bluuebunny/crossref_metadata_2025 huggingface dataset:
A minimax procedure in the context of sequential testing problems in psychodiagnostics
Authors: Vos Hans J.
Published: February 2003

The purpose of this paper is to derive optimal rules for sequential testing problems in psychodiagnostics. In sequential psychodiagnostic testing, each time a patient is exposed to a new treatment, the decision then is to declare this new treatment effective, ineffective, or to continue testing and exposing the new treatment to another random patient suffering from the same mental health problem. The framework of minimax sequential decision theory is proposed for solving such testing problems; that is, optimal rules are obtained by minimizing the maximum expected losses associated with all possible decision rules at each stage of testing. The main advantage of this approach is that costs of testing can be explicitly taken into account. The sequential testing procedure is applied to an empirical example for determining the effectiveness of a cognitive‐analytic therapy for patients suffering from anorexia nervosa. For a given maximum number of patients to be tested, the appropriate action is indicated at each stage of testing for different numbers of positive reactions to the cognitive‐analytic therapy. The paper concludes with a simulation study, in which the minimax sequential strategy is compared for the anorexia nervosa example with other procedures that exist for similar classification decision problems in the literature in terms of average number of patients to be tested, classification accuracy and average loss.
Criticality of predictors in multiple regression
Authors: Azen Razia , Budescu David V. , Reiser Benjamin
Published: February 2003

A new method is proposed for comparing all predictors in a multiple regression model. This method generates a measure of predictor criticality, which is distinct from and has several advantages over traditional indices of predictor importance.

Using the bootstrapping (resampling with replacement) procedure, a large number of samples are obtained from a given data set which contains one response variable and p predictors. For each sample, all 2p − 1 subset regression models are fitted and the best subset model is selected. Thus, the (multinomial) distribution of the probability that each of the 2p − 1 subsets is ‘the best’ model for the data set is obtained.

A predictor's criticality is defined as a function of the probabilities associated with the models that include the predictor. That is, a predictor which is included in a large number of probable models is critical to the identification of the best‐fitting regression model and, therefore, to the prediction of the response variable.

The procedure can be applied to fixed and random regression models and can use any measure of goodness of fit (e.g., adjusted R2, Cp, AIC) for identifying the best model. Several criticality measures can be defined by using different combinations of the probabilities of the best‐fitting models, and asymptotic confidence intervals for each variable's criticality can be derived. The procedure is illustrated with several examples.
Common factor models of validity and reliability for use with ordinal data in k×k tables
Authors: Grayson D. A.
Published: February 2003

Three common factor models are proposed for the analysis of k × k ordinal data arising from test validity or reliability situations. These models represent an extension of the polychoric correlation model and item response theory. Identification is complete in the most usual reliability situation, where data from only two indicators (raters) are available. Full maximum likelihood estimation is available together with associated informative deviance tests and goodness‐of‐fit tests, examples of which are provided.
Seriation of asymmetric matrices using integer linear programming
Authors: Brusco Michael J.
Published: February 2003

Integer linear programming approaches for seriation of asymmetric n × n proximity matrices have generally been perceived as intractable for problems of practical size. However, to date, no computational evidence has been provided regarding the effectiveness (or ineffectiveness) of such approaches. This paper presents an evaluation of an integer linear programming method for asymmetric seriation using five moderately sized matrices (15 ≤ n ≤ 36) from the psychological literature, as well as 80 synthetic matrices (20 ≤ n ≤ 30). The solution to the linear programming relaxation of the integer‐programming model was integer‐optimal for each of the five empirical matrices and many of the synthetic matrices. In such instances, branch‐and‐bound integer programming was not required and optimal orderings were obtained within a few seconds. In all cases where the solution to the linear programming relaxation was not integer‐optimal, branch‐and‐bound integer programming was able to find an optimal seriation in 18 minutes or less. A pragmatic solution strategy for larger matrices (n > 30) is also presented. This approach exploits the fact that, in many instances, only a modest percentage of all possible transitivity constraints are required to obtain an optimal solution.
Transformation works for non‐normality? On one‐sample transformation trimmed t methods
Authors: Luh Wei‐Ming , Guo Jiin‐Huarng
Published: February 2003

If the assumption of normality is not satisfied, there is no simple solution to this problem for the one‐sample t test. The present study proposes Hall's or Johnson's transformation in conjunction with the trimmed mean to deal with the problem. Computer simulation is carried out to evaluate the small‐sample behaviour of the proposed methods in terms of Type I error rate and statistical power. The proposed methods are compared with the conventional Student t, Yuen's trimmed t, Johnson's transformation untrimmed t, and Hall's transformation untrimmed t statistics for one‐sided and two‐sided tests. The simulation results indicate that the proposed methods can control Type I error well in very extreme conditions and are more powerful than the conventional methods.
Bayesian estimation and test for factor analysis model with continuous and polytomous data in several populations
Authors: Song Xin‐Yuan , Lee Sik‐Yum
Published: February 2003

The main purpose of this paper is to develop a Bayesian approach for the multisample factor analysis model with continuous and polytomous variables. Joint Bayesian estimates of the thresholds, the factor scores and the structural parameters subjected to some simple constraints across groups are obtained simultaneously. The Gibbs sampler is used to produce the joint Bayesian estimates. It is shown that the conditional distributions involved in the implementation are the familiar uniform, gamma, normal, univariate truncated normal and Wishart distributions. The Bayes factor is introduced to test hypotheses involving constraints among the structural parameters of the factor analysis models across groups. Two procedures for computing the test statistics are developed, one based on the Schwarz criterion (or Bayesian information criterion), while the other computes the posterior densities and likelihood ratios by means of draws from the appropriate conditional distributions via the Gibbs sampler. The empirical performance of the proposed Bayesian procedure and its sensitivity to prior distributions are illustrated by some simulation results and two real‐life examples.
Individual differences in paired comparison data
Authors: Böckenholt Ulf , Tsai Rung‐Ching
Published: February 2003

Thurstonian models provide a flexible framework for the analysis of multiple paired comparison judgments because they allow a wide range of hypotheses about the judgments' mean and covariance structures to be tested. However, applications have been limited to a large extent by the computational intractability involved in fitting this class of models. This paper demonstrates that the Monte Carlo EM algorithm facilitates maximum likelihood estimation of Thurstonian paired comparison models even when the number of items is large. A paired comparison study is presented in detail to illustrate the estimation approach.
Misspecifying latent class models by mixture binomials
Authors: Formann Anton K.
Published: February 2003

Four scenarios of homogeneity/heterogeneity with respect to the performance of the subjects and the task difficulties are considered: first, the unconstrained latent class model providing for heterogeneity with respect to both; second, the mixture binomial assuming constant task difficulty within each mixing component, but different levels of performance of the subjects; third, the model of independence which is equivalent to the one‐class latent class model allowing for different task difficulties but no variability of the subjects; and fourth, the binomial with success probability constant across tasks and subjects. It is shown that both over‐ and underdispersion may arise in latent class models of which the other three are special cases. As a consequence, the latent class model and the mixture binomial may generate nearly indistinguishable score distributions where overdispersion is present. So the score distribution is not always indicative of the lack of fit of the mixture binomial when in fact the latent class model is true. It may, therefore, be misleading to accept mixture binomials as well‐fitting models without having additionally assessed the fit of latent class models. This, however, is often the case in empirical research. A long series of investigations on Piaget's water‐level tasks serves as a good example.
A nonlinear congeneric model for continuous item responses
Authors: Ferrando Pere J.
Published: February 2003

This paper presents a unidimensional item response model intended for personality and attitude items that use a continuous response format. The model's starting point is the linear congeneric model for item scores, but it takes into account the bounded nature of the item responses and assumes that their conditional distributions for a fixed trait level are truncated normal. This assumption leads to nonlinear item‐trait regressions and considerably modifies some aspects of the linear model. The linear model is considered as an approximation to the modified version, and an interval is defined in which the approximation is satisfactory. Procedures for estimating the item and subject parameters are described. The applicability of the model is illustrated using real data.
Estimation of congeneric scale reliability using covariance structure analysis with nonlinear constraints
Authors: Raykov Tenko
Published: February 2003

A method of composite reliability estimation using covariance structure analysis with nonlinear constraints is outlined. To motivate the developments, initially a short overview of research is presented, demonstrating that in many cases the widely used coefficient alpha is an unsatisfactory index of scale reliability already at the population level. As an alternative, the proposed covariance structure analysis procedure is based on the theoretical formula of the scale reliability coefficient in terms of parameters pertaining to a given set of congeneric components. The described approach is illustrated with several numerical examples and its performance compared with that of coefficient alpha.
Evaluation of a pseudo–R2 measure for panel probit models
Authors: Spiess Martin
Published: February 2003

A simulation study designed to evaluate the pseudo‐R2T proposed in an earlier paper by Spiess and Keller suggests that, for the models considered, this measure represents the goodness of fit not only of the systematic part, but also of the assumed correlation structure in binary panel probit models.
On the direct products of measurement structures
Authors: Chiang Ching‐Yuan
Published: February 2003

This paper considers the direct products of relational systems of the same type and scales defined thereon, as well as the invariance properties of functions on such product structures. The results will be applied to a theory of multidimensional scale types.
A goodness of fit test for sparse 2p contingency tables
Authors: Bartholomew David J. , Leung Shing On
Published: February 2003

When a model is fitted to data in a 2p contingency table many cells are likely to have very small expected frequencies. This sparseness invalidates the usual approximation to the distribution of the chi‐squared or log‐likelihood tests of goodness of fit. We present a solution to this problem by proposing a test based on a comparison of the observed and expected frequencies of the second‐order margins of the table. A χ2 approximation to the sampling distribution is provided using asymptotic moments. This can be straightforwardly calculated from the expected cell frequencies. The new test is applied to several previously published examples relating to the fitting of latent variable models, but its application is quite general.
Estimation of the Wing‐Kristofferson model for discrete motor responses
Authors: Kampen Jarl K. , Snijders Tom A. B.
Published: February 2003

A number of estimation methods for the variance components in the Wing‐Kristofferson model for inter‐response times are examined and compared by means of a simulation study. The estimation methods studied are the method of moments, maximum likelihood, and an alternative approach in which the Wing‐Kristofferson model is recognized as a moving average model.
Comparing the variances of two independent groups
Authors: Wilcox Rand R.
Published: February 2003

Comparing the variances of two independent groups is a classic problem. Methods for comparing alternative measures of dispersion have been derived that perform well in simulations, but all methods for comparing variances have been found to be unsatisfactory in terms of Type I error probabilities or power. Currently, it appears that the most successful method, in terms of controlling the probability of a Type I error, is based on the so‐called mean half‐square successive difference statistic where a bootstrap is used to estimate its standard error, and a calibrated bootstrap is used to control the probability of a Type I error. But the mean half‐square successive difference statistic is not permutation‐invariant, and it is fairly evident that power can be relatively poor. A slight modification of the percentile bootstrap method for computing a 1 — α confidence interval for the difference between the variances is considered that corrects these problems.
Influential observations in the estimation of mean vector and covariance matrix
Authors: Poon Wai‐Yin , Poon Yat Sun
Published: February 2003

Statistical procedures designed for analysing multivariate data sets often emphasize different sample statistics. While some procedures emphasize the estimates of both the mean vector μ and the covariance matrix Σ, 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 μ, of Σ, and of both together. Real data sets are analysed and results are presented to illustrate the effectiveness of the proposed measures.
An inhibition‐based stochastic countable‐time decision model
Authors: Shmulevich Ilya , Van der Ven A. H. G. S.
Published: February 2003

A new stochastic model to account for reaction‐time fluctuation in prolonged work tasks is presented. Transition probabilities from work periods to distraction periods and vice versa are dependent on inhibition, which increases during work and decreases during distractions. The model presented here differs from all other inhibition‐based models in that transitions can take place only at certain random points in time, and is referred to as a countable‐time decision model. It is argued that the proposed model is a more plausible alternative to other existing inhibition‐based models, while at the same time being highly flexible in that it is able to approximate other models arbitrarily well. This model is compared to an existing inhibition‐based continuous‐time decision model and the probability distribution functions for work and distraction periods are derived.
Controlling the rate of Type I error over a large set of statistical tests
Authors: Keselman H.J. , Cribbie Robert , Holland Burt
Published: February 2003

When many tests of significance are examined in a research investigation with procedures that limit the probability of making at least one Type I error—the so‐called familywise techniques of control—the likelihood of detecting effects can be very low. That is, when familywise error controlling methods are adopted to assess statistical significance, the size of the critical value that must be exceeded in order to obtain statistical significance can be extremely large when the number of tests to be examined is also very large. In our investigation we examined three methods for increasing the sensitivity to detect effects when family size is large: the false discovery rate of error control presented by Benjamini and Hochberg (1995), a modified false discovery rate presented by Benjamini and Hochberg (2000) which estimates the number of true null hypotheses prior to adopting false discovery rate control, and a familywise method modified to control the probability of committing two or more Type I errors in the family of tests examined—not one, as is the case with the usual familywise techniques. Our results indicated that the level of significance for the two or more familywise method of Type I error control varied with the testing scenario and needed to be set on occasion at values in excess of 0.15 in order to control the two or more rate at a reasonable value of 0.01. In addition, the false discovery rate methods typically resulted in substantially greater power to detect non‐null effects even though their levels of significance were set at the standard 0.05 value. Accordingly, we recommend the Benjamini and Hochberg (1995, 2000) methods of Type I error control when the number of tests in the family is large.
Fitting structural equation models using estimating equations: A model segregation approach
Authors: Yuan Ke‐Hai , Chan Wai
Published: February 2003

Problems such as improper solution, non‐convergence, subsets of variables having different distribution, and latent variables with single indicators are common in the practice of structural equation modelling. In such cases, it may be feasible to fix some model parameters at prespecified values while concentrating on estimating some other parameters. This paper formulates such a model fitting process through a model segregation approach. The statistical properties of this procedure are studied using the theory of estimating equations and optimal estimating functions. The dependency of the new parameter estimates on those of the prespecified parameter estimates is characterized for several commonly used estimating equations. A rescaled model fit statistic is proposed. Examples illustrate various applications of this procedure.
Outcomes and sample selection: The case of a homelessness and substance abuse intervention
Authors: Sosin Michael R.
Published: February 2003

In the likely event that some clients refuse to participate in a psychosocial field experiment, the estimates of the effects of the experimental treatment on client outcomes may suffer from sample selection bias, regardless of whether the statistical analyses include control variables. This paper explores ways of correcting for this bias with advanced correction strategies, focusing on experiments in which clients refuse assignment into treatment conditions. The sample selection modelling strategy, which is highly recommended but seldom applied to random sample psychosocial experiments, and some alternatives are discussed. Data from an experiment on homelessness and substance abuse are used to compare sample selection, conventional control variable, instrumental variable, and propensity score matching correction strategies. The empirical findings suggest that the sample selection modelling strategy provides reliable estimates of the effects of treatment, that it and some other correction strategies are awkward to apply when there is post‐assignment rejection, and that the varying correction strategies provide widely divergent estimates. In light of these findings, researchers might wish regularly to compare estimates across multiple correction strategies.
Sampling variability of the success ratio in predictor‐based selection
Authors: de Corte Wilfried
Published: February 2003

In applied settings, the expected benefits of a predictor‐based selection are often expressed in terms of the expected success ratio of the selection. Although Taylor and Russell proposed a formula to estimate its value, simple simulations show that their formula is often inadequate. Also, little is known at present about the sampling variability of the success ratio and, hence, about the accuracy with which the success ratio of any particular selection can be predicted. The above deficiencies are addressed for the three most popular single‐stage selection scenarios: the restricted (or fixed) quota, the threshold (mastery or fixed cut‐off) and the mixed quota/threshold decision. For each scenario it is shown how the sampling distribution of the success rate statistic can be derived. The problems related to the numerical evaluation of the distribution are also discussed, and it is shown by means of example applications that the sampling variability of the success ratio may be quite substantial in many real‐world predictor‐based selections.
Measuring change in controlled longitudinal studies
Authors: Overall John E. , Tonidandel Scott
Published: February 2003

This paper examines the implications of the correlational structure of repeated measurements for three indices of change that can be used to evaluate treatment effects in longitudinal studies with scheduled assessment times and fixed total duration. The generalized least squares (GLS) regression of repeated measurements on time, which is usually reserved for complex mixed model solutions, takes the correlational structure of the repeated measurements into account, whereas simple gain scores and ordinary least squares (OLS) regression calculations do not. Nevertheless, the GLS solution is equivalent to OLS under conditions of compound symmetry and is equivalent to the analysis of simple gain scores in the presence of an autoregressive (order 1) correlational structure. The understanding of these relationships is important with regard to the frequently heard criticisms of the simpler definitions of treatment response in repeated measurement designs.
Cross‐validation by downweighting influential cases in structural equation modelling
Authors: Yuan Ke‐Hai , Marshall Linda L. , Weston Rebecca
Published: February 2003

In the social and behavioural sciences, structural equation modelling has been widely used to test a substantive theory or causal relationship among latent constructs. Cross‐validation (CV) is a valuable tool for selecting the best model among competing structural models. Influential cases or outliers are often present in practical data. Therefore, even the correct model for the majority of the data may not cross‐validate well. This paper discusses various drawbacks of CV based on sample covariance matrices, and develops a procedure for using robust covariance matrices in the model calibration and validation stages. Examples illustrate that the CV index based on sample covariance matrices is very sensitive to influential cases, and even a single outlier can cause the CV index to support a wrong model. The CV index based on robust covariance matrices is much less sensitive to influential cases and thus leads to a more valid conclusion about the practical value of a model structure.
Examining group differences in reliability of multiple‐component instruments
Authors: Raykov Tenko
Published: February 2003

A covariance structure modelling method for examining group differences in scale reliability of multi‐component measuring instruments is proposed. The procedure is based on a set of appropriate parameter constraints imposed in a multiple‐population structural model. Unlike tests of group differences in coefficient alpha that in general do not evaluate the discrepancies in scale reliability coefficients, the described approach permits one to examine differences in the latter quantities of actual interest when concerned with questions pertaining to reliability of composite scores. The method can be used to test whether a given measuring instrument has identical reliabilities in studied populations, and/or whether distinct instruments, or modes of administration of the same instrument, have equal reliabilities across groups. The proposed procedure is illustrated with a pair of examples.
Asymptotic standard errors of estimated standard errors in structural equation modelling
Authors: Ogasawara Haruhiko
Published: February 2003

Asymptotic standard errors of the estimated asymptotic standard errors for parameter estimates in structural equation modelling are derived using the delta method with the assumption of multivariate normality for observed variables. The derivation covers the cases with and without restrictions on parameters. The result can be used to derive the asymptotic standard error of the z score (a parameter estimate divided by its estimated standard error), which is frequently substantially different from one. The case of standardized observed variables is dealt with as a typical example with restrictions on parameters. For actual covariance (correlation) structure models, the exploratory factor analysis model with factor rotation and the confirmatory factor analysis model are presented with numerical examples. Simulations are performed to assess the accuracy of our method for normally and non‐normally distributed variables.
A mixed approach and a distribution‐free multiple imputation technique for the estimation of a multivariate probit model with missing values
Authors: Spiess Martin , Keller Ferdinand
Published: February 2003

In the present paper a mixed generalized estimating/pseudo‐score equations (GEPSE) approach together with a distribution‐free multiple imputation technique is proposed for the estimation of regression and correlation structure parameters of multivariate probit models with missing values for an ordered categorical timeinvariant variable. Furthermore, a generalization of the squared trace correlation (R2T) for multivariate probit models, denoted by pseudo‐R2T, is proposed. A simulation study was conducted, simulating a probit model with an equicorrelation structure in the errors of an underlying regression model and using two different missing mechanisms. For a low ‘true’ correlation the difference between the GEPSE, a generalized estimating equations (GEE) and a maximum likelihood (ML) estimator were negligible. For a high ‘true’ correlation the GEPSE estimator turned out to be more efficient than the GEE and very efficient relative to the ML estimator. Furthermore, the pseudo‐R2T was close to R2T of the underlying linear model. The mixed approach is illustrated using a psychiatric data set of depressive in‐patients. The results of this analysis suggest that the depression score at discharge from a psychiatric hospital and the occurrence of stressful life events seem to increase the probability of having an episode of major depression within a one‐year interval after discharge. Furthermore, the correlation structure points to short‐time effects on having or not having a depressive episode, not accounted for in the systematic part of the regression model.
Estimation of item parameters for the three‐parameter logistic model using the marginal likelihood of summed scores
Authors: Chen Wen‐Hung , Thissen David
Published: February 2003

While existing algorithms used in item response theory (IRT) for the estimation of three‐parameter logistic item parameters are based on response pattern frequencies, the alternative algorithm proposed here uses the summed scores. The development here of the maximum marginal summed‐score likelihood (MMSSL) algorithm contributes to the understanding of the relation of IRT with the traditional test theory, and other developments in IRT, based on summed scores. Simulated data are used to evaluate the performance of the MMSSL algorithm. The results show that estimates of the discrimination parameter obtained using the MMSSL algorithm are biased, but estimates of the threshold and lower asymptote parameters are similar to those obtained using the MML algorithm. Estimated scaled scores obtained with the MMSSL parameter estimates are approximately as accurate as those obtained using the MML estimates.
Polytomous item scores and Guttman dependence
Authors: Akkermans Wies
Published: February 2003

Some theoretical relations are established between the score on a polytomous item and the total score on a set of Guttman dependent binary items. Conditions are derived under which these two scores are identically distributed. Application of the theoretical results to three well‐known models for polytomous data yields, among others, the result that the score on a graded response item (Samejima, 1969) is never distributed as the total score on a set of independent binary Rasch items (Rasch, 1969).
A comparison of recent approaches to the analysis of repeated measurements
Authors: Keselman H. J. , Algina James , Kowalchuk Rhonda K. , Wolfinger Russell D.
Published: February 2003

Looney & Stanley's (1989) recommendations regarding analysis strategies for repeated measures designs containing between‐subjects grouping variables and within‐subjects repeated measures variables were re‐examined and compared to recent analysis strategies. That is, corrected degrees of freedom univariate tests, multivariate tests, mixed model tests, and tests due to Keselman, Carriere & Lix (1993) and to Algina (1994), Huynh (1978) and Lecoutre (1991) were compared for rates of Type I error in unbalanced non‐spherical repeated measures designs having varied covariance structures and no missing data on the within‐subjects variable. Heterogeneous within‐subjects and heterogeneous within‐ and between‐subjects structures were investigated along with multivariate non‐normality. Results indicated that the tests due to Keselman et al. and Algina, Huynh and Lecoutre provided effective Type I error control whereas the default mixed model approach computed with PROC MIXED (SAS Institute, 1995) generally did not. Based on power differences, we recommend that applied researchers adopt the Welch‐James type test described by Keselman et al.
Bayesian estimation of Thurstonian ranking models based on the Gibbs sampler
Authors: Yao Grace , Böckenholt Ulf
Published: February 2003

This paper presents a Gibbs sampler for the estimation of Thurstonian ranking models. This approach is useful for the analysis of ranking data with a large number of options. Approaches for assessing the goodness‐of‐fit of Thurstonian ranking models based on posterior predictive distributions are also discussed. Two simulation studies and two ranking studies are presented to illustrate that the Gibbs sampler is a promising solution to the numerical problems that previously plagued the estimation of Thurstonian ranking models.
Measuring effect size: A non‐parametric analogue of ω2
Authors: Wilcox Rand R. , Muska Jan
Published: February 2003

When comparing two groups of subjects, one of the many measures of effect size is ω2. This paper suggests a non‐parametric analogue of ω2 based on the following point of view. Given an observation from one of two groups, but not knowing whether it came from the first or second group, how certain can we be that the observation came from the first group? This is in contrast to ω2 where, given that an observation came from a specific group, say the first group, how much does this reduce our uncertainty about the dependent variable? One problem with ω2 is that it is not robust‐it is a function of the variances‐so it can be misleading for reasons reviewed in the paper. Four estimators of the proposed measure of effect size are described and compared in a simulation study. Contrary to what was expected, the .632 bootstrap estimator performed best in terms of bias and mean squared error.
Bayesian analysis of square ordinal‐ordinal tables
Authors: Poon Wai‐Yin
Published: February 2003

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.
Measuring change: Mixed Markov models for ordinal panel data
Authors: Böckenholt Ulf
Published: February 2003

In an analysis of longitudinal data it is important to distinguish between dependencies caused by within ‐ and between ‐ subject variability. This paper presents mixed Markov models for ordinal data that take into account both sources of variation. In addition, covariates that may capture differences among panel members and timespecific changes are also incorporated in the model. The model is derived by specifying an observation‐driven process at the individual level and allowing for parametric or semi‐parametric representations of random parameter variation across the units of analysis. As a result, the approach is well suited for modelling ordinal panel data with a large number of time points. In an application a three‐week diary study is analysed to test hypotheses about the relationships between emotions and personality factors over time.
On fitting latent class models for binary data: The estimation of standard errors
Authors: De Menezes L. M.
Published: February 2003

We investigate the uncertainty of the estimators in unrestricted latent class analysis applied to binary data. First, we use maximum likelihood theory to obtain asymptotic estimates for the standard errors of the parameters. Second, we consider models that were fitted to data from two large surveys and compare the asymptotic standard errors with empirical estimates obtained using a parametric bootstrap. Third we investigate the variability of the asymptotic standard errors via a simulation study, which gives insights into their reliability. Results show the importance of estimating and reporting standard errors. However, they also indicate serious problems due to very small or very large parameter values, sparse data or sample sizes that are not sufficiently large to enable the estimation of the asymptotic standard errors. Finally, we discuss alternatives designed to handle some of the problems identified in this study, especially circumstances where the parametric bootstrap becomes a useful tool.
Logit models and logistic regressions for social networks: II. Multivariate relations
Authors: Pattison Philippa , Wasserman Stanley
Published: February 2003

The research described here builds on our previous work by generalizing the univariate models described there to models for multivariate relations. This family, labelled p*, generalizes the Markov random graphs of Frank and Strauss, which were further developed by them and others, building on Besag's ideas on estimation. These models were first used to model random variables embedded in lattices by Ising, and have been quite common in the study of spatial data. Here, they are applied to the statistical analysis of multigraphs, in general, and the analysis of multivariate social networks, in particular. In this paper, we show how to formulate models for multivariate social networks by considering a range of theoretical claims about social structure. We illustrate the models by developing structural models for several multivariate networks.
Two practical issues in using LISCOMP for analysing continuous and ordered categorical variables
Authors: Poon Wai‐Yin , Lee Sik‐Yum
Published: February 2003

Practitioners use a variety of models and computer programs for structural equation modelling. In particular, the approach developed by Muthen and the associated computer program LISCOMP are often used when there are continuous and ordered categorical measurement variables. Two problems are frequently encountered in practical applications: determining the sample size required to produce reliable results; and whether continuous variables should be standardized. The objective of this paper is to address these problems using Monte Carlo studies based on the confirmatory factor analysis model. We show that when using LISCOMP continuous variables should be standardized and their correlation matrix analysed, even though in theory the covariance matrix can also be used. Furthermore, dependable results may be expected for correlation structure analysis only if the sample size is large enough. These findings have important implications for the use of LISCOMP.
Extracting knowledge structures from observed data
Authors: Schrepp Martin
Published: February 2003

How can we construct a knowledge structure for a given set of problems? A natural construction method is to collect a large set of response patterns to these problems and then extract the knowledge structure from the data. We describe an algorithm for the construction of a best‐fitting knowledge structure from a given data set. The ability of the algorithm to reconstruct the correct knowledge structure from the data is tested in a simulation study. The results show that the algorithm is able to reconstruct the underlying knowledge structure with high accuracy if enough response patterns are available.
Statistical analysis of nonlinear factor analysis models
Authors: Zhu Hong‐Tu , Lee Sik‐Yum
Published: February 2003

This paper presents a Bayesian analysis of a general nonlinear factor analysis model. Both the non‐informative and conjugate prior distributions are considered. A hybrid algorithm that combines the Metropolis‐Hastings algorithm and the Gibbs sampler is implemented to produce direct estimates of the latent factor scores and the structural parameters. Standard errors estimates as well as a goodness‐of‐fit statistic for assessing the posited model are presented. To illustrate the methodology, results from a simulation study and a real example are reported.
Stochastic three‐mode models for mean and covariance structures
Authors: Oort Frans J.
Published: February 2003

With three‐mode models, the three modes are analysed simultaneously. Examples are the analysis of multitrait‐multimethod data where the modes are traits, methods and subjects, and the analysis of multivariate longitudinal data where the modes are variables, occasions and subjects. If we consider the subjects mode as random, and the other modes as fixed, such data can be analysed using stochastic three‐mode models. Three‐mode factor analysis models and composite direct product models are special cases, but they are models for the covariance structure only. Stochastic three‐mode models for mean and covariance structures are presented, and the identification, estimation and interpretation of the model parameters are discussed. Interpretation is facilitated by introducing a new terminology and by considering various special cases. Analyses of real data from the field of economic psychology serve as an illustration.
Using repeaters for estimating comparable scores
Authors: Liou Michelle , Cheng Philip E. , Wu Chieh‐Jung
Published: February 2003

Multiple forms of a test for placement services and for licensure or certification exams are developed for measuring the same competence and need to be scaled so that observed scores on these forms are comparable. Conventionally, comparable studies have used the common‐item design to adjust selection‐bias between candidates taking different forms. In practice, the common‐item scores may not be immediately available for comparability studies; even when available, they can be contaminated by non‐random error due to test disclosure. On the other hand, scores from candidates repeatedly taking more than one form of text can be used to scale test forms to achieve comparability. This study considers comparability studies using repeaters as an incomplete‐data problem, that is, most candidates have observed scores on one form and missing scores on the other, but repeaters have both. We propose a general model for estimating score distributions for test forms that would have observed if all takers had no missing scores. The score distributions based on all candidates can then be used to find comparable scores by the equipercentile method. The model parameters are estimated by maximizing the incomplete‐data likelihood via an EM algorithm. The standard errors of comparable scores are also derived under the proposed model. The use of repeaters for establishing score comparability is investigated in an empirical study.
Model evaluation and information criteria in covariance structure analysis
Authors: Ichikawa Masanori , Konishi Sadanori
Published: February 2003

The normal theory based likelihood ratio test statistic, often used to evaluate the goodness of fit of a model in covariance structure analysis, has several problems that may make it inflexible and sometimes unreliable. We introduce criteria for evaluating the models in covariance structure analysis from an information‐theoretic point of view. The basic idea behind the present approach is to express a model in the form of a probability distribution and then evaluate the model by the Kullback‐Leibler information. We consider four types of information criteria that are constructed by correcting the upward bias of the sample based log‐likelihood as a natural estimate of the Kullback‐Leibler information or, equivalently, the expected log‐likelihood. Monte Carlo experiments are conducted to examine the performance of the information criteria under various sample sizes and degrees of deviations from both structural and distributional assumptions. We show that the variance of the bootstrap bias estimate caused by bootstrap simulation can be considerably reduced without any analytical derivations.
A powerful transformation trimmed mean method for one‐way fixed effects ANOVA model under non‐normality and inequality of variances
Authors: Luh Wei‐Ming , Guo Jiin‐Huarng
Published: February 2003

The present study proposes a transformation trimmed mean method for the one‐way fixed effects ANOVA model to deal with non‐normality and inequality of variances. First, the data are trimmed on both sides. Then the test statistic is transformed by Johnson's method for each group to deal with non‐normality. Finally, an approximate test such as the Alexander‐Govern test, the James second‐order test or the Welch test is applied to test the equality of population trimmed means. Monte Carlo simulation results show that, under non‐normal cases, the proposed method can control Type I error rates, and has greater power than other competitors, such as the original Alexander‐Govern method, the Alexander‐Govern trimmed mean method, the James second‐order trimmed mean method, the Welch trimmed mean method, as well as the corresponding bootstrapping methods. The Johnson's transformation trimmed mean method can be programmed easily and is highly recommended as an alternative for the one‐way fixed effects ANOVA model if normality or equality of variances cannot be assumed.
Retrieved memories of childhood sexual abuse
Authors: Delmonte Michael M.
Published: February 2003

About a century ago Freud sparked off a bitter controversy concerning alleged recollections of childhood sexual abuse: Were they fact or fiction? Recently this debate has been revived with intensity, with some professionals stubbornly taking up entrenched and polarized positions. On the one side there are those who continue to deny the veracity of all ‘recovered memories’, and thus also of the implicated psychological defences of repression and dissociation. At the other extreme are those therapists who assume that particular symptoms invariably imply sexual abuse. There is a growing corpus of anecdotal, clinical and, more recently, research evidence supporting the contention that childhood sexual abuse, like all other trauma, can be forgotten for days, and even for many years, before being recalled. However, the reconstruction of these memories is a complex and, at times, a somewhat fallible process.
Psychometric properties of the Hospital Anxiety and Depression Scale with a population of members of a depression self‐help group
Authors: Dagnan Dave , Chadwick Paul , Trower Peter
Published: February 2003

This paper reports the psychometric properties of the Hospital Anxiety and Depression Scale when used with 341 members of a self‐help group for people with depression. We report internal reliability and factor structure for the complete data set and factor structure for groups split by gender, previous hospitalization for depression and current use of medication for depression. In each instance the factor structure reflects the intended depression and anxiety subscales. We comment on the usefulness of the HADS in studies of cognitive processes in depression and anxiety.
Attitudes towards emotional expression, relational competence and psychological distress
Authors: Surgenor Tammy , Joseph Stephen
Published: February 2003

The association between life events and psychological distress was found to be mediated by attitudes towards emotional expression for those with low social support but not for those with high social support. Passive interpersonal behaviour significantly accounted for additional variance in psychological distress to that accounted for by life events and attitudes.
Attachment style in the context of clinical and health psychology: A proposal for the assessment of valence, incongruence, and accessibility of attachment representations in various working models
Authors: Gerlsma Coby , Luteijn Frans
Published: February 2003

By combining the Adult Attachment Interview and the Autobiographical Memory Test, a structured interview was developed as a ‘quick and dirty’ measure for the assessment of attachment representations in clinical settings. The interview intends to assess valence, incongruence, and accessibility of the attachment representations that are organized in various mental models of relationships. In this study the mental models of relationships involving parents and current partner were addressed, as well as the more general model of self in relationships with significant others. Validity was examined in a sample of couples from the general community. Interrelationships between the different models of attachment relationships were strong enough to warrant the conclusion that each contributes to the same construct (style). Correlations with theoretically proposed consequences of insecure attachment style showed salience of specific working models: valence of self and partner (but not of parents) representations, and incongruence of parental representations related to depressive symptomatology, trait anxiety, relational quality, and communication efficiency. Results indicated that assessment of representations in the working models of different attachment relationships might prove useful in clinical settings, as it might enable localization of those representations that are related to the individual's experience of adversity in interpersonal relationships and affect regulation. Methodological shortcomings are discussed in this context.