Smith Everett V, Ying Yuping, Brown Scott W
Department of Educational Psychology, Chicago, IL 60607, USA.
J Appl Meas. 2012;13(1):23-40.
In this study, we used the Mixed Rasch Model (MRM) to analyze data from the Beliefs and Attitudes About Memory Survey (BAMS; Brown, Garry, Silver, and Loftus, 1997). We used the original 5-point BAMS data to investigate the functioning of the "Neutral" category via threshold analysis under a 2-class MRM solution. The "Neutral" category was identified as not eliciting the model expected responses and observations in the "Neutral" category were subsequently treated as missing data. For the BAMS data without the "Neutral" category, exploratory MRM analyses specifying up to 5 latent classes were conducted to evaluate data-model fit using the consistent Akaike information criterion (CAIC). For each of three BAMS subscales, a two latent class solution was identified as fitting the mixed Rasch rating scale model the best. Results regarding threshold analysis, person parameters, and item fit based on the final models are presented and discussed as well as the implications of this study.
在本研究中，我们使用混合拉施模型（MRM）来分析来自记忆信念与态度调查（BAMS；Brown、Garry、Silver和Loftus，1997）的数据。我们使用原始的5点BAMS数据，通过在二分类MRM解决方案下的阈值分析来研究“中性”类别（选项）的功能。“中性”类别被确定为未引发模型预期的反应，并且该类别中的观测值随后被视为缺失数据。对于没有“中性”类别的BAMS数据，进行了探索性MRM分析，指定了多达5个潜在类别，以使用一致的赤池信息准则（CAIC）来评估数据与模型的拟合度。对于三个BAMS分量表中的每一个，二潜在类别解决方案被确定为最适合混合拉施评分量表模型。呈现并讨论了基于最终模型的阈值分析、个体参数和项目拟合结果以及本研究的意义。
