Configural analysis of oscillating progression
Oscillating series of scores can be approximated with locally optimized smoothing functions. In this article, we describe how such series can be approximated with locally estimated (loess) smoothing, and how Configural Frequency Analysis (CFA) can be used to evaluate and interpret results. Loess fu...
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doaj-32609b765c6f43aaa2f7411c3e7be61a2021-08-26T17:35:03ZengLund University LibraryJournal for Person-Oriented Research2002-02442003-01772021-08-017110.17505/jpor.2021.23448Configural analysis of oscillating progressionAlexander von Eye0Wolfgang Wiedermann1Stefan von Weber2Michigan State University, USAUniversity of Missouri, Columbia, USAUniversity of Furtwangen, Germany Oscillating series of scores can be approximated with locally optimized smoothing functions. In this article, we describe how such series can be approximated with locally estimated (loess) smoothing, and how Configural Frequency Analysis (CFA) can be used to evaluate and interpret results. Loess functions are often hard to describe because they cannot be represented by just one function that has interpretable parameters. In this article, we suggest that specification of the CFA base model be based on the width of the window that is used for local curve optimization, the weight given to data points in the neighborhood of the approximated one, and by the function that is used to locally approximate observed data. CFA types indicate that more cases were found than expected from the local optimization model. CFA antitypes indicate that fewer cases were found. In a real-world data example, the development of Covid-19 diagnoses in France is analyzed for the beginning period of the pandemic. https://journals.lub.lu.se/jpor/article/view/23448Configural Frequency Analysis, base model, loess smoothing, time series, local optimization, Covid-19 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Alexander von Eye Wolfgang Wiedermann Stefan von Weber |
spellingShingle |
Alexander von Eye Wolfgang Wiedermann Stefan von Weber Configural analysis of oscillating progression Journal for Person-Oriented Research Configural Frequency Analysis, base model, loess smoothing, time series, local optimization, Covid-19 |
author_facet |
Alexander von Eye Wolfgang Wiedermann Stefan von Weber |
author_sort |
Alexander von Eye |
title |
Configural analysis of oscillating progression |
title_short |
Configural analysis of oscillating progression |
title_full |
Configural analysis of oscillating progression |
title_fullStr |
Configural analysis of oscillating progression |
title_full_unstemmed |
Configural analysis of oscillating progression |
title_sort |
configural analysis of oscillating progression |
publisher |
Lund University Library |
series |
Journal for Person-Oriented Research |
issn |
2002-0244 2003-0177 |
publishDate |
2021-08-01 |
description |
Oscillating series of scores can be approximated with locally optimized smoothing functions. In this article, we describe how such series can be approximated with locally estimated (loess) smoothing, and how Configural Frequency Analysis (CFA) can be used to evaluate and interpret results. Loess functions are often hard to describe because they cannot be represented by just one function that has interpretable parameters. In this article, we suggest that specification of the CFA base model be based on the width of the window that is used for local curve optimization, the weight given to data points in the neighborhood of the approximated one, and by the function that is used to locally approximate observed data. CFA types indicate that more cases were found than expected from the local optimization model. CFA antitypes indicate that fewer cases were found. In a real-world data example, the development of Covid-19 diagnoses in France is analyzed for the beginning period of the pandemic.
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topic |
Configural Frequency Analysis, base model, loess smoothing, time series, local optimization, Covid-19 |
url |
https://journals.lub.lu.se/jpor/article/view/23448 |
work_keys_str_mv |
AT alexandervoneye configuralanalysisofoscillatingprogression AT wolfgangwiedermann configuralanalysisofoscillatingprogression AT stefanvonweber configuralanalysisofoscillatingprogression |
_version_ |
1721189232020553728 |