Mohamed Nur Anisah, Alanzi Ayed R A, Azizan Noor Azlinna, Azizan Suzana Ariff, Samsudin Nadia, Jenatabadi Hashem Salarzadeh
Faculty of Science, Institute of Mathematical Sciences, Universiti Malaya, Kuala Lumpur, Malaysia.
Department of Mathematics, College of Science and Arts in Gurayat, Jouf University, Gurayat, Saudi Arabia.
Front Psychol. 2023 Feb 22;14:1060963. doi: 10.3389/fpsyg.2023.1060963. eCollection 2023.
Depression and obesity are the main threat among women which have been considered by many research scholars in psychology studies. In their analysis for measuring and estimating obesity and depression they were involving statistical functions.
Regression, Analysis of Variance (ANOVA), and in the last two decades Structural Equation Modeling are the most familiar statistical methods among research scholars. Taguchi algorism process is one the statistical methods which mostly have been applying in engineering studies. In this study we are looking at two main objectives. The first one is to introduce Taguchi algorism process and apply it in a case study in psychology area. The second objective is challenging among four statistical techniques include ANOVA, regression, SEM, and Taguchi technique in a same data. To achieve those aims we involved depression and obesity indices with other familiar indicators contain socioeconomic, screen time, sleep time, and usage fitness and nutrition mobile applications.
Outputs proved that Taguchi technique is able to analyze some correlations which are not achieved by applying ANOVA, regression, and SEM. Moreover, SEM has a special capability to estimate some hidden correlations which are not possible to evaluate them by using ANOVA, regression, and even Taguchi method. In the last, we found that some correlations are significant by SEM, however, in the same data with regression those correlation were not significant. This paper could be a warning for psychology research scholars to be more careful with involving statistical methods for measuring and estimating of their research variables.
抑郁症和肥胖症是女性面临的主要威胁，许多心理学研究学者都对此进行过探讨。在他们对肥胖症和抑郁症的测量与评估分析中，涉及到了统计函数。
回归分析、方差分析（ANOVA）以及在过去二十年中结构方程模型是研究学者们最熟悉的统计方法。田口算法过程是一种主要应用于工程研究的统计方法。在本研究中，我们着眼于两个主要目标。第一个目标是介绍田口算法过程并将其应用于心理学领域的一个案例研究中。第二个目标是在同一数据中对包括方差分析、回归分析、结构方程模型和田口技术在内的四种统计技术进行比较。为实现这些目标，我们纳入了抑郁症和肥胖症指标以及其他一些常见指标，包括社会经济状况、屏幕使用时间、睡眠时间以及健身和营养移动应用程序的使用情况。
结果表明，田口技术能够分析一些方差分析、回归分析和结构方程模型无法实现的相关性。此外，结构方程模型具有一种特殊能力，能够估计一些方差分析、回归分析甚至田口方法都无法评估的隐藏相关性。最后，我们发现某些相关性在结构方程模型分析中显著，但在相同数据的回归分析中却不显著。本文可能为心理学研究学者敲响警钟，提醒他们在使用统计方法测量和评估研究变量时要更加谨慎。
