2022
DOI: 10.1111/lnc3.12479
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Using principal component analysis to explore co‐variation of vowels

Abstract: This paper presents a methodology for exploring systematic co‐variation of vowels using Principal Component Analysis (PCA). As a case study, we examine and build on Brand et al.'s (2021) study of systematic co‐variation amongst the monophthongs of New Zealand English (NZE) across speakers born over a 118‐year time period. We present PCA as a methodology, with information aimed at readers who may themselves want to use it in a related context. We consider tests for the appropriateness of PCA, how to select Prin… Show more

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Cited by 5 publications
(2 citation statements)
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“…In township-level analysis, the principal component analyses (PCA) is used because the original data are quantitative and meaningful. The PCA is a statistical analysis method that reduces the number of variables in data to minimize information loss and create a small number of uncorrelated composite variables (principal components) [15]. Thus, it is possible to understand information in a lower dimension through dimensionality reduction and find values that fall outside the major components of multidimensional data.…”
Section: Extracting Regional Characteristics By Health Factorsmentioning
confidence: 99%
“…In township-level analysis, the principal component analyses (PCA) is used because the original data are quantitative and meaningful. The PCA is a statistical analysis method that reduces the number of variables in data to minimize information loss and create a small number of uncorrelated composite variables (principal components) [15]. Thus, it is possible to understand information in a lower dimension through dimensionality reduction and find values that fall outside the major components of multidimensional data.…”
Section: Extracting Regional Characteristics By Health Factorsmentioning
confidence: 99%
“…In recent years, important research has been done using PCA. In [8], a method for investigating systematic co-variation of vowels has been presented by using PCA. In [9], an application of principal component analysis has been obtained to reduce the dimensionality of variables representing the speech signal.…”
Section: Introductionmentioning
confidence: 99%