估计顺序数据的集群内相关性
Benjamin W Langworthy1,2, Zhaoxun Hou1, Gary C Curhan2,3,4
1Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Journal of applied statistics
|June 12, 2024
概括
估计顺序听力数据的集群内相关性对于可靠性至关重要. 使用累积后勤或试验模型,与线性模型不同,可以减少这些重要的测试/重新测试可靠性估计中的偏差.
科学领域:
- 统计 统计 统计 统计
- 生物统计学 生物统计学
- 听力学 听力学是指听力学.
背景情况:
- 集群内相关性 (ICC) 对于评估集群或重复测量数据的可靠性至关重要.
- 在听力学中常见的顺序数据 (例如听力值) 对ICC估计提出了独特的挑战.
- 现有的方法往往假定连续数据,可能会偏差ICC对顺序结果的估计.
研究的目的:
- 评估估计集群内相关性 (ICC) 的方法,特别针对普通听力值数据.
- 为了比较混合效应累积物流/probit模型与混合效应线性模型的性能,用于使用顺序数据进行ICC估计.
- 通过使用适当的统计模型,评估基于iPhone的听力评估应用程序的测试复试可靠性.
主要方法:
- 为顺序结果数据开发和应用混合效应累积后勤和试验模型.
- 模拟研究用于比较不同ICC估计方法的偏差和性能.
- 来自基于iPhone的应用程序的听力值的ICC估计.
主要成果:
- 混合效应线性模型,假设连续数据,在应用到顺序数据时显示负有限样本偏差.
- 混合效应的累积物流和探针模型显著降低了对常规ICC估计的偏差.
- 与线性模型相比,使用累积后勤/试探模型的ICC对iPhone听力测试的估计较高,表明可靠性评估得到了改进.
结论:
- 对于顺序数据,特别是听力学,混合效应累积后勤或试验模型优于线性模型来估计集群内相关性.
- 这些顺序模型为iPhone听力评估等应用程序提供了不那么有偏见和潜在更准确的测试-重新测试可靠性的测量方法.
- 这些发现提倡使用适合数据分布性质的适当统计模型来得出可靠的科学结论.
相关概念视频
00:55Ordinal Level of Measurement
23.6K
The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
Data measured using an ordinal scale are similar to nominal scale data, but there is one major difference. The ordinal scale data can be ordered. An example of ordinal scale data is a list of the top five national parks...
Data measured using an ordinal scale are similar to nominal scale data, but there is one major difference. The ordinal scale data can be ordered. An example of ordinal scale data is a list of the top five national parks...
23.6K
01:20Spearman's Rank Correlation Test
762
Spearman's rank correlation test, also known as Spearman's rho, is a nonparametric method for assessing the strength and direction of association between two variables. This test is particularly valuable when the data distribution is unknown or when the assumption of normality does not hold. Named after the English psychologist and statistician Dr. Charles Edward Spearman, it serves as the nonparametric counterpart to Pearson's correlation coefficient.
Spearman's test calculates...
Spearman's test calculates...
762
01:21Friedman Two-way Analysis of Variance by Ranks
185
Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
185
01:12Coefficient of Correlation
6.1K
The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable x and the dependent variable y.
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
What the VALUE of r tells us:
The value of r is always between –1 and +1: –1 ≤ r ≤ 1.
The size of the correlation r indicates the...
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
What the VALUE of r tells us:
The value of r is always between –1 and +1: –1 ≤ r ≤ 1.
The size of the correlation r indicates the...
6.1K
01:11Calculating and Interpreting the Linear Correlation Coefficient
5.9K
The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable, x, and the dependent variable, y. Hence, it is also known as the Pearson product-moment correlation coefficient. It can be calculated using the following equation:
5.9K
01:02Ranks
236
Unlike parametric methods, nonparametric statistics are ideal for nominal and ordinal data, requiring fewer assumptions about the population's nature or distribution. This makes nonparametric methods easier to apply and interpret, as they do not depend on parameters like mean or standard deviation. One common approach in nonparametric analysis is to sort data according to a specific criterion. For instance, we might arrange weather data from hottest to coldest days in a month or rank cities...
236