4.5.1.1使用统计工具

这份报告以来围绕顺序数据,而不是基本的数据,传统统计措施与序数等数据的意思是,标准偏差无法计算。以下工具用于分析的目的:

§假设检验(使用T-tests由于小样本大小),
§在于按斯皮尔曼等级相关的方法,
§倍数评分的决心。

相关性是相互依存的程度和协会之间存在两个变量(阿琳,1995)。相关性在于可以用不同的方法计算,但在本研究分析排名数据,爱德华斯皮尔曼等级相关的倍数评分以及肯德尔的倍数评分等级相关(迪克森,1992年)可能被使用。

由斯皮尔曼等级相关系数已广泛用于分析顺序数据(Bryan,1994)。根据沃恩(2003),它可以被视为非参数对应的皮尔森相关系数。其优势源于这样一个事实:它不需要任何假设数据的分布模式。相同的计算公式是:相关性在于= 1 -[6 *∑Diff2]/[没有(No2 – 1)]

4.5.1.1 Statistical Tools Used

Since this report has been structured around ordinal data and not cardinal data, traditional statistical measures associated with ordinal data such as mean, standard deviation could not be calculated. The following tools have been used for the purposes of analysis:

§ Hypothesis testing (using T-tests due to small sample size),
§ Co-efficient of Rank Correlation as per Spearman’s method,
§ Co-efficient of Determination.

Correlation is a measure of the degree of interdependence and association which exist amongst two variables (Arlene, 1995). Correlation co-efficient can be computed in various ways, but for analyzing ranked data in this research, Edward Spearman’s co-efficient of rank correlation as well as Kendall’s co-efficient of rank correlation (Dixon, 1992) may be used.

The rank correlation coefficient devised by Spearman has been widely used for analyzing ordinal data (Bryan, 1994). As per Vaughan (2003), it can be regarded as the non-parametric counterpart of Pearson’s correlation coefficient. Its advantage stems from the fact that it does not require any assumption of the data’s distribution pattern. The formula for calculating the same is:Correlation Co-efficient = 1 – [6 * ∑Diff2]/[No(No2 – 1 )]

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