Estimating the Multilevel Rasch Model: With the<b>lme4</b>Package

Doran, Harold C.; Bates, Douglas M.; Bliese, Paul D.; Dowling, N. Maritza

doi:10.18637/jss.v020.i02

Cited by 125 publications

(111 citation statements)

References 10 publications

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“…Because the response variables were each dichotomous (yes/no), a multilevel Rasch model was implemented. 32 For the remaining analyses, to cope with the many types of emotional responses and multiple combinations of these, replies were grouped in 2 levels: emotional response/no emotional response.…”

Section: Discussionmentioning

confidence: 99%

Emotional Impact of Patient Safety Incidents on Family Physicians and Their Office Staff

O’Beirne¹,

Sterling²,

Palacios-Derflingher³

et al. 2012

The Journal of the American Board of Family Medicine

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Objective: The objective of this study was to investigate the emotional responses and coping strategies that family physicians and their office staff reported in response to a patient safety incident.Method: Two questions contained in a patient safety incident report developed for a study of patient safety in family practice were analyzed. The questions asked reporters to indicate their emotional response to a patient safety incident and how they coped with it. A total of 264 confidential patient safety incident reports collected from September 2007 to August 2010 were analyzed.Results: An emotional response was reported on 82.4% of reports. Of those reports on which an emotional response was reported, a coping strategy was reported on 62.8%. The top 4 reported emotional responses were frustration (48.3%), embarrassment (31.5%), anger (12.6%), and guilt (10.1%). Physicians reported an emotional response more often than clinic staff. An emotional response was reported more often when there was a possibility of harm. Coping strategies were reported as follows: 52% talked to someone about the incident, 37.2% did nothing in response to the incident, 17.9% told the patient about the incident, and 3.6% did something else. Female physicians reported using coping strategies less often than male physicians. A coping strategy was reported more often when there was a possibility of harm. Conclusions: All members of the health care team report experiencing emotions related to patient safety incidents in their practice. Incidents with minor or no harm still invoked emotional responses from the providers. It is important to understand the impact that patient safety incidents have on the medical clinic as a whole. (J Am Board Fam Med 2012;25:177-183.) Keywords: Coping Skills, Family Medicine Research, Medical Errors, Patient SafetyA patient safety incident is an event or process that could have resulted or did result in unnecessary harm to a patient.1 Patient safety incidents affect both the patient and caregivers.2-4 Much work has been done studying the effect of incidents on patients, 5,6 but examining the effect on caregivers, also referred to as "the second victim," 2-4 is in its infancy. Previous studies have focused on the acute care system and medical residents [7][8][9][10][11][12][13][14][15][16] or a compilation of acute and primary care. [17][18][19][20] Only 2 studies have explored the emotional impact of incidents on primary care providers alone. 21,22 The first explored the emotional impact of the most memorable mistake on the family physician and his or her response to a hypothetical scenario. 21 The second used focus groups of family physicians and residents to develop a reference statement for the appropriate management of mistakes in the training environment. 22Other research, primarily in acute care, found that health professionals have varied emotional responses to incidents.

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Section: Discussionmentioning

confidence: 99%

Emotional Impact of Patient Safety Incidents on Family Physicians and Their Office Staff

O’Beirne¹,

Sterling²,

Palacios-Derflingher³

et al. 2012

The Journal of the American Board of Family Medicine

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“…Following Goldhammer, et al [2] (see also [1]), we tested a random item response time model within the generalized linear mixed model (GLMM) framework [39] (see also [40,41]). Conceptually, this modeling approach is based on the 1-parameter logistic (1PL) item response model with fixed and random effects, as given in Model (1).…”

Section: Statistical Analysesmentioning

confidence: 99%

Sometimes More Is Better, and Sometimes Less Is Better: Task Complexity Moderates the Response Time Accuracy Correlation

et al. 2016

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This study addresses the relationship between item response time and item accuracy (i.e., the response time accuracy correlation, RTAC) in figural matrices tests. The dual processing account of response time effects predicts negative RTACs in tasks that allow for relatively automatic processing and positive RTACs in tasks that require controlled processing. Contrary to these predictions, several studies found negative RTACs for reasoning tests. Nevertheless, it was demonstrated that the RTAC is moderated by task complexity (i.e., the interaction between person ability and item difficulty) and that under conditions of high complexity (i.e., low ability and high difficulty) the RTAC was even slightly positive. The goal of this study was to demonstrate that with respect to task complexity the direction of the RTAC (positive vs. negative) can change substantially even within a single task paradigm (i.e., figural matrices). These predictions were tested using a figural matrices test that employs a constructed response format and has a broad range of item difficulties in a sample with a broad range of ability. Confirming predictions, strongly negative RTACs were observed when task complexity was low (i.e., fast responses tended to be correct). With increasing task complexity, the RTAC flipped to be strongly positive (i.e., slow responses tended to be correct). This flip occurred earlier for people with lower ability, and later for people with higher ability. Cognitive load of the items is suggested as an explanation for this phenomenon.

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“…Package mgcv's (Wood 2006) function rig was used for inverse Gaussian models; package gamlss's (Rigby and Stasinopoulos 2005;Stasinopoulos and Rigby 2007) functions rBB, rZIBI and rZIBB were used for beta-binomial, zero-inflated binomial and zero-inflated beta-binomial models fitted using gamlss; package VGAM's (Yee 2010) functions rbetabinom and rzibinom were used for beta-binomial and zero-inflated binomial models fitted using vglm; package lme4's (Bates et al 2015;Doran et al 2007) function simulate was used for generalized linear mixed models fitted using lmer and glmer; package MASS's (Venables and Ripley 2002) function rnegbin for negative binomial models.…”

Section: Simulation Proceduresmentioning

confidence: 99%

Half-Normal Plots and Overdispersed Models in R: The hnp Package

Moral¹,

Hinde²,

Demétrio³

2017

J. Stat. Soft.

260

156

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Count and proportion data may present overdispersion, i.e., greater variability than expected by the Poisson and binomial models, respectively. Different extended generalized linear models that allow for overdispersion may be used to analyze this type of data, such as models that use a generalized variance function, random-effects models, zero-inflated models and compound distribution models. Assessing goodness-of-fit and verifying assumptions of these models is not an easy task and the use of half-normal plots with a simulated envelope is a possible solution for this problem. These plots are a useful indicator of goodness-of-fit that may be used with any generalized linear model and extensions. For GLIM users, functions that generated these plots were widely used, however, in the open-source software R, these functions were not yet available on the Comprehensive R Archive Network (CRAN). We describe a new package in R, hnp, that may be used to generate the half-normal plot with a simulated envelope for residuals from different types of models. The function hnp() can be used together with a range of different model fitting packages in R that extend the basic generalized linear model fitting in glm() and is written so that it is relatively easy to extend it to new model classes and different diagnostics. We illustrate its use on a range of examples, including continuous and discrete responses, and show how it can be used to inform model selection and diagnose overdispersion.

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Estimating the Multilevel Rasch Model: With thelme4Package

Cited by 125 publications

References 10 publications

Emotional Impact of Patient Safety Incidents on Family Physicians and Their Office Staff

Emotional Impact of Patient Safety Incidents on Family Physicians and Their Office Staff

Sometimes More Is Better, and Sometimes Less Is Better: Task Complexity Moderates the Response Time Accuracy Correlation

Half-Normal Plots and Overdispersed Models in R: The hnp Package

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