Selected Publications

DeCarlo, L. T. (2023). Classical item analysis from a signal detection perspective. Journal of Educational Measurement,
    60, 520-547.

DeCarlo, L. T. (2021 ). On joining a signal detection choice model with response time models.
     Journal of Educational Measurement, 58, 438-464.

DeCarlo, L. T. (2021). A signal detection model for multiple-choice exams. Applied Psychological Measurement,
    45, 423-440.   supplementary_material    typo_corrections

DeCarlo, L. T., & Zhou, X. (2021). A latent class signal detection model for rater scoring with ordered
    perceptual distributions. Journal of Educational Measurement, 58, 31-53.
   typo corrections

DeCarlo, L. T. (2020). An item response model for true-false exams based on signal detection theory.
    Applied Psychological Measurement, 44, 215-229.  
pdf     supplementary_material
      Here's the Algebra data!
y1-y16 are raw data with 1=true, 2=false, yc1-yc16 are scores with 1=correct 0=incorrect   Algebra data
      Here's a Latent Gold program to fit the model given in DeCarlo (2020) and data in the appropriate form, in this case with y=1 for true and
      0 for false; z=1 for true and -1 for false:  Algebra_IRSDT program    algebra_IRSDT.dat 

      Notes: the program uses the logistic-normal distribution instead of beta, but compare the results to the 2020 Table 1 (Bayesian estimation),
      they are very similar. Note that the signs of the bias are reversed from those shown in Table 1 because in the current implementation a
      positive b indicates bias towards a response of TRUE, wheres in Table 1 positive value indicates a bias towards FALSE.

      Here's a Latent Gold program to fit the model as an SDT choice model, as described in DeCarlo 2021, with choices of 1 for true and 2 for false;
      the two choices are treated on separate lines (see case) and 0/1 is used to indicate the choice: 1 on the first line is True and 1 on the 2nd line is false.
      The logistic-normal distribution is again used, the results are identical to the IRSDT results. Algebra choice program   algebra choice dat

DeCarlo, L. T. (2019). Insights from reparameterized DINA and beyond. In M. von Davier, & Y.-S. Lee (Eds.),
    Handbook of Diagnostic Classification Models (pp. 223-243). New York: Springer.

Kim, Y. K., DeCarlo, L. T., & Reshetar, R. (2014). Linking with constructed response items: A hierarchical model
    approach with AP data
. KAERA Research Forum, 1, 26-35.

DeCarlo, L. T. (2013). Signal detection models for the same-different task. Journal of Mathematical Psychology,
    57, 43-51. pdf   typo correction

DeCarlo, L. T. (2012). Recognizing uncertainty in the Q-matrix via a Bayesian extension of the DINA model.
    Applied Psychological Measurement, 36, 447-468. pdf

DeCarlo, L. T. (2012). On a signal detection approach to m-alternative forced choice with bias, with maximum
     likelihood and Bayesian approaches to estimation. Journal of Mathematical Psychology, 56, 196-207.
     pdf   typo corrections  new Table 3   corrected SAS program  Ennis SS data for SAS
     R programs to fit multinomial logit and probit models    Ennis SS data for GMNL    Ennis SS data for MNP

     For the multinomial logit model, the parameters will be about 1.28 larger than for the normal.
     The multinomial probit allows for nonzero correlations and different variances, check the package to
      see how the parameters are normalized

DeCarlo, L. T., Kim, Y. K., & Johnson, M. S. (2011). A hierarchical rater model for constructed responses, with a
     signal detection rater model. Journal of Educational Measurement, 48, 333-356. pdf

DeCarlo, L. T. (2011). Signal detection theory with item effects. Journal of Mathematical Psychology, 55, 229-239. pdf

DeCarlo, L. T. (2011). On the analysis of fraction subtraction data: The DINA model, classification, latent class sizes,
     and the Q-matrix. Applied Psychological Measurement, 35, 8-26. pdf     typo corrections

O'Connell, K. A., Shiffman, S., & DeCarlo, L. T. (2011). Does extinction of responses to cigarette cues occur during
     smoking cessation? Addiction, 106, 410-417.

DeCarlo, L. T. (2010). On the statistical and theoretical basis of signal detection theory and extensions: Unequal variance,
     random coefficient, and mixture models. Journal of Mathematical Psychology, 54, 304-313.

DeCarlo, L. T. (2010). Studies of a latent-class signal-detection model for constructed response scoring II: Incomplete and
     hierarchical designs
(ETS Research Report No. RR-10-08). Princeton NJ: ETS.

DeCarlo, L. T. (2008). Studies of a latent-class signal-detection model for constructed response scoring (ETS Research Rep.
     No. RR-08-63). Princeton NJ: ETS. 

DeCarlo, L. T. (2008). Process dissociation and mixture signal detection theory. Journal of Experimental Psychology: Learning,
     Memory, and Cognition
34, 1565-1572. pdf

Teghtsoonian, M., Teghtsoonian, R., & DeCarlo, L. T. (2008). The influence of trial-to trial recalibration on sequential effects in
     cross-modality matching. Psychological Research72, 115-122. pdf

DeCarlo, L. T. (2007). The mirror effect and mixture signal detection theory. Journal of Experimental Psychology: Learning, Memory,
     and Cognition
33, 18-33. pdf

DeCarlo, L. T. (2006). Sequential effects in successive ratio estimation. Perception & Psychophysics, 68, 861-871. pdf

DeCarlo, L. T. (2005). A model of rater behavior in essay grading based on signal detection theory. Journal of Educational Measurement,
     42, 53-76. pdf
    typo correction

DeCarlo, L. T. (2005). On bias in magnitude scaling and some conjectures of Stevens. Perception & Psychophysics67, 886-896. pdf

DeCarlo, L. T. (2003). An application of signal detection theory with finite mixture distributions to source discrimination. Journal of
     Experimental Psychology
: Learning, Memory, and Cognition29, 767-778.

DeCarlo, L. T. (2003). Source monitoring and multivariate signal detection theory, with a model for selection. Journal of Mathematical
47, 292-303.

DeCarlo, L. T. (2003). Using the PLUM procedure of SPSS to fit unequal variance and generalized signal detection models. Behavior
     Research Methods
, Instruments, &
Computers, 35, 49-56. pdf

DeCarlo, L. T. (2003). An application of a dynamic model of judgment to magnitude production. Perception & Psychophysics, 65,

DeCarlo, L. T. (2002). Signal detection theory with finite mixture distributions: Theoretical developments with applications to recognition
     memory. Psychological Review, 109,
710-721. pdf

DeCarlo, L. T. (2002). A latent class extension of signal detection theory, with applications. Multivariate Behavioral Research, 37,

DeCarlo, L. T., & Luthar, S. S. (2000). Analysis and class validation of a measure of parental values perceived by early adolescents: An
     application of a latent class model for rankings. Educational and Psychological Measurement, 60, 578-591. pdf

DeCarlo, L. T. (1998). Signal detection theory and generalized linear models. Psychological Methods, 3, 186-205. pdf    typo correction

DeCarlo, L. T. (1997). On the meaning and use of kurtosis. Psychological Methods, 2, 292-307. pdf

DeCarlo, L. T. (1994). A dynamic theory of proportional judgment: Context and judgment of length, heaviness, and roughness. Journal of
     Experimental Psychology: Human
Perception & Performance, 20, 372-381. pdf

DeCarlo, L. T., & Tryon, W. W. (1993). Estimating and testing autocorrelation with small samples: A comparison of the C-statistic to a
modified estimator. Behavior Research and Therapy,31, 781-788. pdf

DeCarlo, L. T. (1992). Intertrial interval and sequential effects in magnitude scaling. Journal of Experimental Psychology: Human Perception
     & Pe
rformance, 18, 1080-1088. pdf

DeCarlo, L. T. & Cross, D. V. (1990). Sequential effects in magnitude scaling: Models and theory. Journal of Experimental Psychology:
104, 375-396.