The Experts below are selected from a list of 163467 Experts worldwide ranked by ideXlab platform
B.n. Reeves - One of the best experts on this subject based on the ideXlab platform.
-
ICALT - Zing'Em: a Web-based Likert-Scale Student-Team Peer Evaluation Tool
Seventh IEEE International Conference on Advanced Learning Technologies (ICALT 2007), 2007Co-Authors: B.n. ReevesAbstract:We describe the design and implementation of a peer evaluation system named Zing'Em, in use for 3 years and over 60 course sections. Zing'Em supports Likert-Scale items, is easy for professors to set up, simple for students to use and provides analyses of teams for the professor to use in grading and possible interventions. This paper documents the design goals, implementation tradeoffs and experience of users. We critique the system and suggest improvements for future work.
-
zing em a web based Likert Scale student team peer evaluation tool
International Conference on Advanced Learning Technologies, 2007Co-Authors: B.n. ReevesAbstract:We describe the design and implementation of a peer evaluation system named Zing'Em, in use for 3 years and over 60 course sections. Zing'Em supports Likert-Scale items, is easy for professors to set up, simple for students to use and provides analyses of teams for the professor to use in grading and possible interventions. This paper documents the design goals, implementation tradeoffs and experience of users. We critique the system and suggest improvements for future work.
Craig K Enders - One of the best experts on this subject based on the ideXlab platform.
-
a comparison of imputation strategies for ordinal missing data on Likert Scale variables
Multivariate Behavioral Research, 2015Co-Authors: Wei Wu, Craig K EndersAbstract:This article compares a variety of imputation strategies for ordinal missing data on Likert Scale variables (number of categories = 2, 3, 5, or 7) in recovering reliability coefficients, mean Scale scores, and regression coefficients of predicting one Scale score from another. The examined strategies include imputing using normal data models with naive rounding/without rounding, using latent variable models, and using categorical data models such as discriminant analysis and binary logistic regression (for dichotomous data only), multinomial and proportional odds logistic regression (for polytomous data only). The result suggests that both the normal model approach without rounding and the latent variable model approach perform well for either dichotomous or polytomous data regardless of sample size, missing data proportion, and asymmetry of item distributions. The discriminant analysis approach also performs well for dichotomous data. Naively rounding normal imputations or using logistic regression model...
-
a comparison of imputation strategies for ordinal missing data on Likert Scale variables
Multivariate Behavioral Research, 2015Co-Authors: Fan Jia, Craig K EndersAbstract:This article compares a variety of imputation strategies for ordinal missing data on Likert Scale variables (number of categories = 2, 3, 5, or 7) in recovering reliability coefficients, mean Scale scores, and regression coefficients of predicting one Scale score from another. The examined strategies include imputing using normal data models with naive rounding/without rounding, using latent variable models, and using categorical data models such as discriminant analysis and binary logistic regression (for dichotomous data only), multinomial and proportional odds logistic regression (for polytomous data only). The result suggests that both the normal model approach without rounding and the latent variable model approach perform well for either dichotomous or polytomous data regardless of sample size, missing data proportion, and asymmetry of item distributions. The discriminant analysis approach also performs well for dichotomous data. Naively rounding normal imputations or using logistic regression models to impute ordinal data are not recommended as they can potentially lead to substantial bias in all or some of the parameters.
Evan S Dellon - One of the best experts on this subject based on the ideXlab platform.
-
a visual analogue Scale and a Likert Scale are simple and responsive tools for assessing dysphagia in eosinophilic oesophagitis
Alimentary Pharmacology & Therapeutics, 2017Co-Authors: Craig C Reed, W A Wolf, Cary C Cotton, Evan S DellonAbstract:SummaryBackground While symptom scores have been developed to evaluate dysphagia in eosinophilic oesophagitis (EoE), their complexity may limit clinical use. Aim To evaluate a visual analogue Scale (VAS) and a 10-point Likert Scale (LS) for assessment of dysphagia severity before and after EoE treatment. Methods We conducted a prospective cohort study enrolling consecutive adults undergoing out-patient endoscopy. Incident cases of EoE were diagnosed per consensus guidelines. At diagnosis and after 8 weeks of treatment, symptoms were measured using the VAS, LS and the Mayo Dysphagia Questionnaire (MDQ). The percentage change in scores before and after treatment were compared overall, in treatment responders (<15 eos/hpf) and non-responders, and in patients without baseline dilation. Results In 51 EoE cases, the median VAS decreased from 3.6 at baseline to 1.4 post-treatment (71% decrease), the LS decreased from 6 to 2 (67%) and the MDQ decreased from 20 to 10 (49%). The VAS correlated with both the LS (R = 0.77; P < 0.0001) and MDQ (R = 0.46, P = 0.001). After stratification by histological response, the LS decreased 70% in responders vs. 13% in non-responders (P = 0.02). In patients who did not receive baseline dilation, both the VAS and LS decreased significantly more in the histological responders. Conclusions Both the VAS and LS were responsive to successful treatment as measured by histologic improvement. Because the VAS and LS are simple to administer and are responsive to treatment, they can provide an efficient and objective method for assessing dysphagia severity in EoE in clinical practice.
Milos Hauskrecht - One of the best experts on this subject based on the ideXlab platform.
-
SDM - Active Learning of Classification Models with Likert-Scale Feedback.
Proceedings of the ... SIAM International Conference on Data Mining. SIAM International Conference on Data Mining, 2017Co-Authors: Yanbing Xue, Milos HauskrechtAbstract:Annotation of classification data by humans can be a time-consuming and tedious process. Finding ways of reducing the annotation effort is critical for building the classification models in practice and for applying them to a variety of classification tasks. In this paper, we develop a new active learning framework that combines two strategies to reduce the annotation effort. First, it relies on label uncertainty information obtained from the human in terms of the Likert-Scale feedback. Second, it uses active learning to annotate examples with the greatest expected change. We propose a Bayesian approach to calculate the expectation and an incremental SVM solver to reduce the time complexity of the solvers. We show the combination of our active learning strategy and the Likert-Scale feedback can learn classification models more rapidly and with a smaller number of labeled instances than methods that rely on either Likert-Scale labels or active learning alone.
-
active learning of classification models with Likert Scale feedback
SIAM International Conference on Data Mining, 2017Co-Authors: Yanbing Xue, Milos HauskrechtAbstract:Annotation of classification data by humans can be a time-consuming and tedious process. Finding ways of reducing the annotation effort is critical for building the classification models in practice and for applying them to a variety of classification tasks. In this paper, we develop a new active learning framework that combines two strategies to reduce the annotation effort. First, it relies on label uncertainty information obtained from the human in terms of the Likert-Scale feedback. Second, it uses active learning to annotate examples with the greatest expected change. We propose a Bayesian approach to calculate the expectation and an incremental SVM solver to reduce the time complexity of the solvers. We show the combination of our active learning strategy and the Likert-Scale feedback can learn classification models more rapidly and with a smaller number of labeled instances than methods that rely on either Likert-Scale labels or active learning alone.
Craig C Reed - One of the best experts on this subject based on the ideXlab platform.
-
a visual analogue Scale and a Likert Scale are simple and responsive tools for assessing dysphagia in eosinophilic oesophagitis
Alimentary Pharmacology & Therapeutics, 2017Co-Authors: Craig C Reed, W A Wolf, Cary C Cotton, Evan S DellonAbstract:SummaryBackground While symptom scores have been developed to evaluate dysphagia in eosinophilic oesophagitis (EoE), their complexity may limit clinical use. Aim To evaluate a visual analogue Scale (VAS) and a 10-point Likert Scale (LS) for assessment of dysphagia severity before and after EoE treatment. Methods We conducted a prospective cohort study enrolling consecutive adults undergoing out-patient endoscopy. Incident cases of EoE were diagnosed per consensus guidelines. At diagnosis and after 8 weeks of treatment, symptoms were measured using the VAS, LS and the Mayo Dysphagia Questionnaire (MDQ). The percentage change in scores before and after treatment were compared overall, in treatment responders (<15 eos/hpf) and non-responders, and in patients without baseline dilation. Results In 51 EoE cases, the median VAS decreased from 3.6 at baseline to 1.4 post-treatment (71% decrease), the LS decreased from 6 to 2 (67%) and the MDQ decreased from 20 to 10 (49%). The VAS correlated with both the LS (R = 0.77; P < 0.0001) and MDQ (R = 0.46, P = 0.001). After stratification by histological response, the LS decreased 70% in responders vs. 13% in non-responders (P = 0.02). In patients who did not receive baseline dilation, both the VAS and LS decreased significantly more in the histological responders. Conclusions Both the VAS and LS were responsive to successful treatment as measured by histologic improvement. Because the VAS and LS are simple to administer and are responsive to treatment, they can provide an efficient and objective method for assessing dysphagia severity in EoE in clinical practice.
