Description

MULTIPOL-C

In non-cognitive measurement, certain traits such as clinical or psychiatric traits, addictions, extreme beliefs or maladaptive personality traits can be plausibly modeled as unipolar dimensions (Lucke, 2013, 2015; Reise & Waller, 2009). So, the low end of the dimension merely reflects the absence of trait manifestations while the high end reflects the different levels of trait severity or extremeness. Furthermore, it is reasonable to scale this type of dimensions as adopting only positive values and to assume that they are more informative and meaningful at its upper end. Finally, when traits of this type are measured in community populations, an expected result is that the items and test scores have extreme (rightly skewed) distributions, because most of the individuals are expected to have low trait levels and be piled-up at the lower end. So far, Item Response Theory models intended for unipolar traits have been limited to unidimensional measures. In particular, our previous program UNIPOL-GC is able to fit two unidimensional models of this type in which the latent trait distribution is assumed to be lognormal: (a) the Log-Logistic graded response model (LL-GRM; Reise et al. 2021) and (b) the Log-logistic continuous response model (LL-CRM; Ferrando et al. 2023), which can be viewed as transformed versions of the standard Item Response Theory (IRT) Samejima's Graded-Response and Continuous-Response Models (GRM and CRM) (see the description in UNIPOL-GC for further details)

MULTIPOL-C is a multidimensional extension of the LL-CRM above. Conceptually, it can be viewed as a transformed version of Samejima's (1974) multidimensional CRM (see also Bejar, 1977) in which the traits are all scaled to have lognormal (0,1) distributions. Details of MULTIPOL-C are provided in the accompanying guide. For the moment, MULTIPOL-C is offered as a stand-alone program. In the future, however, we plan to offer a comprehensive, unified package that integrates both unidimensional and multidimensional unipolar models for graded and continuous item scores. Multipol-C has been developed in R version 4.1.2 and has been successfully tested in previous versions such as 3.6.2. It runs in any operating system that supports R (Windows, Linux, Mac OS). If you think the program can be useful in your research or practice, please, give it a try. You can freely download here the package and a detailed manual. A working example that shows how the program functions is also included so as to be used with the X10_2F.csv database and is explained in the guide.

References

Bejar, I. I. (1977). An application of the continuous response level model to personality measurement. Applied Psychological Measurement, 1(4), 509-521.
https://doi.org/10.1177/014662167700100407

Ferrando, P. J., Morales-Vives, F., Hernandez-Dorado, A. (2023). Measuring unipolar traits with continuous-response items: Some methodological and substantive developments. Educational and Psychological Measurement.
https://doi.org/10.1177/00131644231181889

Ferrando, P. J., Morales-Vives, F., Casas J.M. & Navarro-Gonzalez, D. (2023). A multidimensional continuous response model for measuring unipolar traits (submitted).

Lucke, J. F. (2013). Positive trait item response models. In R. E. Millsap, L. A. van der Ark, D. M. Bolt, and C. M. Woods (Eds.), New developments in quantitative psychology (pp. 199-213). Springer.

Lucke, J. F. (2015). Unipolar item response models. In S. P. Reise and D. A. Revicki (Eds.), Handbook of item response theory modeling: Applications to typical performance assessment (pp. 272-284). Routledge/Taylor & Francis Group.
https://doi.org/10.4324/9781315736013

Reise, S. P., Du, H., Wong, E. F., Hubbard, A. S., & Haviland, M. G. (2021). Matching IRT models to patient-reported outcomes constructs: The graded response and log-logistic models for scaling depression. Psychometrika, 86(3), 800-824.
https://doi.org/10.1007/s11336-021-09802-0

Reise, S. P., & Waller, N. G. (2009). Item response theory and clinical measurement. Annual review of clinical psychology, 5, 27-48.
https://doi.org/10.1146/annurev.clinpsy.032408.153553