Statistics using IBM SPSS: an integrative approach
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
New York, NY
Cambridge University Press
2015
|
| Ausgabe: | Third edition |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis Inhaltsverzeichnis |
| Beschreibung: | vorherige Auflagen unter dem Titel: Statistics using SPSS. - Literaturverzeichnis: Seite 588-591 |
| Beschreibung: | xix, 606 Seiten Illustrationen, Diagramme |
| ISBN: | 1107461227 9781107461222 |
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Datensatz im Suchindex
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| adam_text | Contents
Preface page 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 DISTRIBUTIONS 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 * *
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
viii
CONTENTS
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 j 57
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 ^2
Other Visual Displays of Bivariate Relationships j ¿7
Selection of Appropriate Statistic/Graph to Summarize a Relationship j jq
Exercises j7j 6
6 SIMPLE LINEAR REGRESSION 183
The “Best-Fitting” Linear Equation j
The Accuracy of Prediction Using the Linear Regression Model , qn
CONTENTS ix
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 CIS KNOWN 264
Estimating the Population Mean, [i, When the Population Standard Deviation,
0, 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
X
CONTENTS
Type II Error and the Concept of Power 280
Increasing the Level of Significance, a 284
Increasing the Effect Size, 5 284
Decreasing the Standard Error of the Mean, 284
Closing Remarks 285
Exercises 286
11 INFERENCES INVOLVING THE MEAN WHEN a IS NOT KNOWN: ONE- AND
TWO-SAMPLE DESIGNS 290
Single Sample Designs When the Parameter of Interest Is the Mean and a
Is Not Known 290
The f Distribution 291
Degrees of Freedom for the One-Sample f-Test 292
Violating the Assumption of a Normally Distributed Parent Population in the
One-Sample i-Test 293
Confidence Intervals for the One-Sample i-Test 294
Hypothesis Tests: The One-Sample f-Test 298
Effect Size for the One-Sample f-Test 300
Two Sample Designs When the Parameter of Interest Is ¡л, and a 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 f-Test 307
Effect Size for the Independent Samples f-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 f-Test 323
The Bootstrap 323
Summary 327
Exercises 328
12 RESEARCH 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
CONTENTS
xi
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 i-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 CORRELATION 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, p 451
Revisiting Simple Linear Regression for Prediction 454
Estimating the Population Standard Error of Prediction, ay|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 R2 To a2r^ 463
xii
CONTENTS
Testing R2 for 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, j% and RYn When the Predictors Are Not Correlated 493
Equations for b, fi, and RYl2 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 R2 Directly: 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 (y2) 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
CONTENTS xffi
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
;·· ֊
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| spelling | Weinberg, Sharon Lawner 1947- Verfasser (DE-588)1050691148 aut Statistics using SPSS Statistics using IBM SPSS an integrative approach Sharon Lawner Weinberg, New York University, Sarah Knapp Abramowitz, Drew University Third edition New York, NY Cambridge University Press 2015 xix, 606 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier vorherige Auflagen unter dem Titel: Statistics using SPSS. - Literaturverzeichnis: Seite 588-591 Mathematical statisticsxData processing Mathematical statisticsvTextbooks Statistik (DE-588)4056995-0 gnd rswk-swf SPSS (DE-588)4056588-9 gnd rswk-swf SPSS (Computer file) SPSS (DE-588)4056588-9 s Statistik (DE-588)4056995-0 s DE-604 Abramowitz, Sarah Knapp 1967- Sonstige (DE-588)173601332 oth DE-601 pdf/application http://www.gbv.de/dms/bowker/toc/9781107461222.pdf Inhaltsverzeichnis Digitalisierung UB Bamberg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029135735&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Weinberg, Sharon Lawner 1947- Statistics using IBM SPSS an integrative approach Mathematical statisticsxData processing Mathematical statisticsvTextbooks Statistik (DE-588)4056995-0 gnd SPSS (DE-588)4056588-9 gnd |
| subject_GND | (DE-588)4056995-0 (DE-588)4056588-9 |
| title | Statistics using IBM SPSS an integrative approach |
| title_alt | Statistics using SPSS |
| title_auth | Statistics using IBM SPSS an integrative approach |
| title_exact_search | Statistics using IBM SPSS an integrative approach |
| title_full | Statistics using IBM SPSS an integrative approach Sharon Lawner Weinberg, New York University, Sarah Knapp Abramowitz, Drew University |
| title_fullStr | Statistics using IBM SPSS an integrative approach Sharon Lawner Weinberg, New York University, Sarah Knapp Abramowitz, Drew University |
| title_full_unstemmed | Statistics using IBM SPSS an integrative approach Sharon Lawner Weinberg, New York University, Sarah Knapp Abramowitz, Drew University |
| title_short | Statistics using IBM SPSS |
| title_sort | statistics using ibm spss an integrative approach |
| title_sub | an integrative approach |
| topic | Mathematical statisticsxData processing Mathematical statisticsvTextbooks Statistik (DE-588)4056995-0 gnd SPSS (DE-588)4056588-9 gnd |
| topic_facet | Mathematical statisticsxData processing Mathematical statisticsvTextbooks Statistik SPSS |
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