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Statistics Using SPSS: An Integrative Approach 2nd Revised edition [Kõva köide]

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(New York University), (Drew University, New Jersey)
  • Formaat: Hardback, 780 pages, kõrgus x laius x paksus: 262x202x38 mm, kaal: 1690 g
  • Ilmumisaeg: 03-Mar-2008
  • Kirjastus: Cambridge University Press
  • ISBN-10: 0521899222
  • ISBN-13: 9780521899222
Teised raamatud teemal:
Statistics Using SPSS: An Integrative Approach 2nd Revised editionSuurem pilt
  • Formaat: Hardback, 780 pages, kõrgus x laius x paksus: 262x202x38 mm, kaal: 1690 g
  • Ilmumisaeg: 03-Mar-2008
  • Kirjastus: Cambridge University Press
  • ISBN-10: 0521899222
  • ISBN-13: 9780521899222
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780521899222.html
Märksõnad:
Applied statistics text updated to be consistent with SPSS version 15, ideal for classroom use or self study.

This is an introductory applied statistics text that can be used for a one- or two-semester course at either the undergraduate or graduate level. Central features are its hands-on approach; the use of real data; the wealth of exercises and illustrated examples using these data; the complete set of detailed answers to exercises in an appendix; the presentation of statistical methods with a clear, conceptual emphasis that includes an historical account of each method; and the integration of SPSS in a way that reflects statistical practice. Step-by-step instructions for using SPSS are provided as each new analytic procedure is introduced. A data CD is included with the text so that students may conduct their own statistical analyses and learn firsthand how statistics is used in practice.

Arvustused

'A hands-on approach and a wealth of exercises and examples immerse the reader in the process of data analysis from the very beginning. Students and practitioners alike will benefit from the book's careful, gentle prose, its use of real data, and its step-by-step demonstrations of analytical techniques in SPSS. Perhaps best of all, the authors remain firmly grounded in application.' Sean P. Corcoran, Steinhardt School of Education, New York University

Muu info

Applied statistics text updated to be consistent with SPSS version 15, ideal for classroom use or self study.
Preface xiii
Acknowledgments xv
Introduction
1(19)
The Role of the Computer in Data Analysis
1(1)
Statistics: Descriptive and Inferential
2(1)
Variables and Constants
2(1)
The Measurement of Variables
3(5)
Discrete and Continuous Variables
8(3)
Setting a Context with Real Data
11(1)
Exercises
12(8)
Examining Univariate Distributions
20(40)
Counting the Occurrence of Data Values
20(15)
When Variables Are Measured at the Nominal Level
20(1)
Bar Graphs
21(2)
Pie Graphs
23(1)
When Variables Are Measured at the Ordinal, Interval, or Ratio Level
24(1)
Frequency and Percent Distribution Tables
24(2)
Stem-and-Leaf Displays
26(3)
Histograms
29(2)
Line Graphs
31(2)
Describing the Shape of a Distribution
33(2)
Accumulating Data
35(10)
Cumulative Percent Distributions
35(1)
Ogive Curves
35(1)
Percentile Ranks
36(1)
Percentiles
37(3)
Five-Number Summaries and Boxplots
40(5)
Exercises
45(15)
Measures of Location, Spread, and Skewness
60(31)
Characterizing the Location of a Distribution
60(10)
The Mode
60(3)
The Median
63(2)
The Arithmetic Mean
65(2)
Comparing the Mode, Median, and Mean
67(3)
Characterizing the Spread of a Distribution
70(7)
The Range and Interquartile Range
72(2)
The Variance
74(2)
The Standard Deviation
76(1)
Characterizing the Skewness of a Distribution
77(1)
Selecting Measures of Location and Spread
78(1)
Applying What We Have Learned
79(3)
Exercises
82(9)
Re-Expressing Variables
91(30)
Linear and Nonlinear Transformations
91(1)
Linear Transformations: Addition, Subtraction, Multiplication, and Division
91(12)
The Effect on the Shape of a Distribution
93(2)
The Effect on Summary Statistics of a Distribution
95(1)
Common Linear Transformations
95(2)
Standard Scores
97(1)
z-Scores
98(2)
Using z-Scores to Detect Outliers
100(1)
Using z-Scores to Compare Scores in Different Distributions
101(1)
Relating z-Scores to Percentile Ranks
102(1)
Nonlinear Transformations: Square Roots and Logarithms
103(7)
Nonlinear Transformations: Ranking Variables
110(1)
Other Transformations: Recoding and Combining Variables
111(3)
Recoding Variables
111(2)
Combining Variables
113(1)
Exercises
114(7)
Exploring Relationships Between Two Variables
121(37)
When Both Variables Are at Least Interval-Leveled
121(12)
Scatterplots
122(4)
The Pearson Product Moment Correlation Coefficient
126(4)
Interpreting the Pearson Correlation Coefficient
130(2)
The Effect of Linear Transformations
132(1)
Restriction of Range
132(1)
The Shape of the Underlying Distributions
133(1)
The Reliability of the Data
133(1)
When at Least One Variable Is Ordinal and the Other Is at Least Ordinal: The Spearman Rank Correlation Coefficient
133(2)
When at Least One Variable Is Dichotomous: Other Special Cases of the Pearson Correlation Coefficient
135(9)
The Point Biserial Correlation Coefficient: The Case of One at-Least-Interval and One Dichotomous Variable
135(5)
The Phi Coefficient: The Case of Two Dichotomous Variables
140(4)
Other Visual Displays of Bivariate Relationships
144(3)
Selection of Appropriate Statistic/Graph to Summarize a Relationship
147(1)
Exercises
148(10)
Simple Linear Regression
158(24)
The ``Best-Fitting'' Linear Equation
158(6)
The Accuracy of Prediction Using the Linear Regression Model
164(1)
The Standardized Regression Equation
165(1)
Ras a Measure of the Overall Fit of the Linear Regression Model
165(4)
Simple Linear Regression When the Independent Variable Is Dichotomous
169(3)
Using r and Ras Measures of Effect Size
172(1)
Emphasizing the Importance of the Scatterplot
172(2)
Exercises
174(8)
Probability Fundamentals
182(13)
The Discrete Case
182(2)
The Complement Rule of Probability
184(1)
The Additive Rules of Probability
184(3)
First Additive Rule of Probability
185(1)
Second Additive Rule of Probability
186(1)
The Multiplicative Rule of Probability
187(2)
The Relationship between Independence and Mutual Exclusivity
189(1)
Conditional Probability
190(1)
The Law of Large Numbers
191(1)
Exercises
192(3)
Theoretical Probability Models
195(22)
The Binomial Probability Model and Distribution
195(9)
The Applicability of the Binomial Probability Model
200(4)
The Normal Probability Model and Distribution
204(6)
Using the Normal Distribution to Approximate the Binomial Distribution
210(1)
Exercises
210(7)
The Role of Sampling in Inferential Statistics
217(17)
Samples and Populations
217(1)
Random Samples
218(3)
Obtaining a Simple Random Sample
219(2)
Sampling with and without Replacement
221(2)
Sampling Distributions
223(1)
Describing the Sampling Distribution of Means Empirically
223(3)
Describing the Sampling Distribution of Means Theoretically: The Central Limit Theorem
226(4)
Central Limit Theorem (CLT)
227(3)
Estimators and BIAS
230(1)
Exercises
231(3)
Inferences Involving the Mean of a Single Population When σ Is Known
234(25)
Estimating the Population Mean μ When the Population Standard Deviation σ Is Known
234(2)
Interval Estimation
236(3)
Relating the Length of a Confidence Interval, the Level of Confidence, and the Sample Size
239(1)
Hypothesis Testing
239(8)
The Relationship between Hypothesis Testing and Interval Estimation
247(1)
Effect Size
248(1)
Type II Error and the Concept of Power
249(5)
Increasing the Level of Significance, α
253(1)
Increasing the Effect Size, δ
253(1)
Decreasing the Standard Error of the Mean, σ x
253(1)
Closing Remarks
254(1)
Exercises
254(5)
Inferences Involving The Mean When σ Is Not Known: One-And Two-Sample Designs
259(56)
Single Sample Designs When the Parameter of Interest Is the Mean and σ Is Not Known
259(14)
The t-Distribution
260(1)
Degrees of Freedom for the One-Sample t-Test
261(1)
Violating the Assumption of a Normally Distributed Parent Population in the One-Sample t-Test
262(1)
Confidence Intervals for the One-Sample t-Test
263(4)
Hypothesis Tests: The One-Sample t-Test
267(2)
Effect Size for the One-Sample t-Test
269(4)
Two-Sample Designs When the Parameter of Interest Is μ, and σ Is Not Known
273(21)
Independent (or Unrelated) and Dependent (or Related) Samples
274(1)
Independent Samples t-Test and Confidence Interval
275(2)
The Assumptions of the Independent Samples t-Test
277(8)
Effect Size for the Independent Samples t-Test
285(3)
Paired Samples t-Test and Confidence Interval
288(1)
The Assumptions of the Paired Samples t-Test
289(4)
Effect Size for the Paired Samples t-Test
293(1)
Summary
294(1)
The Standard Error of the Mean Difference for Independent Samples: A More Complete Account (Optional)
295(5)
Case 1: σ Known
295(2)
Case 2: σ Not Known
297(2)
Step 1: Estimating σ2 Using the Variance Estimators σ 2/1 and σ 2/2
299(1)
Step 2: Estimating the Standard Error of the Mean Difference, σx1 - x2 Using σ2
299(1)
Exercises
300(15)
One-Way Analysis of Variance
315(35)
The Disadvantage of Multiple t-Tests
315(2)
The One-Way Analysis of Variance
317(11)
A Graphical Illustration of the Role of Variance in Tests on Means
317(1)
ANOVA as an Extension of the Independent Samples t-Test
318(1)
Developing an Index of Separation for the Analysis of Variance
319(1)
Carrying Out the ANOVA Computation
319(1)
The Between-Group Variance (MSB)
320(1)
The Within-Group Variance (MSW)
321(1)
The Assumptions of the One-Way ANOVA
321(1)
Testing the Equality of Population Means: The F-Ratio
322(2)
How to Read the Tables and to Use the SPSS Compute Statement for the F-Distribution
324(3)
ANOVA Summary Table
327(1)
Measuring the Effect Size
328(2)
Post-HOC Multiple Comparison Tests
330(10)
The Bonferroni Adjustment: Testing Planned Comparisons
340(3)
The Bonferroni Tests on Multiple Measures
343(2)
Exercises
345(5)
Two-Way Analysis of Variance
350(41)
The Two-Factor Design
350(3)
The Concept of Interaction
353(5)
The Hypotheses That Are Tested by a Two-Way Analysis of Variance
358(1)
Assumptions of the Two-Way ANOVA
358(2)
Balanced versus Unbalanced Factorial Designs
360(1)
Partitioning the Total Sum of Squares
360(1)
Using the F-Ratio to Test the Effects in Two-Way ANOVA
361(1)
Carrying Out the Two-Way ANOVA Computation by Hand
362(4)
Decomposing Score Deviations about the Grand Mean
366(1)
Modeling Each Score as a Sum of Component Parts
367(1)
Explaining the Interaction as a Joint (or Multiplicative) Effect
368(1)
Measuring Effect Size
368(4)
Fixed versus Random Factors
372(1)
Post-Hoc Multiple Comparison Tests
373(6)
Summary of Steps to Be Taken in a Two-Way ANOVA Procedure
379(4)
Exercises
383(8)
Correlation and Simple Regression as Inferential Techniques
391(44)
The Bivariate Normal Distribution
391(3)
Testing Whether the Population Pearson Product Moment Correlation Equals Zero
394(3)
Using a Confidence Interval to Estimate the Size of the Population Correlation Coefficient, ρ
397(3)
Revisiting Simple Linear Regression for Prediction
400(10)
Estimating the Population Standard Error of Prediction, σ Yix
400(1)
Testing the b-Weight for Statistical Significance
401(4)
Explaining Simple Regression Using an Analysis of Variance Framework
405(2)
Measuring the Fit of the Overall Regression Equation: Using R and R2
407(1)
Relating R2 to σ2 yix
408(1)
Testing R2 for Statistical Significance
409(1)
Estimating the True Population R2: The Adjusted R2
409(1)
Exploring the Goodness of Fit of the Regression Equation: Using Regression Diagnostics
410(14)
Residual Plots: Evaluating the Assumptions Underlying Regression
413(2)
Detecting Influential Observations: Discrepancy and Leverage
415(2)
Using SPSS to Obtain Leverage
417(1)
Using SPSS to Obtain Discrepancy
417(1)
Using SPSS to Obtain Influence
418(4)
Using the Prediction Model to Predict Ice Cream Sales
422(1)
Simple Regression When the Predictor Is Dichotomous
422(2)
Exercises
424(11)
An Introduction to Multiple Regression
435(50)
The Basic Equation with Two Predictors
436(1)
Equations for b, β, and RY.12 When the Predictors Are Not Correlated
437(1)
Equations for b, β, and RY.12 When the Predictors Are Correlated
438(2)
Summarizing and Expanding on Some Important Principles of Multiple Regression
440(4)
Testing the b-Weights for Statistical Significance
444(1)
Assessing the Relative Importance of the Independent Variables in the Equation
445(1)
Measuring the Decrease in R2 Directly: An Alternative to the Squared Part Correlation
446(1)
Evaluating the Statistical Significance of the Change in R2
446(2)
The b-Weight as a Partial Slope in Multiple Regression
448(2)
Multiple Regression When One of the Two Independent Variables Is Dichotomous
450(4)
The Concept of Interaction between Two Variables That Are at Least Interval-Leveled
454(2)
Testing the Statistical Significance of an Interaction Using SPSS
456(4)
Centering First-Order Effects to Achieve Meaningful Interpretations of b-Weights
460(1)
Understanding the Nature of a Statistically Significant Two-Way Interaction
460(3)
Interaction When One of the Independent Variables Is Dichotomous and the Other Is Continuous
463(3)
Putting It All Together: A Student Project Reprinted
466(1)
Measuring the Variables
467(1)
Examining the Variables Individually and in Pairs
468(3)
Examining the Variables Multivariately with Mathematics Achievement as the Criterion
471(4)
Exercises
475(10)
Nonparametric Methods
485(34)
Parametric versus Nonparametric Methods
485(1)
Nonparametric Methods When the Dependent Variable Is at the Nominal Level
486(1)
The Chi-Square Distribution (X2)
486(19)
The Chi-Square Goodness-of-Fit Test
489(4)
The Chi-Square Test of Independence
493(4)
Assumptions of the Chi-Square Test of Independence
497(2)
Fisher's Exact Test
499(2)
Calculating the Fisher Exact Test by Hand Using the Hypergeometric Distribution
501(4)
Nonparametric Methods When the Dependent Variable Is Ordinal-Leveled
505(9)
Wilcoxon Sign Test
505(3)
The Mann-Whitney U-Test
508(4)
The Kruskal-Wallis Analysis of Variance
512(2)
Exercises
514(5)
APPENDIX A. DATA SET DESCRIPTIONS
519(12)
Anscombe
519(1)
Basket
519(1)
Blood
519(1)
Brainsz
520(1)
Currency
520(1)
Framingham
520(2)
Hamburg
522(1)
Ice Cream
522(1)
Impeach
522(1)
Learndis
523(1)
Mandex.sav
524(1)
Marijuan
524(1)
NELS
524(4)
Skulls
528(1)
States
529(1)
Stepping
529(1)
Temp
530(1)
Wages
530(1)
APPENDIX B. GENERATING DISTRIBUTIONS FOR CHAPTERS 8 AND 9 USING SPSS SYNTAX
531(6)
Creating a New Data Set File with ID Values for 75 Cases
531(1)
The SPSS Syntax Program (Called, in General, a Macro) to Generate the Set of 50,000 Sample Means Used to Form the Sampling Distribution of Means Graphed as the Histogram of Figure 9.2
532(1)
The SPSS Syntax Program to Generate the Set of 1,000 Normally Distributed Scores with Mean = 15 and SD = 3 as Illustrated by the Histogram of Figure 9.3
533(1)
The SPSS Syntax Program to Generate the Set of 1,000 Normally Distributed Scores with Mean = 15 and SD = 3 as Illustrated by the Histogram of Figure 9.4
534(1)
The SPSS Syntax Program to Generate the Set of 1,000 Positively Skewed Distributed Scores with Mean = 8 and SD = 4 as Illustrated by the Histogram of Figure 9.5
534(1)
The SPSS Syntax Program, Sampdisver2.Sps, to Generate the Set of 5,000 Sample Means Used to Form the Sampling Distribution of Means Graphed as the Histogram of Figure 9.6
535(2)
APPENDIX C. STATISTICAL TABLES
537(17)
Table
1. Areas under the Standard Normal Curve (to the Right of the z-Score)
537(1)
Table
2. Distribution of t-Values for Right-Tail Areas
538(1)
Table
3. Distribution of F-Values for Right-Tail Areas
539(4)
Table
4. Binomial Distribution Table
543(5)
Table
5. Chi-Square Distribution Values for Right-Tailed Areas
548(1)
Table
6. The Critical q-Values
549(1)
Table
7. The Critical U-Values
550(4)
APPENDIX D. REFERENCES
554(3)
APPENDIX E. SOLUTIONS TO EXERCISES
557(195)
Solutions
557(2)
Solutions
559(20)
Solutions
579(18)
Solutions
597(10)
Solutions
607(19)
Solutions
626(14)
Solutions
640(1)
Solutions
641(3)
Solutions
644(4)
Solutions
648(1)
Solutions
649(24)
Solutions
673(16)
Solutions
689(14)
Solutions
703(12)
Solutions
715(28)
Solutions
743(9)
Index 752


Sharon L. Weinberg is Professor of Quantitative Methods and Psychology and former Vice Provost for Faculty Affairs at New York University. She is widely published, with over 50 publications in her field, including books, book chapters, journal articles, and major reports. She is the recipient of numerous grants and author with Kenneth P. Goldberg of Statistics for the Behavioral Sciences (Cambridge, 1990). Sarah Knapp Abramowitz is Assistant Professor of Mathematics and Computer Science at Drew University. She received her Ph.D. from New York University and is an Associate Editor of the Journal of Statistics Education.