Problem: Several approaches to analyze survey data have been proposed in the literature. One method that is not popular in survey research methodology is the use of item response theory (IRT). Since accurate methods to make prediction behaviors are based upon observed data, the design model must overcome computation challenges, but also consideration towards calibration and proficiency estimation. The IRT model deems to be offered those latter options. We review that model and apply it to an observational survey data. We then compare the findings with the more popular weighted logistic regression. Method: Apply IRT model to the observed data from 136 sites within the Commonwealth of Virginia over five years collected in a two stage systematic stratified proportional to size sampling plan. Results: A relationship within data is found and is confirmed using the weighted logistic regression model selection. Practical Application: The IRT method may allow simplicity and better fit in the prediction within complex methodology: the model provides tools for survey analysis.
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机译:应用项目反应理论对友谊质量量表修定—以结构方程模型、层面理论多种技术支持修定量表的质量 Modifying Friendship Quality Questionnaire with Item Response Theory Approach——Validating with Facet Theory and Structure Equation Techniques
机译:Data mining of Arabidopsis thaliana salt-response proteins based on bioinformatics analysis Guo Meili, Gao Weixi, Yu Xuejuan, Zhou Chunxi, Liu Fujun, Liu Xin Abstract
机译:UtilizaçãodaTeoria da Resposta ao Item(TRI)para aorganizaçãodeum banco de itens destinadosavaliaçãodoraciocíniowordalthe Item Response Theory(IRT)用于构建项目库以评估口头推理