ANALYSIS OF MULTIPLICATIVE COMBINATION RULES WHEN THE CAUSAL VARIABLES ARE MEASURED WITH ERROR
ANALYSIS OF MULTIPLICATIVE COMBINATION RULES WHEN THE CAUSAL VARIABLES ARE MEASURED WITH ERROR
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DOI：
10.1037/0033-2909.93.3.549
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发表时间：
1983-01-01
影响因子：
22.4
通讯作者：
JONES, LE
中科院分区：
文献类型：
--
作者：
BUSEMEYER, JR;JONES, LE
评估的有效性的观察方法用于测试乘法组合规则相对于2个测量问题：测量水平（即，通过允许单调变换的措施所产生的影响）和测量误差（即，通过使用不可靠的因果变量的措施所产生的影响）。评估是基于结构模型（一组方程的理论结构相互关联）和测量模型（一组方程的理论结构观察到的措施）之间的理论区别。它的结论是，分层回归分析是不足以确定是否结构模型是加性或乘法的原因有2个：第一，一个加性结构模型可能会产生乘法效应通过一个非线性测量模型。第二，由于倍增测量误差，倍增结构模型可能产生不可检测的倍增效应。分层回归分析的一些替代方案进行了说明。(35参考）（PsycINFO数据库记录（c）2016阿帕，保留所有权利）
Evaluates the validity of the observational method used to test multiplicative combination rules with respect to 2 measurement issues: measurement level (ie, the effects produced by allowing monotonic transformations of the measures) and measurement error (ie, the effects produced by using unreliable measures of the causal variables). The evaluation is based on a theoretical distinction between the structural model (the set of equations relating theoretical constructs to each other) and the measurement model (the set of equations relating the theoretical constructs to the observed measures). It is concluded that hierarchical regression analysis is inadequate for determining whether the structural model is additive or multiplicative for 2 reasons: First, an additive structural model may produce multiplicative effects through a nonlinear measurement model. Second, a multiplicative structural model may produce nondetectable multiplicative effects because of multiplicative measurement error. Some alternatives to hierarchical regression analysis are described.(35 ref)(PsycINFO Database Record (c) 2016 APA, all rights reserved)