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Integrating PLS-SEM and NVivo in Mixed-Methods Educational Research: A Comprehensive Evaluation of Quantitative and Qualitative Analytical Tools

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Title: Integrating PLS-SEM and NVivo in Mixed-Methods Educational Research: A Comprehensive Evaluation of Quantitative and Qualitative Analytical Tools
Language: English
Authors: Mahadi Hasan Miraz (ORCID 0000-0003-3008-7090), Sanmugam Annamalah (ORCID 0000-0002-9438-2710), Rohana Sham (ORCID 0000-0003-2000-7448)
Source: Educational Process: International Journal. Article e2025531 2025 19.
Availability: UNIVERSITEPARK Limited. iTOWER Plaza (No61, 9th floor) Merkez Mh Akar Cd No3, Sisli, Istanbul, Turkey 34382. e-mail: editor@edupij.com; Web site: http://www.edupij.com/
Peer Reviewed: Y
Page Count: 34
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Descriptors: Evaluation Methods, Educational Research, Structural Equation Models, Data Analysis, Qualitative Research, Statistical Analysis, Multiple Regression Analysis, Computer Software, Research Tools
ISSN: 2147-0901
2564-8020
Abstract: Background/purpose: It revisits Partial Least Squares Structural Equation Modeling (PLS-SEM) as a robust tool for analyzing non-normal data and small samples, offering predictive modeling advantages. This study also compares the merits, practical applications, and added value of both tools in tackling complicated research issues, notably in education and social sciences, rather than reviewing their techniques. Simultaneously, it evaluates NVivo as a leading qualitative data analysis (QDA) tool, focusing on its effectiveness in organizing, coding, querying, and visualizing diverse qualitative datasets. Materials/Methods: The study places both tools in real-world educational research settings to help researchers choose and utilize methodologies that align with their data and goals. This mixed-methods research employed two approaches. A utilized empirical data to assess PLS-SEM's performance using statistical metrics such as R[superscript 2], Q[superscript 2], and Composite Reliability. It compared PLS-SEM with MRA, CB-SEM, and Factor Analysis. Method B involved surveys, interviews, usability testing, and case studies to evaluate NVivo's capabilities. NVivo was compared with ATLAS.ti, MAXQDA, and Dedoose on parameters like coding flexibility, usability, visualization, and collaborative features. Results. The manuscript demonstrates how PLS-SEM can model latent concepts, such as student engagement, learning outcomes, and institutional support, while NVivo can analyze qualitative data, including interview transcripts, reflective diaries, and classroom discourse. NVivo outperformed competing QDA tools in advanced coding, data visualization, and integration features, with 72% of surveyed researchers preferring it for its effectiveness and usability. Usability testing revealed NVivo had a 30% higher task efficiency and a high user satisfaction score (8.5/10), despite a moderate learning curve. NVivo was particularly effective in thematic exploration and supported collaborative research. Conclusion. PLS-SEM proves to be a robust and adaptable statistical method for complex quantitative research, especially when data quality or sample size is constrained. NVivo stands out as a versatile and user-friendly QDA tool, enhancing the rigor and efficiency of qualitative analysis. Together, these tools offer a methodological advancement for researchers undertaking mixed-methods studies, promoting more accurate, predictive, and interpretable research outcomes across disciplines.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1488986
Database: ERIC
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  Data: Integrating PLS-SEM and NVivo in Mixed-Methods Educational Research: A Comprehensive Evaluation of Quantitative and Qualitative Analytical Tools
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  Data: <searchLink fieldCode="AR" term="%22Mahadi+Hasan+Miraz%22">Mahadi Hasan Miraz</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-3008-7090">0000-0003-3008-7090</externalLink>)<br /><searchLink fieldCode="AR" term="%22Sanmugam+Annamalah%22">Sanmugam Annamalah</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-9438-2710">0000-0002-9438-2710</externalLink>)<br /><searchLink fieldCode="AR" term="%22Rohana+Sham%22">Rohana Sham</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-2000-7448">0000-0003-2000-7448</externalLink>)
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  Data: UNIVERSITEPARK Limited. iTOWER Plaza (No61, 9th floor) Merkez Mh Akar Cd No3, Sisli, Istanbul, Turkey 34382. e-mail: editor@edupij.com; Web site: http://www.edupij.com/
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  Data: Background/purpose: It revisits Partial Least Squares Structural Equation Modeling (PLS-SEM) as a robust tool for analyzing non-normal data and small samples, offering predictive modeling advantages. This study also compares the merits, practical applications, and added value of both tools in tackling complicated research issues, notably in education and social sciences, rather than reviewing their techniques. Simultaneously, it evaluates NVivo as a leading qualitative data analysis (QDA) tool, focusing on its effectiveness in organizing, coding, querying, and visualizing diverse qualitative datasets. Materials/Methods: The study places both tools in real-world educational research settings to help researchers choose and utilize methodologies that align with their data and goals. This mixed-methods research employed two approaches. A utilized empirical data to assess PLS-SEM's performance using statistical metrics such as R[superscript 2], Q[superscript 2], and Composite Reliability. It compared PLS-SEM with MRA, CB-SEM, and Factor Analysis. Method B involved surveys, interviews, usability testing, and case studies to evaluate NVivo's capabilities. NVivo was compared with ATLAS.ti, MAXQDA, and Dedoose on parameters like coding flexibility, usability, visualization, and collaborative features. Results. The manuscript demonstrates how PLS-SEM can model latent concepts, such as student engagement, learning outcomes, and institutional support, while NVivo can analyze qualitative data, including interview transcripts, reflective diaries, and classroom discourse. NVivo outperformed competing QDA tools in advanced coding, data visualization, and integration features, with 72% of surveyed researchers preferring it for its effectiveness and usability. Usability testing revealed NVivo had a 30% higher task efficiency and a high user satisfaction score (8.5/10), despite a moderate learning curve. NVivo was particularly effective in thematic exploration and supported collaborative research. Conclusion. PLS-SEM proves to be a robust and adaptable statistical method for complex quantitative research, especially when data quality or sample size is constrained. NVivo stands out as a versatile and user-friendly QDA tool, enhancing the rigor and efficiency of qualitative analysis. Together, these tools offer a methodological advancement for researchers undertaking mixed-methods studies, promoting more accurate, predictive, and interpretable research outcomes across disciplines.
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PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1488986
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    Languages:
      – Text: English
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        PageCount: 34
    Subjects:
      – SubjectFull: Evaluation Methods
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      – SubjectFull: Educational Research
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      – SubjectFull: Structural Equation Models
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      – SubjectFull: Data Analysis
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      – SubjectFull: Qualitative Research
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      – SubjectFull: Statistical Analysis
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      – SubjectFull: Multiple Regression Analysis
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              Y: 2025
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