A Dyadic IRT Model

Psychometrika - We propose a dyadic Item Response Theory (dIRT) model for measuring interactions of pairs of individuals when the responses to items represent the actions (or behaviors,...     

Personality disorders and personality dimensions in anorexia nervosa

Information on the relationship between anorexia nervosa (AN) and personality disorders (PDs) and dimensions of temperament and character (measured by the Temperament and Character Inventory [TCI; Cloninger, Przybeck Svrakic, & Wetzel, 1994]) is limited. This study examines the predictive validity of the TCI for PD diagnoses assessed by the International Personality Disorder Examination-ICD-10 (IPDE-ICD-10; Loranger, Janca, & Sartorius, 1997) interviews of 46 women with DSM-IV-defined AN. Patients with a PD reported higher levels of harm-avoidance and lower levels of self-directedness than those without a PD. Scores on the TCI were predictive of the number of PD features present, particularly for those PDs in the anankastic, anxious, and dependent groups accounting for 40% to 51% of the variance. Cluster analysis based on scores on the TCI identified a subgroup of patients characterized by low levels of novelty seeking, self-directedness, and cooperativeness and high levels of harm avoidance. This cluster included the majority of those with avoidant, anxious, or dependent PDs. Assessment of particular personality dimensions was able to predict PDs in an anorexic sample. Since normal personality dimensions have greater validity than the categorical PDs, a consideration of normal temperament and character may assist in clinical decisionmaking and considerations concerning treatment.     

Regional variation of cloze probabilities for sentence contexts

In 1980, Bloom and Fischler published a paper describing a set of sentence contexts (SCs) and their cloze probabilities (CPs). This material has subsequently been employed in numerous studies of linguistic processing. We sought to define the completion words and their CPs for Bloom and Fischler’s sentences in an inner-city British population in order to establish reliable norms for subsequent studies in the U.K. One hundred and fifty incomplete SCs were presented to 73 volunteers. The CPs for each of the words used to complete the SCs were computed. We then compared the CPs from our sample with those from Bloom and Fischler. There were significant differences between CPs from each sample in 14% of the SCs analyzed (p < .01). Our data suggest that studies employing SCs and CPs may require locally defined norms if the test population differs substantially from the original one. The consequences of employing SCs and CPs to study linguistic processing without normalization are discussed.     

Multilevel and Latent Variable Modeling with Composite Links and Exploded Likelihoods

Composite links and exploded likelihoods are powerful yet simple tools for specifying a wide range of latent variable models. Applications considered include survival or duration models, models for rankings, small area estimation with census information, models for ordinal responses, item response models with guessing, randomized response models, unfolding models, latent class models with random effects, multilevel latent class models, models with log-normal latent variables, and zero-inflated Poisson models with random effects. Some of the ideas are illustrated by estimating an unfolding model for attitudes to female work participation. We wish to thank The Research Council of Norway for a grant supporting our collaboration.     

Bayesian Comparison of Latent Variable Models: Conditional Versus Marginal Likelihoods

Psychometrika - Typical Bayesian methods for models with latent variables (or random effects) involve directly sampling the latent variables along with the model parameters. In high-level software...     

The Role of Conditional Likelihoods in Latent Variable Modeling

Psychometrika - In psychometrics, the canonical use of conditional likelihoods is for the Rasch model in measurement. Whilst not disputing the utility of conditional likelihoods in measurement, we...     

Additive Multilevel Item Structure Models with Random Residuals: Item Modeling for Explanation and Item Generation

An additive multilevel item structure (AMIS) model with random residuals is proposed. The model includes multilevel latent regressions of item discrimination and item difficulty parameters on covariates at both item and item category levels with random residuals at both levels. The AMIS model is useful for explanation purposes and also for prediction purposes as in an item generation context. The parameters can be estimated with an alternating imputation posterior algorithm that makes use of adaptive quadrature, and the performance of this algorithm is evaluated in a simulation study.     

CFA Models with a General Factor and Multiple Sets of Secondary Factors

We propose a class of confirmatory factor analysis models that include multiple sets of secondary or specific factors and a general factor. The general factor accounts for the common variance among manifest variables, whereas multiple sets of secondary factors account for the remaining source-specific dependency among subsets of manifest variables. A special case of the model is further proposed which constrains the specific factor loadings to be proportional to the general factor loadings. This proportional model substantially reduces the number of model parameters while preserving the essential structure of the general model. Furthermore, the proportional model allows for the interpretation of latent variables as the expected values of the observed manifest variables, decomposition of the variances, and the inclusion of interactions, similar to generalizability theory. We provide two applications to illustrate the utility of the proposed class of models.     

IMPS 2008 PLANNED SESSIONS

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IMPS 2008 PLANNED SESSIONS     

Recognizing one's own face

We report two studies of facial self-perception using individually tailored, standardized facial photographs of a group of volunteers and their partners. A computerized morphing procedure was used to merge each target face with an unknown control face. In the first set of experiments, a discrimination task revealed a delayed response time for the more extensively morphed self-face stimuli. In a second set of experiments, functional magnetic resonance imaging (fMRI) was used to measure brain activation while subjects viewed morphed versions of either their own or their partner's face, alternating in blocks with presentation of an unknown face. When subjects viewed themselves (minus activation for viewing an unknown face), increased blood oxygenation was detected in right limbic (hippocampal formation, insula, anterior cingulate), left prefrontal cortex and superior temporal cortex. In the partner (versus unknown) experiment, only the right insula was activated. We suggest that a neural network involving the right hemisphere in conjunction with left-sided associative and executive regions underlies the process of visual self-recognition. Together, this combination produces the unique experience of self-awareness.