Jay Magidson
Biographic Data
| ID | 4168570 |
|---|---|
| NAME | Jay Magidson |
| GIVEN NAMES | Jay |
| FAMILY NAME | Magidson |
| SIGNATURE | MAGIDSON J |
| AFFILIATIONS | Abt Global (United States) |
| VERIFIED | No |
| TOTAL WORKS | 8 |
| TOTAL CITATIONS | 66 |
| AUTHOR COUNT | 8 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 1981 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 3 |
Linear Logistic Scoring Equations for Latent Class and Latent Profile Models: A Simple Method for Classifying New Cases
Researchers are often interested in using latent class or latent profile parameter estimates to obtain posterior class membership probabilities for observations other than those of the original sample. In this paper, we demonstrate that these probabilities typically take on the form of linear logistic equations with coefficients which are functions of the original model parameters. In other words, the posterior class membership probabilities can …
How to Perform Three-Step Latent Class Analysis in the Presence of Measurement Non-Invariance or Differential Item Functioning
The practice of latent class (LC) modeling using a bias-adjusted three-step approach has become widely popular. However, the current three-step approach has one important drawback–its key assumption of conditional independence between external variables and latent class indicators is often violated in practice, such as when a (nominal) covariate represents subgroups showing measurement non-invariance (MNI) or differential item functioning (DIF). …
Latent Class Analysis With Sampling Weights: A Maximum-Likelihood Approach
The authors illustrate how to perform maximum-likelihood estimation in latent class (LC) analysis when there are sampling weights. The methods are natural extensions of the approaches proposed by Clogg and Eliason (1987) and Magidson (1987) for dealing with sampling weights in the log-linear analysis of frequency tables. For the log-linear form of the LC model, the approach corresponds to a special case of Haberman's (1979) log-linear LC model wi…
Latent Class Models
Latent Class Cluster Analysis
INTRODUCTION Kaufman and Rousseeuw (1990) define cluster analysis as the classification of similar objects into groups, in which the number of groups as well as their forms are unknown. The form of a group refers to the parameters of cluster; that is, to its cluster-specific means, variances, and covariances that also have a geometrical interpretation. A similar definition is given by Everitt (1993), who speaks about deriving a useful division in…
Latent Class Factor and Cluster Models, Bi-Plots, and Related Graphical Displays
We propose an alternative method of conducting exploratory latent class analysis that utilizes latent class factor models, and compare it to the more traditional approach based on latent class cluster models. We show that when formulated in terms of R mutually independent, dichotomous latent factors, the LC factor model has the same number of distinct parameters as an LC cluster model with R+1 clusters. Analyses over several data sets suggest tha…
Qualitative variance, entropy, and correlation ratios for nominal dependent variables
Estimating Nonhierarchical and Nested Log-Linear Models
The recent literature on-log-linear models incorrectly implies that the Iterative Proportional Fitting (IPF) algorithm and associated computer programs such as ECTA can only be used to estimate hierarchical (not nonhierarchical) log-linear models. While ECTA and similar programs are designed for the estimation of hierarchical models, it is shown here that the IPF algorithm (and existing computer programs such as ECTA) can be used to estimate any …
Latent Class Factor and Cluster Models, Bi-Plots, and Related Graphical Displays
We propose an alternative method of conducting exploratory latent class analysis that utilizes latent class factor models, and compare it to the more traditional approach based on latent class cluster models. We show that when formulated in terms of R mutually independent, dichotomous latent factors, the LC factor model has the same number of distinct parameters as an LC cluster model with R+1 clusters. Analyses over several data sets suggest tha…
Latent Class Analysis With Sampling Weights: A Maximum-Likelihood Approach
The authors illustrate how to perform maximum-likelihood estimation in latent class (LC) analysis when there are sampling weights. The methods are natural extensions of the approaches proposed by Clogg and Eliason (1987) and Magidson (1987) for dealing with sampling weights in the log-linear analysis of frequency tables. For the log-linear form of the LC model, the approach corresponds to a special case of Haberman's (1979) log-linear LC model wi…
Qualitative variance, entropy, and correlation ratios for nominal dependent variables
Estimating Nonhierarchical and Nested Log-Linear Models
The recent literature on-log-linear models incorrectly implies that the Iterative Proportional Fitting (IPF) algorithm and associated computer programs such as ECTA can only be used to estimate hierarchical (not nonhierarchical) log-linear models. While ECTA and similar programs are designed for the estimation of hierarchical models, it is shown here that the IPF algorithm (and existing computer programs such as ECTA) can be used to estimate any …
Qualitative variance, entropy, and correlation ratios for nominal dependent variables
Estimating Nonhierarchical and Nested Log-Linear Models
The recent literature on-log-linear models incorrectly implies that the Iterative Proportional Fitting (IPF) algorithm and associated computer programs such as ECTA can only be used to estimate hierarchical (not nonhierarchical) log-linear models. While ECTA and similar programs are designed for the estimation of hierarchical models, it is shown here that the IPF algorithm (and existing computer programs such as ECTA) can be used to estimate any …
Latent Class Factor and Cluster Models, Bi-Plots, and Related Graphical Displays
We propose an alternative method of conducting exploratory latent class analysis that utilizes latent class factor models, and compare it to the more traditional approach based on latent class cluster models. We show that when formulated in terms of R mutually independent, dichotomous latent factors, the LC factor model has the same number of distinct parameters as an LC cluster model with R+1 clusters. Analyses over several data sets suggest tha…
Latent Class Cluster Analysis
INTRODUCTION Kaufman and Rousseeuw (1990) define cluster analysis as the classification of similar objects into groups, in which the number of groups as well as their forms are unknown. The form of a group refers to the parameters of cluster; that is, to its cluster-specific means, variances, and covariances that also have a geometrical interpretation. A similar definition is given by Everitt (1993), who speaks about deriving a useful division in…
Latent Class Models
Latent Class Analysis With Sampling Weights: A Maximum-Likelihood Approach
The authors illustrate how to perform maximum-likelihood estimation in latent class (LC) analysis when there are sampling weights. The methods are natural extensions of the approaches proposed by Clogg and Eliason (1987) and Magidson (1987) for dealing with sampling weights in the log-linear analysis of frequency tables. For the log-linear form of the LC model, the approach corresponds to a special case of Haberman's (1979) log-linear LC model wi…
How to Perform Three-Step Latent Class Analysis in the Presence of Measurement Non-Invariance or Differential Item Functioning
The practice of latent class (LC) modeling using a bias-adjusted three-step approach has become widely popular. However, the current three-step approach has one important drawback–its key assumption of conditional independence between external variables and latent class indicators is often violated in practice, such as when a (nominal) covariate represents subgroups showing measurement non-invariance (MNI) or differential item functioning (DIF). …
Linear Logistic Scoring Equations for Latent Class and Latent Profile Models: A Simple Method for Classifying New Cases
Researchers are often interested in using latent class or latent profile parameter estimates to obtain posterior class membership probabilities for observations other than those of the original sample. In this paper, we demonstrate that these probabilities typically take on the form of linear logistic equations with coefficients which are functions of the original model parameters. In other words, the posterior class membership probabilities can …
Mathematics (8 works) · Statistics (8 works) · Computer Science (7 works) · Latent class model (6 works) · Artificial Intelligence (4 works) · Artificial Intelligence (4 works) · Advanced Statistical Methods and Models (3 works) · Bayesian Methods and Mixture Models (3 works) · Class (philosophy) (3 works) · Data mining (3 works)