Sample Size Determination in Test-Retest and Cronbach Alpha Reliability Estimates

Imasuen Kennedy
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引用次数: 14

Abstract

The estimation of reliability in any research is a very important thing. For us to achieve the goal of the research, we are usually faced with the issue of when the measurements are repeated, are we sure we will get the same result? Reliability is the extent to which an experiment, test, or any measuring procedure yields the same result on repeated trials. If a measure is perfectly reliable, there is no error in measurement, that is, everything we observe is the true score. However, it is the amount/degree of error that indicates how reliable, a measurement is. The issue of sample size determination has been a major problem for researchers and psychometricians in reliability studies. Existing approaches to determining sample size for psychometric studies have been varied and are not straightforward. This has made the psychometric literature contain a wide range of articles that propose a variety of sample sizes. This paper investigated sample sizes in test-retest and Cronbach alpha reliability estimates. The study was specifically concerned with identifying and analyzing differences in test-retest and Cronbach alpha reliability estimate of an instrument using various sample sizes of 20,30,40,50,100,150,200,300, and 400. Four hundred and eight (408) senior secondary school students from thirty-eight (38) public senior secondary schools in Benin metropolis part took in the study. The Open Hemisphere Brain Dominance Scale, by Eric Jorgenson was used for data collection. Data were analyzed using Pearson Product Moment Correlation Coefficient (r) and Cronbach alpha. The findings revealed that the sample sizes of 20 and 30 were not reliable, but the reliability of the instrument became stronger when the sample size was at least 100. The interval estimate (Fisher's confidence interval) gave a better reliability estimate than the point estimate for all samples. Based on the findings, it was, therefore, recommended that for a high-reliability estimate, at least one hundred (100) subjects should be used. Observed or field-tested values should always be used in the estimation of the reliability of any measuring instrument, and reliability should not be reported as a point estimate, but as an interval.
测试-重测和Cronbach Alpha信度估计中的样本量测定
在任何研究中,可靠性的估计都是非常重要的。为了实现我们的研究目标,我们通常面临的问题是,当我们重复测量时,我们确定我们会得到相同的结果吗?可靠性是指一个实验、测试或任何测量过程在重复试验中产生相同结果的程度。如果一个测量是完全可靠的,那么测量中就没有误差,也就是说,我们观察到的一切都是真实的分数。然而,正是误差的数量/程度表明了测量的可靠性。在信度研究中,样本大小的确定一直是困扰研究者和心理测量学家的一个主要问题。现有的确定心理测量学研究样本大小的方法多种多样,而且并不直截了当。这使得心理测量学文献包含了广泛的文章,提出了各种各样的样本量。本文研究了重测和Cronbach α信度估计的样本量。该研究特别关注识别和分析使用不同样本量(20、30、40、50、100、150、200、300和400)的仪器的重测和Cronbach α信度估计的差异。来自贝宁大都市38所公立高中的448(408)名高中生参与了这项研究。埃里克·乔根森(Eric Jorgenson)的开放半球大脑优势量表(Open Hemisphere Brain Dominance Scale)用于数据收集。采用Pearson积矩相关系数(r)和Cronbach alpha对数据进行分析。结果表明,20和30的样本量不可靠,但当样本量至少为100时,仪器的可靠性变得更强。区间估计(费雪置信区间)给出了比所有样本的点估计更好的信度估计。因此,根据研究结果,建议为获得高可靠性的估计,至少应该使用一百(100)个受试者。在估计任何测量仪器的可靠性时,应始终使用观察值或现场测试值,可靠性不应作为点估计报告,而应作为区间报告。
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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