A Hierarchical Assessment of Latent Traits by Using Latent Guttman Scaling

1992 ◽  
Vol 19 (2) ◽  
pp. 97-116
Author(s):  
Nobuoki Eshima
Keyword(s):  
Guttman Scaling ◽  
Latent Traits ◽  
Hierarchical Assessment

Expected A Posteriori Estimation of Multiple Latent Traits

2005 ◽  
Author(s):  
Damon U. Bryant ◽  
Ashley K. Smith ◽  
Sandra G. Alexander ◽  
Kathlea Vaughn ◽  
Kristophor G. Canali
Keyword(s):  
A Posteriori ◽  
Latent Traits ◽  
Posteriori Estimation

Two Comments on Guttman Scaling

1969 ◽  
Vol 75 (2) ◽  
pp. 278-280
Author(s):  
Peter Weldon ◽  
J. David Martin ◽  
Louis N. Gray
Keyword(s):  
Guttman Scaling

scholarly journals Power System Transient Stability Assessment Based on Snapshot Ensemble LSTM Network

2021 ◽  
Vol 13 (12) ◽  
pp. 6953
Author(s):  
Yixing Du ◽  
Zhijian Hu
Keyword(s):  
Power Systems ◽  
Power System ◽  
Transient Stability ◽  
Short Term Memory ◽  
Risk Function ◽  
Stability Margin ◽  
Risk Level ◽  
Stability Assessment ◽  
Lstm Network ◽  
Hierarchical Assessment

Data-driven methods using synchrophasor measurements have a broad application prospect in Transient Stability Assessment (TSA). Most previous studies only focused on predicting whether the power system is stable or not after disturbance, which lacked a quantitative analysis of the risk of transient stability. Therefore, this paper proposes a two-stage power system TSA method based on snapshot ensemble long short-term memory (LSTM) network. This method can efficiently build an ensemble model through a single training process, and employ the disturbed trajectory measurements as the inputs, which can realize rapid end-to-end TSA. In the first stage, dynamic hierarchical assessment is carried out through the classifier, so as to screen out credible samples step by step. In the second stage, the regressor is used to predict the transient stability margin of the credible stable samples and the undetermined samples, and combined with the built risk function to realize the risk quantification of transient angle stability. Furthermore, by modifying the loss function of the model, it effectively overcomes sample imbalance and overlapping. The simulation results show that the proposed method can not only accurately predict binary information representing transient stability status of samples, but also reasonably reflect the transient safety risk level of power systems, providing reliable reference for the subsequent control.

scholarly journals Elderly and caregiver demand: proposal for a care need classification

2018 ◽  
Vol 71 (suppl 2) ◽  
pp. 844-850 ◽  
Author(s):  
Daniella Pires Nunes ◽  
Tábatta Renata Pereira de Brito ◽  
Ligiana Pires Corona ◽  
Tiago da Silva Alexandre ◽  
Yeda Aparecida de Oliveira Duarte
Keyword(s):  
Well Being ◽  
The Elderly ◽  
Cross Sectional Study ◽  
Full Time ◽  
Cross Sectional ◽  
Everyday Activities ◽  
Aging Study ◽  
Good Internal Consistency ◽  
Caregiver Demand ◽  
Guttman Scaling

ABSTRACT Objective: To propose a care need classification for elderly people by identifying their functional demands. Method: Cross-sectional study carried out in São Paulo, in 2006, with 1,413 elderly (≥ 60 years old), participants in the Health, Well-being and Aging study (SABE – Saúde, Bem Estar e Envelhecimento). For the care need classification, we used the Guttman Scaling method e the frequency of assistance required by the elderly. Results: The hierarchy of activities of daily living had good internal consistency (α = 0.92) and satisfactory coefficients of reproducibility (98%), scalability (84%) and minimum marginal reproducibility (87%). Care need was categorized into: no need (requires no caregiver), minimum need (requires caregiver sporadically), moderate need (requires caregiver intermittently) and maximum need (requires full-time caregiver). Conclusion: This classification will allow identifying elderly that need assistance in everyday activities and will orientante health professionals in the development of a line of care.

Mokken Scale Analysis: Discussion and Application

2021 ◽  
Vol 8 (3) ◽  
pp. 672-695
Author(s):  
Thomas DeVaney
Keyword(s):  
Item Response Theory ◽  
Item Response ◽  
Rating Scale ◽  
Scale Analysis ◽  
Guttman Scale ◽  
Response Theory ◽  
Data Set ◽  
Mokken Scale Analysis ◽  
Irt Models ◽  
Guttman Scaling

This article presents a discussion and illustration of Mokken scale analysis (MSA), a nonparametric form of item response theory (IRT), in relation to common IRT models such as Rasch and Guttman scaling. The procedure can be used for dichotomous and ordinal polytomous data commonly used with questionnaires. The assumptions of MSA are discussed as well as characteristics that differentiate a Mokken scale from a Guttman scale. MSA is illustrated using the mokken package with R Studio and a data set that included over 3,340 responses to a modified version of the Statistical Anxiety Rating Scale. Issues addressed in the illustration include monotonicity, scalability, and invariant ordering. The R script for the illustration is included.

scholarly journals Impairments in balance and mobility identify delirium in patients with comorbid dementia

2018 ◽  
Vol 31 (5) ◽  
pp. 749-753 ◽  
Author(s):  
Neus Gual ◽  
Sarah J. Richardson ◽  
Daniel H. J. Davis ◽  
Giuseppe Bellelli ◽  
Wolfgang Hasemann ◽  
...  
Keyword(s):  
Motor Function ◽  
Mini Mental State Examination ◽  
The Elderly ◽  
Motor Dysfunction ◽  
Cross Sectional ◽  
Dsm 5 ◽  
Hierarchical Assessment ◽  
State Examination ◽  
Rehabilitation Hospitals ◽  
Delirium Superimposed On Dementia

ABSTRACTDiagnosing delirium superimposed on dementia (DSD) remains challenging because of a lack of specific tools, though motor dysfunction in delirium has been relatively under-explored. This study aimed to use dysfunction in balance and mobility (with the Hierarchical Assessment of Balance And Mobility: HABAM) to identify DSD. This is a cross-sectional multicenter study, recruiting consecutive patients ≥70 years admitted to five acute or rehabilitation hospitals in Ireland, Italy, Portugal, and Switzerland. Delirium was diagnosed using DSM-5 criteria; dementia was determined by the Mini-Mental State Examination and the Questionnaire of Cognitive Decline in the Elderly. HABAM score was recorded at admission. Out of 114 patients (mean age ± SD = 82 ± 7; 54% female), dementia alone was present in 24.6% (n = 28), delirium alone in 18.4% (n = 21) and DSD in 27.2% (n = 31). Patients with DSD had a mean HABAM score 7 points greater than those with dementia alone (19.8 ± 8.7 vs 12.5 ± 9.5; p < 0.001); 70% of participants with DSD were correctly identified using the HABAM at a cut off of 22 (sensitivity 61%, specificity 79%, AUC = 0.76). Individuals with delirium have worse motor function than those without delirium, even in the context of comorbid dementia. Measuring motor function using the HABAM in older people at admission may help to diagnose DSD.

Guttman Scaling: An Error Paradigm

1973 ◽  
Vol 16 (1) ◽  
pp. 5-26
Author(s):  
Richard R. Clayton
Keyword(s):  
Guttman Scaling ◽  
Error Paradigm
Sign in / Sign up

Export Citation Format

Share Document