Three Cs in measurement models: causal indicators, composite indicators, and covariates.
Three Cs in measurement models: causal indicators, composite indicators, and covariates.
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DOI：
10.1037/a0024448
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发表时间：
2011-09
影响因子：
7
通讯作者：
Bauldry S
中科院分区：
文献类型：
--
作者：
Bollen KA;Bauldry S
在过去的二十年里，人们越来越关注因果(和形成性)指标。伴随这种增长而来的是一种信念，即我们可以将指标分为两类，即影响(反映)指标和因果(形成)指标。本文认为，这种二分法的观点过于简单。相反，潜在变量依赖于效应指标和三类变量：因果指标、复合(形成性)指标和协变量(“三个C”)。因果指标具有概念统一性，其对潜在变量的影响是结构性的。协变量不是概念度量，而是要控制的变量，以避免在估计度量与潜在变量之间的关系时出现偏差(S)。复合(形成性)指标形成了变量的精确线性组合，不需要共享一个概念。它们的系数是权重，而不是结构效应，复合材料是一个方便的问题。三个C的区分不清导致了混乱，引发了诸如：因果指标和形成性指标对于同一指标类型是不是不同的名称？一个带有因果或形成性指标的方程式应该有一个错误项吗？因果指标的系数是否不如效果指标稳定？区分因果指标和复合指标以及协变量对消除这种混淆大有裨益。我们强调主题专业知识在作出这些区别方面所起的关键作用。我们为处理这些变量类型提供了新的指导方针，包括识别模型、缩放潜在变量、参数估计和有效性评估。一个关于自我感觉健康的实证例子说明了我们的主要观点。
In the last two decades attention to causal (and formative) indicators has grown. Accompanying this growth has been the belief that we can classify indicators into two categories, effect (reflective) indicators and causal (formative) indicators. This paper argues that the dichotomous view is too simple. Instead, there are effect indicators and three types of variables on which a latent variable depends: causal indicators, composite (formative) indicators, and covariates (the “three Cs”). Causal indicators have conceptual unity and their effects on latent variables are structural. Covariates are not concept measures, but are variables to control to avoid bias in estimating the relations between measures and latent variable(s). Composite (formative) indicators form exact linear combinations of variables that need not share a concept. Their coefficients are weights rather than structural effects and composites are a matter of convenience. The failure to distinguish the “three Cs” has led to confusion and questions such as: are causal and formative indicators different names for the same indicator type? Should an equation with causal or formative indicators have an error term? Are the coefficients of causal indicators less stable than effect indicators? Distinguishing between causal and composite indicators and covariates goes a long way toward eliminating this confusion. We emphasize the key role that subject matter expertise plays in making these distinctions. We provide new guidelines for working with these variable types, including identification of models, scaling latent variables, parameter estimation, and validity assessment. A running empirical example on self-perceived health illustrates our major points.
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DOI：
10.1080/10705510903008261
发表时间：
2009-01-01
影响因子：
6
作者：
Bollen, Kenneth A.;Davis, Walter R.
通讯作者：
Davis, Walter R.
影响因子：
3.8
作者：
Grace, James B.;Bollen, Kenneth A.
通讯作者：
Bollen, Kenneth A.
影响因子：
22.4
作者：
BOLLEN, K;LENNOX, R
通讯作者：
LENNOX, R
影响因子：
1.2
作者：
COHEN, P;COHEN, J;VELEZ, CN
通讯作者：
VELEZ, CN
影响因子：
2
作者：
Bollen, Kenneth A.;Lennox, Richard D.;Dahly, Darren L.
通讯作者：
Dahly, Darren L.