Selected Publications
DeCarlo, L. T. (2023). Classical item analysis from a signal detection perspective. Journal of Educational Measurement, DeCarlo, L. T. (2021 ). On joining a signal detection choice model with response time models. DeCarlo, L. T. (2019). Insights from reparameterized DINA and beyond. In M. von Davier, & Y.-S. Lee (Eds.), Kim, Y. K., DeCarlo, L. T., & Reshetar, R. (2014). Linking with constructed response items: A hierarchical model DeCarlo, L. T. (2013). Signal detection models for the same-different task. Journal of Mathematical Psychology, DeCarlo, L. T. (2010). Studies of a latent-class signal-detection model for constructed response scoring II: Incomplete and DeCarlo, L. T. (2008). Studies of a latent-class signal-detection model
for constructed response scoring (ETS Research Rep. DeCarlo, L. T. (2008). Process dissociation
and mixture signal detection theory. Journal of Experimental Psychology:
Learning,
60, 520-547.
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
Handbook of Diagnostic Classification Models (pp. 223-243). New York: Springer. pdf
approach with AP data. KAERA Research Forum, 1, 26-35. pdf
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. pdf
hierarchical designs (ETS Research Report No. RR-10-08). Princeton NJ: ETS. pdf
No. RR-08-63). Princeton NJ: ETS. pdf
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 Research, 72,
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 & Psychophysics, 67, 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 Cognition, 29, 767-778.
pdf
DeCarlo, L. T. (2003). Source monitoring and multivariate signal
detection theory, with a model for selection. Journal
of Mathematical
Psychology, 47, 292-303.
pdf
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,
152-162. pdf
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,
423-451. pdf
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
& Performance, 18, 1080-1088. pdf
DeCarlo, L. T. & Cross, D. V. (1990). Sequential effects
in magnitude scaling: Models and theory. Journal of Experimental Psychology:
General, 104, 375-396.
pdf
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