IBM® SPSS® Amos™ 25 User’s Guide IBM SPSS Amos User
IBM_SPSS_Amos_User_Guide
User Manual:
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- IBM® SPSS® Amos™ 25 User’s Guide
- Contents
- 1 Introduction
- 2 Tutorial: Getting Started with Amos Graphics
- Introduction
- About the Data
- Launching Amos Graphics
- Creating a New Model
- Specifying the Data File
- Specifying the Model and Drawing Variables
- Naming the Variables
- Drawing Arrows
- Constraining a Parameter
- Altering the Appearance of a Path Diagram
- Setting Up Optional Output
- Performing the Analysis
- Viewing Output
- Printing the Path Diagram
- Copying the Path Diagram
- Copying Text Output
- 1 Estimating Variances and Covariances
- 2 Testing Hypotheses
- 3 More Hypothesis Testing
- 4 Conventional Linear Regression
- 5 Unobserved Variables
- 6 Exploratory Analysis
- 7 A Nonrecursive Model
- 8 Factor Analysis
- 9 An Alternative to Analysis of Covariance
- Introduction
- Analysis of Covariance and Its Alternative
- About the Data
- Analysis of Covariance
- Model A for the Olsson Data
- Identification
- Specifying Model A
- Results for Model A
- Searching for a Better Model
- Model B for the Olsson Data
- Results for Model B
- Model C for the Olsson Data
- Results for Model C
- Fitting All Models At Once
- Modeling in VB.NET
- 10 Simultaneous Analysis of Several Groups
- 11 Felson and Bohrnstedt’s Girls and Boys
- 12 Simultaneous Factor Analysis for Several Groups
- 13 Estimating and Testing Hypotheses about Means
- 14 Regression with an Explicit Intercept
- 15 Factor Analysis with Structured Means
- 16 Sörbom’s Alternative to Analysis of Covariance
- Introduction
- Assumptions
- About the Data
- Changing the Default Behavior
- Model A
- Results for Model A
- Model B
- Results for Model B
- Model C
- Results for Model C
- Model D
- Results for Model D
- Model E
- Results for Model E
- Fitting Models A Through E in a Single Analysis
- Comparison of Sörbom’s Method with the Method of Example 9
- Model X
- Modeling in Amos Graphics
- Results for Model X
- Model Y
- Results for Model Y
- Model Z
- Results for Model Z
- Modeling in VB.NET
- 17 Missing Data
- 18 More about Missing Data
- 19 Bootstrapping
- 20 Bootstrapping for Model Comparison
- 21 Bootstrapping to Compare Estimation Methods
- 22 Specification Search
- Introduction
- About the Data
- About the Model
- Specification Search with Few Optional Arrows
- Specifying the Model
- Selecting Program Options
- Performing the Specification Search
- Viewing Generated Models
- Viewing Parameter Estimates for a Model
- Using BCC to Compare Models
- Viewing the Akaike Weights
- Using BIC to Compare Models
- Using Bayes Factors to Compare Models
- Rescaling the Bayes Factors
- Examining the Short List of Models
- Viewing a Scatterplot of Fit and Complexity
- Adjusting the Line Representing Constant Fit
- Viewing the Line Representing Constant C – df
- Adjusting the Line Representing Constant C – df
- Viewing Other Lines Representing Constant Fit
- Viewing the Best-Fit Graph for C
- Viewing the Best-Fit Graph for Other Fit Measures
- Viewing the Scree Plot for C
- Viewing the Scree Plot for Other Fit Measures
- Specification Search with Many Optional Arrows
- Limitations
- 23 Exploratory Factor Analysis by Specification Search
- Introduction
- About the Data
- About the Model
- Specifying the Model
- Opening the Specification Search Window
- Making All Regression Weights Optional
- Setting Options to Their Defaults
- Performing the Specification Search
- Using BCC to Compare Models
- Viewing the Scree Plot
- Viewing the Short List of Models
- Heuristic Specification Search
- Performing a Stepwise Search
- Viewing the Scree Plot
- Limitations of Heuristic Specification Searches
- 24 Multiple-Group Factor Analysis
- 25 Multiple-Group Analysis
- 26 Bayesian Estimation
- 27 Bayesian Estimation Using a Non-Diffuse Prior Distribution
- 28 Bayesian Estimation of Values Other Than Model Parameters
- 29 Estimating a User-Defined Quantity in Bayesian SEM
- 30 Data Imputation
- 31 Analyzing Multiply Imputed Datasets
- 32 Censored Data
- 33 Ordered-Categorical Data
- 34 Mixture Modeling with Training Data
- 35 Mixture Modeling without Training Data
- 36 Mixture Regression Modeling
- 37 Using Amos Graphics without Drawing a Path Diagram
- 38 Simple User-Defined Estimands I
- 39 Simple User-Defined Estimands II
- A 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
- Notices
- Bibliography
- Index