Analysis of ordinal categorical data with misclassification

We develop a method for the analysis of multivariate ordinal categorical data with misclassification based on the latent normal variable approach. Misclassification arises if a subject has been classified into a category that does not truly reflect its actual state, and can occur with one or more variables. A basic framework is developed to enable the analysis of two types of data. The first corresponds to a single sample that is obtained from a fallible design that may lead to misclassified data. The other corresponds to data that is obtained by double sampling. Double sampling data consists of two parts: a sample that is obtained by classifying subjects using the fallible design only and a sample that is obtained by classifying subjects using both fallible and true designs, which is assumed to have no misclassification. A unified expectation-maximization approach is developed to find the maximum likelihood estimate of model parameters. Simulation studies and examples that are based on real data are used to demonstrate the applicability and practicability of the proposed methods.

On the modelling and estimation of attribute rankings with ties in the Thurstonian framework

A Thurstonian type approach is applied to modelling ranking data with ties. It uses a non-totally differentiable discriminational process instead of the conventional totally differential one to relate the observed rankings and the underlying subjective values. A Monte Carlo expectation-maximization algorithm is proposed to find the maximum likelihood estimates together with the standard errors of the parameters. The approach is examined numerically by means of an artificial example and a simulation study and is applied to a study of attribute assessment.

Estimating the polychoric correlation from misclassified data

Many variables that are used in social and behavioural science research are ordinal categorical or polytomous variables. When more than one polytomous variable is involved in an analysis, observations are classified in a contingency table, and a commonly used statistic for describing the association between two variables is the polychoric correlation. This paper investigates the estimation of the polychoric correlation when the data set consists of misclassified observations. Two approaches for estimating the polychoric correlation have been developed. One assumes that the probabilities in relation to misclassification are known, and the other uses a double sampling scheme to obtain information on misclassification. A parameter estimation procedure is developed, and statistical properties for the estimates are discussed. The practicability and applicability of the proposed approaches are illustrated by analysing data sets that are based on real and generated data. Excel programmes with visual basic for application (VBA) have been developed to compute the estimate of the polychoric correlation and its standard error. The use of the structural equation modelling programme Mx to find parameter estimates in the double sampling scheme is discussed.

Influence analysis for the factor analysis model with ranking data

Influence analysis is an important component of data analysis, and the local influence approach has been widely applied to many statistical models to identify influential observations and assess minor model perturbations since the pioneering work of Cook (1986). The approach is often adopted to develop influence analysis procedures for factor analysis models with ranking data. However, as this well-known approach is based on the observed data likelihood, which involves multidimensional integrals, directly applying it to develop influence analysis procedures for the factor analysis models with ranking data is difficult. To address this difficulty, a Monte Carlo expectation and maximization algorithm (MCEM) is used to obtain the maximum-likelihood estimate of the model parameters, and measures for influence analysis on the basis of the conditional expectation of the complete data log likelihood at the E-step of the MCEM algorithm are then obtained. Very little additional computation is needed to compute the influence measures, because it is possible to make use of the by-products of the estimation procedure. Influence measures that are based on several typical perturbation schemes are discussed in detail, and the proposed method is illustrated with two real examples and an artificial example.

A latent normal distribution model for analysing ordinal responses with applications in meta-analysis

We consider the comparison of two treatments (or a treatment and a control/placebo) with responses that are classified into ordinal categories. By operating on the assumption that the responses are manifestations of some underlying continuous variables and that the definitions of the categories for the treatment group and the placebo group are the same in the same clinical test centre, we develop a model to examine the possible treatment effects. These treatment effects can be identified as location effect or dispersion effect. The method can be generalized to analyse clinical test results coming from different centres, where each centre may have its own standard in classifying responses. The method is technically undemanding and can be implemented in a very simple and straightforward way by using easily accessible software that can be downloaded at no cost. Real data sets are analysed for illustration.

Comparison of Approaches in Estimating Interaction and Quadratic Effects of Latent Variables

Various approaches using the maximum likelihood (ML) option of the LISREL program and products of indicators have been proposed to analyze structural equation models with non-linear latent effects on the basis of Kenny and Judd's formulation. Recently, some methods based on the Bayesian approach and the exact ML approaches have been developed. This article reviews, elaborates and compares several approaches for analyzing nonlinear models with interaction and/or quadratic effects. A total of four approaches are examined, including the product indicator ML approaches proposed by Jaccard and Wan (1995) and Joreskog and Yang (1996), a Bayesian approach and an exact ML approach. The empirical performances of these approaches are assessed using simulation studies in terms of their capabilities in producing reliable parameter and standard error estimates. It is found that whilst the Bayesian and the exact ML approaches produce satisfactory results in all the settings under consideration, and are in general very reliable; the product indicator ML approaches can only produce reasonable results in simple models with large sample sizes.

Identification of influential cells in the analysis of ordinal square tables

For square tables arising from ordinal categorical variables which can be considered as manifestations of underlying continuous variables, it is possible to model the underlying continuous variables in a form that facilitates the comparison of their relative locations and dispersions. An efficient estimation method for such a model is available in the literature and the object of this paper is to develop an influence analysis procedure to accompany the estimation method. The local influence approach is used to obtain the diagnostic measures, and real data sets are analysed to illustrate the practicability of the proposed measures.

Multisample Analysis of Multivariate Ordinal Categorical Variables

We study a multiple group model with ordinal categorical observed variables that are manifestations of underlying normal variables. When the objective of an analysis is to compare the locations and dispersions of the underlying continuous variables in different groups, traditional approaches use exact linear constraints on thresholds across groups to identify the model. Though the resultant model facilitates interpretation in multiple group analysis, in some cases the exact linear relationships on thresholds are not appropriate for describing the reality. However, these constraints must be imposed to identify the model. In view of this, we propose to apply across group stochastic constraints on thresholds to identify the model. Stochastic constraints are more practical and flexible than exact constraints, and subsume exact constraints as a special case, and therefore enable the structure of the data to be described in a more realistic way. Using stochastic constraints, we can achieve an identified model that allows the comparison of underlying continuous variables in different groups relatively, and at the same time accommodate the possible differences in thresholds. A Bayesian approach is employed to analyze the model, and prior knowledge can be incorporated into the analysis. It is demonstrated that the parameter estimates can be produced conveniently using the Mx software program, and an illustrative sample Mx input script is presented. A real data set is analyzed with the proposed approach, and results are compared to those obtained by using other prevailing approaches.

Influential observations in the estimation of mean vector and covariance matrix

Statistical procedures designed for analysing multivariate data sets often emphasize different sample statistics. While some procedures emphasize the estimates of both the mean vector mu and the covariance matrix Sigma, others may emphasize only one of these two sample quantities. In effect, while an unusual observation in a data set has a deleterious impact on the results from an analysis that depends heavily on the covariance matrix, its effect when dependence is on the mean vector may be minimal. The aim of this paper is to develop diagnostic measures for identifying influential observations of different kinds. Three diagnostic measures, based on the local influence approach, are constructed to identify observations that exercise undue influence on the estimate of mu of Sigma, and of both together. Real data sets are analysed and results are presented to illustrate the effectiveness of the proposed measures.

Conditional local influence in case-weights linear regression

The local influence approach proposed by Cook (1986) makes use of the normal curvature and the direction achieving the maximum curvature to assess the local influence of minor perturbation of statistical models. When the approach is applied to the linear regression model, the result provides information concerning the data structure different from that contributed by Cook's distance. One of the main advantages of the local influence approach is its ability to handle the simultaneous effect of several cases, namely, the ability to address the problem of 'masking'. However, Lawrance (1995) points out that there are two notions of 'masking' effects, the joint influence and the conditional influence, which are distinct in nature. The normal curvature and the direction of maximum curvature are capable of addressing effects under the category of joint influences but not conditional influences. We construct a new measure to define and detect conditional local influences and use the linear regression model for illustration. Several reported data sets are used to demonstrate that new information can be revealed by this proposed measure.

A local influence approach to identifying multiple multivariate outliers

We make use of Cook's local influence approach and its recent modification by Poon and Poon to develop measures for detecting multivariate outliers. The motivation and the foundation of the theory are geometrical and are different from classical approaches; however, whilst the proposed measure exhibits a form similar to those in the literature, it still has a considerable advantage in having transformed the classical measures to the unit interval. The new approach unifies outlier identification measures using geometrical concepts. It involves no distributional assumption or large-sample properties, and allows the flexibility of identifying outliers with respect to different metrics. The approach therefore provides a valid reason for using the various measures in complicated situations, such as in non-normal cases and in small-sample problems.

Bayesian analysis of square ordinal-ordinal tables

We analyse square contingency tables with ordered categories. Assuming that the observed ordinal categorical variables are manifestations of underlying continuous variables, we formulate a model which allows the comparisons of locations and dispersions between variables. We identify the model by imposing stochastic constraints on the thresholds that define the relationship between the observed and the underlying variables. As a result, the underlying continuous variables' location and dispersion parameters which were not estimable before can be estimated by the Bayesian approach. Illustrative examples are given based on several reported data sets.

A two-stage estimation of structural equation models with continuous and polytomous variables

This paper develops a computationally efficient procedure for analysis of structural equation models with continuous and polytomous variables. A partition maximum likelihood approach is used to obtain the first stage estimates of the thresholds and the polyserial and polychoric correlations in the underlying correlation matrix. Then, based on the joint asymptotic distribution of the first stage estimator and an appropriate weight matrix, a generalized least squares approach is employed to estimate the structural parameters in the correlation structure. Asymptotic properties of the estimators are derived. Some simulation studies are conducted to study the empirical behaviours and robustness of the procedure, and compare it with some existing methods.