Intervention effects and relative variation as dimensions in experts' use of visual inference

Recent research indicates that when analyzing graphically presented single-subject data, subjects trained in visual inference appear to attend to large changes between phases regardless of relative variation and do not differentiate among common intervention effect patterns. In this follow-up study, experts in applied behavior analysis completed a free-sort task designed to assess the effects of these dimensions on their use of visual inference. The results indicate that they tended to differentiate among common intervention effect patterns but did not attend to relative variation in the data.     

A comparison of severalk sample tests for ordered alternatives in completely randomized designs

The importance of appropriate test selection for a given research endeavor cannot be over-emphasized. Using samples drawn from eleven populations (differing in shape, peakedness, and density in the tails), this study investigates the small sample empirical powers of ninek-sample tests against ordered location alternatives under completely randomized designs. The results then are intended to aid the researcher in the selection of a particular procedure appropriate for a given endeavor. To highlight this an industrial psychology application involving work productivity is presented.Research was supported in part by the Scholastic Assistance Program, Baruch College. The author wishes to thank Professors Matthew Goldstein, Shulamith Gross, David Levine, and Edward Wolf for their helpful comments when writing this paper. In addition, the author wishes to thank the referees and editor for their useful suggestions for improving the paper.     

A rasch model for partial credit scoring

A unidimensional latent trait model for responses scored in two or more ordered categories is developed. This “Partial Credit” model is a member of the family of latent trait models which share the property of parameter separability and so permit “specifically objective” comparisons of persons and items. The model can be viewed as an extension of Andrich's Rating Scale model to situations in which ordered response alternatives are free to vary in number and structure from item to item. The difference between the parameters in this model and the “category boundaries” in Samejima's Graded Response model is demonstrated. An unconditional maximum likelihood procedure for estimating the model parameters is developed. Preparation of this paper was supported by grants from the Spencer Foundation and the National Institute for Justice. I would like to thank Professor Benjamin D. Wright of the University of Chicago for his very kind help with the various drafts of this paper.     

Social interest,extreme response style,and implications for adjustment

As part of a series of studies examining Adler's proposition that social interest, or interest and concern for others, is a major factor facilitating personal adjustment, the research investigated the relation between social interest and Extreme Response Style (ERS). ERS was chosen as an indirect measure of maladjustment since research has confirmed Adler's contention that it is especially prevalent among neurotic individuals. In three studies involving a total of 221 subjects, inverse relations were found between a measure of social interest and ERS scores derived from four questionnaires concerning attitudes toward self, others, and a wide variety of attitudinal issues. The data also indicated that the results were not due to either apathy or repression being associated with social interest. The results provided further evidence that positive relations between social interest and adjustment are not limited to self-reports of the latter.     

Structural analysis of subjective categorical data

A general approach to the analysis of subjective categorical data is considered, in which agreement matrices of two or more raters are directly expressed in terms of error and agreement parameters. The method provides focused analyses of ratings from several raters for whom ratings have measurement error distributions that may induce bias in the evaluation of substantive questions of interest. Each rater's judgment process is modeled as a mixture of two components: an error variable that is unique for the rater in question as well as an agreement variable that operationalizes the true values of the units of observation. The statistical problems of identification, estimation, and testing of such measurement models are discussed.The general model is applied in several special cases. The most simple situation is that underlying Cohen's Kappa, where two raters place units into unordered categories. The model provides a generalization and systematization of the Kappa-idea to correct for agreement by chance. In applications with typical research designs, including a between-subjects design and a mixed within-subjects, between-subjects design, the model is shown to disentangle structural and measurement components of the observations, thereby controlling for possible confounding effects of systematic rater bias. Situations considered include the case of more than two raters as well as the case of ordered categories. The different analyses are illustrated by means of real data sets.The authors wish to thank Lawrence Hubert and Ivo Molenaar for helpful and detailed comments on a previous draft of this paper. Thanks are also due to Jens Möller und Bernd Strauß for the data from the 1992 Olympic Games. We thank the editor and three anonymous reviewers for valuable comments on an earlier draft.     

Probability matrix decomposition models

In this paper, we consider a class of models for two-way matrices with binary entries of 0 and 1. First, we considerBoolean matrix decomposition, conceptualize it as alatent response model (LRM) and, by making use of this conceptualization, generalize it to a larger class of matrix decomposition models. Second,probability matrix decomposition (PMD) models are introduced as a probabilistic version of this larger class of deterministic matrix decomposition models. Third, an algorithm for the computation of the maximum likelihood (ML) and the maximum a posteriori (MAP) estimates of the parameters of PMD models is presented. This algorithm is an EM-algorithm, and is a special case of a more general algorithm that can be used for the whole class of LRMs. And fourth, as an example, a PMD model is applied to data on decision making in psychiatric diagnosis. This paper is based on a chapter of the first author's doctoral dissertation, written at the University of Leuven and supervised by Paul De Boeck.     

Bilateral comparison of occipital lobe sulci: A sulcus identifying algorithm

A procedure for automatic identification of sulci of cerebral hemispheres in magnetic resonance images is presented. The algorithm departs from information of the spatial location of a given feature (e.g., sulcal contour) in one hemisphere and automatically defines its course in the other hemisphere. The automatic tracing of sulci was successfully performed in the mesial aspect both hemispheres in 18 normal individuals. The sulcal location in the target hemisphere was first approximated by assuming an affine transformation between hemispheres, and then refined by local edge analysis. The method produced reliable results in comparing the intersulcal areas (the cuneus), sulcal length, their complexity, and the angle between the pariteooccipital and retrocalcarine fissures.     

ANOVA and ANCOVA of pre- and post-test,ordinal data

With random assignment to treatments and standard assumptions, either a one-way ANOVA of post-test scores or a two-way, repeated measures ANOVA of pre- and post-test scores provides a legitimate test of the equal treatment effect null hypothesis for latent variable . In an ANCOVA for pre- and post-test variablesX andY which are ordinal measures of and , respectively, random assignment and standard assumptions ensure the legitimacy of inferences about the equality of treatment effects on latent variable . Sample estimates of adjustedY treatment means are ordinal estimators of adjusted post-test means on latent variable .     

The conjunctive model of hierarchical classes

This paper describes the conjunctive counterpart of De Boeck and Rosenberg's hierarchical classes model. Both the original model and its conjunctive counterpart represent the set-theoretical structure of a two-way two-mode binary matrix. However, unlike the original model, the new model represents the row-column association as a conjunctive function of a set of hypothetical binary variables. The conjunctive nature of the new model further implies that it may represent some conjunctive higher order dependencies among rows and columns. The substantive significance of the conjunctive model is illustrated with empirical applications. Finally, it is shown how conjunctive and disjunctive hierarchical classes models relate to Galois lattices, and how hierarchical classes analysis can be useful to construct lattice models of empirical data.The research reported in this paper was supported by NATO (Grant CRG.921321 to Iven Van Mechelen and Seymour Rosenberg) and by the Research Fund of Katholieke Universiteit Leuven (Grants PDM92/19 and POR93/3 to Iven Van Mechelen; Grants OT89/9 and F91/56 to Paul De Boeck).     

Estimating correlations with missing data,a bayesian approach

The posterior distribution of the bivariate correlation is analytically derived given a data set wherex is completely observed buty is missing at random for a portion of the sample. Interval estimates of the correlation are then constructed from the posterior distribution in terms of highest density regions (HDRs). Various choices for the form of the prior distribution are explored. For each of these priors, the resulting Bayesian HDRs are compared with each other and with intervals derived from maximum likelihood theory.