Back to Basics: The Importance of Measurement Properties in Biological Psychiatry.
Back to Basics: The Importance of Measurement Properties in Biological Psychiatry.
复制标题
DOI：
10.1016/j.neubiorev.2021.01.008
复制
发表时间：
2021-04
影响因子：
8.2
通讯作者：
Alloy LB
中科院分区：
文献类型：
--
作者：
Moriarity DP;Alloy LB
生物精神病学是资助心理健康研究的组织的主要资助重点（例如，美国国立卫生研究院）。尽管如此，有些人认为，该领域尚未实现其对精神病理学的分类、诊断和治疗产生有意义影响的巨大承诺。这可能部分归因于缺乏对生物变量的关键测量特性（“物理测量学”）的研究，因为它们通常用于生物精神病学研究。具体而言，由生理测量学提供信息的研究设计更有可能是可复制的，避免导致错误估计的测量结果，并在时间，金钱和分析次数方面最大限度地提高效率。这篇评论描述了五个关键的生理测量原则（内部一致性，维度，方法特异性方差，时间稳定性和时间特异性），说明了缺乏对这些特征的理解如何对研究造成有意义的限制，并回顾了以各种流行的生物变量为特征的生理测量学研究的例子，以说明如何进行这项研究，并得出关于以下变量的实质性结论：兴趣
Biological psychiatry is a major funding priority for organizations that fund mental health research (e.g., National Institutes of Health). Despite this, some have argued that the field has fallen short of its considerable promise to meaningfully impact the classification, diagnosis, and treatment of psychopathology. This may be attributable in part to a paucity of research about key measurement properties (“physiometrics”) of biological variables as they are commonly used in biological psychiatry research. Specifically, study designs informed by physiometrics are more likely to be replicable, avoid poor measurement that results in misestimation, and maximize efficiency in terms of time, money, and the number of analyses conducted. This review describes five key physiometric principles (internal consistency, dimensionality, method-specific variance, temporal stability, and temporal specificity), illustrates how lack of understanding about these characteristics imposes meaningful limitations on research, and reviews examples of physiometric studies featuring a variety of popular biological variables to illustrate how this research can be done and substantive conclusions drawn about the variables of interest.
登录
查看更多内容
DOI：
10.1016/j.bpsc.2016.12.004
发表时间：
2017-04
期刊：
Biological psychiatry. Cognitive neuroscience and neuroimaging
影响因子：
--
作者：
Luking KR;Nelson BD;Infantolino ZP;Sauder CL;Hajcak G
通讯作者：
Hajcak G
影响因子：
3.7
作者：
Holiga Š;Sambataro F;Luzy C;Greig G;Sarkar N;Renken RJ;Marsman JC;Schobel SA;Bertolino A;Dukart J
通讯作者：
Dukart J
影响因子：
22.4
作者：
Cummings, Colleen M.;Caporino, Nicole E.;Kendall, Philip C.
通讯作者：
Kendall, Philip C.
影响因子：
18.6
作者：
Giannobile, William V.;Beikler, Thomas;Wong, David T.
通讯作者：
Wong, David T.
影响因子：
3.6
作者：
Clark, LA;Watson, D
通讯作者：
Watson, D