Graphical regression models for polytomous variables

When modeling the relationship between two nominal categorical variables, it is often desirable to include covariates to understand how individuals differ in their response behavior. Typically, however, not all the relevant covariates are available, with the result that the measured variables cannot fully account for the associations between the nominal variables. Under the assumption that the observed and unobserved variables follow a homogeneous conditional Gaussian distribution, this paper proposesRC(M) regression models to decompose the residual associations between the polytomous variables. Based on Goodman's (1979, 1985)RC(M) association model, a distinctive feature ofRC(M) regression models is that they facilitate the joint estimation of effects due to manifest and omitted (continuous) variables without requiring numerical integration. TheRC(M) regression models are illustrated using data from the High School and Beyond study (Tatsuoka & Lohnes, 1988). This article was accepted for publication, when Willem J. Heiser was the Editor ofPsychometrika. This research was supported by grants from the National Science Foundation (#SBR96-17510 and #SBR94-09531) and the Bureau of Educational Research at the University of Illinois. We thank Jee-Seon Kim for comments and computational assistance.     

The Influence of Arbitrary Breakpoints on Judgments of Maximum Output

Consumers often wonder about the product's maximum output: the highest rotation speed of a blender or the best printing quality of a printer. We examine how the number of levels (e.g., a blender with 3 vs. 7 speeds) influences judgments of maximum product output. Objectively speaking, the number of levels is no more than a set of breakpoints in an already predetermined continuum from the product's minimum to maximum output. Nevertheless, because of the ubiquitous association between number of breakpoints and quantity in daily life, consumers do not simply view more levels as a signal of greater precision (i.e., giving consumers more control over the possible outputs). They also incorrectly believe that the product has greater power (i.e., a higher maximum output), even when such an inference is in conflict with diagnostic attribute information (e.g., watts). A series of five studies documents the phenomenon, its asymmetric nature, and its boundary conditions. Reliance on the inaccurate “more levels, more power” lay theory weakens when participants consider a reduction rather than an increase in number of levels, and it disappears when the consumer is presented with an explicit relationship between each level and its corresponding output value (e.g., level 4:400 W).     

Parent-child interaction during the first year of life in infants at elevated likelihood of autism spectrum disorder

Autism spectrum disorder (ASD) likely emerges from a complex interaction between pre-existing neurodevelopmental vulnerabilities and the environment. The interaction with parents forms a key aspect of an infant’s social environment, but few prospective studies of infants at elevated likelihood (EL) for ASD (who have an older sibling with ASD) have examined parent-child interactions in the first year of life. As part of a European multisite network, parent-child dyads of free play were observed at 5 months (62 EL infants, 47 infants at typical likelihood (TL)) and 10 months (101 EL siblings, 77 TL siblings). The newly-developed Parent-Infant/Toddler Coding of Interaction (PInTCI) scheme was used, focusing on global characteristics of infant and parent behaviors. Coders were blind to participant information. Linear mixed model analyses showed no significant group differences in infant or parent behaviors at 5 or 10 months of age (all ps≥0.09, d≤0.36), controlling for infant’s sex and age, and parental educational level. However, without adjustments, EL infants showed fewer and less clear initiations at 10 months than TL infants (p = 0.02, d = 0.44), but statistical significance was lost after controlling for parental education (p = 0.09, d = 0.36), which tended to be lower in the EL group. Consistent with previous literature focusing on parent-infant dyads, our findings suggest that differences between EL and TL dyads may only be subtle during the first year of life. We discuss possible explanations and implications for future developmental studies.     

Functional connectivity abnormalities during contextual processing in schizophrenia and in Parkinson’s disease

Functional connectivity was evaluated in patients with schizophrenia (SC) and in patients with Parkinson’s disease (PD) during the performance of a local contextual processing paradigm, to investigate the proposition that functional disconnection is involved with contextual processing deficits in these populations. To this end, we utilized event-related EEG signals, synchronization likelihood and graph theoretical analysis. Local context was defined as the occurrence of a predictive sequence of stimuli before the presentation of a target event. In the SC patients, we observed a decrease in path length (L) in the beta band, for the predictive sequence and for predicted and random targets, compared with controls. These abnormalities were associated with weaker frontal-temporal-parietal connections. In the PD patients we found longer L (theta band) for predicted targets, and higher cluster coefficients for both the predictive sequence (theta band) and predicted targets (alpha and theta bands), compared with controls. Detection of predicted targets was associated with weaker frontal-parietal connections in PD. No group differences were found for randomized standard stimuli in both SC and PD patients. These findings provide evidence of task-specific functional connectivity abnormalities within frontal networks during local contextual processing.     

A Computationally More Efficient and More Accurate Stepwise Approach for Correcting for Sampling Error and Measurement Error

Over the last decade or two, multilevel structural equation modeling (ML-SEM) has become a prominent modeling approach in the social sciences because it allows researchers to correct for sampling and measurement errors and thus to estimate the effects of Level 2 (L2) constructs without bias. Because the latent variable modeling software Mplus uses maximum likelihood (ML) by default, many researchers in the social sciences have applied ML to obtain estimates of L2 regression coefficients. However, one drawback of ML is that covariance matrices of the predictor variables at L2 tend to be degenerate, and thus, estimates of L2 regression coefficients tend to be rather inaccurate when sample sizes are small. In this article, I show how an approach for stabilizing covariance matrices at L2 can be used to obtain more accurate estimates of L2 regression coefficients. A simulation study is conducted to compare the proposed approach with ML, and I illustrate its application with an example from organizational research.     

Estimating Optimal Weights for Compound Scores: A Multidimensional IRT Approach

A method is proposed for constructing indices as linear functions of variables such that the reliability of the compound score is maximized. Reliability is defined in the framework of latent variable modeling [i.e., item response theory (IRT)] and optimal weights of the components of the index are found by maximizing the posterior variance relative to the total latent variable variance. Three methods for estimating the weights are proposed. The first is a likelihood-based approach, that is, marginal maximum likelihood (MML). The other two are Bayesian approaches based on Markov chain Monte Carlo (MCMC) computational methods. One is based on an augmented Gibbs sampler specifically targeted at IRT, and the other is based on a general purpose Gibbs sampler such as implemented in OpenBugs and Jags. Simulation studies are presented to demonstrate the procedure and to compare the three methods. Results are very similar, so practitioners may be suggested the use of the easily accessible latter method. A real-data set pertaining to the 28-joint Disease Activity Score is used to show how the methods can be applied in a complex measurement situation with multiple time points and mixed data formats.     

Indirect scaling methods for testing quantitative emotion theories

Two studies investigated the utility of indirect scaling methods, based on graded pair comparisons, for the testing of quantitative emotion theories. In Study 1, we measured the intensity of relief and disappointment caused by lottery outcomes, and in Study 2, the intensity of disgust evoked by pictures, using both direct intensity ratings and graded pair comparisons. The stimuli were systematically constructed to reflect variables expected to influence the intensity of the emotions according to theoretical models of relief/disappointment and disgust, respectively. Two probabilistic scaling methods were used to estimate scale values from the pair comparison judgements: Additive functional measurement (AFM) and maximum likelihood difference scaling (MLDS). The emotion models were fitted to the direct and indirect intensity measurements using nonlinear regression (Study 1) and analysis of variance (Study 2). Both studies found substantially improved fits of the emotion models for the indirectly determined emotion intensities, with their advantage being evident particularly at the level of individual participants. The results suggest that indirect scaling methods yield more precise measurements of emotion intensity than rating scales and thereby provide stronger tests of emotion theories in general and quantitative emotion theories in particular.     

Solute drag or diffusion processes in a migrating thick interface

Hillert proposed a new solute drag formula applicable not only to the migration of grain boundaries, but also to that of interfaces. Svoboda and co-workers derived an expression for the velocity of a migrating thick interface by applying the principle of maximum dissipation. In this article, it is shown that the expressions for the interface velocity, one obtained from Hillert's solute drag formula, and the other derived from the principle of maximum dissipation, are equal for steady-state conditions.     

A concise derivation of the contact conditions at a migrating sharp interface

A sharp interface model can be used to simulate the kinetics of diffusional phase transformations provided the transformation process is controlled by migration of the interface and by diffusion of components in the bulk phases only. The contact conditions at the migrating sharp interface can be derived by applying the principles of mass balance and maximum dissipation. A concise derivation of the contact conditions on this basis is presented in this work.     

College Men's Perceived Likelihood to Perpetrate Sexual Aggression

This study implemented a prospective design to explore the relationship between college men's perceived likelihood to perpetrate sexual aggression and their perpetration of sexual aggression over a three-month interim (N = 414). Compared to men's report of their likelihood to use physical force to obtain sex play or sexual intercourse, college men reported higher levels of perceived likelihood to use arguments or pressure to obtain sex play or sexual intercourse as well as drugs and alcohol to obtain sexual intercourse. Prospective analyses revealed that the majority of men who perpetrated sexual aggression over the follow-up period indicated that they were at some risk to do so at the pretest assessment. Implications for sexual assault prevention programming are discussed.