Path analysis (statistics)

About: Path analysis (statistics) is a research topic. Over the lifetime, 1494 publications have been published within this topic receiving 162924 citations.

Using multivariate statistics

Abstract: In this Section: 1. Brief Table of Contents 2. Full Table of Contents 1. BRIEF TABLE OF CONTENTS Chapter 1 Introduction Chapter 2 A Guide to Statistical Techniques: Using the Book Chapter 3 Review of Univariate and Bivariate Statistics Chapter 4 Cleaning Up Your Act: Screening Data Prior to Analysis Chapter 5 Multiple Regression Chapter 6 Analysis of Covariance Chapter 7 Multivariate Analysis of Variance and Covariance Chapter 8 Profile Analysis: The Multivariate Approach to Repeated Measures Chapter 9 Discriminant Analysis Chapter 10 Logistic Regression Chapter 11 Survival/Failure Analysis Chapter 12 Canonical Correlation Chapter 13 Principal Components and Factor Analysis Chapter 14 Structural Equation Modeling Chapter 15 Multilevel Linear Modeling Chapter 16 Multiway Frequency Analysis 2. FULL TABLE OF CONTENTS Chapter 1: Introduction Multivariate Statistics: Why? Some Useful Definitions Linear Combinations of Variables Number and Nature of Variables to Include Statistical Power Data Appropriate for Multivariate Statistics Organization of the Book Chapter 2: A Guide to Statistical Techniques: Using the Book Research Questions and Associated Techniques Some Further Comparisons A Decision Tree Technique Chapters Preliminary Check of the Data Chapter 3: Review of Univariate and Bivariate Statistics Hypothesis Testing Analysis of Variance Parameter Estimation Effect Size Bivariate Statistics: Correlation and Regression. Chi-Square Analysis Chapter 4: Cleaning Up Your Act: Screening Data Prior to Analysis Important Issues in Data Screening Complete Examples of Data Screening Chapter 5: Multiple Regression General Purpose and Description Kinds of Research Questions Limitations to Regression Analyses Fundamental Equations for Multiple Regression Major Types of Multiple Regression Some Important Issues. Complete Examples of Regression Analysis Comparison of Programs Chapter 6: Analysis of Covariance General Purpose and Description Kinds of Research Questions Limitations to Analysis of Covariance Fundamental Equations for Analysis of Covariance Some Important Issues Complete Example of Analysis of Covariance Comparison of Programs Chapter 7: Multivariate Analysis of Variance and Covariance General Purpose and Description Kinds of Research Questions Limitations to Multivariate Analysis of Variance and Covariance Fundamental Equations for Multivariate Analysis of Variance and Covariance Some Important Issues Complete Examples of Multivariate Analysis of Variance and Covariance Comparison of Programs Chapter 8: Profile Analysis: The Multivariate Approach to Repeated Measures General Purpose and Description Kinds of Research Questions Limitations to Profile Analysis Fundamental Equations for Profile Analysis Some Important Issues Complete Examples of Profile Analysis Comparison of Programs Chapter 9: Discriminant Analysis General Purpose and Description Kinds of Research Questions Limitations to Discriminant Analysis Fundamental Equations for Discriminant Analysis Types of Discriminant Analysis Some Important Issues Comparison of Programs Chapter 10: Logistic Regression General Purpose and Description Kinds of Research Questions Limitations to Logistic Regression Analysis Fundamental Equations for Logistic Regression Types of Logistic Regression Some Important Issues Complete Examples of Logistic Regression Comparison of Programs Chapter 11: Survival/Failure Analysis General Purpose and Description Kinds of Research Questions Limitations to Survival Analysis Fundamental Equations for Survival Analysis Types of Survival Analysis Some Important Issues Complete Example of Survival Analysis Comparison of Programs Chapter 12: Canonical Correlation General Purpose and Description Kinds of Research Questions Limitations Fundamental Equations for Canonical Correlation Some Important Issues Complete Example of Canonical Correlation Comparison of Programs Chapter 13: Principal Components and Factor Analysis General Purpose and Description Kinds of Research Questions Limitations Fundamental Equations for Factor Analysis Major Types of Factor Analysis Some Important Issues Complete Example of FA Comparison of Programs Chapter 14: Structural Equation Modeling General Purpose and Description Kinds of Research Questions Limitations to Structural Equation Modeling Fundamental Equations for Structural Equations Modeling Some Important Issues Complete Examples of Structural Equation Modeling Analysis. Comparison of Programs Chapter 15: Multilevel Linear Modeling General Purpose and Description Kinds of Research Questions Limitations to Multilevel Linear Modeling Fundamental Equations Types of MLM Some Important Issues Complete Example of MLM Comparison of Programs Chapter 16: Multiway Frequency Analysis General Purpose and Description Kinds of Research Questions Limitations to Multiway Frequency Analysis Fundamental Equations for Multiway Frequency Analysis Some Important Issues Complete Example of Multiway Frequency Analysis Comparison of Programs

Significance tests and goodness of fit in the analysis of covariance structures

Abstract: Factor analysis, path analysis, structural equation modeling, and related multivariate statistical methods are based on maximum likelihood or generalized least squares estimation developed for covariance structure models. Large-sample theory provides a chi-square goodness-of-fit test for comparing a model against a general alternative model based on correlated variables. This model comparison is insufficient for model evaluation: In large samples virtually any model tends to be rejected as inadequate, and in small samples various competing models, if evaluated, might be equally acceptable. A general null model based on modified independence among variables is proposed to provide an additional reference point for the statistical and scientific evaluation of covariance structure models. Use of the null model in the context of a procedure that sequentially evaluates the statistical necessity of various sets of parameters places statistical methods in covariance structure analysis into a more complete framework. The concepts of ideal models and pseudo chi-square tests are introduced, and their roles in hypothesis testing are developed. The importance of supplementing statistical evaluation with incremental fit indices associated with the comparison of hierarchical models is also emphasized. Normed and nonnormed fit indices are developed and illustrated.

Principles and Procedures of Statistics: A Biometrical Approach

Abstract: Observations probability sampling from a normal distribution comparisons involving two sample means principles of experimental design analysis of variance I - the one-way classification mutiple comparisons analysis of variance II - multiway classification linear regression linear correlation matrix notation linear regression in matrix notation multiple and partial regression and correlation analysis of variance III - factorial experiments analysis of variance analysis of covariance IV analysis of covariance analysis of variance V - unequal subclass numbers some uses of chi-square enumeration data I - one-way classifications enumeration data II - contingency tables categorical models some discrete distributions nonparametric statistics sampling finite populations.

Multivariate data analysis : a global perspective

Abstract: 1 Introduction: Models and Model Building Section I Understanding and Preparing for Multivariate Analysis 2 Cleaning and Transforming Data 3 Factor Analysis Section II Analysis Using Dependece Techniques 4 Simple and Multiple Regression Analysis 5 Canonical correlation 6 Conjoint analysis 7 Multiple Discriminant Analysis and Logistic Regression 8 ANOVA and MANOVA Section III Analysis using Interdependence Techniques 9 Group data and Cluster Analysis 10 MDS and Correspondence Analysis Structural Equation Modeling 11 SEM: An Introduction 12 Application of SEM

Multiple regression in behavioral research

Abstract: One of the dilemmas facing those who teach sociological methods and statistics these days is how to present the three main applied analytical models which derive from the general linear hypothesis-analysis of variance, regression, and analysis of covariance. The reason for this dilemma is that whereas there now exist in the sociological literature a number of theoretical expositions integrating these various models, nowhere has there existed a reference or, for that matter, a set of references which provided the computational integration in sufficient clarity that the teacher could assign them to his class and be assured that the student would obtain a clear picture of how the three models were computationally interrelated and interchangeable. Kerlinger and Pedhazur have painstakingly provided such a resource. For those looking for such a text (or reference book), it is a teacher's delight! The authors provide one with a consistency of framework which opens in Part 1 (five chapters). Those chapters are a review of the foundations of multiple regression and can be easily read by students who have had an introductory course in statistics. The review is, however, more than just a rehash of regression theory and procedures, as the authors are also developing a framework for the later integration of analysis of variance, analysis of covariance, time series analysis, path analysis and multivariate analysis (multivariate analysis of variance, canonical regression, and discriminant analysis). Part 2, which consists of six chapters, is the focal point of the book. For example, chapters 5, 6, and 7 give an introduction to the use of dummy coding to achieve the same results as one gets in one-way analysis of variance. Chapter 8 extends the procedures to multiple categorical variables and how they can be handled in the multiple regression framework to achieve the same results one would obtain via ANOV computational procedures in factorial designs. Chapter 9 departs from this theme to open considerations of testing for linear and curvilinear regression when working with continu'ous variables. Chapter 10 weaves these considerations into those developed earlier regarding categorical variables and discusses regression procedures for handling both continuous and categorical regressors in the same equation. (I have found this to be a topic of great interest among sociology students who wonder how to use