Liu Chen-Wei, Wang Wen-Chung
The Chinese University of Hong Kong, Sha Tin, New Territories, Hong Kong.
The Education University of Hong Kong, Tai Po, New Territories, Hong Kong.
Appl Psychol Meas. 2019 May;43(3):195-210. doi: 10.1177/0146621618762743. Epub 2018 Apr 16.
It is commonly known that respondents exhibit different response styles when responding to Likert-type items. For example, some respondents tend to select the extreme categories (e.g., strongly disagree and strongly agree), whereas some tend to select the middle categories (e.g., disagree, neutral, and agree). Furthermore, some respondents tend to disagree with every item (e.g., strongly disagree and disagree), whereas others tend to agree with every item (e.g., agree and strongly agree). In such cases, fitting standard unfolding item response theory (IRT) models that assume no response style will yield a poor fit and biased parameter estimates. Although there have been attempts to develop dominance IRT models to accommodate the various response styles, such models are usually restricted to a specific response style and cannot be used for unfolding data. In this study, a general unfolding IRT model is proposed that can be combined with a softmax function to accommodate various response styles via scoring functions. The parameters of the new model can be estimated using Bayesian Markov chain Monte Carlo algorithms. An empirical data set is used for demonstration purposes, followed by simulation studies to assess the parameter recovery of the new model, as well as the consequences of ignoring the impact of response styles on parameter estimators by fitting standard unfolding IRT models. The results suggest the new model to exhibit good parameter recovery and seriously biased estimates when the response styles are ignored.
众所周知，在回答李克特式项目时，受访者会表现出不同的回答方式。例如，一些受访者倾向于选择极端类别（如强烈不同意和强烈同意），而一些受访者则倾向于选择中间类别（如不同意、中立和同意）。此外，一些受访者倾向于对每个项目都持否定态度（如强烈不同意和不同意），而另一些受访者则倾向于对每个项目都持肯定态度（如同意和强烈同意）。在这种情况下，拟合假设不存在回答方式的标准展开项目反应理论（IRT）模型将导致拟合效果不佳和参数估计有偏差。尽管已经有人尝试开发优势IRT模型来适应各种回答方式，但这类模型通常局限于特定的回答方式，不能用于展开数据。在本研究中，提出了一种通用的展开IRT模型，该模型可以与softmax函数相结合，通过评分函数来适应各种回答方式。新模型的参数可以使用贝叶斯马尔可夫链蒙特卡罗算法进行估计。使用一个实证数据集进行演示，随后进行模拟研究，以评估新模型的参数恢复情况，以及通过拟合标准展开IRT模型而忽略回答方式对参数估计量的影响所产生的后果。结果表明，当忽略回答方式时，新模型表现出良好的参数恢复能力，但估计值存在严重偏差。
