The SAS Macro-Program %AnaQol to Estimate the Parameters of Item Responses Theory Models

The analysis of quality of life questionnaires is taking a great importance in clinical research. Usual and general statistical packages like SAS do not allow users to perform classical analysis of items or to estimate parameters of most used models in this specific field: the practitioners must use various specific software to analyze a quality of life scale. In this article, we present an easy to use SAS macro-program that enables SAS users to obtain classical indices, usual graphical representations, and estimation of parameters of five usual Item Response models. We illustrate capabilities of our macro-program by presenting some practical real Quality of Life examples.     

Two-scale spatial models for binary data

A spatial lattice model for binary data is constructed from two spatial scales linked through conditional probabilities. A coarse grid of lattice locations is specified, and all remaining locations (which we call the background) capture fine-scale spatial dependence. Binary data on the coarse grid are modelled with an autologistic distribution, conditional on the binary process on the background. The background behaviour is captured through a hidden Gaussian process after a logit transformation on its Bernoulli success probabilities. The likelihood is then the product of the (conditional) autologistic probability distribution and the hidden Gaussian–Bernoulli process. The parameters of the new model come from both spatial scales. A series of simulations illustrates the spatial-dependence properties of the model and likelihood-based methods are used to estimate its parameters. Presence–absence data of corn borers in the roots of corn plants are used to illustrate how the model is fitted.     

A Shorter and Multidimensional Version of the Gambling Attitudes and Beliefs Survey (GABS-23)

The Gambling Attitudes and Beliefs Survey (GABS) is a questionnaire which explores gambling-related dysfunctional beliefs in an unidimensional way. The present research aims to investigate the dimensionality of the scale. 343 undergraduate student gamblers and 75 pathological gamblers seeking treatment completed the GABS and the south oaks gambling screen. Exploratory and confirmatory factor analyses revealed that the original one-factor structure of the GABS did not fit the data effectively. We then proposed a shorter version of the GABS (GABS-23) with a new five-factor structure, which fitted with the data more efficiently. The comparisons between students (problem vs. non-problem gamblers) and pathological gamblers seeking treatment indicated that the GABS-23 can discriminate between problem and non-problem gamblers as efficiently as the original GABS. To ensure the validity and the stability of the new structure of the GABS-23, analyses were replicated in an independent sample that consisted of 628 gamblers (256 non problem gamblers, 169 problem gamblers who are not treatment-seeking and 203 problem gamblers seeking treatment). Analyses showed satisfactory results, and the multidimensional structure of the GABS-23 was then confirmed. The GABS-23 seems to be a valid and useful assessment tool for screening gambling-related beliefs, emotions and attitudes among problem and non-problem gamblers. Moreover, it presents the advantage of being shorter than the original GABS, and of screening irrational beliefs and attitudes about gambling in a multidimensional way. The five-factor model of the GABS-23 is discussed based on the theory of locus of control.     

Markov Chain Markov Field dynamics: Models and statistics

This study deals with time dynamics of Markov fields defined on a finite set of sites with state space <$>E<$>, focussing on Markov Chain Markov Field (MCMF) evolution. Such a model is characterized by two families of potentials: the instantaneous interaction potentials, and the time delay potentials. Four models are specified: auto-exponential dynamics (<$>E = {\of R}^+<$>), auto-normal dynamics (<$>E = {\of R}<$>), auto-Poissonian dynamics (<$>E = {\of N}<$>) and auto-logistic dynamics ( E qualitative and finite). Sufficient conditions ensuring ergodicity and strong law of large numbers are given by using a Lyapunov criterion of stability, and the conditional pseudo-likelihood statistics are summarized. We discuss the identification procedure of the two Markovian graphs and look for validation tests using martingale central limit theorems. An application to meteorological data illustrates such a modelling.     

Test de différence de contrastes et somme pondérée de khi-deux

After recalling the framework of minimum-contrast estimation, its consistency and its asymptotic normality, we highlight the fact that these results do not require any stationarity or ergodicity assumptions. The asymptotic distribution of the underlying contrast difference test is a weighted sum of independent chi-square variables having one degree of freedom each. We illustrate these results in three contexts: (1) a nonhomogeneous Markov chain with likelihood contrast; (2) a Markov field with coding, pseudolikelihood or likelihood contrasts; (3) a not necessarily Gaussian time series with Whittle's contrast. In contexts (2) and (3), we compare experimentally the power of the likelihood-ratio test with those of other contrast-difference tests.