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

Introduction to Mediation, Moderation, a

📁 Introduction to Mediation, Moderation, and Conditional Process Analysis. A Regression-based Approach

✍ Andrew F. Hayes 📂 Library 📅 2018 🏛 Guilford Press 🌐 English

Lauded for its easy-to-understand, conversational discussion of the fundamentals of mediation, moderation, and conditional process analysis, this book has been fully revised with 50% new content, including sections on working with multicategorical antecedent variables, the use of PROCESS version 3 f

Introduction to Mediation, Moderation, a

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

✍ Andrew F. Hayes 📂 Library 📅 2018 🏛 Guilford Publications 🌐 English

Lauded for its easy-to-understand, conversational discussion of the fundamentals of mediation, moderation, and conditional process analysis, this book has been fully revised with 50% new content, including sections on working with multicategorical antecedent variables, the use of PROCESS version 3 f

Introduction to Mediation, Moderation, a

📁 Introduction to Mediation, Moderation, and Conditional Process Analysis, Third Edition: A Regression-Based Approach (Methodology in the Social Sciences)

✍ Andrew F. Hayes 📂 Library 📅 2022 🏛 The Guilford Press 🌐 English

Acclaimed for its thorough presentation of mediation, moderation, and conditional process analysis, this book has been updated to reflect the latest developments in PROCESS for SPSS, SAS, and, new to this edition, R. Using the principles of ordinary least squares regression, Andrew F. Hayes i

Introduction to Mediation, Moderation, a

📁 Introduction to Mediation, Moderation, and Conditional Process Analysis

✍ Andrew F. Hayes 📂 Library 📅 2022 🏛 Guilford Press 🌐 English

Understanding Regression Analysis: A Con

📁 Understanding Regression Analysis: A Conditional Distribution Approach

✍ Peter H. Westfall, Andrea L. Arias 📂 Library 📅 2020 🏛 Chapman and Hall/CRC 🌐 English

Understanding Regression Analysis unifies diverse regression applications including the classical model, ANOVA models, generalized models including Poisson, Negative binomial, logistic, and survival, neural networks, and decision trees under a common umbrella -- namely, the conditional d

Model-Free Prediction and Regression: A

📁 Model-Free Prediction and Regression: A Transformation-Based Approach to Inference

✍ Dimitris N. Politis (auth.) 📂 Library 📅 2015 🏛 Springer International Publishing 🌐 English

The Model-Free Prediction Principle expounded upon in this monograph is based on the simple notion of transforming a complex dataset to one that is easier to work with, e.g., i.i.d. or Gaussian. As such, it restores the emphasis on observable quantities, i.e., current and future data, as oppos

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

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