Academic Research Analysis: Fundamentals, Quality, and Contextually Productive Application

To mitigate the challenges faced by early researchers in the analyses and presentation of their survey findings, this article explains and illustrates critically fundamental aspects of academic research data analyses. The contextual presentation, and illustration are an asset to the student-researcher or instructor who desires to quickly look up how to interpret: Likert Scale survey results, Difference tests, and correlation r-values. Also, this applied research clearly explains the concept of parametric and non-parametric tests using case studies to illustrate when non-parametric tests such as Mann-Whitney U test, Wilcoxon Signed Test, Kruskal-Wallis test, Chi square test, Spearman Rank correlation, and Puri and Sen test can be used. These are sequentially organized to promote constructive research manuscripts and contextually productive application by academics. A review of over 45 correlation analysis in published articles as presented in the findings revealed the need to call for standardization in the interpretation/reporting of educational surveyed data. Also, a statistically consistent approach to interpreting the mean of a Likert Scale test result was illustrated. The choice to maintain a standard/universal approach to the interpretation of test results is a choice to advance, standardize, and promote logical consistency, as well as quality in academic research analysis reporting.