A statistical approach to quantitative data validation focused on the assessment of students' perceptions about biotechnology

Detalhes bibliográficos
Autor(a) principal: Maria João Fonseca
Data de Publicação: 2013
Outros Autores: Patrício Costa, Leonor Lencastre, Fernando Tavares
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: https://hdl.handle.net/10216/70407
Resumo: Student awareness levels are frequently used to evaluate the effectiveness of educational policies to promote scientific literacy. Over the last years several studies have been developed to assess students' perceptions towards science and technology, which usually rely on quantitative methods to achieve broad characterizations, and obtain quantifiable and comparable data. Although the usefulness of this information depends on its validity and reliability, validation is frequently neglected by researchers with limited background in statistics. In this context, we propose a guideline to implement a statistical approach to questionnaire validation, combining exploratory factor analysis and reliability analysis. The work focuses on the psychometric analysis of data provided by a questionnaire assessing 1196 elementary and high school students' perceptions about biotechnology. Procedural guidelines to enhance the efficiency of quantitative inquiry surveys are given, by discussing essential methodological aspects and relevant criteria to integrate theory into practice. (c) 2013 Fonseca et al.
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spelling A statistical approach to quantitative data validation focused on the assessment of students' perceptions about biotechnologyStudent awareness levels are frequently used to evaluate the effectiveness of educational policies to promote scientific literacy. Over the last years several studies have been developed to assess students' perceptions towards science and technology, which usually rely on quantitative methods to achieve broad characterizations, and obtain quantifiable and comparable data. Although the usefulness of this information depends on its validity and reliability, validation is frequently neglected by researchers with limited background in statistics. In this context, we propose a guideline to implement a statistical approach to questionnaire validation, combining exploratory factor analysis and reliability analysis. The work focuses on the psychometric analysis of data provided by a questionnaire assessing 1196 elementary and high school students' perceptions about biotechnology. Procedural guidelines to enhance the efficiency of quantitative inquiry surveys are given, by discussing essential methodological aspects and relevant criteria to integrate theory into practice. (c) 2013 Fonseca et al.20132013-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/10216/70407eng2193-180110.1186/2193-1801-2-496Maria João FonsecaPatrício CostaLeonor LencastreFernando Tavaresinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-29T15:12:52Zoai:repositorio-aberto.up.pt:10216/70407Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T00:18:09.406460Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv A statistical approach to quantitative data validation focused on the assessment of students' perceptions about biotechnology
title A statistical approach to quantitative data validation focused on the assessment of students' perceptions about biotechnology
spellingShingle A statistical approach to quantitative data validation focused on the assessment of students' perceptions about biotechnology
Maria João Fonseca
title_short A statistical approach to quantitative data validation focused on the assessment of students' perceptions about biotechnology
title_full A statistical approach to quantitative data validation focused on the assessment of students' perceptions about biotechnology
title_fullStr A statistical approach to quantitative data validation focused on the assessment of students' perceptions about biotechnology
title_full_unstemmed A statistical approach to quantitative data validation focused on the assessment of students' perceptions about biotechnology
title_sort A statistical approach to quantitative data validation focused on the assessment of students' perceptions about biotechnology
author Maria João Fonseca
author_facet Maria João Fonseca
Patrício Costa
Leonor Lencastre
Fernando Tavares
author_role author
author2 Patrício Costa
Leonor Lencastre
Fernando Tavares
author2_role author
author
author
dc.contributor.author.fl_str_mv Maria João Fonseca
Patrício Costa
Leonor Lencastre
Fernando Tavares
description Student awareness levels are frequently used to evaluate the effectiveness of educational policies to promote scientific literacy. Over the last years several studies have been developed to assess students' perceptions towards science and technology, which usually rely on quantitative methods to achieve broad characterizations, and obtain quantifiable and comparable data. Although the usefulness of this information depends on its validity and reliability, validation is frequently neglected by researchers with limited background in statistics. In this context, we propose a guideline to implement a statistical approach to questionnaire validation, combining exploratory factor analysis and reliability analysis. The work focuses on the psychometric analysis of data provided by a questionnaire assessing 1196 elementary and high school students' perceptions about biotechnology. Procedural guidelines to enhance the efficiency of quantitative inquiry surveys are given, by discussing essential methodological aspects and relevant criteria to integrate theory into practice. (c) 2013 Fonseca et al.
publishDate 2013
dc.date.none.fl_str_mv 2013
2013-01-01T00:00:00Z
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dc.language.iso.fl_str_mv eng
language eng
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