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Robustness of parametric and nonparametric tests when distances between points change on an ordinal measurement scale.

机译:当点之间的距离按顺序测量尺度变化时,参数和非参数测试的鲁棒性。

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摘要

Ordinal measures currently enjoy widespread use. The use of a limited number of categories with equal lengths between points (the placements of categorical labels) on the scale may cause a loss of information. Many well-known studies claim that only nonparametric tests are appropriate for the nominal or ordinal level data, that parametric tests are appropriate for the interval or ratio level data; and ordinal numbers (equal lengths between points) cannot be meaningfully added, subtracted, multiplied, and divided. This citation has been one of the most controversial statements in applied statistics.;The purpose of this research was to evaluate the effect on parametric and nonparametric tests using ordinal data when the distances between points changed on the measurement scale. The research examined the performance of Type I and Type II error rates using selected parametric and nonparametric tests.;Three experiments were conducted by generating simulation data on a seven-point Likert-scale using one uniform, three normal, and three gamma populations. Various unequal distance changes were made between points in the four phases of the experiments.;One and two random samples of simulation data were selected from seven populations. Selected parametric tests and nonparametric tests were used to examine the equality of means, medians, deviations, and distributions between two populations. Several computer programs were written in FORTRAN 77 to implement the algorithm of data simulation, parametric tests, and nonparametric tests. The simulation data were tested by the programs.;The results were analyzed in terms of Type I and Type II error rates with different sample sizes, populations, and phases. In summary, the nonparametric tests produced the same results when the distances between points changed on the scale. However, parametric tests show different results when the distances between points changed. The power of parametric and nonparametric tests were evaluated as underlying assumptions were violated in the location parameters.
机译:当前,序数度量得到广泛使用。使用数量有限的类别,在磅秤上的点(类别标签的位置)之间具有相等的长度,可能会导致信息丢失。许多著名的研究声称,只有非参数检验适用于名义或有序水平数据,而参数检验则适用于区间或比率水平数据。和序数(点之间的相等长度)不能有意义地相加,相减,相乘和相除。该引用一直是应用统计学中最具争议的陈述之一。这项研究的目的是,当点之间的距离在测量范围上变化时,使用有序数据来评估对参数和非参数测试的影响。该研究使用选定的参数检验和非参数检验检验了I型和II型错误率的性能。通过使用一个均匀,三个法向和三个伽马种群在七点李克特量表上生成模拟数据,进行了三个实验。在实验的四个阶段中,各点之间进行了各种不等距离变化。从七个总体中选择了一个和两个随机的模拟数据样本。使用选定的参数检验和非参数检验来检验均值,中位数,偏差和两个总体之间的分布是否相等。在FORTRAN 77中编写了一些计算机程序来实现数据模拟,参数测试和非参数测试的算法。通过程序对仿真数据进行了测试。根据不同样本大小,总体和阶段的I型和II型错误率对结果进行了分析。总之,当点之间的距离在比例尺上变化时,非参数检验会产生相同的结果。但是,当点之间的距离改变时,参数测试会显示不同的结果。由于在位置参数中违反了基本假设,因此评估了参数和非参数测试的功效。

著录项

  • 作者

    Chen, Andrew Hwa-Fen.;

  • 作者单位

    University of North Texas.;

  • 授予单位 University of North Texas.;
  • 学科 Management.;Statistics.;Computer science.
  • 学位 Ph.D.
  • 年度 1994
  • 页码 153 p.
  • 总页数 153
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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