Faktör Analizinde Faktör Döndürme Yöntemleri: Ziraat Verisi Üzerinde Bir Uygulama

Ayşenur Can, Özgür Koşkan, M. Ergin
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Abstract

In this study, the rotation stage of factor analysis, which is one of the multivariate analysis methods, was examined. All stages of factor analysis have been defined. The material of the study consisted of a data set obtained from barley planted in 20 plots (replication) having 9 variables. In each plot, the average of 6 plants selected from that plot was used. The variables emphasized in the study were plant height, number of leaves, spike length, spike weight, grain yield, flowering period (days), harvest index, yield, and 1000-grain weight. Factors were obtained by principal component analysis, which is a factor extraction method, from the data set that met the prerequisites of the analysis. The criteria used in different factor rotations are given and based on these criteria, the formula that gives the optimum rotation angle for each data set was obtained. As a result, the formulas obtained for orthomax, varimax, quartimax, and equamax were applied to the factors obtained from the data set and the results were interpreted. As a result of factor rotation, when varimax, quartimax, and equamax methods were used, the values of the variables in terms of factor loads differed in each factor. This is a desirable situation for factor analysis results.
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因子分析中的因子旋转方法:农业数据应用
在本研究中,对作为多元分析方法之一的因子分析旋转阶段进行了研究。因子分析的所有阶段均已确定。研究材料包括从 20 个地块(重复)种植的大麦中获得的数据集,其中有 9 个变量。在每个小区中,使用了从该小区中选取的 6 株植物的平均值。研究强调的变量包括株高、叶片数、穗长、穗重、谷物产量、花期(天数)、收获指数、产量和千粒重。通过主成分分析(一种因子提取方法)从符合分析前提条件的数据集中提取因子。给出了不同因子旋转所使用的标准,并根据这些标准得出了每组数据的最佳旋转角度公式。因此,正交、变异、四交和等交的公式被应用于从数据集中获得的因子,并对结果进行了解释。因子旋转的结果是,在使用变异、四变和等变方法时,每个因子中变量的因子载荷值都不相同。对于因子分析结果来说,这是一种理想的情况。
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