Creating performance categories from continuous motor skill data using a Rasch measurement model.

Journal of outcome measurement Pub Date : 1999-01-01
B Hands, B Sheridan, D Larkin
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Abstract

This paper reports the use of the Extended Logistic Model (ELM) of Rasch (Andrich, 1988), based on Item Response Theory, to validate the reduction of continuous motor skill data to categories of performance. The data were gathered from the performances of 5 and 6 year old children on 24 fundamental movement skills and involved different measurement units such as seconds, centimetres, scores and counts. In order to compare results across all skills the data were collapsed into discrete sets of categories. Several alternative cut-off locations based on normative data were considered. A feature of the ELM is that it can account for correct scoring of the response categories, but only if the threshold estimates derived from the data by the measurement model are correctly ordered in a hierarchical fashion, from lowest to highest. Should this be the case, a valid scoring function has been established. In this study, the data were successfully reduced to three categories based on the 15th and 85th percentile allowing further analysis to proceed.

使用Rasch测量模型从连续的运动技能数据中创建性能类别。
本文采用Rasch (Andrich, 1988)基于项目反应理论的扩展逻辑模型(Extended Logistic Model, ELM)来验证将连续运动技能数据简化为表现类别的有效性。数据来自5 - 6岁儿童在24项基本动作技能上的表现,涉及不同的测量单位,如秒、厘米、分数和计数。为了比较所有技能的结果,数据被分解成离散的类别集。考虑了基于规范数据的几个备选截止点。ELM的一个特征是,它可以对响应类别进行正确的评分,但前提是测量模型从数据中得出的阈值估计以层次方式正确排序,从最低到最高。如果是这种情况,则建立了一个有效的评分函数。在本研究中,基于第15和第85百分位的数据成功地简化为三类,以便进行进一步的分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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