Accurate Confidence and Bayesian Interval Estimation for Non-centrality Parameters and Effect Size Indices.

Accurate Confidence and Bayesian Interval Estimation for Non-centrality Parameters and Effect Size Indices.
复制标题

非中心参数和效果大小指数的准确置信度和贝叶斯间隔估计。

DOI:
10.1007/s11336-022-09899-x
复制
发表时间:
2023-03
期刊:
影响因子:
3
通讯作者:
Vandekar, Simon
Vandekar, Simon
中科院分区:
心理学4区
文献类型:
--
作者:
Kang, Kaidi;Jones, Megan T.;Armstrong, Kristan;Avery, Suzanne;McHugo, Maureen;Heckers, Stephan;Vandekar, Simon

文献摘要

参考文献

相似文献

报告效应量指数估计及其置信区间(CI)是同时传达观察到的证据的强度和精度的一种很好的方式。我们最近提出了一个鲁棒效应量指数(RESI),它比普通指数更有优势,因为它广泛适用于不同类型的数据。在这里,我们使用统计理论和模拟来开发和评估依赖于不同协方差估计量的RESI估计量和置信/可信区间。我们的结果表明:(1)与直觉相反,协变量的随机性降低了卡方和F CI的覆盖率;(2)当估计估计量的方差时,使用参数和稳健RESI估计量的非中心卡方和F CI无法覆盖名义水平上的真实效应大小。使用鲁棒估计沿着与拟议的非参数自助或贝叶斯(可信)区间提供了有效的推理RESI,即使模型假设可能会被违反。这项工作形成了一个统一的效应量报告程序,使得具有置信/可信区间的效应量可以很容易地以方差分析(ANOVA)表格格式报告。在线版本包含补充材料,可通过10.1007/s11336-022-09899-x获得。
Reporting effect size index estimates with their confidence intervals (CIs) can be an excellent way to simultaneously communicate the strength and precision of the observed evidence. We recently proposed a robust effect size index (RESI) that is advantageous over common indices because it’s widely applicable to different types of data. Here, we use statistical theory and simulations to develop and evaluate RESI estimators and confidence/credible intervals that rely on different covariance estimators. Our results show (1) counter to intuition, the randomness of covariates reduces coverage for Chi-squared and F CIs; (2) when the variance of the estimators is estimated, the non-central Chi-squared and F CIs using the parametric and robust RESI estimators fail to cover the true effect size at the nominal level. Using the robust estimator along with the proposed nonparametric bootstrap or Bayesian (credible) intervals provides valid inference for the RESI, even when model assumptions may be violated. This work forms a unified effect size reporting procedure, such that effect sizes with confidence/credible intervals can be easily reported in an analysis of variance (ANOVA) table format. The online version contains supplementary material available at 10.1007/s11336-022-09899-x.
DOI: 10.1037/a0028086
发表时间: 2012-06-01
影响因子: 7
作者:
Kelley, Ken;Preacher, Kristopher J.
通讯作者: Preacher, Kristopher J.
DOI: 10.1093/schbul/sbaa081
发表时间: 2021-01-01
影响因子: 6.6
作者:
Avery, Suzanne N.;Armstrong, Kristan;Heckers, Stephan
通讯作者: Heckers, Stephan
DOI: 10.1007/s11336-020-09698-2
发表时间: 2020-03-30
期刊: PSYCHOMETRIKA
影响因子: 3
作者:
Vandekar, Simon;Tao, Ran;Blume, Jeffrey
通讯作者: Blume, Jeffrey
DOI: 10.1016/j.schres.2018.06.049
发表时间: 2018-12-01
影响因子: 4.5
作者:
Armstrong, Kristan;Avery, Suzanne;Heckers, Stephan
通讯作者: Heckers, Stephan
DOI: 10.1037//1082-989x.7.1.105
发表时间: 2002-03-01
影响因子: 7
作者:
Morris, SB;DeShon, RP
通讯作者: DeShon, RP