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 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.

Guttman Scaling: An Error Paradigm

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

Universal Patterns in Cultural Evolution: An Empirical Analysis Using Guttman Scaling

2004 ◽  
Vol 106 (1) ◽  
pp. 145-149 ◽  
Author(s):  
PETER N. PEREGRINE ◽  
CAROL R. EMBER ◽  
MELVIN EMBER
Keyword(s):  
Empirical Analysis ◽  
Cultural Evolution ◽  
Guttman Scaling

Reading Comprehension Test Performance and Hierarchical Classification

1979 ◽  
Vol 11 (4) ◽  
pp. 381-385
Author(s):  
Jean Wallace Gillet ◽  
Herbert C. Richards
Keyword(s):  
Test Performance ◽  
Hierarchical Classification ◽  
Third Graders ◽  
Reading Test ◽  
Stepwise Multiple Regression ◽  
Stepwise Multiple Regression Analysis ◽  
Test Items ◽  
Single Score ◽  
Classification Tasks ◽  
Guttman Scaling

Items from a widely used standardized reading achievement test were rated by trained judges according to the degree to which they required an understanding of hierarchical classification. 2 subtests were constructed from subsets of items that were identified by their extreme ratings: Subtest A was judged to require classification operations from respondents; Subtest B, not to require them. 22 third graders were assessed on 5 types of Piagetian classification tasks. After Guttman scaling, each was assigned a single score for classification ability. Stepwise multiple regression analysis revealed that classification ability was significantly predictive of Subtest A performance, even when performance on Subtest B was controlled statistically. The results were interpreted to mean that reading test performance is partially influenced by one's mastery of hierarchical classification because some test items require this ability. Such items probably discriminate among children on the basis of developmental maturity rather than on instruction-related knowledge.

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