当调查不可观察时,复杂的特征,数据收集和聚合过程可以为数据引入独特的特征,如有界性,测量误差,聚类,异常值,和异方差。未能共同解决这些特征可能会导致统计挑战,从而阻止对有关这些特征的假设的调查。本研究旨在证明贝叶斯β比例广义线性潜在和混合模型(β比例GLLAMM)的有效性(Rabe-Hesketh等人。,Psychometrika,69(2)、167-90,2004a,计量经济学杂志,128(2)、301-23,2004c,2004b;Skrondal和Rabe-Hesketh2004)在探索有关语音清晰度的研究假设时处理数据特征。为了实现这一目标,该研究重新检查了Boonen等人最初收集的自发语音样本的转录数据。(儿童语言杂志,50(1)、78-103,2023年)。将数据汇总为熵得分。研究比较了β-比例GLLAMM与正态线性混合模型(LMM)的预测精度(Holmes等.,2019年),并研究了其从熵分数估计潜在可懂度的能力。该研究还说明了如何使用所提出的模型来探索有关说话者相关因素对可懂度的影响的假设。beta比例GLLAMM并非没有挑战;其实施需要制定有关数据生成过程的假设以及概率编程语言的知识,都是贝叶斯方法的核心。然而,结果表明,该模型在预测经验现象方面优于正常LMM,以及它量化潜在可理解性的能力。此外,所提出的模型有助于探索有关说话者相关因素和可理解性的假设。最终,这项研究对对定量测量复杂的研究人员和数据分析师有意义,在准确预测经验现象的同时,无法观察到的结构。
When investigating unobservable, complex traits, data collection and aggregation processes can introduce distinctive features to the data such as boundedness, measurement error, clustering,
outliers, and heteroscedasticity. Failure to collectively address these features can result in statistical challenges that prevent the investigation of hypotheses regarding these traits. This study aimed to demonstrate the efficacy of the Bayesian beta-proportion generalized linear latent and mixed model (beta-proportion GLLAMM) (Rabe-Hesketh et al., Psychometrika, 69(2), 167-90, 2004a, Journal of Econometrics, 128(2), 301-23, 2004c, 2004b; Skrondal and Rabe-Hesketh 2004) in handling data features when exploring research hypotheses concerning speech intelligibility. To achieve this objective, the study reexamined data from transcriptions of spontaneous speech samples initially collected by Boonen et al. (Journal of Child Language, 50(1), 78-103, 2023). The data were aggregated into entropy scores. The research compared the prediction accuracy of the beta-proportion GLLAMM with the normal linear mixed model (LMM) (Holmes et al., 2019) and investigated its capacity to estimate a latent intelligibility from entropy scores. The study also illustrated how hypotheses concerning the impact of speaker-related factors on intelligibility can be explored with the proposed model. The beta-proportion GLLAMM was not free of challenges; its implementation required formulating assumptions about the data-generating process and knowledge of probabilistic programming languages, both central to Bayesian methods. Nevertheless, results indicated the superiority of the model in predicting empirical phenomena over the normal LMM, and its ability to quantify a latent potential intelligibility. Additionally, the proposed model facilitated the exploration of hypotheses concerning speaker-related factors and intelligibility. Ultimately, this research has implications for researchers and data analysts interested in quantitatively measuring intricate, unobservable constructs while accurately predicting the empirical phenomena.