A novel method for assessing rival models of recognition memory

A general comparison is made between the multinomial processing tree (MPT) approach and a strength-based approach for modeling recognition memory measurement. Strength models include the signal-detection model and the dual-process model. Existing MPT models for recognition memory and a new generic MPT model, called the Multistate (MS) model, are contrasted with the strength models. Although the ROC curves for the MS model and strength model are similar, there is a critical difference between existing strength models and MPT models that goes beyond the assessment of the ROC. This difference concerns the question of stochastic mixtures for foil test trials. The hazard function and the reverse hazard function are powerful methods for detecting the presence of a probabilistic mixture. Several new theorems establish a novel method for obtaining information about the hazard function and reverse hazard function for the latent continuous distributions that are assumed in the strength approach to recognition memory. Evidence is provided that foil test trials involve a stochastic mixture. This finding occurred for both short-term memory procedures, such as the Brown–Peterson task, and long-term list-learning procedures, such as the paired-associate task. The effect of mixtures on foil trials is problematic for existing strength models but can be readily handled by MPT models such as the MS model. Other phenomena, such as the mirror effect and the effect of target-foil similarity, are also predicted accurately by the MPT modeling framework.     

Narrowing the Creativity Gap: The Moderating Effects of Perceived Support for Creativity

This article examines the role of 3 types of perceived support for creativity in moderating the relation between creative self-efficacy and self-perceived creativity. The findings suggest significant interaction effects for perceived work-group support and supervisor support, but not for perceived organizational support. This study is among the first to (a) examine the importance of perceived support for creativity in unlocking creative potential and increasing creativity in organizations and (b) use interaction terms in structural equation modeling (SEM) to investigate moderator effects in an applied research setting. These results imply that organizational interventions focused on training supervisors and work-group members to support creativity in the workplace may be more effective than broader and less focused interventions at the organizational level.     

Model Fit and Item Factor Analysis: Overfactoring,Underfactoring, and a Program to Guide Interpretation

In exploratory item factor analysis (IFA), researchers may use model fit statistics and commonly invoked fit thresholds to help determine the dimensionality of an assessment. However, these indices and thresholds may mislead as they were developed in a confirmatory framework for models with continuous, not categorical, indicators. The present study used Monte Carlo simulation methods to investigate the ability of popular model fit statistics (chi-square, root mean square error of approximation, the comparative fit index, and the Tucker–Lewis index) and their standard cutoff values to detect the optimal number of latent dimensions underlying sets of dichotomous items. Models were fit to data generated from three-factor population structures that varied in factor loading magnitude, factor intercorrelation magnitude, number of indicators, and whether cross loadings or minor factors were included. The effectiveness of the thresholds varied across fit statistics, and was conditional on many features of the underlying model. Together, results suggest that conventional fit thresholds offer questionable utility in the context of IFA.     

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.     

Multilevel Investigation into the Community Context of Child Maltreatment

Official rates of child maltreatment vary considerably from community to community. Extensive research on the role of the community context in maltreatment suggests that the neighborhood in which families live could impact their maltreatment behavior. However, the vast majority of existing studies and literature reviews on the topic have not used statistical methods to appropriately control for the impact of these variables at the individual level. This literature review approaches the question of whether the community context affects maltreatment behavior above and beyond the individual-level variables by critiquing the studies that have successfully measured maltreatment risks at both the individual and community level using multilevel modeling.     

Trends in optimization models of sales force management

In the last half-century, significant advances have been made in directing sales force behavior with the use of optimization and decision models. The present paper both presents the current state-of-the art in sales force decision modeling, and also discusses key issues and trends in contemporary modeling of relevance to sales force researchers. The paper begins by exploring critical concepts regarding the estimation of the sales response function, and then discusses critical problems of endogeneity, heterogeneity, and temporal variation that are faced by modelers in this task. Modern approaches to dealing with these issues are presented. We then discuss areas of importance concerning finding model solutions, including closed form versus simulation, and optimization versus heuristic solutions. The paper next moves to areas of practical importance where models can help, including call planning, sales force size, territory allocation, and compensation design. Finally, we discuss trends that will likely impact on sales force modeling in coming years, including the use of big data and data mining, the possible breakdown of rationality, the rise of the Internet and social media, and the potential of agent-based modeling.     

Why experiments matter*

Experimentation is traditionally considered a privileged means of confirmation. However, why and how experiments form a better confirmatory source relative to other strategies is unclear, and recent discussions have identified experiments with various modeling strategies on the one hand, and with ‘natural’ experiments on the other hand. We argue that experiments aiming to test theories are best understood as controlled investigations of specimens. ‘Control’ involves repeated, fine-grained causal manipulation of focal properties. This capacity generates rich knowledge of the object investigated. ‘Specimenhood’ involves possessing relevant properties given the investigative target and the hypothesis in question. Specimens are thus representative members of a class of systems, to which a hypothesis refers. It is in virtue of both control and specimenhood that experiments provide powerful confirmatory evidence. This explains the distinctive power of experiments: although modelers exert extensive control, they do not exert this control over specimens; although natural experiments utilize specimens, control is diminished.     

Attachment insecurity as a moderator of cardiovascular arousal effects following dyadic support

We examine the cardiovascular arousal effects of emotional support receipt, and the moderation of these by the support recipient’s and provider’s attachment. Seventy couples engaged in a laboratory dyadic supportive interaction, while their ECG was monitored. With more emotional support, men with high attachment anxiety showed greater arousal reduction during the dyadic interaction, whereas men with low attachment anxiety showed less reduction; additionally, women coupled with partners with high attachment anxiety showed greater arousal reduction, whereas women coupled with partners with low attachment anxiety showed less reduction. Men and women with high attachment avoidance showed less arousal reduction, whereas those with low attachment avoidance showed greater reduction. These results highlight the differential ways in which support gets under the skin.     

Discrete response patterns in the upper range of hypnotic suggestibility: A latent profile analysis

High hypnotic suggestibility is a heterogeneous condition and there is accumulating evidence that highly suggestible individuals may be comprised of discrete subtypes with dissimilar cognitive and phenomenological profiles. This study applied latent profile analysis to response patterns on a diverse battery of difficult hypnotic suggestions in a sample of individuals in the upper range of hypnotic suggestibility. Comparisons among models indicated that a four-class model was optimal. One class was comprised of very highly suggestible (virtuoso) participants, two classes included highly suggestible participants who were alternately more responsive to inhibitory cognitive suggestions or posthypnotic amnesia suggestions, and the fourth class consisted primarily of medium suggestible participants. These results indicate that there are discrete response profiles in high hypnotic suggestibility. They further provide a number of insights regarding the optimization of hypnotic suggestibility measurement and have implications for the instrumental use of hypnosis for the modeling of different psychological conditions.