This study investigated the item parameter recovery of two methods of factor analysis. The methods researched were a traditional factor analysis of tetrachoric correlation coefficients and an IRT approach to factor analysis which utilizes marginal maximum likelihood estimation using an EM algorithm (MMLE-EM). Dichotomous item response data was generated under the 2-parameter normal ogive model (2PNOM) using PARDSIM software. Examinee abilities were sampled from both the standard normal and uniform distributions. True item discrimination, a, was normal with a mean of .75 and a standard deviation of .10. True b, item difficulty, was specified as uniform [-2, 2]. The two distributions of abilities were completely crossed with three test lengths (n= 30, 60, and 100) and three sample sizes (N = 50, 500, and 1000). Each of the 18 conditions was replicated 5 times, resulting in 90 datasets. PRELIS software was used to conduct a traditional factor analysis on the tetrachoric correlations. The IRT approach to factor analysis was conducted using BILOG 3 software. Parameter recovery was evaluated in terms of root mean square error, average signed bias, and Pearson correlations between estimated and true item parameters. ANOVAs were conducted to identify systematic differences in error indices. Based on many of the indices, it appears the IRT approach to factor analysis recovers item parameters better than the traditional approach studied. Future research should compare other methods of factor analysis to MMLE-EM under various non-normal distributions of abilities.

This article explains what we mean by the unidimensionality of a measurement instrument, therefore we present some definitions and  theoretical contributions about this subject. Factor analysis is proposed as one of the many methods for assessing the unidimensionality of a measurement instrument. The use of Pearson correlations matrices on item-level factor analysis is identified as an important problem. Those correlations are problematic because items didn’t carry out the necessary assumptions in order to apply the Pearson correlation: interval-level measurement and normal distribution of the variable. As an alternative we propose and exemplify the use of tetrachoric and polychoric correlations. / Este artículo explica qué es lo que implica la unidimensionalidad de un instrumento de medición. Para ello se presentan algunas definiciones y aportes teóricos sobre el tema. Luego, el análisis factorial es propuesto como uno de los métodos para evaluar la dimensionalidad de un instrumento de medición. Se señala como un problema importante el uso de las matrices de correlaciones de Pearson en los análisis factoriales a nivel de ítems. Estas correlaciones son problemáticas porque los ítems no cumplen con los supuestos necesarios para aplicar la correlación de Pearson: nivel de medición de intervalo y distribución normal de la variable. Como alternativa se postula y ejemplifica el uso de las correlaciones tetracóricas y policóricas.