Related Experiment Video
Updated: Aug 20, 2025
06:46
Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
Published on: September 27, 2024
340
autoRasch: An R Package to Do Semi-Automated Rasch Analysis
Feri Wijayanto1,2, Ioan Gabriel Bucur1, Perry Groot1
1Institute for Computing and Information Sciences, Radboud University, Nijmegen, The Netherlands.
Applied Psychological Measurement
|November 25, 2022
Summary
The autoRasch R package automates Rasch analysis using the in-plus-out-of-questionnaire log-likelihood (IPOQ-LL) for optimized survey instrument fit. It supports model reevaluation with standard Rasch statistics.
Area of Science:
- Psychometrics
- Statistical Software Development
- Survey Methodology
Background:
- Rasch analysis is crucial for evaluating survey instrument quality.
- Automating Rasch analysis can improve efficiency and objectivity.
- Existing methods may require significant user expertise.
Purpose of the Study:
- To introduce the autoRasch R package for semi-automated Rasch analysis.
- To implement optimization criteria like IPOQ-LL and IPOQ-LL-DIF.
- To provide tools for both automated and manual Rasch analysis workflows.
Main Methods:
- Utilizes the generalized partial credit model (GPCM) and GPCM with differential item functioning (GPCM-DIF).
- Employs penalized joint maximum likelihood estimation (PJMLE) for model fitting.
- Optimizes in-plus-out-of-questionnaire log-likelihood (IPOQ-LL) for item selection.
Main Results:
- The autoRasch package offers a streamlined approach to Rasch analysis.
- It provides criteria for assessing survey fit based on item inclusion.
- Standard Rasch statistics (outfit, infit, reliability) are available for reevaluation.
Conclusions:
- autoRasch facilitates efficient and robust Rasch analysis.
- The package aids in developing and refining survey instruments.
- It supports informed decision-making through automated and manual analysis options.
Related Concept Videos
01:11Introduction to R
538
R is a powerful software environment for statistical computing and graphics. Originating as an implementation of the S language, developed at Bell Laboratories, R has evolved into a robust, open-source statistical software favored by statisticians and data scientists worldwide. Its comprehensive suite includes data manipulation, calculation, and graphical display capabilities, making it versatile for data analysis and visualization. Its programming language is at the core of R's...
538
01:16Response Surface Methodology
217
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:
217
01:22Statistical Package for the Social Sciences (SPSS)
470
The Statistical Package for the Social Sciences, or SPSS, is a data management and analysis software suite. Developed by SPSS Inc. in 1968 and acquired by IBM in 2009, this tool was initially designed for social science data analysis, evolving to serve a wider range of disciplines. It was later renamed to Statistical Product and Service Solutions.
SPSS streamlines the process from data preparation to analysis and reporting. It is characterized by its user-friendly interface, which conceals...
SPSS streamlines the process from data preparation to analysis and reporting. It is characterized by its user-friendly interface, which conceals...
470
01:18Introduction To Survival Analysis
335
Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
The primary goal of survival analysis is to estimate survival time—the time...
335
01:21Friedman Two-way Analysis of Variance by Ranks
274
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...
274
01:15Assumptions of Survival Analysis
173
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
173