Latent variable model

About: Latent variable model is a research topic. Over the lifetime, 3589 publications have been published within this topic receiving 235061 citations.

Squeezing interval change from ordinal panel data: latent growth curves with ordinal outcomes.

Abstract: A didactic on latent growth curve modeling for ordinal outcomes is presented. The conceptual aspects of modeling growth with ordinal variables and the notion of threshold invariance are illustrated graphically using a hypothetical example. The ordinal growth model is described in terms of 3 nested models: (a) multivariate normality of the underlying continuous latent variables (yt) and its relationship with the observed ordinal response pattern (Yt), (b) threshold invariance over time, and (c) growth model for the continuous latent variable on a common scale. Algebraic implications of the model restrictions are derived, and practical aspects of fitting ordinal growth models are discussed with the help of an empirical example and Mx script (M. C. Neale, S. M. Boker, G. Xie, & H. H. Maes, 1999). The necessary conditions for the identification of growth models with ordinal data and the methodological implications of the model of threshold invariance are discussed.

Measuring sensorial quality of Iberian Ham by Rasch model

Abstract: This paper focuses on a fresh way to determine quality of ham according to sensorial analysis. It is an application of latent traits theory to biometry. “Quality of Iberian Ham” can be considered a latent variable defined by a set of sensorial analysis factors (items). The theoretical background is Item Response Theory (IRT), which suggests that if we can understand how each item in a set of items operates with an object, then we can estimate a measure for the object. The Rasch model is the most common model for that theory. This technique has been applied to data from 8 different hams assessed by 15 expert judges tasting ham in order to obtain Rasch measurements for hams and calibrations for the items.

An Applied Comparison of Methods for Least- Squares Factor Analysis of Dichotomous Variables

Abstract: A statistical simulation was performed to com pare four least-squares methods of factor analysis on datasets comprising dichotomous variables. In put matrices were: (1) phi correlation coefficients...

Factor analysis with (mixed) observed and latent variables in the exponential family

Abstract: We develop a general approach to factor analysis that involves observed and latent variables that are assumed to be distributed in the exponential family. This gives rise to a number of factor models not considered previously and enables the study of latent variables in an integrated methodological framework, rather than as a collection of seemingly unrelated special cases. The framework accommodates a great variety of different measurement scales and accommodates cases where different latent variables have different distributions. The models are estimated with the method of simulated likelihood, which allows for higher dimensional factor solutions to be estimated than heretofore. The models are illustrated on synthetic data. We investigate their performance when the distribution of the latent variables is mis-specified and when part of the observations are missing. We study the properties of the simulation estimators relative to maximum likelihood estimation with numerical integration. We provide an empirical application to the analysis of attitudes.

Longitudinal analysis of activity and travel pattern dynamics using generalized mixed markov latent class models

Abstract: Understanding the dynamics of time allocation by households and their household members is becoming increasingly important for travel demand forecasting. A unique opportunity to understand day-to-day and year-to-year behavioral change, is provided by data from multi-day travel diaries combined with yearly observation of the same individuals over time (panel surveys). In fact, the “repeated” nature of the data allows to distinguish units that over time change their behavior from those that are not and to uncover the underlying stochastic behavior generating the data. In this paper data from the Puget Sound Transportation Panel (PSTP) are analyzed to identify change in the patterns of time allocation by the panel participants (i.e., patterns of activity participation and travel). The data analyzed are sequences of states in categorical data from reported individuals' daily activity participation and travel indicators. This is done separately for activity participation and trip making using probabilistic models that generalize the restrictive Markov chain models by incorporating unobserved variables of change. The PSTP data analysis here suggests the more likely presence of multiple paths of change for time allocation to activities, non-stationary switching of activity participation from one year to the next, and day-to-day stationarity in activity participation pattern switching. Travel pattern change is best explained by a single path of change with stationary day-to-day pattern transition probabilities that are different from their year-to-year counterparts.