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Articles 1 - 4 of 4
Full-Text Articles in Physical Sciences and Mathematics
The Influence Of Reliability On Four Rules For Determining The Number Of Components To Retain, Gibbs Y. Kanyongo
The Influence Of Reliability On Four Rules For Determining The Number Of Components To Retain, Gibbs Y. Kanyongo
Journal of Modern Applied Statistical Methods
Imperfectly reliable scores impact the performance of factor analytic procedures. A series of Monte Carlo studies was conducted to generate scores with known component structure from population matrices with varying levels of reliability. The scores were submitted to four procedures: Kaiser rule, scree plot, parallel analysis, and modified Horn’s parallel analysis to find if each procedure accurately determines the number of components at the different reliability levels. The performance of each procedure was judged by the percentage of the number of times that the procedure was correct and the mean components that each procedure extracted in each cell. Generally, the …
Exploratory Factor Analysis In Two Measurement Journals: Hegemony By Default, J. Thomas Kellow
Exploratory Factor Analysis In Two Measurement Journals: Hegemony By Default, J. Thomas Kellow
Journal of Modern Applied Statistical Methods
Exploratory factor analysis studies in two prominent measurement journals were explored. Issues addressed were: (a) factor extraction methods, (b) factor retention rules, (c) factor rotation strategies, and (d) saliency criteria for including variables. Many authors continue to use principal components extraction, orthogonal (varimax) rotation, and retain factors with eigenvalues greater than 1.0.
Determining The Correct Number Of Components To Extract From A Principal Components Analysis: A Monte Carlo Study Of The Accuracy Of The Scree Plot, Gibbs Y. Kanyongo
Determining The Correct Number Of Components To Extract From A Principal Components Analysis: A Monte Carlo Study Of The Accuracy Of The Scree Plot, Gibbs Y. Kanyongo
Journal of Modern Applied Statistical Methods
This article pertains to the accuracy of the of the scree plot in determining the correct number of components to retain under different conditions of sample size, component loading and variable-tocomponent ratio. The study employs use of Monte Carlo simulations in which the population parameters were manipulated, and data were generated, and then the scree plot applied to the generated scores.
Insights Into Latent Class Analysis, Margaret S. Pepe, Holly Janes
Insights Into Latent Class Analysis, Margaret S. Pepe, Holly Janes
UW Biostatistics Working Paper Series
Latent class analysis is a popular statistical technique for estimating disease prevalence and test sensitivity and specificity. It is used when a gold standard assessment of disease is not available but results of multiple imperfect tests are. We derive analytic expressions for the parameter estimates in terms of the raw data, under the conditional independence assumption. These expressions indicate explicitly how observed two- and three-way associations between test results are used to infer disease prevalence and test operating characteristics. Although reasonable if the conditional independence model holds, the estimators have no basis when it fails. We therefore caution against using …