15 research outputs found
The Estimation of Item Response Models with the lmer Function from the lme4 Package in R
In this paper we elaborate on the potential of the lmer function from the lme4 package in R for item response (IRT) modeling. In line with the package, an IRT framework is described based on generalized linear mixed modeling. The aspects of the framework refer to (a) the kind of covariates -- their mode (person, item, person-by-item), and their being external vs. internal to responses, and (b) the kind of effects the covariates have -- fixed vs. random, and if random, the mode across which the effects are random (persons, items). Based on this framework, three broad categories of models are described: Item covariate models, person covariate models, and person-by-item covariate models, and within each category three types of more specific models are discussed. The models in question are explained and the associated lmer code is given. Examples of models are the linear logistic test model with an error term, differential item functioning models, and local item dependency models. Because the lme4 package is for univariate generalized linear mixed models, neither the two-parameter, and three-parameter models, nor the item response models for polytomous response data, can be estimated with the lmer function.
Uncovering perceived identification accuracy of in-vehicle biometric sensing
Biometric techniques can help make vehicles safer to drive, authenticate users, and provide personalized in-car experiences. However, it is unclear to what extent users are willing to trade their personal biometric data for such benefits. In this early work, we conducted an open card sorting study (N=11) to better understand how well users perceive their physical, behavioral and physiological features can personally identify them. Findings showed that on average participants clustere
A practitioner’s perspective on coaching effectiveness
In the past decades, coaching has grown into a two-billion-dollar industry worldwide, and organizations increasingly rely on it as a human-resource development (HRD) tool. Consequently, the question of how coaching effectiveness can be measured is a central topic of discussion in both coaching practice and research. Currently, there is no unified answer to this question because researchers have used diverse indicators of coaching success. Three meta-analytic investigations showed that coaching can have positive effects (Jones, Wood, & Guillemeau, 2016; Sonesh et al., 2015; Theeboom, Beersma, & van Vianen, 2014) and that the different classifications are possible to examine coaching success indicators (CSIs). To date, there is no research that investigated whether these CSIs are actually utilized by coaching practitioners to evaluate the effectiveness of their interventions. As such, this chapter uses a concept-mapping approach to shed light on the CSIs that coaching practitioners use to assess the effectiveness of their own interventions and the relative importance that these practitioners ascribe to these CSIs. Specifically, it intends to explore and describe practitioners’ mental representation of coaching effectiveness
The Estimation of Item Response Models with the lmer Function from the lme4 Package in R
In this paper we elaborate on the potential of the lmer function from the lme4 package in R for item response (IRT) modeling. In line with the package, an IRT framework is described based on generalized linear mixed modeling. The aspects of the framework refer to (a) the kind of covariates -- their mode (person, item, person-by-item), and their being external vs. internal to responses, and (b) the kind of effects the covariates have -- fixed vs. random, and if random, the mode across which the effects are random (persons, items). Based on this framework, three broad categories of models are described: Item covariate models, person covariate models, and person-by-item covariate models, and within each category three types of more specific models are discussed. The models in question are explained and the associated lmer code is given. Examples of models are the linear logistic test model with an error term, differential item functioning models, and local item dependency models. Because the lme4 package is for univariate generalized linear mixed models, neither the two-parameter, and three-parameter models, nor the item response models for polytomous response data, can be estimated with the lmer function
Demographic characteristics per training condition.
<p>Demographic characteristics per training condition.</p
Mean reactions times (RT) to negative probe scenarios (NP) and positive probe scenarios (PP) during training sessions for the two training groups.
<p>Mean reactions times (RT) to negative probe scenarios (NP) and positive probe scenarios (PP) during training sessions for the two training groups.</p
Statistics of the original and final models for all hypotheses.
<p>Statistics of the original and final models for all hypotheses.</p
Parameters estimates of significant effects.
<p>Parameters estimates of significant effects.</p