Measurement error and outcome distributions: Methodological issues in regression analyses of behavioral coding data.
Measurement error and outcome distributions: Methodological issues in regression analyses of behavioral coding data.
复制标题
DOI：
10.1037/adb0000091
复制
发表时间：
2015-12
期刊：
影响因子：
--
通讯作者：
Atkins DC
中科院分区：
文献类型：
--
作者：
Holsclaw T;Hallgren KA;Steyvers M;Smyth P;Atkins DC
行为编码越来越多地用于研究物质使用障碍（SUD）的心理社会治疗的变化机制。然而，行为编码数据通常包括在回归分析中可能存在问题的特征，包括自变量中的测量误差、计数结果变量的非正态分布以及预测变量和结果变量与第三变量（例如会话长度）的合并。计量经济学的方法研究表明，这些问题可能导致有偏的参数估计，不准确的标准误差，以及增加I型和II型错误率，但这些统计问题在SUD治疗研究中并不广为人知，或者更普遍地说，在心理治疗编码研究中。使用最低限度的技术语言，旨在为广大观众的SUD治疗研究人员，本文件说明了这些数据问题的性质是有问题的。我们利用真实世界的数据和基于模拟的示例来说明这些数据特征如何影响参数估计和模型解释。引入加权负二项回归作为普通线性回归的替代，其适当地解决了SUD治疗行为编码数据的共同数据特征。最后，我们展示了如何使用和解释这些模型的数据从动机访谈的研究。用于加权负二项回归模型的SPSS和R语法包含在补充材料中。
Behavioral coding is increasingly used for studying mechanisms of change in psychosocial treatments for substance use disorders (SUDs). However, behavioral coding data typically include features that can be problematic in regression analyses, including measurement error in independent variables, non-normal distributions of count outcome variables, and conflation of predictor and outcome variables with third variables, such as session length. Methodological research in econometrics has shown that these issues can lead to biased parameter estimates, inaccurate standard errors, and increased type-I and type-II error rates, yet these statistical issues are not widely known within SUD treatment research, or more generally, within psychotherapy coding research. Using minimally-technical language intended for a broad audience of SUD treatment researchers, the present paper illustrates the nature in which these data issues are problematic. We draw on real-world data and simulation-based examples to illustrate how these data features can bias estimation of parameters and interpretation of models. A weighted negative binomial regression is introduced as an alternative to ordinary linear regression that appropriately addresses the data characteristics common to SUD treatment behavioral coding data. We conclude by demonstrating how to use and interpret these models with data from a study of motivational interviewing. SPSS and R syntax for weighted negative binomial regression models is included in supplementary materials.