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Methods to Analyze Likert-Type Data in Educational Technology Research.

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Title: Methods to Analyze Likert-Type Data in Educational Technology Research.
Authors: Li-Ting Chen1 litingc@unr.edu, Leping Liu1 liu@unr.edu
Source: Journal of Educational Technology Development & Exchange. 2020, Vol. 13 Issue 2, p39-60. 22p.
Subject Terms: *Education research, *Data analysis, *Educational technology, Logistic regression analysis, Dependent variables, Statistics
Abstract: Likert-type items are commonly used in education and related fields to measure attitudes and opinions. Yet there is no consensus on how to analyze data collected from these items. In this paper, we first provided a synthesis of the existing literature on methods to analyze Likert-type data and computing tools for these methods. Secondly, to examine the use and analysis of Likert-type data in the field of educational technology, we reviewed 424 articles that were published in the journal Educational Technology Research and Development between 2016 and 2020. Our review showed that about 50% of the articles reported Likert-type data. A total of 139 articles used Likert-type data as a dependent variable, among which 86% employed parametric methods to analyze the data. In addition, less than 3% of the 139 articles used an ordered probit/logit model, transformation, or strategy for rescaling Likert-type data to interval data to perform statistical analysis. Finally, to empower educational technology researchers to handle Likert-type data effectively, we concluded the paper with our suggestions and insight regarding alternative strategies and methods. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Educational Technology Development & Exchange is the property of Society of International Chinese in Educational Technology (SICET) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Educational+Technology+Development+%26+Exchange%22">Journal of Educational Technology Development & Exchange</searchLink>. 2020, Vol. 13 Issue 2, p39-60. 22p.
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  Data: *<searchLink fieldCode="DE" term="%22Education+research%22">Education research</searchLink><br />*<searchLink fieldCode="DE" term="%22Data+analysis%22">Data analysis</searchLink><br />*<searchLink fieldCode="DE" term="%22Educational+technology%22">Educational technology</searchLink><br /><searchLink fieldCode="DE" term="%22Logistic+regression+analysis%22">Logistic regression analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Dependent+variables%22">Dependent variables</searchLink><br /><searchLink fieldCode="DE" term="%22Statistics%22">Statistics</searchLink>
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  Data: Likert-type items are commonly used in education and related fields to measure attitudes and opinions. Yet there is no consensus on how to analyze data collected from these items. In this paper, we first provided a synthesis of the existing literature on methods to analyze Likert-type data and computing tools for these methods. Secondly, to examine the use and analysis of Likert-type data in the field of educational technology, we reviewed 424 articles that were published in the journal Educational Technology Research and Development between 2016 and 2020. Our review showed that about 50% of the articles reported Likert-type data. A total of 139 articles used Likert-type data as a dependent variable, among which 86% employed parametric methods to analyze the data. In addition, less than 3% of the 139 articles used an ordered probit/logit model, transformation, or strategy for rescaling Likert-type data to interval data to perform statistical analysis. Finally, to empower educational technology researchers to handle Likert-type data effectively, we concluded the paper with our suggestions and insight regarding alternative strategies and methods. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of Educational Technology Development & Exchange is the property of Society of International Chinese in Educational Technology (SICET) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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