Why and When Statistics is Required, and How to Simplify Choosing Appropriate Statistical Techniques During Ph.D. Program in India?

Ganesha H. R., P. Aithal
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Abstract

Purpose: The purpose of this article is to explain the key reasons for the existence of statistics in doctoral-level research, why and when statistical techniques are to be used, how to statistically describe the units of analysis/samples, how to statistically describe the data collected from units of analysis/samples; how to statistically discover the relationship between variables of the research question; a step-by-step process of statistical significance/hypothesis test, tricks for selecting an appropriate statistical significance test, and most importantly which is the most user-friendly and free software for carrying out statistical analyses. In turn, guiding Ph.D. scholars to choose appropriate statistical techniques across various stages of the doctoral-level research process to ensure a high-quality research output. Design/Methodology/Approach: Postmodernism philosophical paradigm; Inductive research approach; Observation data collection method; Longitudinal data collection time frame; Qualitative data analysis. Findings/Result: As long as the Ph.D. scholars can understand i) they need NOT be an expert in Mathematics/Statistics and it is easy to learn statistics during Ph.D.; ii) the difference between measures of central tendency and dispersion; iii) the difference between association, correlation, and causation; iv) difference between null and research/alternate hypotheses; v) difference between Type I and Type II errors; vi) key drivers for choosing a statistical significance test; vi) which is the best software for carrying out statistical analyses. Scholars will be able to (on their own) choose appropriate statistical techniques across various steps of the doctoral-level research process and comfortably claim their research findings. Originality/Value: There is a vast literature about statistics, probability theory, measures of central tendency and dispersion, formulas for finding the relationship between variables, and statistical significance tests. However, only a few have explained them together comprehensively which is conceivable to Ph.D. scholars. In this article, we have attempted to explain the reasons for the existence, objectives, purposes, and essence of ‘Statistics’ briefly and comprehensively with simple examples and tricks that would eradicate fear among Ph.D. scholars about ‘Statistics’. Paper Type: Conceptual.
为什么和何时需要统计,以及如何简化在印度博士课程中选择适当的统计技术?
目的:本文的目的是解释统计学在博士级研究中存在的关键原因,为什么以及何时使用统计技术,如何统计地描述分析单位/样本,如何统计地描述从分析单位/样本收集的数据;如何统计地发现研究问题的变量之间的关系;统计显著性/假设检验的一步一步的过程,技巧选择一个适当的统计显著性检验,最重要的是,这是最友好的和免费的软件进行统计分析。反过来,指导博士学者在博士级研究过程的各个阶段选择适当的统计技术,以确保高质量的研究成果。设计/方法论/途径:后现代主义哲学范式;归纳研究法;观测数据采集方法;纵向数据收集时间框架;定性数据分析。发现/结果:博士学者只要能理解i)他们不需要是数学/统计学专家,在博士期间很容易学习统计学;Ii)集中趋势和离散度度量之间的差异;Iii)关联、相关和因果关系的区别;Iv)零假设和研究假设/备选假设之间的差异;v)第一类和第二类错误的区别;Vi)选择统计显著性检验的关键驱动因素;Vi)这是进行统计分析的最佳软件。学者们将能够(自主地)在博士级研究过程的各个步骤中选择适当的统计技术,并轻松地宣布他们的研究成果。原创性/价值:有大量关于统计学、概率论、集中趋势和分散的度量、寻找变量之间关系的公式和统计显著性检验的文献。然而,只有少数人能够全面地解释它们,这是博士学者可以想象的。在这篇文章中,我们试图用简单的例子和技巧,简要而全面地解释“统计学”存在的原因、目标、目的和本质,以消除博士学者对“统计学”的恐惧。论文类型:概念性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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