Person Parameter Estimation for IRT Models of Forced-Choice Data: Merits and Perils of Pseudo-Likelihood Approaches

2020 ◽  
pp. 31-43
Author(s):  
Safir Yousfi
Keyword(s):  
Parameter Estimation ◽  
Forced Choice ◽  
Choice Data ◽  
Irt Models ◽  
Person Parameter ◽  
Pseudo Likelihood

scholarly journals GGUM-RANK Statement and Person Parameter Estimation With Multidimensional Forced Choice Triplets

2018 ◽  
Vol 43 (3) ◽  
pp. 226-240 ◽  
Author(s):  
Philseok Lee ◽  
Seang-Hwane Joo ◽  
Stephen Stark ◽  
Oleksandr S. Chernyshenko
Keyword(s):  
Parameter Estimation ◽  
Forced Choice ◽  
Future Research ◽  
Simulation Studies ◽  
Scoring Methods ◽  
Mcmc Algorithm ◽  
High Stake ◽  
Parameter Recovery ◽  
Irt Model ◽  
Person Parameter

Historically, multidimensional forced choice (MFC) measures have been criticized because conventional scoring methods can lead to ipsativity problems that render scores unsuitable for interindividual comparisons. However, with the recent advent of item response theory (IRT) scoring methods that yield normative information, MFC measures are surging in popularity and becoming important components in high-stake evaluation settings. This article aims to add to burgeoning methodological advances in MFC measurement by focusing on statement and person parameter recovery for the GGUM-RANK (generalized graded unfolding-RANK) IRT model. Markov chain Monte Carlo (MCMC) algorithm was developed for estimating GGUM-RANK statement and person parameters directly from MFC rank responses. In simulation studies, it was examined that how the psychometric properties of statements composing MFC items, test length, and sample size influenced statement and person parameter estimation; and it was explored for the benefits of measurement using MFC triplets relative to pairs. To demonstrate this methodology, an empirical validity study was then conducted using an MFC triplet personality measure. The results and implications of these studies for future research and practice are discussed.

Person Parameter Estimation and Measurement in Rasch Models

2013 ◽  
pp. 63-78 ◽  
Author(s):  
Svend Kreiner ◽  
Karl Bang Christensen
Keyword(s):  
Parameter Estimation ◽  
Rasch Models ◽  
Person Parameter

scholarly journals Can High-Dimensional Questionnaires Resolve the Ipsativity Issue of Forced-Choice Response Formats?

2020 ◽  
pp. 001316442093486
Author(s):  
Niklas Schulte ◽  
Heinz Holling ◽  
Paul-Christian Bürkner
Keyword(s):  
Rating Scales ◽  
Forced Choice ◽  
High Dimensional ◽  
Choice Response ◽  
Scoring Methods ◽  
High Stakes ◽  
Irt Models ◽  
Response Formats ◽  
Response Biases ◽  
Very High

Forced-choice questionnaires can prevent faking and other response biases typically associated with rating scales. However, the derived trait scores are often unreliable and ipsative, making interindividual comparisons in high-stakes situations impossible. Several studies suggest that these problems vanish if the number of measured traits is high. To determine the necessary number of traits under varying sample sizes, factor loadings, and intertrait correlations, simulations were performed for the two most widely used scoring methods, namely the classical (ipsative) approach and Thurstonian item response theory (IRT) models. Results demonstrate that while especially Thurstonian IRT models perform well under ideal conditions, both methods yield insufficient reliabilities in most conditions resembling applied contexts. Moreover, not only the classical estimates but also the Thurstonian IRT estimates for questionnaires with equally keyed items remain (partially) ipsative, even when the number of traits is very high (i.e., 30). This result not only questions earlier assumptions regarding the use of classical scores in high-dimensional questionnaires, but it also raises doubts about many validation studies on Thurstonian IRT models because correlations of (partially) ipsative scores with external criteria cannot be interpreted in a usual way.

Correcting Two-Alternative Forced-Choice Data for Response Bias

1971 ◽  
Vol 32 (2) ◽  
pp. 533-534 ◽  
Author(s):  
Carl Auerbach
Keyword(s):  
Normal Distribution ◽  
Response Bias ◽  
Forced Choice ◽  
Choice Data

A method for correcting two-alternative forced-choice data for response bias is presented which requires only a table of integrals of a normal distribution.

scholarly journals Bias effects in a two-stage recognition paradigm: A challenge for ‘pure’ threshold and signal detection models

2021 ◽  
Author(s):  
Qiuli Ma ◽  
Jeffrey Joseph Starns ◽  
David Kellen
Keyword(s):  
Signal Detection ◽  
Forced Choice ◽  
Dual Process ◽  
High Threshold ◽  
Word Target ◽  
Choice Data ◽  
Two Stage ◽  
Unequal Variance ◽  
Single Item

We explored a two-stage recognition memory paradigm in which people first make single-item “studied”/“not studied” decisions and then have a chance to correct their errors in forced-choice trials. Each forced-choice trial included one studied word (“target”) and one non-studied word (“lure”) that received the same previous single-item response. For example, a “studied”-“studied” trial would have a target that was correctly called “studied” and a lure that was incorrectly called “studied.” The two-high-threshold (2HT) model and the unequal-variance signal detection (UVSD) model predict opposite effects of biasing the initial single-item responses on subsequent forced-choice accuracy. Results from two experiments showed that the bias effect is actually near zero and well out of the range of effects predicted by either model. Follow-up analyses showed that the model failures were not a function of experiment artifacts like changing memory states between the two types of recognition trials. Follow-up analyses also showed that the dual process signal detection (DPSD) model made better predictions for the forced-choice data than 2HT and UVSD models.

scholarly journals InDisc: An R Package for Assessing Person and Item Discrimination in Typical-Response Measures

2020 ◽  
Vol 44 (4) ◽  
pp. 327-328
Author(s):  
Pere J. Ferrando ◽  
David Navarro-González
Keyword(s):  
R Package ◽  
Main Function ◽  
Irt Models ◽  
Discriminating Power ◽  
Person Parameter ◽  
Attitude Measures ◽  
Continuous Responses ◽  
Response Measures ◽  
The Individual ◽  
Dual Models

InDisc is an R Package that implements procedures for estimating and fitting unidimensional Item Response Theory (IRT) Dual Models (DMs). DMs are intended for personality and attitude measures and are, essentially, extended standard IRT models with an extra person parameter that models the discriminating power of the individual. The package consists of a main function, which calls subfunctions for fitting binary, graded, and continuous responses. The program, a detailed user’s guide, and an empirical example are available at no cost to the interested practitioner.

Sign in / Sign up

Export Citation Format

Share Document