Applied statistics II. Multivariable and multivariate techniques / (2020)

• Record Type: Book
• Other Titles: Multivariable and multivariate techniques
• Further Information: Note: Rebecca M. Warner.
• Authors: Warner, Rebecca M
• Contents: 1. The New Statistics; Required Background; What is the “New Statistics”?; Common Misinterpretations of p Values; Problems with NHST Logic The Replication Crises; Common Misuses of NHST; The Replication Crisis; Some Proposed Remedies for NHST Problems; Review of Confidence Intervals; Effect Size; Brief Introduction to Meta-Analysis; Recommendations for Better Research and Analysis; Summary; 2. Advanced Data Screening: Outliers and Missing Values; Introduction; Variable Names and File Management; Sources of Bias; Screening Sample Data; Possible Remedy for Skewness: Nonlinear Data Transformations; Identification of Outliers; Handling Outliers; Testing Linearity Assumptions; Evaluation of Other Assumptions Specific to Analyses; Describing Amount of Missing Data; How Missing Data Arise; Patterns in Missing Data; Empirical Example: Detecting Type a Missingness; Possible Remedies for Missing Data; Empirical Example: Multiple Imputation to Replace Missing Values; Data Screening Checklist; Reporting Guidelines; Summary; Appendix 2 A Brief Note About Zero Inflated Binomial or Poisson Regression; 3. Statistical Control: What Can Happen When You Add a Third Variable?; What is Statistical Control?; First Research Example: Controlling for a Categorical X2 Variable; Assumptions for Partial Correlation Between X1 and Y, Controlling for X2; Notation for Partial Correlation; Computing Partial Correlation: Use of Bivariate Regressions to Remove Variance Predictable by X2 from Both X1 and Y; Partial Correlation Makes No Sense if There is An X1 x X2 Interaction; Computation of Partial r From Bivariate Pearson Correlations; Significance Tests, Confidence Intervals, and Statistical Power for Partial Correlations; Comparing Outcomes for ry1.2 and ry1; Introduction to Path Models; Possible Paths Among X1, Y, and X2; One Possible Model: X1 and Y are Not Related Whether You Control for X2 or Not; Possible Model: Correlation Between X1 and Y is the Same Whether X2 is Statistically Controlled or Not (X2 is Irrelevant to the X1, Y Relationship); When You Control for X2, Correlation Between X1 and Y Drops to 0; When You Control for X2, the Correlation Between X1 and Y Becomes Smaller (But Does not Drop to 0 or Change Sign); Some Forms of Suppression: When You Control for X2, r1y.2 Becomes Larger Than r1y or Opposite in Sign to r1y; “None of the Above”; Results Section; Summary; 4. Regression Analysis and Statistical Control; Introduction; Hypothetical Research Example; Graphic Representation of Regression Plane; Semipartial (or “Part”) Correlation; Partition of Variance In Y in Regression with Two Predictors; Assumptions for Regression With Two Predictors; Formulas for Regression With Two Predictors; SPSS Regression; Conceptual Basis: Factors that Affect the Magnitude and Sign of ? and b in Regression With Two Predictors; Tracing Rules for Path Models; Comparison of Equations for ?, b, pr, and sr; Nature of Predictive Relationships; Effect Size Information in Regression with Two Predictors; Statistical Power; Issues in Planning a Study; Results; Summary; 5. Multiple Regression with Multiple Predictors; Research Questions; Empirical Example; Screening for Violations of Assumptions; Issues in Planning a Study; Computation of Regression Coefficients with k Predictor Variables; Methods of Entry for Predictor Variables; Variance Partitioning in Standard Regression Versus Hierarchical and Statistical Regression; Significance Test for an Overall Regression Model; Significance Tests for Individual Predictors in Multiple Regression; Effect Size; Changes in F and R as Additional Predictors Are Added to a Model in Sequential or Statistical Regression; Statistical Power; Nature of the Relationship Between Each X Predictor and Y (Controlling for Other Predictors); Assessment of Multivariate Outliers in Regression; SPSS Examples and Results; Summary; Appendix 5 A Use of Matrix Algebra to Estimate Regression Coefficients for Multiple Predictors; Appendix 5 B Tables for Wilkinson and Dallal (1981) Test of Significance of Multiple R2 in Forward Statistical Regression; 6. Dummy Predictor Variables in Multiple Regression; What Dummy Variables Are and When They Are Used; Empirical Example; Screening for Violations of Assumptions; Issues in Planning a Study; Parameter Estimates and Significance Tests for Regressions with Dummy Predictor Variables; Group Mean Comparisons Using One-Way Between-S ANOVA; Three Methods of Coding for Dummy Variables; Regression Models That Include Both Dummy and Quantitative Predictor Variables; Effect Size and Statistical Power; Nature of the Relationship and/or Follow-Up Tests; Results; Summary; 7. Moderation: Interaction in Multiple Regression; Terminology; Interaction Between Two Categorical Predictors: Factorial ANOVA; Interaction Between One Categorical and One Quantitative Predictor; Preliminary Data Screening: One Categorical and One Quantitative Predictor; Scatterplot for Preliminary Assessment of Possible Interaction Between Categorical and Quantitative Predictor; Regression to Assess Statistical Significance of Interaction Between One Categorical and One Quantitative Predictor; Interaction Analysis With More Than Three Categories; Example With Different Data: Significant Sex by Years Interaction; Follow-Up: Analysis of Simple Main Effects; Interaction Between Two Quantitative Predictors; SPSS Example of Interaction Between Two Quantitative Predictors; Results for Interaction of Age and Habits as Predictors of Symptoms; Graphing Interaction for Two Quantitative Predictors; Results Section for Interaction of Two Quantitative Predictors; Additional Issues and Summary; Appendix 7 A Graphing Interactions Between Quantitative Variables “By Hand”; 8. Analysis of Covariance; Research Situations for ANCOVA; Empirical Example; Screening for Violations of Assumptions; Variance Partitioning in ANCOVA; Issues in Planning a Study; Formulas for ANCOVA; Computation of Adjusted Effects and Adjusted Y* Means; Conceptual Basis: Factors that Affect the Magnitude of SSAadj and SSresidual and the Pattern of Adjusted Group Means; Effect Size; Statistical Power; Nature of the Relationship and Follow-Up Tests: Information to Include in the Results Section; SPSS Analysis and Results; Additional Discussion of ANCOVA Results; Summary; Appendix 8 A Alternative Methods for the Analysis of Pretest/Posttest Data; 9. Mediation; Definition of Mediation; Hypothetical Research Example; Limits of “Causal” Models; Questions in a Mediation Analysis; Issues in Designing a Mediation Analysis Study; Assumptions in Mediation Analysis and Preliminary Data Screening; Path Coefficient Estimation; Conceptual Issues: Assessment of Direct Versus Indirect Paths; Evaluating Statistical Significance; Effect Size Information; Sample Size and Statistical Power; Additional Examples of Mediation; Note About Use of Structural Equation Modeling Programs to Test Mediation Hypotheses; Results Section; Summary; 10. Discriminant Analysis; Research Situations and Research Questions; Introduction to Empirical Example; Screening for Violations of Assumptions; Issues in Planning a Study; Equations for Discriminant Analysis; Conceptual Basis: Factors That Affect the Magnitude of Wilks’s L; Effect Size; Statistical Power and Sample Size Recommendations; Follow-Up Tests to Assess What Pattern of Scores Best Differentiates Groups; Results; One-Way ANOVA on Scores on Discriminant Functions; Summary; Appendix 10 A The Eigenvalue/ Eigenvector Problem; Appendix 10 B Additional Equations for Discriminant Analysis; 11. Multivariate Analysis of Variance (MANOVA); Research Situations and Research Questions; First Research Example: One-Way MANOVA; Why Include Multiple Outcome Measures?; Equivalence of MANOVA and DA<br /&gt
• Edition: Third edition
• Publisher Details: Los Angeles : SAGE
• Publication Date: 2020
• Extent: 1 online resource
• Subjects: 519.535; Social sciences -- Statistical methods; Psychology -- Statistical methods; Multivariate analysis
• Languages: English
• ISBNs: 9781506352879
• Related ISBNs: 9781544398723
• Notes: Note: Includes bibliographical references.; Note: Description based on CIP data; resource not viewed.
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