估计趋势与差异性项目运作:比较五种基于IRT的方法
Oskar Engels1,2, Oliver Lüdtke1,2, Alexander Robitzsch1,2
1IPN-Leibniz Institute for Science and Mathematics Education, Kiel, Germany.
Educational and psychological measurement
|March 16, 2026
概括
使用平滑贝叶斯信息标准 (SBIC) 规范化估计项目参数漂移 (IPD) 下的最佳估计趋势. 这种方法保持了低偏差和根平均平方误差 (RMSE),在纵向评估中表现优于其他方法.
科学领域:
- 心理测量 心理测量 心理测量
- 教育测量教育的测量
- 统计建模 统计建模
背景情况:
- 纵向评估依赖于测试来估计随时间推移的趋势.
- 项目参数漂移 (IPD) 可以扭曲时间点比较,需要强大的统计方法.
- 准确的趋势估计对于有效的纵向数据解释至关重要.
研究的目的:
- 使用双参数后勤 (2PL) 模型,在项目参数漂移 (IPD) 下比较五种趋势估计方法.
- 为了评估并发校准,固定校准,强大的链接,部分不变性和规则化的估计的性能.
- 确定用于IPD纵向评估中准确估计趋势的最有效方法.
主要方法:
- 他们比较了五种趋势估计方法:并发校准,固定校准,强大的链接 (哈伯曼,海巴拉与Lp或L0损失),部分不变性 (使用概率比测试或RMSD) 和规范估计 (SBIC).
- 评估偏差和相对根平均平方误差 (RMSE) 趋势估计 (平均值和SD) 在T2.
- 用合成纵向读取数据作为实证示例.
主要成果:
- 使用SBIC进行规范估计显示出最佳表现,在各种条件中显示出低偏差和RMSE.
- 强大的链接方法,特别是哈伯曼与L0损失的链接,表现强,在不平衡的IPD下表现优于部分不变性方法.
- 同步和固定校准在不平衡的IPD下产生了最差的趋势恢复.
结论:
- 建议使用SBIC进行规范化估计,以便在项目参数可能偏移时准确地估计纵向趋势.
- 强大的连接方法提供了一个可行的替代方案,特别是哈伯曼连接与L0损失,用于处理IPD.
- 同步和固定校准方法不适用于具有显著项目参数偏移的纵向研究.
相关概念视频
01:21Friedman Two-way Analysis of Variance by Ranks
538
Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
538
01:52Regression Toward the Mean
7.3K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
7.3K
01:13Multiple Comparison Tests
4.5K
Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
4.5K
01:09Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test
7.0K
In parametric statistics, two fundamental tests stand out for their utility and wide application: the Student's t-test and goodness-of-fit tests. These tests provide researchers with a robust method for drawing insights from data, testing hypotheses, and making informed decisions based on their findings.
The Student's t-test is a statistical test that examines if there is a statistically significant difference between the means of two groups. This test is instrumental when dealing with...
The Student's t-test is a statistical test that examines if there is a statistically significant difference between the means of two groups. This test is instrumental when dealing with...
7.0K
01:09Comparing Experimental Results: Student's t-Test
6.2K
The t-test is a statistical method used to compare the sample mean with a population mean or compare two means from two data sets. The test statistic is calculated from the standard deviation, mean, and number of measurements in the data set at a selected confidence interval and then compared to a table of critical values at this confidence level. If the test statistic is smaller than the critical value, the null hypothesis is accepted. In this case, we state that the difference between the...
6.2K
01:19Expected Frequencies in Goodness-of-Fit Tests
8.8K
A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n) to the number of categories (k).
8.8K