ANÁLISE FATORIAL E ANÁLISE DE AGRUPAMENTO NO MAPEAMENTO DE CONCEPÇÖES EPISTEMOLÓGICAS DE PROFESSORES SOBRE A CIÊNCIA E A ECOLOGIA/Factor analysis and cluster analysis to map teacher's epistemological beliefs about science and ecology
The construction and validation of new tools for epistemological studies in science education is very important and few studies perform a validation process using techniques from multivariate statistics. As suggested by the literature, there are several tools to identify epistemological views about...
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Veröffentlicht in: | Investigações em ensino de ciências 2016-12, Vol.21 (3), p.152 |
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Sprache: | por |
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Zusammenfassung: | The construction and validation of new tools for epistemological studies in science education is very important and few studies perform a validation process using techniques from multivariate statistics. As suggested by the literature, there are several tools to identify epistemological views about the science as a whole, but few focused on the mapping of field-dependent beliefs, or views about specific scientific areas, such as the ecology, which has received less attention from researchers. Thus, this paper aimed to: i) validate new questionnaires for identification of epistemological beliefs about the science, the professional ecology, and the school ecology (teaching ecology), and ii) characterize the profiles of epistemological beliefs of the teachers who answered these questionnaires. We designed three questionnaires with Likert five-point scale, and carried out a cross-sectional online survey, using non-probabilistic convenience sampling. The sample size was 80 participants. The factor analysis and the internal consistency analysis (Cronbach's alpha) allowed to evaluate the validity and reliability of our questionnaires, which is performed only in a qualitative/subjective way by most researchers. In addition, the cluster analysis allowed to map which groups of teachers showed sophisticated epistemological beliefs, and which presented naïve views. Therefore, the statistical techniques contributed offering more objective criteria for the interpretation of the results. |
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ISSN: | 1518-9384 1518-8795 |
DOI: | 10.22600/1518-8795.ienci2016v21n3p152 |