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An Attempt to Lower Sources of Systematic Measurement Error Using Hierarchical Generalized Linear Modeling (HGLM)

机译:使用分层广义线性建模(HGLM)尝试降低系统测量误差的来源

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The purpose of the present studies was to test the effects of systematic sources of measurement error on the parameter estimates of scales using the Rasch model. Studies 1 and 2 tested the effects of mood and affectivity. Study 3 evaluated the effects of fatigue. Last, studies 4 and 5 tested the effects of motivation on a number of parameters of the Rasch model (e.g., ability estimates). Results indicated that (a) the parameters of interest and the psychometric properties of the scales were substantially distorted in the presence of all systematic sources of error, and, (b) the use of HGLM provides a way of adjusting the parameter estimates in the presence of these sources of error. It is concluded that validity in measurement requires a thorough evaluation of potential sources of error and appropriate adjustments based on each occasion.
机译:本研究的目的是测试使用Rasch模型的系统测量误差源对尺度参数估计的影响。研究1和2测试了情绪和情感的影响。研究3评估了疲劳的影响。最后,研究4和5测试了动机对Rasch模型的许多参数(例如能力估计)的影响。结果表明(a)在所有系统性误差源均存在的情况下,(a)感兴趣的参数和量表的心理计量学特性严重失真,并且(b)HGLM的使用提供了一种在存在该误差的情况下调整参数估计的方法这些错误来源。结论是,测量的有效性要求对潜在的误差源进行全面评估,并根据每种情况进行适当的调整。

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