Quantitative Models in Psychology 1st Edition by Robert Mcgrath 1433809591 9781433809590
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ISBN 10: 1433809591
ISBN 13: 978-1433809590
Author: Robert E. Mcgrath
With clear prose and a reader-friendly format, Robert E. McGrath introduces a conceptual framework for understanding the entire spectrum of quantitative modeling procedures used in psychology while providing a solid grounding in its methods and practices. The result is a comprehensive survey of quantitative methods and concepts in psychology that covers everything needed at the graduate level and beyond, including generalizing from samples to populations, using measurement instruments to generate quantitative scales, discovering alternatives to null hypothesis significance testing, and modeling real-world patterns and relationships.
This book presents the most important and practically relevant quantitative models for the behavioral and social sciences and encourages psychologists and graduate students to think critically about the limitations of the methods currently in use.
Quantitative Models in Psychology 1st Table of contents:
Chapter 1: Introduction to Quantitative Psychology
- The Role of Quantitative Models in Psychology
- Overview of Basic Mathematical and Statistical Concepts
- Importance of Data in Psychological Research
- Historical Development of Quantitative Models in Psychology
Chapter 2: Basic Statistical Methods for Psychological Research
- Descriptive Statistics: Measures of Central Tendency and Variability
- Inferential Statistics: Hypothesis Testing and Confidence Intervals
- Correlation and Regression Analysis
- Introduction to Experimental Design and Sampling
Chapter 3: Probability Theory in Psychology
- Probability Concepts and Their Applications
- Random Variables and Probability Distributions
- Bayes’ Theorem in Psychological Modeling
- The Role of Probability in Decision Making and Cognitive Models
Chapter 4: Psychometric Models
- Classical Test Theory
- Item Response Theory
- Reliability and Validity in Psychological Testing
- Factor Analysis and Its Role in Understanding Constructs
Chapter 5: Linear Models in Psychology
- Introduction to Linear Regression and Multiple Regression
- General Linear Models (GLM)
- Analysis of Covariance (ANCOVA)
- Structural Equation Modeling (SEM) Overview
Chapter 6: Nonlinear Models and Nonparametric Methods
- Nonlinear Regression and Its Applications
- Logistic Regression and Categorical Data Analysis
- Nonparametric Tests and When to Use Them
- Fitting Complex Models to Psychological Data
Chapter 7: Cognitive and Neural Modeling
- Mathematical Models of Cognitive Processes
- Computational Models of Memory, Attention, and Decision Making
- Neural Networks and their Role in Psychology
- Simulating Psychological Phenomena Using Computers
Chapter 8: Models of Learning and Behavior
- Classical and Operant Conditioning Models
- Reinforcement Learning Models
- Bayesian Models of Learning
- Cognitive Models of Perception and Action
Chapter 9: Dynamic Systems and Chaos Theory in Psychology
- Introduction to Dynamic Systems Theory
- Applying Chaos Theory to Human Behavior
- Modeling Psychological Systems Over Time
- Applications in Developmental and Clinical Psychology
Chapter 10: Multivariate Analysis in Psychology
- Principal Component Analysis (PCA)
- Multivariate Regression and Discriminant Analysis
- Cluster Analysis and Its Applications
- Canonical Correlation and Path Analysis
Chapter 11: Advanced Topics in Quantitative Psychology
- Multilevel and Hierarchical Models
- Latent Variable Modeling
- Bayesian Statistics in Psychological Research
- Big Data and Machine Learning in Psychology
Chapter 12: Applications of Quantitative Models in Psychology
- Applications in Clinical Psychology and Diagnosis
- Quantitative Models in Social Psychology
- Cognitive Psychology and Quantitative Research
- Psychological Testing and Assessment Using Quantitative Models
Chapter 13: Ethical and Practical Considerations in Quantitative Psychology
- Ethical Issues in Statistical Analysis
- Addressing Bias and Errors in Psychological Modeling
- Challenges in Data Collection and Interpretation
- The Role of Quantitative Models in Evidence-Based Practice
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