Advances in Psychological Science ›› 2024, Vol. 32 ›› Issue (4): 700-714.doi: 10.3724/SP.J.1042.2024.00700
• Research Method • Previous Articles
Received:
2023-08-29
Online:
2024-04-15
Published:
2024-02-29
Contact:
LIU Hongyun
E-mail:hyliu@bnu.edu.cn
CLC Number:
LUO Xiaohui, LIU Hongyun. Estimating test reliability of intensive longitudinal studies: Perspectives on multilevel structure and dynamic nature[J]. Advances in Psychological Science, 2024, 32(4): 700-714.
题目 | 基于多层验证性因子分析 | 基于动态因子分析a | 基于动态结构方程模型 | ||
---|---|---|---|---|---|
个体间信度 | 个体内信度 | 个体内信度 | 个体间信度 | 个体内信度 | |
题目1 | 0.954 [0.929, 0.979] | 0.511 [0.047, 0.550] | 0.649 [0.566, 0.704] | 0.973 [0.961, 0.985] | 0.514 [0.500, 0.528] |
题目2 | 0.731 [0.631, 0.831] | 0.305 [0.266, 0.344] | 0.472 [0.365, 0.556] | 0.851 [0.802, 0.900] | 0.329 [0.311, 0.343] |
题目3 | 0.905 [0.864, 0.946] | 0.689 [0.654, 0.724] | 0.753 [0.677, 0.796] | 0.930 [0.908, 0.952] | 0.677 [0.658, 0.687] |
题目4 | 0.903 [0.854, 0.952] | 0.689 [0.644, 0.734] | 0.733 [0.657, 0.783] | 0.948 [0.930, 0.966] | 0.682 [0.667, 0.694] |
题目5 | 0.946 [0.922, 0.970] | 0.623 [0.586, 0.660] | 0.747 [0.657, 0.788] | 0.966 [0.952, 0.980] | 0.599 [0.585, 0.609] |
题目6 | 0.963 [0.939, 0.987] | 0.652 [0.615, 0.689] | 0.747 [0.670, 0.792] | 0.990 [0.982, 0.998] | 0.618 [0.603, 0.629] |
测验 | 0.982 [0.976, 0.988] | 0.892 [0.882, 0.902] | 0.919 [0.890, 0.937] | 0.990 [0.988, 0.992] | 0.847 [0.840, 0.852] |
题目 | 基于多层验证性因子分析 | 基于动态因子分析a | 基于动态结构方程模型 | ||
---|---|---|---|---|---|
个体间信度 | 个体内信度 | 个体内信度 | 个体间信度 | 个体内信度 | |
题目1 | 0.954 [0.929, 0.979] | 0.511 [0.047, 0.550] | 0.649 [0.566, 0.704] | 0.973 [0.961, 0.985] | 0.514 [0.500, 0.528] |
题目2 | 0.731 [0.631, 0.831] | 0.305 [0.266, 0.344] | 0.472 [0.365, 0.556] | 0.851 [0.802, 0.900] | 0.329 [0.311, 0.343] |
题目3 | 0.905 [0.864, 0.946] | 0.689 [0.654, 0.724] | 0.753 [0.677, 0.796] | 0.930 [0.908, 0.952] | 0.677 [0.658, 0.687] |
题目4 | 0.903 [0.854, 0.952] | 0.689 [0.644, 0.734] | 0.733 [0.657, 0.783] | 0.948 [0.930, 0.966] | 0.682 [0.667, 0.694] |
题目5 | 0.946 [0.922, 0.970] | 0.623 [0.586, 0.660] | 0.747 [0.657, 0.788] | 0.966 [0.952, 0.980] | 0.599 [0.585, 0.609] |
题目6 | 0.963 [0.939, 0.987] | 0.652 [0.615, 0.689] | 0.747 [0.670, 0.792] | 0.990 [0.982, 0.998] | 0.618 [0.603, 0.629] |
测验 | 0.982 [0.976, 0.988] | 0.892 [0.882, 0.902] | 0.919 [0.890, 0.937] | 0.990 [0.988, 0.992] | 0.847 [0.840, 0.852] |
题目 | 基于动态因子分析a | 基于动态结构方程模型 | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
最小值 | 最大值 | 中位数 | 均值 | 标准差 | 最小值 | 最大值 | 中位数 | 均值 | 标准差 | |
题目1 | 0.027 | 0.978 | 0.655 | 0.648 | 0.172 | 0.041 | 0.994 | 0.534 | 0.515 | 0.204 |
题目2 | 0.047 | 1.000 | 0.474 | 0.471 | 0.262 | 0.034 | 0.984 | 0.290 | 0.329 | 0.204 |
题目3 | 0.014 | 1.000 | 0.782 | 0.753 | 0.178 | 0.032 | 0.999 | 0.724 | 0.676 | 0.236 |
题目4 | 0.110 | 1.000 | 0.795 | 0.737 | 0.205 | 0.030 | 0.999 | 0.745 | 0.683 | 0.238 |
题目5 | 0.219 | 1.000 | 0.760 | 0.746 | 0.148 | 0.055 | 0.999 | 0.641 | 0.599 | 0.238 |
题目6 | 0.144 | 1.000 | 0.770 | 0.745 | 0.157 | 0.056 | 0.999 | 0.638 | 0.619 | 0.214 |
测验 | 0.651 | 1.000 | 0.931 | 0.919 | 0.055 | 0.296 | 0.976 | 0.891 | 0.847 | 0.123 |
题目 | 基于动态因子分析a | 基于动态结构方程模型 | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
最小值 | 最大值 | 中位数 | 均值 | 标准差 | 最小值 | 最大值 | 中位数 | 均值 | 标准差 | |
题目1 | 0.027 | 0.978 | 0.655 | 0.648 | 0.172 | 0.041 | 0.994 | 0.534 | 0.515 | 0.204 |
题目2 | 0.047 | 1.000 | 0.474 | 0.471 | 0.262 | 0.034 | 0.984 | 0.290 | 0.329 | 0.204 |
题目3 | 0.014 | 1.000 | 0.782 | 0.753 | 0.178 | 0.032 | 0.999 | 0.724 | 0.676 | 0.236 |
题目4 | 0.110 | 1.000 | 0.795 | 0.737 | 0.205 | 0.030 | 0.999 | 0.745 | 0.683 | 0.238 |
题目5 | 0.219 | 1.000 | 0.760 | 0.746 | 0.148 | 0.055 | 0.999 | 0.641 | 0.599 | 0.238 |
题目6 | 0.144 | 1.000 | 0.770 | 0.745 | 0.157 | 0.056 | 0.999 | 0.638 | 0.619 | 0.214 |
测验 | 0.651 | 1.000 | 0.931 | 0.919 | 0.055 | 0.296 | 0.976 | 0.891 | 0.847 | 0.123 |
比较维度 | 基于多层验证性因子分析 | 基于动态因子分析 | 基于动态结构方程模型 |
---|---|---|---|
数据适配度 | 体现密集追踪数据的多层结构 | 体现密集追踪数据的动态特性 | 体现密集追踪数据的多层结构和动态特性 |
可估的信度 | 个体内信度和个体间信度 | 个体特定信度和个体内信度 | 个体特定信度、个体内信度和个体间信度 |
估计方法 | 极大似然估计 | 贝叶斯估计 | 贝叶斯估计 |
软件需求 | 只需Mplus即可完成 | 需在R中调用Mplus | 需要Mplus和其它统计软件(如, R) |
运行耗时a | 可忽略不计(本例中, 小于1 s) | 较短(本例中, 约10 min) | 较长(本例中, 约2 h) |
主要局限 | ①对数据有较强的假设 ②无法考察信度的个体差异 ③没有考虑数据的动态特性 | ①混淆特质和状态成分, 信度估计不准 ②忽视多层结构, 无法估计个体间信度 ③某些个体模型可能无法拟合 | 操作相对复杂, 耗时较长, 不够简便 |
比较维度 | 基于多层验证性因子分析 | 基于动态因子分析 | 基于动态结构方程模型 |
---|---|---|---|
数据适配度 | 体现密集追踪数据的多层结构 | 体现密集追踪数据的动态特性 | 体现密集追踪数据的多层结构和动态特性 |
可估的信度 | 个体内信度和个体间信度 | 个体特定信度和个体内信度 | 个体特定信度、个体内信度和个体间信度 |
估计方法 | 极大似然估计 | 贝叶斯估计 | 贝叶斯估计 |
软件需求 | 只需Mplus即可完成 | 需在R中调用Mplus | 需要Mplus和其它统计软件(如, R) |
运行耗时a | 可忽略不计(本例中, 小于1 s) | 较短(本例中, 约10 min) | 较长(本例中, 约2 h) |
主要局限 | ①对数据有较强的假设 ②无法考察信度的个体差异 ③没有考虑数据的动态特性 | ①混淆特质和状态成分, 信度估计不准 ②忽视多层结构, 无法估计个体间信度 ③某些个体模型可能无法拟合 | 操作相对复杂, 耗时较长, 不够简便 |
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