Data analysis for the behavioral sciences using SPSS:
"This text is written from the perspective that statistics is an integrated set of tools used to uncover the story contained in numerical data. The presentation is informal, yet rigorous; it is based on a conceptual approach supported by an understanding of underlying mathematical foundations....
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cambridge, UK
Cambridge University Press
2002
|
| Schlagworte: | |
| Online-Zugang: | Publisher description Table of contents Inhaltsverzeichnis |
| Zusammenfassung: | "This text is written from the perspective that statistics is an integrated set of tools used to uncover the story contained in numerical data. The presentation is informal, yet rigorous; it is based on a conceptual approach supported by an understanding of underlying mathematical foundations. Students learn that more than one method of analysis is typically needed and that an ample characterization of results is a critical component of any data analytic plan. The use of real data contained in an accompanying disk not only enables a greater emphasis on conceptual understanding and interpretation, but also allows students to study statistics in a way that reflects statistical practice."--BOOK JACKET. |
| Beschreibung: | Includes bibliographical references (p. 591-592) and index |
| Beschreibung: | XIV, 737 S. graph. Darst. 1 floppy disk (3 1/2 in.) |
| ISBN: | 0521632080 0521635616 |
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| 264 | 1 | |a Cambridge, UK |b Cambridge University Press |c 2002 | |
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| 520 | 1 | |a "This text is written from the perspective that statistics is an integrated set of tools used to uncover the story contained in numerical data. The presentation is informal, yet rigorous; it is based on a conceptual approach supported by an understanding of underlying mathematical foundations. Students learn that more than one method of analysis is typically needed and that an ample characterization of results is a critical component of any data analytic plan. The use of real data contained in an accompanying disk not only enables a greater emphasis on conceptual understanding and interpretation, but also allows students to study statistics in a way that reflects statistical practice."--BOOK JACKET. | |
| 630 | 0 | 4 | |a SPSS (Computer file) |
| 650 | 4 | |a SPSS (Logiciel) | |
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Datensatz im Suchindex
| _version_ | 1804129317428396032 |
|---|---|
| adam_text | Preface page xiii
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
Nominal Level 3
Ordinal Level 4
Interval Level 4
Ratio Level 5
Choosing a Scale of Measurement 5
Discrete and Continuous Variables 7
Setting a Context with Real Data 10
Exercises 11
2 EXAMINING UNIVARIATE DISTRIBUTIONS 17
Counting the Occurrence of Data Values 17
When Variables are Measured at the Nominal Level 17
Frequency and Percent Distribution Tables, 17* Bar
Graphs, 18 • Pie Graphs, 21
When Variables are Measured at the Ordinal, Interval, or Ratio Level 22
Frequency and Percent Distribution Tables, 22 • Stem and Leaf
Displays, 24 • Histograms, 27 • Line Graphs, 29
Describing the Shape of a Distribution 30
Accumulating Data 32
Cumulative Percent Distributions 33
Ogive Curves 33
Percentile Ranks 34
Percentiles 35
Five Number Summaries and Boxplots 37
Exercises 43
3 MEASURES OF LOCATION, SPREAD, AND SKEWNESS 61
Characterizing the Location of a Distribution 61
The Mode 61
The Median 64
The Arithmetic Mean 66
Comparing the Mode, Median, and Mean 68
CONTENTS
Characterizing the Spread of a Distribution 70
The Range and Interquartile Range 73
The Variance 75
The Standard Deviation 77
Characterizing the Skewness of a Distribution 78
Selecting Measures of Location and Spread 79
Applying What We Have Learned 80
Exercises 84
4 RE EXPRESSING VARIABLES 95
Linear and Nonlinear Transformations 95
Linear Transformations: Addition, Subtraction, Multiplication, and Division 96
The Effect on the Shape of a Distribution 98
The Effect on Summary Statistics of a Distribution 98
Common Linear Transformations 100
Standard Scores 101
z Scores 102
Using z Scores to Detect Outliers, 105 • Using z Scores to Compare Scores in
Different Distributions, 105 • Relating z Scores to Percentile Ranks, 106
Nonlinear Transformations: Square Roots and Logarithms 107
Nonlinear Transformations: Ranking Variables 114
Other Transformations: Recoding and Combining Variables 115
Recoding Variables 115
Combining Variables 117
Exercises 117
5 EXPLORING RELATIONSHIPS BETWEEN TWO VARIABLES 126
When Both Variables Are at Least Interval Leveled 126
Scatterplots 128
The Pearson Product Moment Correlation Coefficient 131
Interpreting the Pearson Correlation Coefficient, 135 • The Effect of Linear
Transformations, 137 • Restriction of Range, 137 • The Shape of the
Underlying Distribution, 138 • The Reliability of the Data, 138
When One Variable Is Ordinal and the Other Is At Least Ordinal:
The Spearman Rank Correlation Coefficient 139
When at Least One Variable Is Dichotomous: Other Special Cases of the
Pearson Correlation Coefficient 140
The Point Biserial Correlation Coefficient: The Case of One At Least Interval and
One Dichotomous Variable 140
The Phi Coefficient: The Case of Two Dichotomous Variables 144
Other Visual Displays of Bivariate Relationships 149
Exercises 153
6 SIMPLE LINEAR REGRESSION 168
The Best Fitting Linear Equation 168
The Accuracy of Prediction Using the Linear Regression Model 174
The Standardized Regression Equation 175
R as a Measure of the Overall Fit of the Linear Regression Model 175
Simple Linear Regression When the Independent Variable Is Dichotomous 179
Using r and R as Measures of Effect Size 181
CONTENTS
Emphasizing the Importance of the Scatterplot 181
Exercises 183
7 PROBABILITY FUNDAMENTALS 195
The Discrete Case 195
The Complement Rule of Probability 197
The Additive Rules of Probability 197
First Additive Rule of Probability 198
Second Additive Rule of Probability 199
The Multiplicative Rule of Probability 200
The Relationship between Independence and Mutual Exclusivity 202
Conditional Probability 203
The Law of Large Numbers 204
Exercises 205
8 THEORETICAL PROBABILITY MODELS 209
The Binomial Probability Model and Distribution 209
The Applicability of the Binomial Probability Model 214
The Normal Probability Model and Distribution 218
Using the Normal Distribution to Approximate the Binomial Distribution 224
Exercises 224
9 THE ROLE OF SAMPLING IN INFERENTIAL STATISTICS 231
Samples and Populations 231
Random Samples 232
Obtaining a Simple Random Sample 233
Sampling with and without Replacement 235
Sampling Distributions 237
Describing the Sampling Distribution of Means Empirically 237
Describing the Sampling Distribution of Means Theoretically: The Central
Limit Theorem 240
Central Limit Theorem (CLT) 241
Estimators and Bias 244
Exercises 245
10 INFERENCES INVOLVING THE MEAN OF A SINGLE POPULATION WHEN a
IS KNOWN 248
Estimating the Population Mean, /x, When the Population Standard Deviation,
a, Is Known 248
Interval Estimation 250
Relating the Length of a Confidence Interval, the Level of Confidence, and
the Sample Size 252
Hypothesis Testing 253
The Relationship between Hypothesis Testing and Interval Estimation 261
Effect Size 262
Type II Error and the Concept of Power 263
Increasing the Level of Significance 267
Increasing the Effect Size 267
Decreasing the Standard Error of the Mean 267
CONTENTS
Closing Remarks 268
Exercises 269
11 INFERENCES INVOLVING THE MEAN WHEN a IS NOT KNOWN:
ONE AND TWO SAMPLE DESIGNS 273
Single Sample Designs When the Parameter of Interest Is the Mean and a Is
Not Known 273
The f Distribution 274
Degrees of Freedom for the One Sample f Test 275
Violating the Assumption of a Normally Distributed Parent Population in the One
Sample f Test 276
Confidence Intervals for the One Sample f Test 277
Hypothesis Tests: The One Sample f Test 281
Effect Size for the One Sample f Test 283
Two Sample Designs When the Parameter of Interest Is jl and CTls Not Known 287
Independent (or Unrelated) and Dependent (or Related) Samples 288
Independent Samples f Test and Confidence Interval 289
The Assumptions of the Independent Samples f Test 291
Effect Size for the Independent Samples f Test, 299
Paired Samples f Test and Confidence Interval 302
The Assumptions of the Paired Samples f Test 303
Effect Size for the Paired Samples f Test 307
Summary 308
The Standard Error of the Mean Difference for Independent Samples: A More
Complete Account (OPTIONAL) 309
Case 1: a Known 309
Case 2: ex Not Known 313
Step 1: Estimating Using the Variance Estimators ] and
cH, 313 • Step 2: Estimating the Standard Error of the Mean Difference,
o^ Using o2, 314
Exercises 315
12 ONE WAY ANALYSIS OF VARIANCE 337
The Disadvantage of Multiple t Tests 337
The One Way Analysis of Variance 339
A Graphical Illustration of the Role of Variance in Tests on Means 339
ANOVA as an Extension of the Independent Groups f Test 340
Developing an Index of Separation for the Analysis of Variance 341
Carrying out the ANOVA Computation 341
The Between Group Variance, 342 • The Within Group Variance, 343
The Assumptions of the One Way ANOVA 343
Testing the Equality of Population Means: The F Ratio 344
How to Read the Tables and Use the SPSS Compute Statement for the F
Distribution 346
ANOVA Summary Table 349
Measuring the Effect Size 350
Post Hoc Multiple Comparison Tests 352
The Bonferroni Adjustment: Testing Planned Comparisons 363
The Bonferroni Tests on Multiple Measures 365
Exercises 366
CONTENTS
13 TWO WAY ANALYSIS OF VARIANCE 373
The Two Factor Design 373
The Concept of Interaction 377
The Hypotheses that are Tested by a Two Way Analysis of Variance 381
Assumptions of the Two Way Analysis of Variance 381
Balanced Versus Unbalanced Factorial Designs 383
Partitioning the Total Sum of Squares 383
Using the F Ratio to Test the Effects in Two Way ANOVA 384
Carrying out the Two Way ANOVA Computation by Hand 384
Decomposing Score Deviations about the Grand Mean 389
Modeling Each Score as a Sum of Component Parts 390
Explaining the Interaction as a Joint (or Multiplicative) Effect 390
Measuring Effect Size 391
Fixed Versus Random Factors 395
Post Hoc Multiple Comparison Tests 396
Summary of Steps to be Taken in a Two Way ANOVA Procedure 401
Exercises 405
14 CORRELATION AND SIMPLE REGRESSION AS INFERENTIAL TECHNIQUES 419
The Bivariate Normal Distribution 419
Testing Whether the Population Pearson Product Moment Correlation Equals Zero 422
Using a Confidence Interval to Estimate the Size of the Population Correlation
Coefficient, p 425
Revisiting Simple Linear Regression for Prediction 429
Estimating the Population Standard Error of Prediction, oY x 429
Testing the fr Weight for Statistical Significance 430
Explaining Simple Regression Using an Analysis of Variance Framework 434
Measuring the Fit of the Overall Regression Equation: Using R and R~ 436
Relating R2 to cfY x 437
Testing R2 for Statistical Significance 438
Estimating the True Population R2: The Adjusted R2 439
Exploring the Goodness of Fit of the Regression Equation: Using Regression
Diagnostics 440
Residual Plots: Evaluating the Assumptions Underlying Regression, 441 •
Detecting Influential Observations: Discrepancy and Leverage, 444 •
Using SPSS to Obtain Leverage, 445 • Using SPSS to Obtain
Discrepancy, 445 • Using SPSS to Obtain Influence, 446 • Using Diagnostics
to Evaluate the Ice Cream Sales Example, 447
Using the Prediction Model to Predict Ice Cream Sales 450
Simple Regression When the Predictor Is Dichotomous 450
Exercises 452
15 AN INTRODUCTION TO MULTIPLE REGRESSION 469
The Basic Equation with Two Predictors 470
Equations for b, /}, and RYA2 When the Predictors Are Not Correlated 471
Equations for b, f), and RYU When the Predictors Are Correlated 472
Summarizing and Expanding on Some Important Principles of Multiple Regression 474
Testing the b Weights for Statistical Significance 479
CONTENTS
Assessing the Relative Importance of the Independent Variables in the Equation 480
Measuring the Drop in R2 Directly: An Alternative to the Squared Part Correlation 481
Evaluating the Statistical Significance of the Change in R2 481
The b Weight as a Partial Slope in Multiple Regression 482
Multiple Regression When One of the Two Independent Variables is Dichotomous 485
The Concept of Interaction between Two Variables that are At Least Interval Leveled 488
Testing the Statistical Significance of an Interaction Using SPSS 490
Centering First Order Effects to Achieve Meaningful Interpretations of b Weights 494
Understanding the Nature of a Statistically Significant Two Way Interaction 494
Interaction When One of the Independent Variables is Dichotomous and the
Other is Continuous 497
Putting It All Together: A Student Project Reprinted 501
Measuring the Variables 501
Examining the Variables Individually and in Pairs 502
Examining the Variables Multivariately with Mathematics Achievement as the
Criterion 505
Exercises 509
16 NONPARAMETRIC METHODS 527
Parametric Versus Nonparametric Methods 527
Nonparametric Methods When the Dependent Variable is at the Nominal Level 528
The Chi Square Distribution (j2) 528
The Chi Square Goodness of Fit Test 531
The Chi Square Test of Independence 535
Assumptions of the Chi Square Test of Independence, 539
Nonparametric Methods When the Dependent Variable is Ordinal Leveled 542
The Sign Test 543
The Mann Whitney U Test 545
The Kruskal Wallis Analysis of Variance 549
Exercises 551
APPENDIX A. DATA SET DESCRIPTIONS 559
Anscombe.sav 559
Basket.sav 559
Blood.sav 559
Brainsz.sav 560
Colleges.sav 560
Currency.sav 561
Hamburg.sav 561
HR.sav 561
Icecream.sav 562
Impeach.sav 562
Learndis.sav 563
Mandex. sav 563
Marijuan.sav 564
NELS.sav 564
Skulls.sav 568
States.sav 568
Stress.sav 559
CONTENTS
Temp.sav 569
Wages.sav 570
APPENDIX B. SPSS MACRO TO GENERATE A SAMPLING DISTRIBUTION OF MEANS 571
APPENDIX C. STATISTICAL TABLES 573
Table 1. Areas Under the Standard Normal Curve (to the Right of the z Score) 573
Table 2. t Distribution Values for Right Tailed Areas 574
Table 3. F Distribution Values for Right Tailed Areas 575
Table 4. Binomial Distribution Table 580
Table 5. Chi Square Distribution Table for Right Tailed Areas 585
Table 6. The Critical q Values 586
Table 7. The Critical [/ Values 587
APPENDIX D. REFERENCES 591
APPENDIX E. SOLUTIONS TO EXERCISES 593
Chapter 1 Solutions 593
Chapter 2 Solutions 595
Chapter 3 Solutions 610
Chapter 4 Solutions 620
Chapter 5 Solutions 625
Chapter 6 Solutions 638
Chapter 7 Solutions 644
Chapter 8 Solutions 645
Chapter 9 Solutions 648
Chapter 10 Solutions 650
Chapter 11 Solutions 651
Chapter 12 Solutions 668
Chapter 13 Solutions 676
Chapter 14 Solutions 687
Chapter 15 Solutions 697
Chapter 16 Solutions 716
Index 723
|
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| spelling | Weinberg, Sharon Lawner Verfasser aut Data analysis for the behavioral sciences using SPSS Sharon Lawner Weinberg ; Sarah Knapp Abramowitz Cambridge, UK Cambridge University Press 2002 XIV, 737 S. graph. Darst. 1 floppy disk (3 1/2 in.) txt rdacontent n rdamedia nc rdacarrier Includes bibliographical references (p. 591-592) and index "This text is written from the perspective that statistics is an integrated set of tools used to uncover the story contained in numerical data. The presentation is informal, yet rigorous; it is based on a conceptual approach supported by an understanding of underlying mathematical foundations. Students learn that more than one method of analysis is typically needed and that an ample characterization of results is a critical component of any data analytic plan. The use of real data contained in an accompanying disk not only enables a greater emphasis on conceptual understanding and interpretation, but also allows students to study statistics in a way that reflects statistical practice."--BOOK JACKET. SPSS (Computer file) SPSS (Logiciel) Data-analyse gtt Dataprocessing gtt SPSS gtt Sociale wetenschappen gtt Statistique mathématique - Informatique Datenverarbeitung Sozialwissenschaften Mathematical statistics Data processing Floppy disks Abramowitz, Sarah Knapp Sonstige oth http://www.loc.gov/catdir/description/cam021/2001035262.html Publisher description http://www.loc.gov/catdir/toc/cam025/2001035262.html Table of contents HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=009886674&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Weinberg, Sharon Lawner Data analysis for the behavioral sciences using SPSS SPSS (Computer file) SPSS (Logiciel) Data-analyse gtt Dataprocessing gtt SPSS gtt Sociale wetenschappen gtt Statistique mathématique - Informatique Datenverarbeitung Sozialwissenschaften Mathematical statistics Data processing |
| title | Data analysis for the behavioral sciences using SPSS |
| title_auth | Data analysis for the behavioral sciences using SPSS |
| title_exact_search | Data analysis for the behavioral sciences using SPSS |
| title_full | Data analysis for the behavioral sciences using SPSS Sharon Lawner Weinberg ; Sarah Knapp Abramowitz |
| title_fullStr | Data analysis for the behavioral sciences using SPSS Sharon Lawner Weinberg ; Sarah Knapp Abramowitz |
| title_full_unstemmed | Data analysis for the behavioral sciences using SPSS Sharon Lawner Weinberg ; Sarah Knapp Abramowitz |
| title_short | Data analysis for the behavioral sciences using SPSS |
| title_sort | data analysis for the behavioral sciences using spss |
| topic | SPSS (Computer file) SPSS (Logiciel) Data-analyse gtt Dataprocessing gtt SPSS gtt Sociale wetenschappen gtt Statistique mathématique - Informatique Datenverarbeitung Sozialwissenschaften Mathematical statistics Data processing |
| topic_facet | SPSS (Computer file) SPSS (Logiciel) Data-analyse Dataprocessing SPSS Sociale wetenschappen Statistique mathématique - Informatique Datenverarbeitung Sozialwissenschaften Mathematical statistics Data processing Floppy disks |
| url | http://www.loc.gov/catdir/description/cam021/2001035262.html http://www.loc.gov/catdir/toc/cam025/2001035262.html http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=009886674&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT weinbergsharonlawner dataanalysisforthebehavioralsciencesusingspss AT abramowitzsarahknapp dataanalysisforthebehavioralsciencesusingspss |