Confirmatory and Structural Categorical Latent Variables Models

The paper proposes a general framework for modeling multiple categorical latent variables (MCLV). The MCLV models extend latent class analysis or latent transition analysis to allow flexible measurement and structural components between endogenous categorical latent variables and exogenous covariates. Therefore, modeling frameworks in conventional structural equation models, for example, CFA and MIMIC models are feasible in the MCLV circumstances. Parameter estimations for the MCLV models are performed by using generalized expectation–maximization (E–M) algorithm. In addition, the adjusted Bayesian information criterion provides help for model selections. A substantive study of reading development is analyzed to illustrate the feasibility of MCLV models.     

Latent Markov Modelling of Recidivism Data

This article discusses the application of latent Markov modelling for the analysis of recidivism data. We briefly examine the relations of Markov modelling with log–linear analysis, pointing out pertinent differences as well. We show how the restrictive Markov model may be more easily applicable by adding latent variables to the model, in which case the latent Markov model is a dynamic version of the latent class model. As an illustration, we apply latent Markov analysis on an empirical data set of juvenile prosecution careers, showing how the Markov analyses producing well-fitting and interpretable solutions. We end by comparing the possible contributions of Markov modelling in recidivism research, outlining its drawbacks as well. Recommendations and directions for future research conclude the article.     

Does school matter for educational achievement? a two-way nested-error components analysis

School effectiveness is a much-debated question both at an analytical level and as a policy issue. In this paper we used a two-way nested-error components educational production function model to evaluate the relative importance of latent class and school variables in explaining student school achievement. The analysis is carried out with a sample of Montreal francophone public elementary school students. As for the observable variables, the empirical results suggest that the latent class and school variables are negligible with respect to the latent personal and socioeconomic variables. Related policy issues are discussed in the conclusion.     

Latent variable models for time series: A frequency domain approach with an application to the permanent income hypothesis

The theory of estimation and inference in a very general class of latent variable models for time series is developed by showing that the distribution theory for the finite Fourier transform of the observable variables in latent variable models for time series is isomorphic to that for the observable variables themselves in classical latent variable models. This implies that analytic work on classical latent variable models can be adapted to latent variable models for time series, an implication which is illustrated here in the context of a general canonical form. To provide an empirical example a latent variable model for permanent income is developed, its parameters are shown to be identified, and a variety of restrictions on these parameters implied by the permanent income hypothesis are tested.     

Latent Class Analysis of Respondent Scalability

The psychometric literature contains many indices to detect aberrant respondents. A different, promising approach is using ordered latent class analysis with the goal to distinguish latent classes of respondents that are scalable, from latent classes of respondents that are not scalable (i.e., aberrant) according to the scaling model adopted. This article examines seven Latent Class models for a cumulative scale. A simulation study was performed to study the efficacy of different models for data that follow the scale model perfectly. A second simulation study was performed to study how well these models detect aberrant respondents.     

Estimating latent class model parameters for filter questions with skip patterns

Filter questions with skip patterns have been widely used in survey research, and latent class models (LCM) are often used to analyze this type of categorical data. The LCM parameters are usually estimated by means of an EM (expectation maximization) algorithm. When the pattern is present, the non-response of the skip pattern cannot be treated as random missingness. We thus propose a modified algorithm to estimate the latent class parameters when non-response is present, and the approach is attractive for two reasons. First, the latent class model with the algorithm is very flexible in the sense that it can model the association of variables with the skip patterns under study. Secondly, the algorithm can be easily implemented using any computer language. An empirical example is used to demonstrate the usefulness of the algorithm. The algorithm may also be flexibly generalized to more complex surveys, for example, polytomous responses.     

Some Comments on Latent Structure Analysis, Arising Out of a Paper by R.D. Gill

The model of latent structure analysis for k discrete-valued variates was explained and illustrated by R.D. G ill [5], Here the latent structure model is related to a condition for a covariance matrix, as in factor analysis theory, and a word of warning is given for the interpretation of a factor analysis from discrete valued variables.     

A latent time–budget model

Time–budgets summarize how the time of objects is distributed over a number of categories. Usually they are collected in object by category matrices with the property that rows of this data matrix add up to one. In this paper we discuss a model for the analysis of time–budgets that used this property. The model approximates the observed time–budgets by weighted sums of a number of latent time–budgets. These latent time–budgets determine the behavior of all objects. Special attention is given to the identification of the model. The model is compared with logcontrast principal component analysis.     

Bayesian modeling of joint and conditional distributions

In this paper, we study a Bayesian approach to flexible modeling of conditional distributions. The approach uses a flexible model for the joint distribution of the dependent and independent variables and then extracts the conditional distributions of interest from the estimated joint distribution. We use a finite mixture of multivariate normals (FMMN) to estimate the joint distribution. The conditional distributions can then be assessed analytically or through simulations. The discrete variables are handled through the use of latent variables. The estimation procedure employs an MCMC algorithm. We provide a characterization of the Kullback–Leibler closure of FMMN and show that the joint and conditional predictive densities implied by the FMMN model are consistent estimators for a large class of data generating processes with continuous and discrete observables. The method can be used as a robust regression model with discrete and continuous dependent and independent variables and as a Bayesian alternative to semi- and non-parametric models such as quantile and kernel regression. In experiments, the method compares favorably with classical nonparametric and alternative Bayesian methods.     

Technical efficiency with multi-output,heterogeneous production: a latent class,distance function model of english football

This study combines the output distance function approach with a latent class model to estimate technical efficiency in English football in the presence of productive heterogeneity within a stochastic frontier analysis framework. The distance function approach allows the researcher to estimate technical efficiency including both on-field and off-field production, which is important in the case of English football where clubs are generally thought to maximize something other than profit. On-field production is measured using total league points, and off-field production is measured using total revenue. The data set consists of 2177 club-level observations on 88 clubs that competed in the four divisions of professional football in England over the 29-season period from 1981/82 to 2009/10. The results show evidence of three separate productivity classes in English football. As might be expected, technical efficiency estimated using the latent class model is, on average, higher than technical efficiency using an alternative method which confines heterogeneity to the intercept coefficient. Specifically, average efficiency for the sample is 87.3 and 93.2% for the random-intercept model and the latent class model respectively.     

Assessing the Cross-Sex Validity of the Goldthorpe Class Schema Using Log-linear Models with Latent Variables

In this paper we use log-linear models with latent variables to estimate and compare the structure of job characteristics and their relation to positions in the Goldthorpe class schema for male and female employees. We proceed by estimating latent class models of job attributes and then evaluate the degree of similarity in these models between men and women. Sex differences in the patterns of association between the various latent types of job attribute and between those attributes and the Goldthorpe class schema are then assessed. The structure of job attributes is found to be generally similar for men and women; and the Goldthorpe schema is shown to be a similarly effective predictor of these attributes for both sexes. The class structure of employment relations is therefore argued to be equivalent across the sexes, so confirming the generality of class relations as conceptualised by Goldthorpe and his colleagues.     

A note on the multiple indicator-multiple cause model with several latent variables

Jöreskog/Goldberger [1975] use a maximum likelihood procedure to estimate the parameters of a model in which one observes multiple indicators and multiple causes of a single latent variable. This note extends their analysis to cover the case of several latent variables.     

Portfolio selection in a data-rich environment

We model portfolio weights as a function of latent factors that summarize the information in a large number of economic variables. This approach (hereafter diffusion index approach) offers the opportunity to exploit a much richer information base to improve portfolio selection. We use factor analysis to estimate the space spanned by the factors. This provides consistent estimates for the optimal weights as the number of economic variables and sample size go to infinity. We consider an empirical application to illustrate the practical usefulness of our approach. The results indicate that the diffusion index approach helps to improve the portfolio performance.     

异质性的技术俱乐部与中国地区工业增长——基于潜类别随机前沿模型的研究

本文利用中国1999~2007年30个省份工业的面板数据,以各省份的人力资本水平、对外开放程度作为类别的条件变量,通过建立潜类别随机前沿模型,考虑技术俱乐部的异质性,从而分析了我国的工业经济增长过程。结果表明,我国30个省份存在两个技术俱乐部,使用不同的技术前沿,生产函数存在巨大差异,并且俱乐部A的技术效率高于俱乐部B。技术进步是各省份工业产出变化最重要的决定因素,且B俱乐部要高于A俱乐部。投入变动起着很重要的作用,而效率变化的作用却非常小。     

The Ordinal Controversy Revisited

We present a discussion of the different dimensions of the ongoing controversy about the analysis of ordinal variables. The source of this controversy is traced to the earliest possible stage, measurement theory. Three major approaches in analyzing ordinal variables, called the non-parametric, the parametric, and the underlying variable approach, are identified and the merits and drawbacks of each of these approaches are pointed out. We show that the controversy on the exact definition of an ordinal variable causes problems with regard to defining ordinal association, and therefore to the interpretation of many recently designed models for ordinal variables, e.g., structure equation models using polychoric correlations, latent class models and ordinal response models. We conclude that the discussion with regard to ordinal variable modeling can only be fruitful if one makes a distinction between different types of ordinal variables. Five types of ordinal variables were identified. The problems concerning the analysis of these five types of ordinal variables are solved in some cases and remain a problem for others.     

Local structural quantile effects in a model with a nonseparable control variable

I consider a semiparametric version of the nonseparable triangular model of Chesher [Chesher, A., 2003. Identification in nonseparable models. Econometrica 71, 1405–1441]. The proposed model is linear in coefficients, where the coefficients are unknown functions of unobserved latent variables. Using a control variable idea and quantile regression methods, I propose a simple two-step estimator for the coefficients evaluated at particular values of the latent variables. Under the condition that the instruments are locally relevant (i.e. they affect a particular conditional quantile of interest of the endogenous variable) I establish consistency and asymptotic normality. Simulation experiments confirm the theoretical results.     

A PLS path model to investigate the relations between institutions and human development

The paper studies the relations between institutions and human development, in particular the causal effects of the different types of institutions on different components of human development. We assume development to be created by aggregate demand; in particular that aggregate demand determines the material components of human development. We thus divide institutions into those that create demand and those that are determined by the whole process of development. Similarly we divide human development in its three traditional components (economic development, health, knowledge). Both human development and institutions are assumed to be multidimensional constructs; all the main components of these constructs are defined as latent variables, and the relations between them as structural relations. A partial least squares (PLS) path model is developed: it is the aggregation (and simultaneous estimation) of an outer model relating observed or manifest variables to their own latent variable and of a structural model (inner model) relating some endogenous latent variable to other latent variables. From the goodness of fit point of view, our results seem to validate our theoretical assumptions.     

Servitization across countries and sectors: Evidence from world input-output data

This paper uses the supply tables underlying WIOT data to explore the provision of services by manufacturing. The service shares differ substantially across countries and sectors, while they remain largely stable over time. A latent class analysis assigns broadly defined manufacturing sectors to economy-wide growth models, differentiating between service- and manufacturing-driven models in catching-up and developed economies. Servitization increases with labor productivity. The service intensities in the sectoral production mix are lower in countries with higher manufacturing shares. This holds for both catching-up and developed economies. However, servitization is largely unrelated to productivity and employment growth. We therefore argue that the degree of servitization is contingent on and an attribute of the respective economic model in which a sector operates.     

An assessment of equivalence between Internet and paper-based surveys: evidence from collectivistic cultures

Little research exists that addresses the equivalence in collectivistic cultures of paper- versus Internet-based surveys. This study addressed this gap and examined the measurement equivalence of individual innovativeness scales between Internet surveys and paper-based surveys within a collectivistic culture (with China serving as our example). The study analyzed and compared survey data from both paper and web-based surveys using confirmatory factor analysis. The assessment of invariance included the levels of configural, metric, scalar, and covariance invariance. The means and variance of latent variables were also compared. The results show that measurements are invariant at the two levels (configural and metric), and the covariances between latent variables are also equivalent, but the mean and variance differences of latent variables are apparent. The results indicate that when conducting research in collectivistic cultures and collecting data from distinct survey modes, researchers should concern themselves with the potential of extreme response patterns and the inclination of social desirability responding, as well as considering the measurement invariance across survey modes.