Ohio State University

About: Ohio State University is a education organization based out in Columbus, Ohio, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 102421 authors who have published 222715 publications receiving 8373403 citations. The organization is also known as: Ohio State & The Ohio State University.

Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach

Abstract: I. FUNDAMENTAL CONCEPTS 1. Introduction 1.1. A Scientist in Training 1.2. Questions of Whether, If, How, and When 1.3. Conditional Process Analysis 1.4. Correlation, Causality, and Statistical Modeling 1.5. Statistical Software 1.6. Overview of this Book 1.7. Chapter Summary 2. Simple Linear Regression 2.1. Correlation and Prediction 2.2. The Simple Linear Regression Equation 2.3. Statistical Inference 2.4. Assumptions for Interpretation and Statistical Inference 2.5. Chapter Summary 3. Multiple Linear Regression 3.1. The Multiple Linear Regression Equation 3.2. Partial Association and Statistical Control 3.3. Statistical Inference in Multiple Regression 3.4. Statistical and Conceptual Diagrams 3.5. Chapter Summary II. MEDIATION ANALYSIS 4. The Simple Mediation Model 4.1. The Simple Mediation Model 4.2. Estimation of the Direct, Indirect, and Total Effects of X 4.3. Example with Dichotomous X: The Influence of Presumed Media Influence 4.4. Statistical Inference 4.5. An Example with Continuous X: Economic Stress among Small Business Owners 4.6. Chapter Summary 5. Multiple Mediator Models 5.1. The Parallel Multiple Mediator Model 5.2. Example Using the Presumed Media Influence Study 5.3. Statistical Inference 5.4. The Serial Multiple Mediator Model 5.5. Complementarity and Competition among Mediators 5.6. OLS Regression versus Structural Equation Modeling 5.7. Chapter Summary III. MODERATION ANALYSIS 6. Miscellaneous Topics in Mediation Analysis 6.1. What About Baron and Kenny? 6.2. Confounding and Causal Order 6.3. Effect Size 6.4. Multiple Xs or Ys: Analyze Separately or Simultaneously? 6.5. Reporting a Mediation Analysis 6.6. Chapter Summary 7. Fundamentals of Moderation Analysis 7.1. Conditional and Unconditional Effects 7.2. An Example: Sex Discrimination in the Workplace 7.3. Visualizing Moderation 7.4. Probing an Interaction 7.5. Chapter Summary 8. Extending Moderation Analysis Principles 8.1. Moderation Involving a Dichotomous Moderator 8.2. Interaction between Two Quantitative Variables 8.3. Hierarchical versus Simultaneous Variable Entry 8.4. The Equivalence between Moderated Regression Analysis and a 2 x 2 Factorial Analysis of Variance 8.5. Chapter Summary 9. Miscellaneous Topics in Moderation Analysis 9.1. Truths and Myths about Mean Centering 9.2. The Estimation and Interpretation of Standardized Regression Coefficients in a Moderation Analysis 9.3. Artificial Categorization and Subgroups Analysis 9.4. More Than One Moderator 9.5. Reporting a Moderation Analysis 9.6. Chapter Summary IV. CONDITIONAL PROCESS ANALYSIS 10. Conditional Process Analysis 10.1. Examples of Conditional Process Models in the Literature 10.2. Conditional Direct and Indirect Effects 10.3. Example: Hiding Your Feelings from Your Work Team 10.4. Statistical Inference 10.5. Conditional Process Analysis in PROCESS 10.6. Chapter Summary 11. Further Examples of Conditional Process Analysis 11.1. Revisiting the Sexual Discrimination Study 11.2. Moderation of the Direct and Indirect Effects in a Conditional Process Model 11.3. Visualizing the Direct and Indirect Effects 11.4. Mediated Moderation 11.5. Chapter Summary 12. Miscellaneous Topics in Conditional Process Analysis 12.1. A Strategy for Approaching Your Analysis 12.2. Can a Variable Simultaneously Mediate and Moderate Another Variable's Effect? 12.3. Comparing Conditional Indirect Effects and a Formal Test of Moderated Mediation 12.4. The Pitfalls of Subgroups Analysis 12.5. Writing about Conditional Process Modeling 12.6. Chapter Summary Appendix A. Using PROCESS Appendix B. Monte Carlo Confidence Intervals in SPSS and SAS

Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models

Abstract: Hypotheses involving mediation are common in the behavioral sciences. Mediation exists when a predictor affects a dependent variable indirectly through at least one intervening variable, or mediator. Methods to assess mediation involving multiple simultaneous mediators have received little attention in the methodological literature despite a clear need. We provide an overview of simple and multiple mediation and explore three approaches that can be used to investigate indirect processes, as well as methods for contrasting two or more mediators within a single model. We present an illustrative example, assessing and contrasting potential mediators of the relationship between the helpfulness of socialization agents and job satisfaction. We also provide SAS and SPSS macros, as well as Mplus and LISREL syntax, to facilitate the use of these methods in applications.

Alternative Ways of Assessing Model Fit

Abstract: This article is concerned with measures of fit of a model. Two types of error involved in fitting a model are considered. The first is error of approximation which involves the fit of the model, wi...

Analyzing real-time PCR data by the comparative C(T) method.

Abstract: Two different methods of presenting quantitative gene expression exist: absolute and relative quantification. Absolute quantification calculates the copy number of the gene usually by relating the PCR signal to a standard curve. Relative gene expression presents the data of the gene of interest relative to some calibrator or internal control gene. A widely used method to present relative gene expression is the comparative C(T) method also referred to as the 2 (-DeltaDeltaC(T)) method. This protocol provides an overview of the comparative C(T) method for quantitative gene expression studies. Also presented here are various examples to present quantitative gene expression data using this method.

SPSS and SAS procedures for estimating indirect effects in simple mediation models.

Abstract: Researchers often conduct mediation analysis in order to indirectly assess the effect of a proposed cause on some outcome through a proposed mediator. The utility of mediation analysis stems from its ability to go beyond the merely descriptive to a more functional understanding of the relationships among variables. A necessary component of mediation is a statistically and practically significant indirect effect. Although mediation hypotheses are frequently explored in psychological research, formal significance tests of indirect effects are rarely conducted. After a brief overview of mediation, we argue the importance of directly testing the significance of indirect effects and provide SPSS and SAS macros that facilitate estimation of the indirect effect with a normal theory approach and a bootstrap approach to obtaining confidence intervals, as well as the traditional approach advocated by Baron and Kenny (1986). We hope that this discussion and the macros will enhance the frequency of formal mediation tests in the psychology literature. Electronic copies of these macros may be downloaded from the Psychonomic Society's Web archive at www.psychonomic.org/archive/.