The relevance of determinacy coefficients as indicators for the validity of factor score predictors has regularly been emphasized. Previous simulation studies revealed biased determinacy coefficients for factor score predictors based on categorical variables. Therefore, and because there are different possibilities to compute determinacy coefficients, the present study compared bias of determinacy coefficients for the best linear factor score predictor and for a correlation-preserving factor score predictor based on confirmatory factor models with observed variables with 2, 4, 6, and 8 categories and maximum likelihood estimation, diagonally weighted least squares estimation, and Bayesian estimation. Positive bias was found when data were based on variables with two categories, population factors were correlated, and when there were unmodeled cross-loadings. Based on the results, the correction for sampling error, the use of maximum likelihood or Bayesian parameters, and data with at least four categories are recommended to avoid overestimation of parameter-based determinacy coefficients.

翻译：确定性系数作为因子得分预测有效性指标的相关性已得到反复强调。以往的模拟研究表明，基于分类变量的因子得分预测的确定性系数存在偏差。因此，且由于计算确定性系数存在多种可能性，本研究比较了基于观测变量（分为2、4、6和8个类别）的验证性因子模型中的最佳线性因子得分预测和保相关因子得分预测的确定性系数偏差，并采用了最大似然估计、对角线加权最小二乘估计和贝叶斯估计。当数据基于两类变量、总体因子相关且存在未建模的交叉载荷时，发现了正向偏差。基于结果，建议通过抽样误差校正、使用最大似然或贝叶斯参数以及包含至少四个类别的数据，以避免基于参数的确定性系数被高估。