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        <title type="main" level="a">A structural equation model to measure logical competences</title>
        <author>
          <persName n="1" ref="https://orcid.org/0000-0001-8097-3870" type="ORCID">
            <forename>Silvia</forename>
            <surname>Bacci</surname>
            <placeName type="affiliation">University of Florence, Italy</placeName>
          </persName>
          <persName n="2" ref="https://orcid.org/0000-0002-5816-2964" type="ORCID">
            <forename>Bruno</forename>
            <surname>Bertaccini</surname>
            <placeName type="affiliation">University of Florence, Italy</placeName>
          </persName>
          <persName n="3" ref="https://orcid.org/0000-0003-2695-0058" type="ORCID">
            <forename>Riccardo</forename>
            <surname>Bruni</surname>
            <placeName type="affiliation">University of Florence, Italy</placeName>
          </persName>
          <persName n="4" ref="https://orcid.org/0000-0002-0701-4398" type="ORCID">
            <forename>Federico</forename>
            <surname>Crescenzi</surname>
            <placeName type="affiliation">University of Florence, Italy</placeName>
          </persName>
          <persName n="5" ref="https://orcid.org/0000-0002-4707-8476" type="ORCID">
            <forename>Beatrice</forename>
            <surname>Donati</surname>
            <placeName type="affiliation">University of Florence, Italy</placeName>
          </persName>
        </author>
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          <resp>This is a section of <title>ASA 2021 Statistics and Information Systems for Policy Evaluation  </title>(DOI: <idno type="DOI">10.36253/978-88-5518-461-8</idno>) by </resp>
          <name>Alessandra Petrucci, Bruno Bertaccini, Luigi Fabbris</name>
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      <publicationStmt>
        <publisher>Firenze University Press</publisher>
        <pubPlace>Firenze</pubPlace>
        <date when="2021">2021</date>
        <idno type="DOI">https://doi.org/10.36253/978-88-5518-461-8.08</idno>
        <availability>
          <p>Available for academic research purposes</p>
          <p>Open Access</p>
          <p>Copyright Author(s)</p>
          <licence source="text" target="https://creativecommons.org/licenses/by/4.0/legalcode">
            <p>Content licence CC BY 4.0</p>
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          <licence source="metadata" target="https://creativecommons.org/publicdomain/zero/1.0/legalcode">
            <p>Metadata licence CC0 1.0</p>
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        <p>This is original content, published for academic research purposes</p>
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      <abstract xml:lang="en">
        <p>Logical abilities are a ubiquitous ingredient in all those contexts that take into account soft skills, argumentative skills or critical thinking. However, the relationship between logical models and the enhancement of these abilities is rarely explicitly considered. Two aspects of the issue are particularly critical in our opinion, namely: (i) the lack of statistically relevant data concerning these competences; (ii) the absence of reliable indices that might be used to detect and measure the possession of abilities underlying the aforementioned skills. This paper addresses both aspects of this topic by presenting the results of a research that we conducted between October and December 2020 on students enrolled in various degree courses at the University of Florence. The dataset has been collected by a three-stage initiative. We started from an entrance examination to assess the students' initial abilities. This test included ten questions, each regarding a specific aspect of logical reasoning. Then, students underwent a short training course to strengthen their knowledge of logic. At the end of the short course, students underwent a final examination to evaluate the effectiveness of the training.
To verify the effectiveness of the training program we used students’ characteristics into a Structural Equation Model comparing the distribution of abilities before and after the training.</p>
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        <keywords>
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            <item>SEM</item>
            <item>Logical abilities</item>
            <item>Training course effectiveness</item>
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      <p>It is available online at https://doi.org/10.36253/978-88-5518-461-8.08<ref target="https://doi.org/10.36253/978-88-5518-461-8.08" /></p>
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          <head>References</head>
          <bibl n="60448">Bartolucci, F., Bacci, S., and Gnaldi, M. (2019). Statistical analysis of questionnaires: A unified approach based on R and Stata. Chapman and Hall/CRC.</bibl>
          <bibl n="60449">Battauz, M. (2015). equateirt: An r package for irt test equating. Journal of Statistical Software, 68(1):1–22.</bibl>
          <bibl n="60450">Bertaccini, B., Bruni, R., Crescenzi, F., and Donati, B. (2021). Measuring logical competences and soft skills when enrolling in a university degree course. In Bertaccini, B., Fabbris, L., and Petrucci, A., editors, ASA 2021 Statistics and Information Systems for Policy Evaluation: Book of short papers of the opening conference, volume 127. Firenze University Press.</bibl>
          <bibl n="60451">Bollen, K. A. (1989). Structural equations with latent variables, volume 210. John Wiley &amp;amp; Sons.</bibl>
          <bibl n="60452">DeMars, C. (2010). Item response theory. Oxford University Press.</bibl>
          <bibl n="60453">Duncan, O. D. (2014). Introduction to structural equation models. Elsevier.</bibl>
          <bibl n="60454">Rosseel, Y. (2012). Lavaan: An r package for structural equation modeling and more. version 0.5–12 (beta). Journal of statistical software, 48(2):1–36.</bibl>
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