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Lawrence T. DeCarlo

Professional Background

Educational Background

B.A. (Psychology) SUNY at Stony Brook; M.S. (Biostatistics) Columbia University; Ph.D. (Psychology) SUNY at Stony Brook

Scholarly Interests

Statistical modeling of psychological processes, measurement, latent class analysis, categorical data analysis, structural equation modeling, multilevel and longitudinal data analysis, item response theory.

Selected Publications

publications

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

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 correction

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
 
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. pdf

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. pdf

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 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

HUDM 4050: Introduction to measurement

An introduction to basic concepts and issues in measurement. Descriptive statistics, scales of measurement, norms, reliability, validity. Advantages and limitations of measurement techniques are discussed and illustrated.

HUDM 5059: Psychological measurement

Open to doctoral and Ed.M. students in psychology; others only by permission. A previous course in statistics or measurement is recommended. An in-depth examination of measurement and associated techniques, norms, classical test theory, reli-ability, validity, item response theory, issues, and applications.

HUDM 6030: Multilevel longitudinal data analysis

Prerequisite: HUDM 5122. Multilevel models include a broad range of models called by various names, such as random effects models, multi-level models, and growth curve models. This course introduces the background and computer skills needed to understand and utilize these models.

HUDM 6055: Latent structure analysis

Permission required. Prerequisite: HUDM 5122. Recommended: HUDM 6122. Study of latent structure analysis, including measurement models for latent traits and latent classes, path analysis, factor analysis, structural equations, and categorical data analysis.

HUDM 6122: Multivariate analysis I

Permission required. Prerequisite: HUDM 5122 or equivalent; HUDM 5123 is recommended. An introduction to multivariate statistical analysis, including matrix algebra, general linear hypothesis and application, profile analysis, principal components analysis, discriminant analysis, and classification methods.

HUDM 6123: Multivariate analysis II

Permission required. Prerequisite: HUDM 6122. A continuation of multivariate statistical analysis, including canonical analysis, MANOVA, factor analysis, and categorical data analysis.

Documents & Papers

Lawrence T. DeCarlo appeared in the following articles:

When Less is More (3/22/2011)

Employee News (2/11/2011)

Staffing News (12/17/2010)

Calendar (3/24/2009)

TC at AERA, 2008 (3/25/2008)

Teachers College Welcomes Sixteen New Faculty (8/1/1998)