Arbuckle J.L. Amos 17.0 User’s Guide

SPSS, 2008. — 654 p. — ISBN: 1568274025, 9781568274027Amos is short for Analysis of MOment Structures. It implements the general approach to data analysis known as structural equation modeling (SEM), also known as analysis of covariance structures, or causal modeling. This approach includes, as special cases, many well-known conventional techniques, including the general linear model and common factor analysis.Getting StartedTutorial: Getting Started with Amos Graphics
Examples
Estimating Variances and Covariances
Testing Hypotheses
More Hypothesis Testing
Conventional Linear Regression
Unobserved Variables
Exploratory Analysis
A Nonrecursive Model
Factor Analysis
An Alternative to Analysis of Covariance
Simultaneous Analysis of Several Groups
Felson and Bohrnstedt’s Girls and Boys
Simultaneous Factor Analysis for Several Groups
Estimating and Testing Hypotheses about Means
Regression with an Explicit Intercept
Factor Analysis with Structured Means
Sorbom’s Alternative to Analysis of Covariance
Missing Data
More about Missing Data
Bootstrapping
Bootstrapping for Model Comparison
Bootstrapping to Compare Estimation Methods
Specification Search
Exploratory Factor Analysis by Specification Search
Multiple-Group Factor Analysis
Multiple-Group Analysis
Bayesian Estimation
Bayesian Estimation Using a Non-Diffuse Prior Distribution
Bayesian Estimation of Values Other Than Model Parameters
Estimating a User-Defined Quantity in Bayesian SEM
Data Imputation
Analyzing Multiply Imputed Datasets
Censored Data
Ordered-Categorical Data
Mixture Modeling with Training Data
Mixture Modeling without Training Data
Mixture Regression ModelingA Notation
B Discrepancy Functions
C Measures of Fit
D Numeric Diagnosis of Non-Identifiability
E Using Fit Measures to Rank Models
F Baseline Models for Descriptive Fit Measures
G Rescaling of AIC, BCC, and BIC