Integrating Advanced Data Analysis in Islamic Education Research

Soibatul Aslamiah Nasution; Zulfani Sesmiarni; Iswandi Iswandi; Ramadhoni Aulia Gusli

doi:10.58485/jie.v4i3.560

Authors

Soibatul Aslamiah Nasution IAI YAPTIP Chadijah Ismail Pasaman Barat, Indonesia
Zulfani Sesmiarni Universitas Islam Negeri Sjech M. Djamil Djambek Bukittinggi, Indonesia
Iswandi Iswandi IAI YAPTIP Chadijah Ismail Pasaman Barat, Indonesia
Ramadhoni Aulia Gusli Universitas Islam Negeri Sjech M. Djamil Djambek Bukittinggi, Indonesia

DOI:

https://doi.org/10.58485/jie.v4i3.560

Keywords:

Data analysis, mixed methods, quantitative analysis, qualitative analysis, NVivo

Abstract

This study aims to integrate various high-level data analysis techniques in quantitative and qualitative approaches to produce a comprehensive understanding of educational phenomena. A mixed methods approach is used to systematically combine the strengths of numerical and interpretive analysis. In the quantitative stage, data were analyzed using advanced statistical techniques such as regression analysis, multiple regression, principal component analysis (PCA), discriminant analysis, canonical analysis, path analysis, factor analysis, ANAVA, ANCOVA, MANOVA, and MANCOVA to test the significant relationships, differences, and effects between variables. Meanwhile, the qualitative stage uses taxonomy/domain analysis, constant comparative analysis, and symbolic analysis to explore the deeper meaning of field data inductively and contextually. The use of the NVivo application supports the qualitative analysis process through thematic coding, visualization of conceptual relationships, and evidence-based data validation. The integration of these two approaches strengthens the internal and external validity of the research, increases the reliability of the results, and broadens theoretical and practical understanding in the context of modern education. The results of this study indicate that the application of a combination of high-level data analysis methods and qualitative analysis technology support is capable of producing richer, more accurate, and more relevant interpretations for the development of data- and meaning-based educational science.

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