The aim of the paper is to propose the introduction of power prior distributions in the ability estimation of item response theory (IRT) models. In the literature, power priors have been proposed to integrate information coming from historical data with current data within Bayesian parameter estimation for generalized linear models. This approach allows to use a weighted posterior distribution based on the historical study as prior distribution for the parameters in the current study. Applications can be found especially in clinical trials and survival studies. Here, power priors are introduced within a Gibbs sampler scheme in the ability estimation step for a unidimensional IRT model. A Markov chain Monte Carlo algorithm is chosen for the high flexibility and possibility of extension to more complex models. The efficiency of the approach is demonstrated in terms of measurement precision by using data from the Hospital Anxiety and Depression Scale with a small sample.
Quality & Quantity – Springer Journals
Published: Jul 25, 2014
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.
All for just $49/month
Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly
Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.
Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.
Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.
All the latest content is available, no embargo periods.
“Hi guys, I cannot tell you how much I love this resource. Incredible. I really believe you've hit the nail on the head with this site in regards to solving the research-purchase issue.”Daniel C.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud
“I must say, @deepdyve is a fabulous solution to the independent researcher's problem of #access to #information.”@deepthiw
“My last article couldn't be possible without the platform @deepdyve that makes journal papers cheaper.”@JoseServera