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

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. Method A utilized empirical data to assess PLS-SEM's performance using statistical metrics such as R², Q², 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.

Keywords: PLS-SEM, NVivo, statistical tools, mixed methods research, qualitative, quantitative, tools evaluation

References

Abdulkareem, S. A., Mustafa, Y. T., Augustijn, E.-W., & Filatova, T. (2019). Bayesian networks for spatial learning: a workflow on using limited survey data for intelligent learning in spatial agent-based models. Geoinformatica, 23, 243-268. https://doi.org/10.1007/s10707-019-00347-0

Ahammad, T. (2024). Identifying hidden patterns of fake COVID-19 news: An in-depth sentiment analysis and topic modeling approach. Natural Language Processing Journal, 6. https://doi.org/10.1016/j.nlp.2024.100053

Al Mamun, M., Parvin, K., Yu, M., Wan, J., Willan, S., Gibbs, A., Jewkes, R., Naved R.T. (2018). The HERrespect intervention to address violence against female garment workers in Bangladesh: study protocol for a quasi-experimental trial. BMC Public Health 18, 512 (2018). https://doi.org/10.1186/s12889-018-5442-5

Amoroso, D. L., & Cheney, P. H. (1991). Testing a causal model of end-user application effectiveness. Journal of Management Information Systems, 8(1), 63-89. https://doi.org/10.1080/07421222.1991.11517911

Ashraf, M. A., & Ahmed, H. (2022). Approaches to Quality Education in Tertiary Sector: An Empirical Study Using PLS‐SEM. Education Research International, 2022(1), 5491496. https://doi.org/10.1155/2022/5491496

Auld, G. W., Diker, A., Bock, M. A., Boushey, C. J., Bruhn, C. M., Cluskey, M., Edlefsen, M., Goldberg, D. L., Misner, S. L., Olson, B. H., Reicks, M., Wang, C., & Zaghloul, S. (2007). Development of a Decision Tree to Determine Appropriateness of NVivo in Analyzing Qualitative Data Sets. Journal of Nutrition Education and Behavior, 39(1), 37-47. https://doi.org/10.1016/j.jneb.2006.09.006

Azeem, M., Salfi, N. A. (2012). Usage of NVivo software for qualitative data analysis. Academic Research International, 2(1), 262-266. chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/http://www.savap.org.pk/journals/ARInt./Vol.2(1)/2012(2.1-30).pdf

Aziz, F., Al Haque, T., Hossain, M. B., Rahman, A., & Siam, S. A. J. (2023). Customer Behavior Analysis Through Data Analytics in the Bangladeshi Retail Industry. Malaysian E Commerce Journal, 7(2), 78-84. http://doi.org/10.26480/mecj.02.2023.90.96

Bono, R., Blanca, M. J., Arnau, J., & Gómez-Benito, J. (2017). Non-normal distributions commonly used in health, education, and social sciences: A systematic review. Frontiers in Psychology, 8, 1602. https://doi.org/10.3389/fpsyg.2017.01602

Brandão, C. (2015). P. Bazeley and K. Jackson, Qualitative Data Analysis with NVivo (2nd ed.): (2013). London: Sage. Qualitative Research in Psychology, 12(4), 492–494. https://doi.org/10.1080/14780887.2014.992750

Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa

Byrne, B. M. (2013). Structural Equation Modeling with AMOS: Basic Concepts, Applications, and Programming. Routledge. https://doi.org/10.4324/9781410600219

Carrión, G. C., Nitzl, C., & Roldán, J. L. (2017). Mediation analyses in Partial Least Squares Structural Equation Modeling: Guidelines and empirical examples. In H. Latan & R. Noonan (Eds.), Partial Least Squares Path Modeling (pp. 173–195). Cham: Springer. https://doi.org/10.1007/978-3-319-64069-3_8

Cepeda, G., Rold´an, J. L., Sabol, M., Hair, J., & Chong, A. Y. L. (2024). Emerging opportunities for information systems researchers to expand their PLS-SEM analytical toolbox. Industrial Management & Data Systems, 124(6), 2230-2250. https://doi.org/10.1108/IMDS-08-2023-0580

Charmaz, K. (2014). Constructing Grounded Theory. Los Angeles: Sage.

Chen S, Ye J (2023) Understanding consumers’ intentions to purchase smart clothing using PLS-SEM and fsQCA. PLoS ONE 18(9): e0291870. https://doi.org/10.1371/journal.pone.0291870

Ciavolino, E., Aria, M., Cheah, J.-H., & Roldán, J. L. (2022). A tale of PLS structural equation modelling: episode I-a bibliometrix citation analysis. Social Indicators Research, 164(3), 1323-1348. https://doi.org/10.1007/s11205-022-02994-7

Creswell, J. W. (2013). Qualitative Inquiry & Research Design: Choosing among Five Approaches (3rd ed.). Thousand Oaks, CA: SAGE.

Creswell, J.W. and Poth, C.N. (2018) Qualitative Inquiry and Research Design Choosing among Five Approaches. 4th Edition, SAGE Publications, Inc., Thousand Oaks.

Dalton, A. J., & McVilly, K. R. (2004). Ethics guidelines for international, multicenter research involving people with intellectual disabilities 1, 2, 3, 4. Journal of Policy and Practice in Intellectual Disabilities, 1(2), 57-70. https://doi.org/10.1111/j.1741-1130.2004.04010.x

Dash, G., & Paul, J. (2021). CB-SEM vs PLS-SEM methods for research in social sciences and technology forecasting. Technological Forecasting and Social Change, 173, 121092. https://doi.org/10.1016/j.techfore.2021.121092

Davidson J., & di Gregorio, S. (2011). Digital tools in qualitative analysis. In N. K. Denzin & M. D. Giardina (Eds.), Qualitative inquiry and global crisis (pp. 3139). Walnut Creek, CA: Left Coast Press.

Dupin, C. M., & Borglin, G. (2020). Usability and application of a data integration technique (following the thread) for multi-and mixed methods research: a systematic review. International journal of nursing studies, 108, 103608. https://doi.org/10.1016/j.ijnurstu.2020.103608

Fabrigar, L. R., & Wegener, D. T. (2012). Exploratory Factor Analysis. New York: Oxford University Press, Inc.

Fallin, L. (2019). Using nvivo in your research. In C. Opie, D. Brown (Eds.) Using NVivo In Your Research 243-274. SAGE Publications Ltd, https://doi.org/10.4135/9781526480507.n12

Feng, X., & Behar-Horenstein, L. (2019). Maximizing NVivo Utilities to Analyze Open-Ended Responses. The Qualitative Report, 24(3), 563-571. https://doi.org/10.46743/2160-3715/2019.3692

Fornell, C., & Larcker, D. F. (1981) Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18, 39-50. https://doi.org/10.2307/3151312

Gibson, W., Callery, P., Campbell, M., Hall, A., & Richards, D. (2005). The Digital Revolution in Qualitative Research: Working with Digital Audio Data through Atlas. Ti. Sociological Research Online, 10(1), 57-68. https://doi.org/10.5153/sro.1044

Glaser, B. G. & Strauss, A. L. (1967). The Discovery of Grounded Theory. Strategies for Qualitative Research. Chicago: Aldine.

Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., & Thiele, K. O. (2017). Mirror, mirror on the wall: A comparative evaluation of composite-based structural equation modeling methods. Journal of the Academy of Marketing Science, 45(5), 616–632. https://doi.org/10.1007/s11747-017-0517-x

Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (3rd ed.). Sage. https://doi.org/10.1007/978-3-030-80519-7

Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203

Hair, J. F., Sharma, P. N., Sarstedt, M., Ringle, C. M., & Liengaard, B. D. (2024). The shortcomings of equal weights estimation and the composite equivalence index in PLS-SEM. European journal of marketing, 58(13), 30-55. https://doi.org/10.1108/EJM-04-2023-0307

Hanafiah, M. H. (2020). Formative vs. reflective measurement model: Guidelines for structural equation modeling research. International Journal of Analysis and Applications, 18(5), 876-889. DOI: https://doi.org/10.28924/2291-8639-18-2020-876

Hannon, B., Swami, N., Rodin, G., Pope, A., & Zimmermann, C. (2017). Experiences of patients and caregivers with early palliative care: a qualitative study. Palliative medicine, 31(1), 72-81. https://doi.org/10.1177/0269216316649126

Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A New Criterion for Assessing Discriminant Validity in Variance-Based Structural Equation Modeling. Journal of the Academy of Marketing Science, 43, 115-135. https://doi.org/10.1007/s11747-014-0403-8

Henseler, J., & Schuberth, F. (2025). Should PLS become factor-based or should CB-SEM become composite-based? Both! European Journal of Information Systems, 34(3), 551-563. https://doi.org/10.1080/0960085X.2024.2357123

Huang, C. H. (2021). Using PLS-SEM Model to Explore the Influencing Factors of Learning Satisfaction in Blended Learning. Education Sciences, 11(5), 249. https://doi.org/10.3390/educsci11050249

Hutchison, A. J., Johnston, L. H., & Breckon, J. D. (2010). Using QSR‐NVivo to facilitate the development of a grounded theory project: an account of a worked example. International Journal of Social Research Methodology, 13(4), 283-302. https://doi.org/10.1080/13645570902996301

Kline, R. B. (2023). Principles and Practice of Structural Equation Modeling. Guilford Publications.

Kraiwanit, T., Limna, P., & Siripipatthanakul, S. (2023). NVivo for social sciences and management studies: A systematic review. Advance Knowledge for Executives, 2(3), 1-11. https://ssrn.com/abstract=4523829

Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1), 159–174. https://doi.org/10.2307/2529310

Leech, N. L., & Onwuegbuzie, A. J. (2011). Beyond constant comparison qualitative data analysis: Using NVivo. School Psychology Quarterly, 26(1), 70–84. https://doi.org/10.1037/a0022711

Lewis, R. B., & Maas, S. M. (2007). QDA Miner 2.0: Mixed-Model Qualitative Data Analysis Software. Field Methods, 19(1), 87-108. https://doi.org/10.1177/1525822X06296589

Limna, P. (2023). The impact of NVivo in qualitative research: Perspectives from graduate students. Journal of Applied Learning and Teaching, 6(2), 271-282. https://doi.org/10.37074/jalt.2023.6.2.17

Maher, C., Hadfield, M., Hutchings, M., & de Eyto, A. (2018). Ensuring Rigor in Qualitative Data Analysis: A Design Research Approach to Coding Combining NVivo With Traditional Material Methods. International Journal of Qualitative Methods, 17(1). https://doi.org/10.1177/1609406918786362

Major, L., Warwick, P., Rasmussen, I., Ludvigsen, S., & Cook, V. (2018). Classroom dialogue and digital technologies: A scoping review. Education and Information Technologies, 23, 1995-2028. https://doi.org/10.1007/s10639-018-9701-y

McAlearney, A. S., Walker, D. M., Shiu-Yee, K., Crable, E. L., Auritt, V., Barkowski, L., Batty, E. J., Dasgupta, A., Goddard-Eckrich, D., Knudsen, H. K., McCrimmon, T., Scalise, A., Sieck, C., Wood, J., & Drainoni, M.-L. (2023). Embedding Big Qual and Team Science Into Qualitative Research: Lessons From a Large-Scale, Cross-Site Research Study. International Journal of Qualitative Methods, 22. https://doi.org/10.1177/16094069231165933

Modi, D., & Zhao, L. (2021). Social media analysis of consumer opinion on apparel supply chain transparency. Journal of Fashion Marketing and Management: An International Journal, 25(3), 465-481. https://doi.org/10.1108/JFMM-09-2019-0220

Morse, J. M., and Richards, L. (2002). Readme First for a User’s Guide to Qualitative Methods. Thousand Oaks, CA: Sage Publications.

Noakes, T., Harpur, P., & Uys, C. (2023). Noteworthy Disparities With Four CAQDAS Tools: Explorations in Organising Live Twitter Data. Social Science Computer Review, 42(3), 794-811. https://doi.org/10.1177/08944393231204163

Noviana, E., Ridlo, Z. R., & Mursyidah, I. L. (2024). The analysis of the RBL-STEM application in improving student financial literacy in controlling consumptive behavior. Heliyon, 10(12). https://doi.org/10.1016/j.heliyon.2024.e32382

Parveen, K., Phuc, T. Q. B., Alghamdi, A. A., Hajjej, F., Obidallah, W. J., Alduraywish, Y. A., & Shafiq, M. (2024). Unraveling the dynamics of ChatGPT adoption and utilization through Structural Equation Modeling. Scientific Reports, 14(1), 23469. https://doi.org/10.1038/s41598-024-74406-4

Paulus, T.M., & Lester, J. N. (2021). Doing Qualitative Research in a Digital World. Thousand Oaks, CA: Sage.

Paulus, T. M., Woods, M., Atkins, D. P., & Macklin, R. (2017). The discourse of QDAS: Reporting practices of ATLAS.ti and NVivo users with implications for best practices. International Journal of Social Research Methodology, 20(1), 35–47. https://doi.org/10.1080/13645579.2015.1102454

Polit, D. F., & Beck, C. T. (2006). The content validity index: Are you sure you know what’s being reported? Critique and recommendations. Research in Nursing & Health, 29(5), 489–497. https://doi.org/10.1002/nur.20147

Purwanto, A., & Sudargini, Y. (2021). Partial least squares structural squation modeling (PLS-SEM) analysis for social and management research: a literature review. Journal of Industrial Engineering & Management Research, 2(4), 114-123. : https://doi.org/10.7777/jiemar.v2i4

Qostal, A., Bellamy, K., Sabri, Z., Nouib, H., Lakhrissi, Y., & Moumen, A. (2024). Perceived employability of Moroccan engineering students: A PLS-SEM approach. International Journal of Instruction, 17(2), 259-282. Retrieved from https://e-iji.net/ats/index.php/pub/article/view/560

Richards, T. (2002). An intellectual history of NUD* IST and NVivo. International Journal of Social Research Methodology, 5(3), 199-214. https://doi.org/10.1080/13645570210146267

Richards, L. (2005). Handling qualitative data: A practical guide. Sage Publications, Inc.

Rigdon, E. E., Ringle, C. M., & Sarstedt, M. (2010). Structural modeling of heterogeneous data with partial least squares. Review of marketing research, 255-296. https://doi.org/10.1108/S1548-6435(2010)0000007011

Rosen, R. K., Gainey, M., Nasrin, S., Garbern, S. C., Lantini, R., Elshabassi, N., Sultana, S., Hasnin, T., Alam, N. H., Nelson, E. J., & Levine, A. C. (2023). Use of Framework Matrix and Thematic Coding Methods in Qualitative Analysis for mHealth: The FluidCalc app. International Journal of Qualitative Methods, 22. https://doi.org/10.1177/16094069231184123

Salmona, M., Lieber, E., & Kaczynski, D. (2020). Qualitative and Mixed Methods Data Analysis using Dedoose. Thousand Oaks, CA: Sage.

Sarstedt, M., Hair Jr, J. F., Cheah, J.-H., Becker, J.-M., & Ringle, C. M. (2019). How to specify, estimate, and validate higher-order constructs in PLS-SEM. Australasian Marketing Journal, 27(3), 197-211. https://doi.org/10.1016/j.ausmj.2019.05.003

Sarstedt, M., Hair Jr, J. F., & Ringle, C. M. (2023). PLS-SEM: indeed a silver bullet-retrospective observations and recent advances. Journal of Marketing theory and Practice, 31(3), 261-275. https://doi.org/10.1080/10696679.2022.2056488

Sarstedt, M., Ringle, C.M., Hair, J.F. (2022). Partial Least Squares Structural Equation Modeling. In: Homburg, C., Klarmann, M., Vomberg, A. (eds) Handbook of Market Research. Springer, Cham. https://doi.org/10.1007/978-3-319-57413-4_15

Sepasgozar, S. M. E., & Davis, S. (2018). Construction Technology Adoption Cube: An Investigation on Process, Factors, Barriers, Drivers and Decision Makers Using NVivo and AHP Analysis. Buildings, 8(6), 74. https://doi.org/10.3390/buildings8060074

Sergis, S., Sampson, D. G., & Giannakos, M. N. (2018). Supporting school leadership decision making with holistic school analytics: Bridging the qualitative-quantitative divide using fuzzy-set qualitative comparative analysis. Computers in Human Behavior, 89, 355-366. https://doi.org/10.1016/j.chb.2018.06.016

Shmueli, G., Ray, S., Velasquez Estrada, J., et al. (2016) The Elephant in the Room: Evaluating the Predictive Performance of PLS Models. Journal of Business Research, 69, 4552-4564. https://doi.org/10.1016/j.jbusres.2016.03.049

Silver, C. and Lewin, A. (2014) Using Software in Qualitative Research: A Step-by-Step Guide. Sage Publications Ltd., Thousand Oaks. https://doi.org/10.4135/9781473906907

Sinkovics, R. R., & Alfoldi, E. A. (2012). Progressive focusing and trustworthiness in qualitative research: The enabling role of computer-assisted qualitative data analysis software (CAQDAS). Management international review, 52, 817-845. https://doi.org/10.1007/s11575-012-0140-5

Smith, B. (2002) Atlas.ti for Qualitative Data Analysis, Perspectives in Education. Perspectives in Education, 20, 65-76. https://hdl.handle.net/10520/EJC87147

Sotiriadou, P., Brouwers, J., & Le, T.-A. (2014). Choosing a qualitative data analysis tool: A comparison of NVivo and Leximancer. Annals of Leisure Research, 17(2), 218-234. https://doi.org/10.1080/11745398.2014.902292

Trail, G. T., Kim, Y. K., & Alfaro-Barrantes, P. (2024). A critical assessment for sport management research: comparing PLS-SEM and CB-SEM techniques for moderation analysis using formative measures. Journal of Global Sport Management, 9(1), 248-268. https://doi.org/10.1080/24704067.2022.2098802

Vennedey, V., Hower, K. I., Hillen, H., Ansmann, L., Kuntz, L., & Stock, S. (2020). Patients’ perspectives of facilitators and barriers to patient-centred care: insights from qualitative patient interviews. BMJ open, 10(5), e033449. https://doi.org/10.1136/bmjopen-2019-033449

Wah, J. N. K. (2025). Decoding Structural Equation Modeling: Insights on Data Assumptions, Normality, and Model Fit in Advancing Digital Marketing Strategies. Journal of Cases on Information Technology (JCIT), 27(1), 1-20. https://doi.org/10.4018/JCIT.369092

Wahyuddin, W. (2023). Adaptation of Field Experience Practice Teachers in a School Impacted by the Earthquake and the Covid-19 Pandemic. Edunesia: Jurnal Ilmiah Pendidikan, 4(3), 999-1016. https://orcid.org/0000-0001-7092-7184

Weitzman E. A., & Miles, M. B. (1995). Computer programs for qualitative data analysis, Thousand Oaks, CA: Sage Publications, 1995.

Wilk, V., Soutar, G. N., & Harrigan, P. (2019). Tackling social media data analysis: Comparing and contrasting QSR NVivo and Leximancer. Qualitative Market Research: An International Journal, 22(2), 94-113. https://doi.org/10.1108/QMR-01-2017-0021

Wold, H. (1982). Models for Knowledge. In: Gani, J. (eds) The Making of Statisticians. Springer, New York, NY. https://doi.org/10.1007/978-1-4613-8171-6_14

Woods, M., Macklin, R., & Lewis, G. K. (2015). Researcher reflexivity: exploring the impacts of CAQDAS use. International Journal of Social Research Methodology, 19(4), 385–403. https://doi.org/10.1080/13645579.2015.1023964

Yusif, S., Hafeez-Baig, A., Soar, J., & Teik, D. O. L. (2020). PLS-SEM path analysis to determine the predictive relevance of e-Health readiness assessment model. Health and Technology, 10, 1497-1513. https://doi.org/10.1007/s12553-020-00484-9

Zotzmann, K., & O’Regan, J. P. (2016). Critical Discourse Analysis and Identity. In: Preece, S, (ed.) The Routledge Handbook of Language and Identity. (pp. 113-128). Routledge: New York, United States. https://doi.org/10.4324/9781315669816

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