ABSTRACT

When questionnaires include Likert scales, items endorsed by relatively few respondents may result from characteristics of examinees or the constructs under study. Researchers may collapse categories to increase cell sample size; however, effects of this practice have not been systematically investigated. A five-point ordinal scale was simulated where data included few responses in extreme categories. Different estimators were applied to sparsely distributed and collapsed category data; characteristics of sample size, number of categories, number of items including sparse data, and percentage of sparse data were manipulated. Collapsing categories were advantageous for ULSMV and WLSMV, yielding higher convergence rates, more accurate estimation of parameters and standard errors, and chi-square test rejection rates close to the nominal level. With many response categories (e.g., ≥5), treating sparse data as continuous and using MLMV may serve as an alternative, especially when a small percentage of total items contain low cell frequencies.

摘要

当问卷中包含李克特量表时，相对较少的受访者认可的项目可能是由于考生的特征或所研究的结构所致。研究人员可以折叠类别以增加细胞样本的数量。但是，这种做法的效果尚未得到系统的研究。模拟了五点顺序量表，其中的数据很少包含极端类别的响应。对稀疏分布和折叠的类别数据应用了不同的估计量；样本大小，类别数量，项目数量（包括稀疏数据）和稀疏数据的百分比等特征均受到了操纵。折叠类别对ULSMV和WLSMV有利，可产生更高的收敛速度，更准确地估计参数和标准误差，并且卡方检验拒绝率接近标称水平。