Estimating measures of latent variables from m-alternative forced choice responses


Autoři: Chris Bradley aff001;  Robert W. Massof aff001
Působiště autorů: Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America aff001
Vyšlo v časopise: PLoS ONE 14(11)
Kategorie: Research Article
doi: 10.1371/journal.pone.0225581

Souhrn

Signal Detection Theory is the standard method used in psychophysics to estimate person ability in m-alternative forced choice tasks where stimuli are typically generated with known physical properties (e.g., size, frequency, contrast, etc …) and lie at known locations on a physical measurement axis. In contrast, variants of Item Response Theory are preferred in fields such as medical research and educational testing where the axis locations of items on questionnaires or multiple choice tests are not defined by any observable physical property and are instead defined by a latent (or unobservable) variable. We provide an extension of Signal Detection Theory to latent variables that employs the same strategy used in Item Response Theory and demonstrate the practical utility of our method by applying it to a set of clinically relevant face perception tasks with visually impaired individuals as subjects. A key advantage of our approach is that Signal Detection Theory explicitly models the m-alternative forced choice task while Item Response Theory does not. We show that Item Response Theory is inconsistent with key assumptions of the m-alternative forced choice task and is not a valid model for this paradigm. However, the simplest Item Response Theory model–the dichotomous Rasch model–is found to be a special case of SDT and provides a good approximation as long as the number of response alternatives m is small and remains fixed for all items.

Klíčová slova:

Decision making – Emotions – Face – Physical properties – Psychophysics – Social discrimination – Vision – Visual impairments


Zdroje

1. Samejima F. Estimation of latent ability using a response pattern of graded scores. Psychometrika. 1969. Suppl 17.

2. Lord FM. Applications of item response theory to practical testing. Hillsdale NJ: Lawrence Earlbaum Associates; 1980.

3. De Boeck P & Wilson M. Explanatory Item Response Models: A Generalized Linear and Nonlinear Approach. New York: Springer; 2004.

4. Massof RW. Understanding Rasch and Item Response Theory Models: Applications to the Estimation and Validation of Interval Latent Trait Measures from Responses to Rating Scale Questionnaires. Ophthalmic Epidemiol. 2011; 18(1):1–19. https://doi.org/10.3109/09286586.2010.545501

5. Bradley C & Massof RW. Method of successive dichotomizations: an improved method for estimating measures of latent variables from rating scale data. PLoS ONE 2018; 13(10): e0206106. doi: 10.1371/journal.pone.0206106 30335832

6. Green DM & Swets JA. Signal detection theory and psychophysics. Huntington NY: Krieger; 1974.

7. DeCarlo LT. A Latent Class Extension of Signal Detection Theory, with Applications. Multivariate Behav Res. 2002; 37(4): 423–451. doi: 10.1207/S15327906MBR3704_01 26816322

8. DeCarlo LT. A Model of Rater Behavior in Essay Grading Based on Signal Detection Theory. J Educ Meas. 2005; 42(1): 53–76. https://doi.org/10.1111/j.0022-0655.2005.00004.x

9. DeCarlo LT. A Hierarchical Rater Model for Constructed Responses, with a Signal Detection Rater Model. J Educ Meas. 2011; 48(3); 333–356. https://doi.org/10.1111/j.1745-3984.2011.00143.x

10. Birnbaum A. Some latent trait models and their use in inferring an examinee's ability. In Lord FM & Novick MR (Eds). Statistical Theories of Mental Test Scores (394–479). Reading, MA: Addison-Wesley; 1968.

11. Hacker MJ & Ratcliff R. A revised table of d' for M-alternative forced choice. Percept. Psychophys. 1979; 26:168–170. https://doi.org/10.3758/BF03208311

12. Rasch G. Probabilistic models for some intelligence and attainment tests. In: Neyman J, editor. Proceedings of the Fourth Berkeley symposium on Mathematical Statistics and Probability. Berkeley: University of California Press; 1961. Vol. IV. pp. 321–334.

13. Brown LD, Cai TT, DasGupta A. Interval Estimation for a Binomial Proportion. Statist. Sci. 2001; 16(2):101–133. https://doi.org/10.1214/ss/1009213286

14. Bradley C. Extending Signal Detection Theory to Latent Variables. https://sourceforge.net/projects/sdt-latent/files/

15. Lundqvist D, Flykt A, Öhman A. 1998. The Karolinska Directed Emotional Faces–KDEF, CD ROM from Department of Clinical Neuroscience, Psychology section, Karolinska Institutet, ISBN 91-630-7164-9.

16. Goeleven E, De Raedt R, Leyman L, Vershuere B. The Karolinska Directed Emotional Faces: A validation study. Cogn. Emot. 2008; 22(6):1094–1118. https://doi.org/10.1080/02699930701626582

17. Deemer AD, Swenor BK, Fujiwara K, Deremeik JT, Ross NC, Nicole RC, Bradley C, et al. Preliminary Evaluation of Two Digital Image Processing Strategies for Head Mounted Magnification for Low Vision Patients. Tranl. Vis. Sci. Techn. 2019; 8(23). https://doi.org/10.1167/tvst.8.1.23

18. Macmillan NA & Creelman CD. Detection theory: A user's guide (2nd ed.). Mahwah, NJ: Earlbaum; 2005.

19. Petrov AA. Symmetry-based methodology for decision-rule identification in same–different experiments. Psychon B Rev. 2009; 16(6); 1011–1025. https://doi.org/10.3758/PBR.16.6.1011

20. Brown SD & Heathcote AJ. The simplest complete model of choice reaction time: Linear ballistic accumulation. Cogn. Psychol. 2008; 57; 153–178. doi: 10.1016/j.cogpsych.2007.12.002 18243170

21. Peli E, Goldstein R, Young B, Trempe C, Buzney S. Image enhancement for the visually impaired: Simulations and experimental results. Investigat. Ophthalmol. 1991; 32:2337–2350.

22. Boucart M, Dinon J-F, Despretz P, Desmettre T, Hladiuk K, Oliva A. Recognition of facial emotion in low vision: A flexible usage of facial features. Vis. Neurosci. 2008; 25(4):603–609. doi: 10.1017/S0952523808080656 18631411

23. Mienaltowski A, Johnson ER, Wittman R, Wilson A-T, Sturycz C, Norman J. The visual discrimination of negative facial expressions by younger and older adults. Vis Res. 2013; 81:12–17. https://doi.org/10.1016/j.visres.2013.01.006


Článek vyšel v časopise

PLOS One


2019 Číslo 11