The Guttman-Kaiser Criterion as a Predictor of the Number of Common Factors

Yeomans, K. A.; Golder, Paul

doi:10.2307/2987988

Cited by 247 publications

(181 citation statements)

References 14 publications

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“…We also explored the Guttman-Kaiser Criterion [35] and Cattell scree plots [36], to determine the number of factors to extract, the result of which reconfirmed the existence of three hidden ecological factors to the incidence of malaria. In the Guttman-Kaiser Criterion, we have the eigenvalues 2.71, 1.53, 1.02, 0.82, 0.57, 0.29, 0.05 computed using the correlation matrix (see Table 1); however, the rule for extraction is based on the factors whose eigenvalues are greater than unity.…”

Section: Presentation Of Resultsmentioning

confidence: 99%

Towards a Predictive Analytics-Based Intelligent Malaria Outbreak Warning System

et al. 2017

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Malaria, as one of the most serious infectious diseases causing public health problems in the world, affects about two-thirds of the world population, with estimated resultant deaths close to a million annually. The effects of this disease are much more profound in third world countries, which have very limited medical resources. When an intense outbreak occurs, most of these countries cannot cope with the high number of patients due to the lack of medicine, equipment and hospital facilities. The prevention or reduction of the risk factor of this disease is very challenging, especially in third world countries, due to poverty and economic insatiability. Technology can offer alternative solutions by providing early detection mechanisms that help to control the spread of the disease and allow the management of treatment facilities in advance to ensure a more timely health service, which can save thousands of lives. In this study, we have deployed an intelligent malaria outbreak early warning system, which is a mobile application that predicts malaria outbreak based on climatic factors using machine learning algorithms. The system will help hospitals, healthcare providers, and health organizations take precautions in time and utilize their resources in case of emergency. To our best knowledge, the system developed in this paper is the first publicly available application. Since confounding effects of climatic factors have a greater influence on the incidence of malaria, we have also conducted extensive research on exploring a new ecosystem model for the assessment of hidden ecological factors and identified three confounding factors that significantly influence the malaria incidence. Additionally, we deploy a smart healthcare application; this paper also makes a significant contribution by identifying hidden ecological factors of malaria.

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Section: Presentation Of Resultsmentioning

confidence: 99%

Towards a Predictive Analytics-Based Intelligent Malaria Outbreak Warning System

et al. 2017

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“…The commonly used eigenvalues greater than unity (Kaiser-Guttman) criterion (Yeomans and Golder, 1981) has been shown to be inaccurate when the number of variables is below about 20 or above about 50 (cf. Cattell and Vogelmann, 1977;Hakstian, Rogers, and Cattell, 1982;Horn and Engstrom, 1979).…”

Section: Procedures and Methodologymentioning

confidence: 99%

Confirmation of the structural dimensionality of the Stanford-Binet intelligence scale (fourth edition)

Boyle

1989

Personality and Individual Differences

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“…A Monte Carlo study such as Pennell(1968), Yeomans and Golder(1982), Hakstian, Rogers and Cattell(1982) and Tucker, Koopman and Linn(1969) adopts a random loading model in which loadings are generated by some random mechanism. Intending a finer classification, let us call the model in the first two papers a purely random loading model because it is determined randomly except for values of only a few parameters.…”

Section: Introductionmentioning

confidence: 99%

“…The model used in Pennell(1968) or Yeomans and Golder(1982) has only one parameter representing the communality so that the arbitrariness is reduced to minimum. The latter model, however, seems to be unsatisfactory in that the variables are uncorrelated with each other.…”

Section: Introductionmentioning

confidence: 99%

The Number-of-Factors Problem in Least-Squares Factor Analysis With a Random Loading Model

Okamoto

1988

Journal of the Japanese Society of Computational Statistics

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A random loading model is presented as a numerical model in a Monte Carlo experiment of factor analysis. Three methods for the number-of-factors problem, Guttman-Kaiser, likelihood ratio and AIC, and two least-squares estimators, onestep and final, were compared experimentally within each category. On the whole, AIC method using the final estimator fared best, whereas the one-step estimator behaved better than the final estimator in estimating the unique variances.

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The Guttman-Kaiser Criterion as a Predictor of the Number of Common Factors

Abstract: This article evaluates the performance of the Guttman‐Kaiser criterion in determining the number of significant components or factors in a correlation matrix

Cited by 247 publications

References 14 publications

Towards a Predictive Analytics-Based Intelligent Malaria Outbreak Warning System

Towards a Predictive Analytics-Based Intelligent Malaria Outbreak Warning System

Confirmation of the structural dimensionality of the Stanford-Binet intelligence scale (fourth edition)

The Number-of-Factors Problem in Least-Squares Factor Analysis With a Random Loading Model

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