Critical Analysis of Research Methods and Misperceptions of Statistical Analysis

Abstract

Purpose: This study aims to critically analyze research methods and explore the misperceptions surrounding statistical analysis. The importance of methodology cannot be overemphasized in any research. It is the heart of a study, enhances the reproducibility of a study, and is the bedrock on which the research findings can be generalizable. Methodology: The literature review form method was used to identify the strengths and weaknesses of quantitative, qualitative, and mixed research methods, including their differences and applications. Findings: Research designs, including case studies, grounded theory, ethnographies, phenomenologies, and narratives, are used in the qualitative method to comprehend relationships and behaviors. Because the quantitative research method is rooted in scientific discovery, it has become the standard in the research community. Different viewpoints exist about the required sample size, the statistical model employed for data evaluation, and the nature of the data. For this study, statistical misperceptions are categorized under descriptive statistics, design techniques, inferential statistics, and regression. A thorough explanation of statistical misperceptions under these three categories is believed to be sufficient to cover fundamental areas of statistical analysis. Certain conceptual and statistical presumptions must be satisfied for any statistical approach's findings to be considered plausible. Having a sound knowledge of fundamental areas for statistical data analysis will enhance a more plausible result in research. Originality/value: The quantitative research method entails more rigorous statistical tests than the qualitative research method. Using both research methods is referred to as a mixed research method. The biases in qualitative and quantitative methods can be surmounted by using mixed methods.