Structural Equation Modeling of Social Networks: Specification,Estimation, and Application

Psychologists are interested in whether friends and couples share similar personalities or not. However, no statistical models are readily available to test the association between personalities and social relations in the literature. In this study, we develop a statistical model for analyzing social network data with the latent personality traits as covariates. Because the model contains a measurement model for the latent traits and a structural model for the relationship between the network and latent traits, we discuss it under the general framework of structural equation modeling (SEM). In our model, the structural relation between the latent variable(s) and the outcome variable is no longer linear or generalized linear. To obtain model parameter estimates, we propose to use a two-stage maximum likelihood (ML) procedure. This modeling framework is evaluated through a simulation study under representative conditions that would be found in social network data. Its usefulness is then demonstrated through an empirical application to a college friendship network.     

Indistinguishability tests in the actor–partner interdependence model

When considering dyadic data, one of the questions is whether the roles of the two dyad members can be considered equal. This question may be answered empirically using indistinguishability tests in the actor–partner interdependence model. In this paper several issues related to such indistinguishability tests are discussed: the difference between maximum likelihood and restricted maximum likelihood based tests for equality in variance parameters; the choice between the structural equation modelling and multilevel modelling framework; and the use of sequential testing rather than one global test for a set of indistinguishability tests. Based on simulation studies, we provide guidelines for best practice. All different types of tests are illustrated with cross-sectional and longitudinal data, and corroborated with corresponding R code.     

Conscious emotional experience emerges as a function of multilevel, appraisal-driven response synchronization

In this paper we discuss the issue of the processes potentially underlying the emergence of emotional consciousness in the light of theoretical considerations and empirical evidence. First, we argue that componential emotion models, and specifically the Component Process Model (CPM), may be better able to account for the emergence of feelings than basic emotion or dimensional models. Second, we advance the hypothesis that consciousness of emotional reactions emerges when lower levels of processing are not sufficient to cope with the event and regulate the emotional process, particularly when the degree of synchronization between the components reaches a critical level and duration. Third, we review recent neuroscience evidence that bolsters our claim of the central importance of the synchronization of neuronal assemblies at different levels of processing.     

Regularized Finite Mixture Models for Probability Trajectories

Finite mixture models are widely used in the analysis of growth trajectory data to discover subgroups of individuals exhibiting similar patterns of behavior over time. In practice, trajectories are usually modeled as polynomials, which may fail to capture important features of the longitudinal pattern. Focusing on dichotomous response measures, we propose a likelihood penalization approach for parameter estimation that is able to capture a variety of nonlinear class mean trajectory shapes with higher precision than maximum likelihood estimates. We show how parameter estimation and inference for whether trajectories are time-invariant, linear time-varying, or nonlinear time-varying can be carried out for such models. To illustrate the method, we use simulation studies and data from a long-term longitudinal study of children at high risk for substance abuse. This work was supported in part by NIAAA grants R37 AA07065 and R01 AA12217 to RAZ.     

Sensorimotor synchronization with adaptively timed sequences

Most studies of human sensorimotor synchronization require participants to coordinate actions with computer-controlled event sequences that are unresponsive to their behavior. In the present research, the computer was programmed to carry out phase and/or period correction in response to asynchronies between taps and tones, and thereby to modulate adaptively the timing of the auditory sequence that human participants were synchronizing with, as a human partner might do. In five experiments the computer's error correction parameters were varied over a wide range, including "uncooperative" settings that a human synchronization partner could not (or would not normally) adopt. Musically trained participants were able to maintain synchrony in all these situations, but their behavior varied systematically as a function of the computer's parameter settings. Computer simulations were conducted to infer the human participants' error correction parameters from statistical properties of their behavior (means, standard deviations, auto- and cross-correlations). The results suggest that participants maintained a fixed gain of phase correction as long as the computer was cooperative, but changed their error correction strategies adaptively when faced with an uncooperative computer.     

An EM algorithm for fitting two-level structural equation models

Maximum likelihood is an important approach to analysis of two-level structural equation models. Different algorithms for this purpose have been available in the literature. In this paper, we present a new formulation of two-level structural equation models and develop an EM algorithm for fitting this formulation. This new formulation covers a variety of two-level structural equation models. As a result, the proposed EM algorithm is widely applicable in practice. A practical example illustrates the performance of the EM algorithm and the maximum likelihood statistic.We are thankful to the reviewers for their constructive comments that have led to significant improvement on the first version of this paper. Special thanks are due to the reviewer who suggested a comparison with the LISREL program in the saturated means model, and provided its setup and output. This work was supported by National Institute on Drug Abuse grants DA01070, DA00017, and a UNH 2002 Summer Faculty Fellowship.     

When to Refrain from Using Likelihood Surface Methods for Geographic Offender Profiling: An Ex Ante Test of Assumptions

Likelihood surface methods for geographic offender profiling rely on several assumptions regarding the underlying location choice mechanism of an offender. We propose an ex ante test for checking whether a given set of crime locations is compatible with two necessary assumptions: circular symmetry and distance decay. The proposed (SDD) test compares the observed inter point distances of a given series of crimes with a theoretical distribution function governed by these assumptions, using a Monte Carlo simulation procedure for approximating that distribution function. We apply the SDD test to data on serial burglary from both the UK and the Netherlands. In most cases, the assumption of an underlying symmetric distance decay function has to be rejected. Copyright © 2011 John Wiley & Sons, Ltd.     

Mean Comparison: Manifest Variable Versus Latent Variable

Mean comparisons are of great importance in the application of statistics. Procedures for mean comparison with manifest variables have been well studied. However, few rigorous studies have been conducted on mean comparisons with latent variables, although the methodology has been widely used and documented. This paper studies the commonly used statistics in latent variable mean modeling and compares them with parallel manifest variable statistics. Our results indicate that, under certain conditions, the likelihood ratio and Wald statistics used for latent mean comparisons do not always have greater power than the Hotelling T² statistics used for manifest mean comparisons. The noncentrality parameter corresponding to the T² statistic can be much greater than those corresponding to the likelihood ratio and Wald statistics, which we find to be different from those provided in the literature. Under a fixed alternative hypothesis, our results also indicate that the likelihood ratio statistic can be stochastically much greater than the corresponding Wald statistic. The robustness property of each statistic is also explored when the model is misspecified or when data are nonnormally distributed. Recommendations and advice are provided for the use of each statistic. The research was supported by NSF grant DMS-0437167 and Grant DA01070 from the National Institute on Drug Abuse. We would like to thank three referees for suggestions that helped in improving the paper.     

A mathematical model of the interaction between bottom-up and top-down attention controllers in response to a target and a distractor in human beings

Top-down and bottom-up attention are two systems that allocate our neuronal resources for processing different stimuli. To do the tasks efficiently, it is required to suppress irrelevant information. In the presence of both target and distractor, synchronization or desynchronization between the activities of neuronal responses has been observed in different regions of the brain. In the current study, we have proposed a mathematical model to show how the interaction between top-down and bottom-up attention, through synchronization and desynchronization, can lead to the suppression of distractor effects in human beings. The model structure was inspired by the results of neurological studies. The model consists of several oscillating units as a representation of top-down and bottom-up neuronal processing resources. These units communicate with each other through synchronization and desynchronization procedures.Results of simulations showed that how the mutual interaction between top-down and bottom-up units, which was done using synchronization and desynchronization procedures, led to the selective or divided attention between the target and distractor. It was shown that the activity of responsive units to the distractor could be suppressed by a desynchronous signal transmitted from the top-down attention unit. This model suggests a justification for brain waves synchronization or desynchronization during attentionally demand tasks. The proposed model also provides a tool to investigate the effect of some influencing factors such as the distractor intensity or similarity between the distractor and the target on the function of top-down and bottom-up systems.     

Difference in the Formation of Attitude Toward Nuclear Power

Determinants of attitudes toward nuclear power in Japan were assessed in a March 1999 survey. The results suggested that persons with interest in and knowledge about nuclear power judged its acceptability on the basis of their perceptions of the sufficiency of electric power and the risks of nuclear power, whereas persons with no interest in and knowledge of nuclear power did not respond on this basis. Although both types of respondents based acceptability on their trust in nuclear power operation and their perception of efficiency, the influence of perception of efficiency on acceptability was stronger for the interested and knowledgeable respondents than for the others. These results partially support the elaboration likelihood model or dual process theory.