• Assumptions of the Two-Way Analysis of Variance = 413
• Balanced versus Unbalanced Factorial Designs = 414
• Partitioning the Total Sum of Squares = 414
• Using the F-Ratio to Test the Effects in Two-Way ANOVA = 415
• Carrying Out the Two-Way ANOVA Computation by Hand = 416
• Decomposing Score Deviations about the Grand Mean = 420
• Modeling Each Score as a Sum of Component Parts = 421
• Explaining the Interaction as a Joint (or Multiplicative) Effect = 421
• Measuring Effect Size = 422
• Fixed versus Random Factors = 426
• Post-hoc Multiple Comparison Tests = 426
• Summary of Steps to Be Taken in a Two-Way ANOVA Procedure = 432
• Exercises = 437
• 15 C orrelation and Simple Regression as Inferential Techniques = 445
• The Bivariate Normal Distribution = 445
• Testing Whether the Population Pearson Product Moment Correlation Equals Zero = 448
• Using a Confidence Interval to Estimate the Size of the Population Correlation Coefficient, ρ = 451
• Revisiting Simple Linear Regression for Prediction = 454
• Estimating the Population Standard Error of Prediction, σ Y
• X = 455
• Testing the b-Weight for Statistical Significance = 456
• Explaining Simple Regression Using an Analysis of Variance Framework = 459
• Measuring the Fit of the Overall Regression Equation : Using R and R²= 462
• Relating R²To σ²Y
• X = 463
• Testing R²for Statistical Significance = 463
• Estimating the True Population R²: The Adjusted R²= 464
• Exploring the Goodness of Fit of the Regression Equation : Using Regression Diagnostics = 465
• Residual Plots : Evaluating the Assumptions Underlying Regression = 467
• Detecting Influential Observations : Discrepancy and Leverage = 470
• Using SPSS to Obtain Leverage = 472
• Using SPSS to Obtain Discrepancy = 472
• Using SPSS to Obtain Influence = 473
• Using Diagnostics to Evaluate the Ice Cream Sales Example = 474
• Using the Prediction Model to Predict Ice Cream Sales = 478
• Simple Regression When the Predictor Is Dichotomous = 478
• Exercises = 479
• 16 An Introduction to Multiple Regression = 491
• The Basic Equation with Two Predictors = 492
• Equations for b, β, and R$$His_{Y}$$.₁₂ When the Predictors Are Not Correlated = 493
• Equations for b, β, and R$$His_{Y}$$.₁₂ When the Predictors Are Correlated = 494
• Summarizing and Expanding on Some Important Principles of Multiple Regression = 496
• Testing the b-Weights for Statistical Significance = 501
• Assessing the Relative Importance of the Independent Variables in the Equation = 503
• Measuring the Drop in R²Directly : An Alternative to the Squared Part Correlation = 504
• Evaluating the Statistical Significance of the Change in R²= 504
• The b-Weight as a Partial Slope in Multiple Regression = 505
• Multiple Regression When One of the Two Independent Variables Is Dichotomous = 508
• The Concept of Interaction between Two Variables That Are at Least Interval-Leveled = 514
• Testing the Statistical Significance of an Interaction Using SPSS = 516
• Centering First-Order Effects to Achieve Meaningful Interpretations of b-Weights = 521
• Understanding the Nature of a Statistically Significant Two-Way Interaction = 521
• Interaction When One of the Independent Variables Is Dichotomous and the Other Is Continuous = 524
• Exercises = 528
• 17 Nonparametric Methods = 539
• Parametric versus Nonparametric Methods = 539
• Nonparametric Methods When the Dependent Variable Is at the Nominal Level = 540
• The Chi-Square Distribution (χ²) = 540
• The Chi-Square Goodness-of-Fit Test = 542
• The Chi-Square Test of Independence = 547
• Assumptions of the Chi-Square Test of Independence = 550
• Fisher's Exact Test = 552
• Calculating the Fisher Exact Test by Hand Using the Hypergeometric Distribution = 554
• Nonparametric Methods When the Dependent Variable Is Ordinal-Leveled = 558
• Wilcoxon Sign Test = 558
• The Mann-Whitney U Test = 561
• The Kruskal-Wallis Analysis of Variance = 565
• Exercises = 567
• Appendix A : Data Set Descriptions = 573
• Appendix B : Generating Distributions for Chapters 8 and 9 Using SPSS Syntax = 586
• Appendix C : Statistical Tables = 587
• Appendix D : References = 588
• Appendix E : Solutions to Exercises = 592
• Appendix F : The Standard Error of the Mean Difference for Independent Samples : A More Complete Account (Optional) = 593
• Index = 595