000			02001cam a22004338i 4500
999			_c197399 _d197399
020			_a9781119355397
040			_cCUS
082	0	0	_a300.15195 _bNES/S
100	1		_aGrimm, Laurence G.,
245	1	0	_aStatistical applications for the behavioral and social sciences / _cK. Paul Nesselroade, Jr., Asbury University; Laurence G. Grimm, University of Illinois at Chicago.
250			_a2nd edition.
300			_a1 online resource.
500			_aIncludes index.
500			_aEarlier edition published in 1993 as: Statistical applications for the behavioral sciences [by] Laurence G. Grimm.
505			_aDedication Preface Acknowledgements About the companion Website INTRODUCTION: BASIC CONCEPTS IN RESEARCH Chapter 1: Basic Concepts in Research 1.1 The Scientific Method 1.2 The Goals of the Researcher 1.3 Types of Variables 1.4 Controlling Extraneous VariablesBOX 1.1: Is the Scientific Method Broken? The Wallpaper Effect 1.5 Validity IssuesBOX 1.2: Feeling Good and Helping Others: A Study With a Confound 1.6 Causality and Correlation 1.7 The Role of Statistics and the Organization of the TextbookBOX 1.3: A Strategy for Studying Statistics: Distributed Over Mass Practice Summary Key Terms for Chapter 1 Questions and Exercises for Chapter 1 PART 1: DESCRIPTIVE STATISTICS Chapter 2: Scales of Measurement and Data Display 2.1 Scales of MeasurementSPOTLIGHT 2.1 Rensis Likert 2.2 Discrete Variables, Continuous Variables, and the Real Limits of Numbers 2.3 Using Tables to Organize DataBOX 2.1 Some Notes on the History of Statistics 2.4 Using Graphs to Display DataBOX 2.2 Using a Graph to Provide a Visual Display of DataBOX 2.3 Is the Scientific Method Broken? The Misrepresentation of Data/Findings 2.5 The Shape of Things to Come Summary Introduction to Microsoft (R) Excel and SPSS (R) Key Terms for Chapter 2 Question and Exercises for Chapter 2 Chapter 3: Measures of Central Tendency 3.1 Describing a Distribution of Scores 3.2 Parameters and Statistics 3.3 The Rounding Rule 3.4 The Mean 3.5 The MedianBOX 3.1: The Central Tendency of Likert Scales: The Great Debate 3.6 The Mode 3.7 How the Shape of Distributions Affects Measures of Central Tendency 3.8 When to Use the Mean, Median, and Mode 3.9 Experimental Research and the Mean: A Glimpse of Things to ComeBOX 3.2 Learning to Control Our Heart Rate Summary Using Microsoft (R) Excel and SPSS (R) to find measures of centrality Key Formulas for Chapter 3 Key Terms for Chapter 3 Questions and Exercises for Chapter 3 Chapter 4: Measures of Variability 4.1 The Importance of Measures of Variability 4.2 Range 4.3 Mean Deviation 4.4 The VarianceBOX 4.1 The Substantive Importance of the Variance 4.5 The Standard DeviationBOX 4.2 The Origins of the Standard Deviation 4.6 Simple Transformations and Their Effect on the Mean and Variance 4.7 Deciding Which Measure of Variability to UseBOX 4.3 Is the Scientific Method Broken? Demand Characteristics and Shrinking Variation Summary Using Microsoft (R) Excel and SPSS (R) to Find Measures of Variability Key Formulas for Chapter 4 Key Terms for Chapter 4 Questions and Exercises for Chapter 4 Chapter 5: The Normal Curve and Transformations: Percentiles, z Scores and T Scores 5.1 Percentile Rank 5.2 The Normal DistributionsSPOTLIGHT 5.1 Abraham De Moivre 5.3 Standardized Scores (z Scores)BOX 5.1 With z Scores We Can Compare Apples and Oranges Summary Using Microsoft (R) Excel and SPSS (R) to Find z Scores Key Formulas for Chapter 5 Key Terms for Chapter 5 Questions and Exercises for Chapter 5 PART 2: Inferential Statistics: Theoretical Basis Chapter 6: Basic Concepts of Probability 6.1 Theoretical Support for Inferential Statistics 6.2 The Taming of Chance 6.3 What is Probability?BOX 6.1 Is the Scientific Method Broken? Uncertainty, Likelihood, and Clarity 6.4 Sampling with and without Replacement 6.5 A Priori and A Posteriori Approaches to Probability 6.6 The Addition Rule 6.7 The Multiplication Rule 6.8 Conditional Probabilities 6.9 Bayes TheoremSPOTLIGHT 6.1 Thomas Bayes and Bayesianism Summary Key Formulas for Chapter 6 Key Terms for Chapter 6 Questions and Exercises for Chapter 6 Chapter 7: Hypothesis Testing and Sampling Distributions 7.1 Inferential Statistics 7.2 Hypothesis Testing 7.3 Sampling DistributionsBOX 7.1 Playing with the Numbers: Create Our Own Sampling Distribution 7.4 Estimating the Features of Sampling DistributionsBOX 7.2 Is the Scientific Method Broken? The Value of Replication Summary Key Formulas for Chapter 7 Key Terms for Chapter 7 Questions and Exercises for Chapter 7 PART 3: Inferential Statistics: z Test, t Tests, and Power Analysis Chapter 8: Testing a Single Mean: The Single-Sample z and t Tests 8.1 The Research Context 8.2 Using the Sampling Distribution of Means for the Single-Sample z Test 8.3 Type I and Type II ErrorsBOX 8.1 Is the Scientific Method Broken? Type I Errors and the Ioannidis Critique 8.4 Is a Significant Finding "Significant"? 8.5 The Statistical Test for the Mean of a Population When Sigma is unknown: The t DistributionsBOX 8.2 Visual Illusions and Immaculate Perception 8.6 Assumptions of the Single-Sample z and t Test 8.7 Interval Estimation of the Population Mean 8.8 How to Present Formally the Conclusions for a Single-Sample t Test Summary Using Microsoft (R) Excel and SPSS (R) to Run Single-Sample t Tests Key Formulas for Chapter 8 Key Terms for Chapter 8 Questions and Exercises for Chapter 8 Chapter 9: Testing the Difference between Two Means: The Independent-Samples t Test 9.1 The Research ContextSPOTLIGHT 9.1 William Gosset 9.2 The Independent-Sample t TestBOX 9.1 Can Epileptic Seizures Be Controlled By Relaxation Training? 9.3 The Appropriateness of Unidirectional Tests 9.4 Assumptions of the Independent-Samples t Test 9.5 Interval Estimation of the Population Mean Difference 9.6 How to Present Formally the Conclusions for an Independent-Samples t Test Summary Using Microsoft (R) Excel and SPSS (R) to run an Independent-Samples t Test Key Formulas for Chapter 9 Key Terms for Chapter 9 Questions and Exercises for Chapter 9 Chapter 10: Testing the Difference Between Two Means: The Dependent-samples t Test 10.1 The Research Context 10.2 The Sampling Distribution for the Dependent-Samples t Test 10.3 The t Distribution for Dependent Samples 10.4 Comparing the Independent- and Dependent-Samples t Tests 10.5 The One-Tailed t Test RevisitedBOX 10.1 Is the Scientific Method Broken? The Questionable Use of One-Tailed t Tests 10.6 Assumptions of the Dependent-Samples t TestBOX 10.2 The First Application of the t Test 10.7 Interval Estimation of the Population Mean Difference 10.8 How to Present Formally the Conclusions for a Dependent-Samples t Test Summary Using Microsoft (R) Excel and SPSS (R) to Run a Dependent-Samples t Test Key Formulas for Chapter 10 Key Terms for Chapter 10 Questions and Exercises for Chapter 10 Chapter 11: Power Analysis and Hypothesis Testing 11.1 Decision Making While Hypothesis Testing 11.2 Why Study Power? 11.3 The Five Factors that Influence Power 11.4 Decision Criteria that Influence Power 11.5 Using the Power Table 11.6 Determining Effect Size: The Achilles Heel of the Power AnalysisBOX 11.1 Is the Scientific Method Broken? The Need to Take Our Own Advice 11.7 Determining Sample Size for a Single-Sample Test 11.8 Failing to Reject the Null Hypothesis: Can a Power Analysis Help?BOX 11.2 Psychopathy and Frontal Lobe Damage Summary Key Formulas for Chapter 11 Key Term for Chapter 11 Questions and Exercises for Chapter 11 PART 3 REVIEW: The z Test, t Tests, and Power Analysis Review of Concepts Presented in Part 3 Questions and Exercises for Part 3 Review PART 4: Inferential Statistics: Analysis of Variance Chapter 12: One-Way Analysis of Variance 12.1 The Research ContextSPOTLIGHT 12.1 Sir Ronald Fisher 12.2 The Conceptual Basis of ANOVA: Sources of Variation 12.3 The Assumptions of the one-way ANOVA 12.4 The Conceptual Basis of ANOVA: Hypotheses and Error Terms 12.5 Computing the F Ratio in ANOVA 12.6 Testing Null Hypotheses 12.7 The ANOVA Summary Table 12.8 An Example of ANOVA with Unequal Numbers of Participants 12.9 Measuring Effect Size for a One-Way ANOVA 12.10 Locating the Source(s) of SignificanceSPOTLIGHT 12.2 John Wilder TukeyBOX 12.1 Initiation Rites and Club Loyalty 12.11 How to Present Formally the Conclusions for a One-Way ANOVA Summary Using Microsoft (R) Excel and SPSS (R) to Run a One-Way ANOVA Key Formulas for Chapter 12 Key Terms for Chapter 12 Questions and Exercises for Chapter 12 Chapter 13: Two-Way Analysis of Variance 13.1 The Research Context 13.2 The Logic of the Two-Way ANOVA 13.3 Definitional and Computational Formulas for the Two-Way ANOVA 13.4 Using the F Ratios to Test Null HypothesesBOX 13.1 Do Firearms Create Aggression? 13.5 Assumptions of the Two-Way ANOVA 13.6 Measuring Effect Sizes for a Two-Way ANOVA 13.7 Multiple ComparisonsBOX 13.2 Next Steps with ANOVA 13.8 Interpreting the Factors in a Two-Way ANOVA 13.9 How to Present Formally the Conclusions for a Two-Way ANOVA Summary Using Microsoft (R) Excel and SPSS (R) to Run a Two-Way ANOVA Key Formulas for Chapter 13 Key Terms for Chapter 13 Questions and Exercises for Chapter 13 Chapter 14: Repeated-Measures Analysis of Variance 14.1 The Research Context 14.2 The Logic of the Repeated-Measures ANOVA 14.3 The Formulas for the Repeated-Measures ANOVA 14.4 Using the F Ratio to Test the Null Hypothesis 14.5 Interpreting the Findings 14.6 The ANOVA Summary TableBOX 14.1 Next Steps for Repeated-Measures ANOVA's: Mixed-Designs and Quasi-Experimentation 14.7 Assumptions of the Repeated-Measures ANOVA 14.8 Measuring Effect Size for Repeated-Measures ANOVA 14.9 Locating the Source(s) of Statistical EvidenceBOX 14.2 The Inverted U Relationship between Arousal and Task Performance 14.10 How to Present Formally the Conclusions for a Repeated-Measures ANOVA Summary Using Microsoft (R) Excel and SPSS (R) to Run a Repeated-Measures ANOVA Key Formulas for Chapter 14 Key Terms for Chapter 14 Questions and Exercises for Chapter 14 PART 4 REVIEW: Analysis of Variance Review of Concepts Presented in Part 4 Questions and Exercises for Part 4 Review PART 5: Inferential Statistics: Bivariate Data Analyses Chapter 15: Linear Correlation 15.1 The Research ContextSPOTLIGHT 15.1 Karl Pearson 15.2 The Correlation Coefficient and Scatter Diagrams 15.3 The Coefficient of DeterminationBOX 15.1 Next Steps with Correlations: Scale Development 15.4 Using the Pearson r for Hypothesis TestingBOX 15.2 Maternal Cognitions and Aggressive Children 15.5 Factors That Can Create Misleading Correlation Coefficients 15.6 How to Present Formally the Conclusions of a Pearson r Summary Using Microsoft (R) Excel and SPSS (R) to Calculate Pearson r Key Formulas for Chapter 15 Key Terms for Chapter 15 Questions and Exercises for Chapter 15 Chapter 16: Linear Regression 16.1 The Research Context 16.2 Overview of Regression 16.3 Establishing the Regression LineSPOTLIGHT 16.1 Sir Francis Galton 16.4 Putting It All Together: A Worked ProblemBOX 16.1 Why is a Prediction Equation Called a Regression Equation? 16.5 The Coefficient of Determination in the Context of Prediction 16.6 The Pitfalls of Linear RegressionBOX 16.2 Next Steps with Regression Analyses 16.7 How to Present Formally the Conclusions of a Linear Regression Analysis Summary Using Microsoft (R) Excel and SPSS (R) to Create a Linear Regression Line Key Formulas for Chapter 16 Key Terms for Chapter 16 Questions and Exercises for Chapter 16 PART 5 REVIEW: Linear Correlation and Linear Regression Review of Concepts Presented in Part 5 Questions and Exercises for Part 5 Review PART 6: Inferential Statistics: Nonparametric Tests Chapter 17: The Chi-Square Test 17.1 The Research Context 17.2 The Chi-Square Test for One-Way Designs: The Goodness-of-Fit Test 17.3 The Chi-Square Distribution and Degrees of Freedom 17.4 Two-Way Designs: The Chi-Square Test for Independence 17.5 The Chi-Square Test for a 2 x 2 Contingency TableBOX 17.1 What is Beautiful is Good 17.6 A Measure of Effect Size for the Chi-Square Test for Independence 17.7 Which Cells Are Major Contributors to a Significant Chi-Square Test? 17.8 Using the Chi-Square Test with Quantitative Variables 17.9 Assumptions of the Chi-Square Test 17.10 How to Present Formally the Conclusions for a Chi-Square Test Summary Using Microsoft (R) Excel and SPSS (R) to Calculate a Chi-Square Key Formulas for Chapter 17 Key Terms for Chapter 17 Questions and Exercises for Chapter 17 Chapter 18: Other Nonparametric Tests 18.1 The Research Context 18.2 The Use of Ranked Data in Research 18.3 The Spearman Rank Correlation Coefficient 18.4 The Point-Biserial Correlation Coefficient 18.5 The Mann-Whitney U Test 18.6 The Wilcoxon Signed-Ranks TestBOX 18.1 Do Infants Notice the Difference Between Lip Movement and Speech Sounds? 18.7 Using Nonparametric TestsBOX 18.1 Is the Scientific Method Broken? The Limitations of Science 18.8 How to Present Formally the Conclusions for Various Nonparametric Tests Summary Using Microsoft (R) Excel and SPSS (R) to Calculate Various Nonparametrics Key Formulas for Chapter 18 Key Terms for Chapter 18 Questions and Exercises for Chapter 18 PART 6 REVIEW: Nonparametric Tests Review of Concepts Presented in Part 6 Questions and Exercises for Part 6 Review Appendixes A. Statistical Tables 1. z Table 2. t Table 3. Power Table (Finding Power) 4. Power Table (Finding Delta) 5. F Table 6. q Table (Studentized Range) 7. Pearson r Table 8. Spearman rs. Table 9. Chi-Square Table 10. Mann-Whitney U Table 11. Wilcoxon Signed-Ranks Table B. Answers to Questions and Exercises C. Basic Data Entry for Microsoft (R) Excel and SPSS (R) Glossary References List of Formulas List of symbols Index
650		0	_aSocial sciences _xStatistical methods.
942			_cWB16