Details

Statistical Applications for the Behavioral and Social Sciences


Statistical Applications for the Behavioral and Social Sciences


2. Aufl.

von: K. Paul Nesselroade, Laurence G. Grimm

103,99 €

Verlag: Wiley
Format: EPUB
Veröffentl.: 09.11.2018
ISBN/EAN: 9781119355366
Sprache: englisch
Anzahl Seiten: 960

DRM-geschütztes eBook, Sie benötigen z.B. Adobe Digital Editions und eine Adobe ID zum Lesen.

Beschreibungen

<p><b>An updated edition of a classic text on applying statistical analyses to the social sciences, with reviews, new chapters, an expanded set of post-hoc analyses, and information on computing in Excel and SPSS</b></p> <p>Now in its second edition,<i>Statistical Applications for the Behavioral and Social Sciences </i>has been revised and updated and continues to offer an essential guide to the conceptual foundations of statistical analyses (particularly inferential statistics), placing an emphasis on connecting statistical tools with appropriate research contexts. Designed to be accessible, the text contains an applications-oriented, step-by-step presentation of the statistical theories and formulas most often used by the social sciences. The revised text also includes an entire chapter on the basic concepts in research, presenting an overall context for all the book's statistical theories and formulas.</p> <p>The authors cover descriptive statistics and <i>z</i> scores, the theoretical underpinnings of inferential statistics, <i>z</i> and <i>t</i> tests, power analysis, one/two-way and repeated-measures ANOVA, linear correlation and regression, as well as chi-square and other nonparametric tests. The second edition also includes a new chapter on basic probability theory.</p> <p>This important resource:</p> <ul> <li>Contains information regarding the use of statistical software packages; both Excel and SPSS</li> <li>Offers four strategically positioned and accumulating reviews, each containing a set of research-oriented diagnostic questions designed to help students determine which tests are applicable to which research scenarios</li> <li>Incorporates additional statistical information on follow-up analyses such as post-hoc tests and effect sizes</li> <li>Includes a series of sidebar discussions dispersed throughout the text that address, among other topics, the recent and growing controversy regarding the failed reproducibility of published findings in the social sciences</li> <li>Puts renewed emphasis on presentation of data and findings using the APA format</li> <li>Includes supplementary material consisting of a set of "kick-start" quizzes designed to get students quickly back up to speed at the start of an instructional period, and a complete set of ready-to-use PowerPoint slides for in-class use</li> </ul> <p>Written for students in areas such as psychology, sociology, criminology, political science, public health, and others, <i>Statistical Applications for the Behavioral and Social Sciences, Second Edition</i> continues to provide the information needed to understand the foundations of statistical analyses as relevant to the behavioral and social sciences.</p>
<p>Dedication</p> <p>Preface</p> <p>Acknowledgements</p> <p>About the companion Website</p> <p><b>INTRODUCTION: BASIC CONCEPTS IN RESEARCH </b></p> <p><b>Chapter 1: Basic Concepts in Research</b></p> <p>1.1 The Scientific Method</p> <p>1.2 The Goals of the Researcher</p> <p>1.3 Types of Variables</p> <p>1.4 Controlling Extraneous Variables<br /><br />BOX 1.1: Is the Scientific Method Broken? The Wallpaper Effect</p> <p>1.5 Validity Issues<br /><i><br /></i>BOX 1.2: Feeling Good and Helping Others: A Study With a Confound</p> <p>1.6 Causality and Correlation</p> <p>1.7 The Role of Statistics and the Organization of the Textbook<br /><i><br /></i>BOX 1.3: A Strategy for Studying Statistics: Distributed Over Mass Practice</p> <p>Summary</p> <p>Key Terms for Chapter 1</p> <p>Questions and Exercises for Chapter 1</p> <p><b>PART 1: DESCRIPTIVE STATISTICS</b></p> <p><b>Chapter 2: Scales of Measurement and Data Display</b></p> <p>2.1 Scales of Measurement<br /><br />SPOTLIGHT 2.1 Rensis Likert</p> <p>2.2 Discrete Variables, Continuous Variables, and the Real Limits of Numbers</p> <p>2.3 Using Tables to Organize Data<br /><i><br /></i>BOX 2.1 Some Notes on the History of Statistics</p> <p>2.4 Using Graphs to Display Data<br /><i><br /></i>BOX 2.2 Using a Graph to Provide a Visual Display of Data<br /><br />BOX 2.3 Is the Scientific Method Broken? The Misrepresentation of Data/Findings</p> <p>2.5 The Shape of Things to Come</p> <p>Summary</p> <p>Introduction to Microsoft® Excel and SPSS®</p> <p>Key Terms for Chapter 2</p> <p>Question and Exercises for Chapter 2</p> <p><b>Chapter 3: Measures of Central Tendency</b></p> <p>3.1 Describing a Distribution of Scores</p> <p>3.2 Parameters and Statistics</p> <p>3.3 The Rounding Rule</p> <p>3.4 The Mean</p> <p>3.5 The Median<br /><i><br /></i>BOX 3.1: The Central Tendency of Likert Scales: The Great Debate</p> <p>3.6 The Mode</p> <p>3.7 How the Shape of Distributions Affects Measures of Central Tendency</p> <p>3.8 When to Use the Mean, Median, and Mode</p> <p>3.9 Experimental Research and the Mean: A Glimpse of Things to Come<br /><i><br /></i>BOX 3.2 Learning to Control Our Heart Rate</p> <p>Summary</p> <p>Using Microsoft® Excel and SPSS® to find measures of centrality</p> <p>Key Formulas for Chapter 3</p> <p>Key Terms for Chapter 3</p> <p>Questions and Exercises for Chapter 3</p> <p><b>Chapter 4: Measures of Variability</b></p> <p>4.1 The Importance of Measures of Variability</p> <p>4.2 Range</p> <p>4.3 Mean Deviation</p> <p>4.4 The Variance<br /><i><br /></i>BOX 4.1 The Substantive Importance of the Variance</p> <p>4.5 The Standard Deviation<br /><i><br /></i>BOX 4.2 The Origins of the Standard Deviation</p> <p>4.6 Simple Transformations and Their Effect on the Mean and Variance</p> <p>4.7 Deciding Which Measure of Variability to Use<br /><i><br /></i>BOX 4.3 Is the Scientific Method Broken? Demand Characteristics and Shrinking Variation</p> <p>Summary</p> <p>Using Microsoft® Excel and SPSS® to Find Measures of Variability</p> <p>Key Formulas for Chapter 4</p> <p>Key Terms for Chapter 4</p> <p>Questions and Exercises for Chapter 4</p> <p><b>Chapter 5: The Normal Curve and Transformations:</b></p> <p>Percentiles, <i>z</i> Scores and <i>T</i> Scores</p> <p>5.1 Percentile Rank</p> <p>5.2 The Normal Distributions<br /><i><br /></i>SPOTLIGHT 5.1 Abraham De Moivre</p> <p>5.3 Standardized Scores (z Scores)<br /><i><br /></i>BOX 5.1 With z Scores We Can Compare Apples and Oranges</p> <p>Summary</p> <p>Using Microsoft® Excel and SPSS® to Find z Scores</p> <p>Key Formulas for Chapter 5</p> <p>Key Terms for Chapter 5</p> <p>Questions and Exercises for Chapter 5</p> <p><b>PART 2: Inferential Statistics: Theoretical Basis</b></p> <p><b>Chapter 6: Basic Concepts of Probability</b></p> <p>6.1 Theoretical Support for Inferential Statistics</p> <p>6.2 The Taming of Chance</p> <p>6.3 What is Probability?<br /><i><br /></i>BOX 6.1 Is the Scientific Method Broken? Uncertainty, Likelihood, and Clarity</p> <p>6.4 Sampling with and without Replacement</p> <p>6.5 A Priori and A Posteriori Approaches to Probability</p> <p>6.6 The Addition Rule</p> <p>6.7 The Multiplication Rule</p> <p>6.8 Conditional Probabilities</p> <p>6.9 Bayes Theorem<br /><i><br /></i>SPOTLIGHT 6.1 Thomas Bayes and Bayesianism</p> <p>Summary</p> <p>Key Formulas for Chapter 6</p> <p>Key Terms for Chapter 6</p> <p>Questions and Exercises for Chapter 6</p> <p><b>Chapter 7: Hypothesis Testing and Sampling Distributions</b></p> <p>7.1 Inferential Statistics</p> <p>7.2 Hypothesis Testing</p> <p>7.3 Sampling Distributions<br /><i><br /></i>BOX 7.1 Playing with the Numbers: Create Our Own Sampling Distribution</p> <p>7.4 Estimating the Features of Sampling Distributions<br /><i><br /></i>BOX 7.2 Is the Scientific Method Broken? The Value of Replication</p> <p>Summary</p> <p>Key Formulas for Chapter 7</p> <p>Key Terms for Chapter 7</p> <p>Questions and Exercises for Chapter 7</p> <p><b>PART 3: Inferential Statistics: z Test, t Tests, and Power Analysis</b></p> <p><b>Chapter 8: Testing a Single Mean: The Single-Sample z and t Tests</b></p> <p>8.1 The Research Context</p> <p>8.2 Using the Sampling Distribution of Means for the Single-Sample z Test</p> <p>8.3 Type I and Type II Errors<br /><i><br /></i>BOX 8.1 Is the Scientific Method Broken? Type I Errors and the Ioannidis Critique</p> <p>8.4 Is a Significant Finding “Significant”?</p> <p>8.5 The Statistical Test for the Mean of a Population When Sigma is unknown: The t Distributions<br /><i><br /></i>BOX 8.2 Visual Illusions and Immaculate Perception</p> <p>8.6 Assumptions of the Single-Sample z and t Test</p> <p>8.7 Interval Estimation of the Population Mean</p> <p>8.8 How to Present Formally the Conclusions for a Single-Sample t Test</p> <p>Summary</p> <p>Using Microsoft® Excel and SPSS® to Run Single-Sample t Tests</p> <p>Key Formulas for Chapter 8</p> <p>Key Terms for Chapter 8</p> <p>Questions and Exercises for Chapter 8</p> <p><b>Chapter 9: Testing the Difference between Two Means: </b><b>The Independent-Samples <i>t</i> Test</b></p> <p>9.1 The Research Context<br /><i><br /></i>SPOTLIGHT 9.1 William Gosset</p> <p>9.2 The Independent-Sample t Test<br /><i><br /></i>BOX 9.1 Can Epileptic Seizures Be Controlled By Relaxation Training?</p> <p>9.3 The Appropriateness of Unidirectional Tests</p> <p>9.4 Assumptions of the Independent-Samples t Test</p> <p>9.5 Interval Estimation of the Population Mean Difference</p> <p>9.6 How to Present Formally the Conclusions for an Independent-Samples t Test</p> <p>Summary</p> <p>Using Microsoft® Excel and SPSS® to run an Independent-Samples t Test</p> <p>Key Formulas for Chapter 9</p> <p>Key Terms for Chapter 9</p> <p>Questions and Exercises for Chapter 9</p> <p><b>Chapter 10: Testing the Difference Between Two Means: </b><b>The Dependent-samples <i>t</i> Test</b></p> <p>10.1 The Research Context</p> <p>10.2 The Sampling Distribution for the Dependent-Samples t Test</p> <p>10.3 The t Distribution for Dependent Samples</p> <p>10.4 Comparing the Independent- and Dependent-Samples t Tests</p> <p>10.5 The One-Tailed t Test Revisited<br /><i><br /></i>BOX 10.1 Is the Scientific Method Broken? The Questionable Use of One-Tailed t Tests</p> <p>10.6 Assumptions of the Dependent-Samples t Test<br /><i><br /></i>BOX 10.2 The First Application of the t Test</p> <p>10.7 Interval Estimation of the Population Mean Difference</p> <p>10.8 How to Present Formally the Conclusions for a Dependent-Samples t Test</p> <p>Summary</p> <p>Using Microsoft® Excel and SPSS® to Run a Dependent-Samples t Test</p> <p>Key Formulas for Chapter 10</p> <p>Key Terms for Chapter 10</p> <p>Questions and Exercises for Chapter 10</p> <p><b>Chapter 11: Power Analysis and Hypothesis Testing</b></p> <p>11.1 Decision Making While Hypothesis Testing</p> <p>11.2 Why Study Power?</p> <p>11.3 The Five Factors that Influence Power</p> <p>11.4 Decision Criteria that Influence Power</p> <p>11.5 Using the Power Table</p> <p>11.6 Determining Effect Size: The Achilles Heel of the Power Analysis<br /><i><br /></i>BOX 11.1 Is the Scientific Method Broken? The Need to Take Our Own Advice</p> <p>11.7 Determining Sample Size for a Single-Sample Test</p> <p>11.8 Failing to Reject the Null Hypothesis: Can a Power Analysis Help?<br /><i><br /></i>BOX 11.2 Psychopathy and Frontal Lobe Damage</p> <p>Summary</p> <p>Key Formulas for Chapter 11</p> <p>Key Term for Chapter 11</p> <p>Questions and Exercises for Chapter 11</p> <p><b>PART 3 REVIEW: The z Test, t Tests, and Power Analysis</b></p> <p>Review of Concepts Presented in Part 3</p> <p>Questions and Exercises for Part 3 Review</p> <p><b>PART 4: Inferential Statistics: Analysis of Variance</b></p> <p><b>Chapter 12: One-Way Analysis of Variance</b></p> <p>12.1 The Research Context<br /><i><br /></i>SPOTLIGHT 12.1 Sir Ronald Fisher</p> <p>12.2 The Conceptual Basis of ANOVA: Sources of Variation</p> <p>12.3 The Assumptions of the one-way ANOVA</p> <p>12.4 The Conceptual Basis of ANOVA: Hypotheses and Error Terms</p> <p>12.5 Computing the F Ratio in ANOVA</p> <p>12.6 Testing Null Hypotheses</p> <p>12.7 The ANOVA Summary Table</p> <p>12.8 An Example of ANOVA with Unequal Numbers of Participants</p> <p>12.9 Measuring Effect Size for a One-Way ANOVA</p> <p>12.10 Locating the Source(s) of Significance<br /><i><br /></i>SPOTLIGHT 12.2 John Wilder Tukey<br /><br />BOX 12.1 Initiation Rites and Club Loyalty</p> <p>12.11 How to Present Formally the Conclusions for a One-Way ANOVA</p> <p>Summary</p> <p>Using Microsoft® Excel and SPSS® to Run a One-Way ANOVA</p> <p>Key Formulas for Chapter 12</p> <p>Key Terms for Chapter 12</p> <p>Questions and Exercises for Chapter 12</p> <p><b>Chapter 13: Two-Way Analysis of Variance </b></p> <p>13.1 The Research Context</p> <p>13.2 The Logic of the Two-Way ANOVA</p> <p>13.3 Definitional and Computational Formulas for the Two-Way ANOVA</p> <p>13.4 Using the F Ratios to Test Null Hypotheses<br /><i><br /></i>BOX 13.1 Do Firearms Create Aggression?</p> <p>13.5 Assumptions of the Two-Way ANOVA</p> <p>13.6 Measuring Effect Sizes for a Two-Way ANOVA</p> <p>13.7 Multiple Comparisons<br /><i><br /></i>BOX 13.2 Next Steps with ANOVA</p> <p>13.8 Interpreting the Factors in a Two-Way ANOVA</p> <p>13.9 How to Present Formally the Conclusions for a Two-Way ANOVA</p> <p>Summary</p> <p>Using Microsoft® Excel and SPSS® to Run a Two-Way ANOVA</p> <p>Key Formulas for Chapter 13</p> <p>Key Terms for Chapter 13</p> <p>Questions and Exercises for Chapter 13</p> <p><b>Chapter 14: Repeated-Measures Analysis of Variance</b></p> <p>14.1 The Research Context</p> <p>14.2 The Logic of the Repeated-Measures ANOVA</p> <p>14.3 The Formulas for the Repeated-Measures ANOVA</p> <p>14.4 Using the F Ratio to Test the Null Hypothesis</p> <p>14.5 Interpreting the Findings</p> <p>14.6 The ANOVA Summary Table<br /><i><br /></i>BOX 14.1 Next Steps for Repeated-Measures ANOVA’s: Mixed-Designs and Quasi-Experimentation</p> <p>14.7 Assumptions of the Repeated-Measures ANOVA</p> <p>14.8 Measuring Effect Size for Repeated-Measures ANOVA</p> <p>14.9 Locating the Source(s) of Statistical Evidence<br /><i><br /></i>BOX 14.2 The Inverted U Relationship between Arousal and Task Performance</p> <p>14.10 How to Present Formally the Conclusions for a Repeated-Measures ANOVA</p> <p>Summary</p> <p>Using Microsoft® Excel and SPSS® to Run a Repeated-Measures ANOVA</p> <p>Key Formulas for Chapter 14</p> <p>Key Terms for Chapter 14</p> <p>Questions and Exercises for Chapter 14</p> <p><b>PART 4 REVIEW: Analysis of Variance</b></p> <p>Review of Concepts Presented in Part 4</p> <p>Questions and Exercises for Part 4 Review</p> <p><b>PART 5: Inferential Statistics: Bivariate Data Analyses</b></p> <p><b>Chapter 15: Linear Correlation</b></p> <p>15.1 The Research Context<br /><i><br /></i>SPOTLIGHT 15.1 Karl Pearson</p> <p>15.2 The Correlation Coefficient and Scatter Diagrams</p> <p>15.3 The Coefficient of Determination<br /><i><br /></i>BOX 15.1 Next Steps with Correlations: Scale Development</p> <p>15.4 Using the Pearson r for Hypothesis Testing<br /><i><br /></i>BOX 15.2 Maternal Cognitions and Aggressive Children</p> <p>15.5 Factors That Can Create Misleading Correlation Coefficients</p> <p>15.6 How to Present Formally the Conclusions of a Pearson r</p> <p>Summary</p> <p>Using Microsoft® Excel and SPSS® to Calculate Pearson r</p> <p>Key Formulas for Chapter 15</p> <p>Key Terms for Chapter 15</p> <p>Questions and Exercises for Chapter 15</p> <p><b>Chapter 16: Linear Regression</b></p> <p>16.1 The Research Context</p> <p>16.2 Overview of Regression</p> <p>16.3 Establishing the Regression Line<br /><i><br /></i>SPOTLIGHT 16.1 Sir Francis Galton</p> <p>16.4 Putting It All Together: A Worked Problem<br /><i><br /></i>BOX 16.1 Why is a Prediction Equation Called a Regression Equation?</p> <p>16.5 The Coefficient of Determination in the Context of Prediction</p> <p>16.6 The Pitfalls of Linear Regression<br /><i><br /></i>BOX 16.2 Next Steps with Regression Analyses</p> <p>16.7 How to Present Formally the Conclusions of a Linear Regression Analysis</p> <p>Summary</p> <p>Using Microsoft® Excel and SPSS® to Create a Linear Regression Line</p> <p>Key Formulas for Chapter 16</p> <p>Key Terms for Chapter 16</p> <p>Questions and Exercises for Chapter 16</p> <p><b>PART 5 REVIEW: Linear Correlation and Linear Regression</b></p> <p>Review of Concepts Presented in Part 5</p> <p>Questions and Exercises for Part 5 Review</p> <p><b>PART 6: Inferential Statistics: Nonparametric Tests</b></p> <p><b>Chapter 17: The Chi-Square Test</b></p> <p>17.1 The Research Context</p> <p>17.2 The Chi-Square Test for One-Way Designs: The Goodness-of-Fit Test</p> <p>17.3 The Chi-Square Distribution and Degrees of Freedom</p> <p>17.4 Two-Way Designs: The Chi-Square Test for Independence</p> <p>17.5 The Chi-Square Test for a 2 × 2 Contingency Table<br /><i><br /></i>BOX 17.1 What is Beautiful is Good</p> <p>17.6 A Measure of Effect Size for the Chi-Square Test for Independence</p> <p>17.7 Which Cells Are Major Contributors to a Significant Chi-Square Test?</p> <p>17.8 Using the Chi-Square Test with Quantitative Variables</p> <p>17.9 Assumptions of the Chi-Square Test</p> <p>17.10 How to Present Formally the Conclusions for a Chi-Square Test</p> <p>Summary</p> <p>Using Microsoft® Excel and SPSS® to Calculate a Chi-Square</p> <p>Key Formulas for Chapter 17</p> <p>Key Terms for Chapter 17</p> <p>Questions and Exercises for Chapter 17</p> <p><b>Chapter 18: Other Nonparametric Tests</b></p> <p>18.1 The Research Context</p> <p>18.2 The Use of Ranked Data in Research</p> <p>18.3 The Spearman Rank Correlation Coefficient</p> <p>18.4 The Point-Biserial Correlation Coefficient</p> <p>18.5 The Mann-Whitney U Test</p> <p>18.6 The Wilcoxon Signed-Ranks Test<br /><i><br /></i>BOX 18.1 Do Infants Notice the Difference Between Lip Movement and Speech Sounds?</p> <p>18.7 Using Nonparametric Tests<br /><i><br /></i>BOX 18.1 Is the Scientific Method Broken? The Limitations of Science</p> <p>18.8 How to Present Formally the Conclusions for Various Nonparametric Tests</p> <p>Summary</p> <p>Using Microsoft® Excel and SPSS® to Calculate Various Nonparametrics</p> <p>Key Formulas for Chapter 18</p> <p>Key Terms for Chapter 18</p> <p>Questions and Exercises for Chapter 18</p> <p><b>PART 6 REVIEW: Nonparametric Tests</b></p> <p>Review of Concepts Presented in Part 6</p> <p>Questions and Exercises for Part 6 Review</p> <p><b>Appendixes</b></p> <p>A. Statistical Tables</p> <p>1. <i>z</i> Table</p> <p>2. <i>t</i> Table</p> <p>3. Power Table (Finding Power)</p> <p>4. Power Table (Finding Delta)</p> <p>5. <i>F</i> Table</p> <p>6. <i>q</i> Table (Studentized Range)</p> <p>7. Pearson <i>r</i> Table</p> <p>8. Spearman <i>r<sub>s</sub></i>. Table</p> <p>9. Chi-Square Table</p> <p>10. Mann-Whitney <i>U</i> Table</p> <p>11. Wilcoxon Signed-Ranks Table</p> <p>B. Answers to Questions and Exercises</p> <p>C. Basic Data Entry for Microsoft® Excel and SPSS®</p> <p><b>Glossary</b></p> <p><b>References</b></p> <p><b>List of Formulas</b></p> <p><b>List of symbols</b></p> <p><b>Index</b></p>
<p><b>K. PAUL NESSELROADE, JR, P<small>H</small>D</b>, is Professor and Chair of the Psychology Department at Asbury University. <p><b>THE LATE LAURENCE G. GRIMM, P<small>H</small>D,</b> was a clinical psychologist and Emeritus Associate Professor, University of Illinois at Chicago.
<p><b>AN UPDATED EDITION OF A CLASSIC TEXT ON APPLYING STATISTICAL ANALYSES TO THE BEHAVIORAL AND SOCIAL SCIENCES, WITH REVIEWS, NEW CHAPTERS, AN EXPANDED SET OF POST-HOC ANALYSES, AND INFORMATION ON COMPUTING IN MICROSOFT<sup>®</sup> EXCEL AND SPSS<sup>®</sup></b> <p>Now in its second edition,<b></b> <i>Statistical Applications for the Behavioral and Social Sciences</i> has been revised and updated and continues to offer an essential guide to the conceptual foundations of statistical analyses (particularly inferential statistics), placing an emphasis on connecting statistical tools with appropriate research contexts. Designed to be accessible, the text contains an applications-oriented, step-by-step presentation of the statistical theories and formulas most often used by the behavioral and social sciences. The revised text also includes an entire chapter on the basic concepts in research, presenting an overall context for all the book's statistical theories and formulas. <p>The authors cover descriptive statistics and <i>z</i> scores, the theoretical underpinnings of inferential statistics, <i>z</i> and <i>t</i> tests, power analysis, one/two-way and repeated-measures ANOVA, linear correlation and regression, as well as chi-square and other nonparametric tests. The second edition also includes a new chapter on basic probability theory. <p>This important resource: <ul> <li>Contains information regarding the use of statistical software packages; both Microsoft<sup>®</sup> Excel and SPSS<sup>®</sup></li> <li>Offers four strategically positioned and accumulating reviews, each containing a set of research-oriented diagnostic questions designed to help students determine which tests are applicable to which research scenarios</li> <li>Incorporates additional statistical information on follow-up analyses such as post-hoc tests and effect sizes??</li> <li>Includes a series of sidebar discussions dispersed throughout the text that address, among other topics, the recent and growing controversy regarding the failed reproducibility of published findings in the behavioral and social sciences</li> <li>Puts renewed emphasis on presentation of data and findings using the APA format</li> <li>Includes supplementary material consisting of a set of "kick-start" quizzes designed to get students quickly back up to speed at the start of an instructional period, and a complete set of ready-to-use PowerPoint slides for in-class use</li> </ul> <p>Written for students in areas such as psychology, sociology, criminology, political science, public health, and others, <i>Statistical Applications for the Behavioral and Social Sciences, Second Edition</i> continues to provide the information needed to understand the foundations of statistical analyses as relevant to the behavioral and social sciences.

Diese Produkte könnten Sie auch interessieren:

Statistics for Microarrays
Statistics for Microarrays
von: Ernst Wit, John McClure
PDF ebook
90,99 €