Replication Data for: Estimating and fitting the Discrete-scored Polytomous Items under the Weighted-score Logistic Model and its Simulation Study

NIAID Data Ecosystem2026-05-01 收录

下载链接：

https://doi.org/10.7910/DVN/L6TUJ5

下载链接

链接失效反馈

官方服务：

资源简介：

The code for estimating item parameter under the weighted-score Logistic model, and the data for simulating test

创建时间：

2023-09-16

Estimates and their Standard Errors of the Discrimination Parameter for each of the 20 Items in the Body Map for the Comfort Questions.

D(s≥6) is the rank order of the difficulty parameter for obtaining a score of at least 6 from the IRT model, where 1 is the most and 20 the least difficult.

NIAID Data Ecosystem40

Maximum Likelihood Worksheet

This worksheet allows you to see how IRT estimates the ML estimate of theta. Plus in the a and b for an item, as well as the response to each item (correct/incorrect).

Figshare2013-11-15 更新10

Exploratory factor analysis of four- and one-factor solutions and graded response model parameter estimates of one-factor solution.

Factor loadings presented include four-factor and one-factor solutions. Bolded loadings indicate agreement of factor loadings in four-factor solution with Berger Subscales*.

Figshare2020-02-05 更新10

simulating test and estimating item parameter under the weighted-score Logistic model

A new polytomously scored item response theory model, the weighted-score Logistic model, WSLM, is presented. The weighted-score Logistic model is also based on the Logistic function, in which the weig

NIAID Data Ecosystem20

Explained Variance for Bias of Item Discrimination and Item Difficulty Parameter for Standard MML Estimation with the Default Convergence Criterion.

Note. See Table 1.Explained Variance for Bias of Item Discrimination and Item Difficulty Parameter for Standard MML Estimation with the Default Convergence Criterion.

Figshare2015-12-03 更新10