Bayesian Estimation of Multidimensional Item Response Models. A comparison of Analytic and Simulation Algorithms
This study compares the performance of two estimation algorithms of new usage, the Metropolis-Hastings Robins-Monro (MHRM) and the Hamiltonian MCMC (HMC), with two consolidated algorithms in the psychometric literature, the marginal likelihood via EM algorithm (MMLEM) and the Markov chain Monte Carlo (MCMC), in the estimation of multidimensional item response models of various levels of complexity. This paper evaluates the performance of parameter recovery via three simulation studies from a [...]
Landmark vs. Geometry Learning: Explaining Female rats´ selective Preference for a Landmark
Rats were trained in a triangular-shaped pool to find a hidden [...]
The Use of Sounds as Stimuli in Human Electroderman Classical Conditioning
Tradicionalmente, en el estudio del condicionamiento clásico humano se emplearon descargas [...]
Lextale: A test to rapidly and efficiently assess the Spanish vocabulary size
The methods to measure vocabulary size vary across disciplines. This [...]
The Effect of Colour and Size on Attentional Bias to Alcohol-Related Pictures
Attentional bias plays an important role in the development and [...]
