Small numbers are an opportunity, not a problem
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Scientia Medica (Porto Alegre. Online) |
Texto Completo: | https://revistaseletronicas.pucrs.br/scientiamedica/article/view/40128 |
Resumo: | Aims: outcomes of research in education and training are partly a function of the context in which that study takes place, the questions we ask, and what is feasible. Many questions are about learning, which involves repeated measurements in a particular time window, and the practical context is usually such that offering an intervention to some but not to all learners does not make sense or is unethical. For quality assurance and other purposes, education and training centers may have very locally oriented questions that they seek to answer, such as whether an intervention can be considered effective in their context of small numbers of learners. While the rationale behind the design and outcomes of this kind of studies may be of interest to a much wider community, for example to study the transferability of findings to other contexts, people are often discouraged to report on the outcomes of such studies at conferences or in educational research journals. The aim of this paper is to counter that discouragement and instead encourage people to see small numbers as an opportunity instead of as a problem.Method: a worked example of a parametric and a non-parametric method for this type of situation, using simulated data in the zero-cost Open Source statistical program R version 4.0.5.Results: contrary to the non-parametric method, the parametric method can provide estimates of intervention effectiveness for the individual participant, account for trends in different phases of a study. However, the non-parametric method provides a solution in several situations where the parametric method should be used.Conclusion: Given the costs of research, the lessons to be learned from research, and statistical methods available, small numbers should be considered an opportunity, not a problem. |
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Small numbers are an opportunity, not a problemNúmeros pequenos são uma oportunidade, não um problemamixed modelpercentage of all non-overlapping data bayessingle case designsingle case experimental designtime seriesmodelo mistoporcentagem de todos os dados bayes não sobrepostosprojeto de caso únicoprojeto experimental de caso únicoséries temporaisAims: outcomes of research in education and training are partly a function of the context in which that study takes place, the questions we ask, and what is feasible. Many questions are about learning, which involves repeated measurements in a particular time window, and the practical context is usually such that offering an intervention to some but not to all learners does not make sense or is unethical. For quality assurance and other purposes, education and training centers may have very locally oriented questions that they seek to answer, such as whether an intervention can be considered effective in their context of small numbers of learners. While the rationale behind the design and outcomes of this kind of studies may be of interest to a much wider community, for example to study the transferability of findings to other contexts, people are often discouraged to report on the outcomes of such studies at conferences or in educational research journals. The aim of this paper is to counter that discouragement and instead encourage people to see small numbers as an opportunity instead of as a problem.Method: a worked example of a parametric and a non-parametric method for this type of situation, using simulated data in the zero-cost Open Source statistical program R version 4.0.5.Results: contrary to the non-parametric method, the parametric method can provide estimates of intervention effectiveness for the individual participant, account for trends in different phases of a study. However, the non-parametric method provides a solution in several situations where the parametric method should be used.Conclusion: Given the costs of research, the lessons to be learned from research, and statistical methods available, small numbers should be considered an opportunity, not a problem.Objetivo: os resultados da pesquisa em educação e treinamento são, em parte, uma função do contexto em que esse estudo ocorre, das perguntas que fazemos e do que é viável. Muitas perguntas são sobre a aprendizagem, que envolve medições repetidas em uma janela de tempo específica, e o contexto prático, geralmente, é tal, que oferecer uma intervenção a alguns, mas não a todos os alunos, não faz sentido ou é antiético. Para garantia de qualidade e outros propósitos, os centros de educação e treinamento podem ter perguntas orientadas localmente que procuram responder, como, por exemplo, se uma intervenção pode ser considerada eficaz em seu contexto de pequeno número de alunos. Embora a justificativa por trás do projeto e dos resultados deste tipo de estudos possa ser do interesse de uma comunidade muito mais ampla, por exemplo, para estudar a possibilidade de transferência de resultados para outros contextos, as pessoas são frequentemente desencorajadas a relatar os resultados de tais estudos em conferências ou em revistas de pesquisa educacional. O objetivo deste artigo é combater esse desânimo e, em vez disso, incentivar as pessoas a verem os pequenos números como uma oportunidade em vez de um problema.Método: realizado um exemplo de método paramétrico e não paramétrico para este tipo de situação, utilizando dados simulados no programa estatístico Open Source R versão 4.0.5 de custo zero.Resultados: ao contrário do método não paramétrico, o método paramétrico pode fornecer estimativas da eficácia da intervenção para o participante individual, levando em conta as tendências em diferentes fases de um estudo. No entanto, o método não paramétrico fornece uma solução em várias situações, onde o método paramétrico deve ser usado.Conclusão: dados os custos da pesquisa, as lições a serem aprendidas com a pesquisa e os métodos estatísticos disponíveis, pequenos números devem ser considerados uma oportunidade, não um problema.Editora da PUCRS - ediPUCRS2021-06-30info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://revistaseletronicas.pucrs.br/scientiamedica/article/view/4012810.15448/1980-6108.2021.1.40128Scientia Medica; Vol. 31 No. 1 (2021): Single Volume; e40128Scientia Medica; v. 31 n. 1 (2021): Volume Único; e401281980-61081806-556210.15448/1980-6108.2021.1reponame:Scientia Medica (Porto Alegre. Online)instname:Pontifícia Universidade Católica do Rio Grande do Sul (PUCRS)instacron:PUC_RSenghttps://revistaseletronicas.pucrs.br/scientiamedica/article/view/40128/26901Copyright (c) 2021 Scientia Medicahttp://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessLeppink, Jimmie2022-01-25T17:01:59Zoai:ojs.revistaseletronicas.pucrs.br:article/40128Revistahttps://revistaseletronicas.pucrs.br/scientiamedica/PUBhttps://revistaseletronicas.pucrs.br/scientiamedica/oaiscientiamedica@pucrs.br || editora.periodicos@pucrs.br1980-61081806-5562opendoar:2022-01-25T17:01:59Scientia Medica (Porto Alegre. Online) - Pontifícia Universidade Católica do Rio Grande do Sul (PUCRS)false |
dc.title.none.fl_str_mv |
Small numbers are an opportunity, not a problem Números pequenos são uma oportunidade, não um problema |
title |
Small numbers are an opportunity, not a problem |
spellingShingle |
Small numbers are an opportunity, not a problem Leppink, Jimmie mixed model percentage of all non-overlapping data bayes single case design single case experimental design time series modelo misto porcentagem de todos os dados bayes não sobrepostos projeto de caso único projeto experimental de caso único séries temporais |
title_short |
Small numbers are an opportunity, not a problem |
title_full |
Small numbers are an opportunity, not a problem |
title_fullStr |
Small numbers are an opportunity, not a problem |
title_full_unstemmed |
Small numbers are an opportunity, not a problem |
title_sort |
Small numbers are an opportunity, not a problem |
author |
Leppink, Jimmie |
author_facet |
Leppink, Jimmie |
author_role |
author |
dc.contributor.author.fl_str_mv |
Leppink, Jimmie |
dc.subject.por.fl_str_mv |
mixed model percentage of all non-overlapping data bayes single case design single case experimental design time series modelo misto porcentagem de todos os dados bayes não sobrepostos projeto de caso único projeto experimental de caso único séries temporais |
topic |
mixed model percentage of all non-overlapping data bayes single case design single case experimental design time series modelo misto porcentagem de todos os dados bayes não sobrepostos projeto de caso único projeto experimental de caso único séries temporais |
description |
Aims: outcomes of research in education and training are partly a function of the context in which that study takes place, the questions we ask, and what is feasible. Many questions are about learning, which involves repeated measurements in a particular time window, and the practical context is usually such that offering an intervention to some but not to all learners does not make sense or is unethical. For quality assurance and other purposes, education and training centers may have very locally oriented questions that they seek to answer, such as whether an intervention can be considered effective in their context of small numbers of learners. While the rationale behind the design and outcomes of this kind of studies may be of interest to a much wider community, for example to study the transferability of findings to other contexts, people are often discouraged to report on the outcomes of such studies at conferences or in educational research journals. The aim of this paper is to counter that discouragement and instead encourage people to see small numbers as an opportunity instead of as a problem.Method: a worked example of a parametric and a non-parametric method for this type of situation, using simulated data in the zero-cost Open Source statistical program R version 4.0.5.Results: contrary to the non-parametric method, the parametric method can provide estimates of intervention effectiveness for the individual participant, account for trends in different phases of a study. However, the non-parametric method provides a solution in several situations where the parametric method should be used.Conclusion: Given the costs of research, the lessons to be learned from research, and statistical methods available, small numbers should be considered an opportunity, not a problem. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-06-30 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://revistaseletronicas.pucrs.br/scientiamedica/article/view/40128 10.15448/1980-6108.2021.1.40128 |
url |
https://revistaseletronicas.pucrs.br/scientiamedica/article/view/40128 |
identifier_str_mv |
10.15448/1980-6108.2021.1.40128 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://revistaseletronicas.pucrs.br/scientiamedica/article/view/40128/26901 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2021 Scientia Medica http://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2021 Scientia Medica http://creativecommons.org/licenses/by/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Editora da PUCRS - ediPUCRS |
publisher.none.fl_str_mv |
Editora da PUCRS - ediPUCRS |
dc.source.none.fl_str_mv |
Scientia Medica; Vol. 31 No. 1 (2021): Single Volume; e40128 Scientia Medica; v. 31 n. 1 (2021): Volume Único; e40128 1980-6108 1806-5562 10.15448/1980-6108.2021.1 reponame:Scientia Medica (Porto Alegre. Online) instname:Pontifícia Universidade Católica do Rio Grande do Sul (PUCRS) instacron:PUC_RS |
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Pontifícia Universidade Católica do Rio Grande do Sul (PUCRS) |
instacron_str |
PUC_RS |
institution |
PUC_RS |
reponame_str |
Scientia Medica (Porto Alegre. Online) |
collection |
Scientia Medica (Porto Alegre. Online) |
repository.name.fl_str_mv |
Scientia Medica (Porto Alegre. Online) - Pontifícia Universidade Católica do Rio Grande do Sul (PUCRS) |
repository.mail.fl_str_mv |
scientiamedica@pucrs.br || editora.periodicos@pucrs.br |
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1809101752486592512 |