The Application of Statistical Analysis in Guidance and Counseling Research: A Literature Review
Abstract
Research in guidance and counseling increasingly demands systematic, evidence-based methodological approaches to produce valid, generalizable findings, where statistical analysis plays a strategic role in objectively processing data, testing hypotheses, and identifying variable relationship patterns (Creswell & Creswell, 2018). However, literature reveals gaps in comprehensive mapping of current statistical methods applied to BK research, particularly on student academic performance, mental health, and career guidance effectiveness, amid shifts from simple descriptive techniques to complex approaches like multilevel modeling and machine learning. This study aims to systematically review statistical analysis applications in guidance and counseling via Systematic Literature Review (SLR) using PRISMA, analyzing 50 articles from Google Scholar, Scopus, and ERIC (2014-2024). Key findings show dominance of multilevel and longitudinal modeling (38%), followed by regression and variance techniques (34%), latent variable analysis (18%), Bayesian approaches (14%), and machine learning (12%), reflecting methodological evolution to capture hierarchical educational data dynamics. Conclusions emphasize the need for enhanced statistical literacy among BK researchers, mixed-methods integration, and transparent reporting standards to optimize research impact on educational practice and policy.
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References
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DOI: https://doi.org/10.17509/psikoeduko.v6i1.95007
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