ABSTRACT A Monte Carlo simulation examined estimation difficulties and parameter and standard error bias for mean and variance estimates of binary latent growth curve models using mean and variance adjusted diagonally weighted least squares (WLSMV) and robust maximum likelihood (MLR). Small and medium effects of slope means and variances for longitudinal designs with three, five, and seven time points and sample sizes of 100, 200, 500, and 1000 were examined. Results indicated that more time points, larger sample size, and more symmetric distributions were associated with fewer improper solutions, lower parameter and standard error bias, better Type I error rates, and better coverage. WLSMV and MLR performed acceptably with at least five time points and sample size of 500, but WLSMV performance depended on the model specification. Three time points and 100 cases appeared to be too few for accurate estimation of binary latent growth curve models for any method.

中文翻译：

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具有二元变量的潜在增长曲线模型的性能

摘要 蒙特卡罗模拟使用均值和方差调整对角加权最小二乘法 (WLSMV) 和鲁棒最大似然 (MLR) 来检查二元潜在增长曲线模型的均值和方差估计的估计难度和参数和标准误差偏差。检查了具有三个、五个和七个时间点且样本大小为 100、200、500 和 1000 的纵向设计的斜率均值和方差的中小影响。结果表明，更多的时间点、更大的样本量和更对称的分布与更少的不当解决方案、更低的参数和标准误差偏差、更好的 I 类错误率和更好的覆盖率相关。WLSMV 和 MLR 在至少五个时间点和 500 的样本大小下表现可以接受，但 WLSMV 性能取决于模型规范。