Скачать или смотреть Likert Data & ANOVA: Myth vs Fact | Why Large Studies Get Rejected

#drjksachdeva #LikertScale #ANOVA #Statistics #ResearchMethodology #PhDResearch #ThesisWriting #StatisticalMyths #AcademicResearch #DataAnalysis #EvidenceBasedResearch
Many research papers and PhD theses are rejected simply because Likert-scale data are analysed using parametric tests like ANOVA—often citing non-normality as the reason.

In this video, I clarify this common methodological misconception using established statistical literature. You will learn why ANOVA remains valid and robust for large-sample Likert data, how myths about normality arise, and what modern research methodology actually recommends.

This video is useful for PhD scholars, researchers, reviewers, and examiners who want evidence-based clarity instead of outdated statistical rules.

📚 Key references discussed include Norman (2010), Carifio & Perla (2008), Glass et al. (1972), Field (2018), and Tabachnick & Fidell (2019).

🔍 Topics covered:

Likert scales and level of measurement

Normality myths in large samples

ANOVA vs non-parametric tests

What reviewers should look for in large datasets