参数可预测性和响应精度和响应时间的联合建模对能力估计的影响
Maryam Pezeshki1, Susan Embretson1
1School of Psychology, Georgia Institute of Technology, GA, USA.
Applied psychological measurement
|March 3, 2025
概括
自动项目生成可以降低成本,但它对特征估计准确性的影响取决于项目参数的可预测性. 添加响应时间可以提高准确性,特别是在具有较低项目难度的成就测试中.
科学领域:
- 教育测量教育的测量
- 心理测量 心理测量 心理测量
- 项目响应理论 (IRT)
背景情况:
- 保持测试质量需要大量的项目库,往往需要昂贵的经验试验.
- 自动项目生成提供潜在的成本和劳动力减少,如果项目参数是可预测的.
- 项目参数可预测性对IRT模型内特征估计准确性的影响仍然不清楚.
研究的目的:
- 研究项目参数可预测性的不同水平如何影响IRT模型中特征估计的准确性.
- 检查将响应时间纳入作为补充数据源对特征估计准确性的影响.
主要方法:
- 模拟数据使用项目响应理论 (IRT) 模型进行分析.
- 使用项目家族模型和认知复杂性特征来预测项目参数.
- 用和不包括响应时间数据来比较特征估计的准确性.
主要成果:
- 在使用基于项目家族模型的参数与已知的参数时,特征估计准确性显示出最小的差异.
- 通过认知复杂性特征预测项目参数导致略大的特征估计错误.
- 纳入响应时间显著提高了对具有较低项目难度级别的测试的特征估计准确度.
结论:
- 具有可预测参数的自动项目生成可以产生与实证方法可比的准确特征估计.
- 响应时间数据可以提高特征估计的准确性,特别是在成就测试环境中.
- 这些发现对优化自动化项目生成和理解测试有效性的响应过程有影响.
相关概念视频
01:03Prediction Intervals
2.2K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
2.2K
01:16Response Surface Methodology
82
Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
The process of RSM involves several key steps:
82
01:13Sensitivity, Specificity, and Predicted Value
158
In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
Sensitivity is the...
158
01:14Parametric Survival Analysis: Weibull and Exponential Methods
322
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
322
01:23Mechanistic Models: Compartment Models in Individual and Population Analysis
23
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
23
01:25Multiple Regression
2.9K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
2.9K