Diagnostic Classification Model for Forced-Choice Items and Noncognitive Tests

2022 ◽  
pp. 001316442110699
Author(s):  
Hung-Yu Huang
Keyword(s):  
Work Motivation ◽  
Paired Comparison ◽  
Structural Parameters ◽  
Latent Trait ◽  
Forced Choice ◽  
Classification Model ◽  
Diagnostic Classification ◽  
Model Parameters ◽  
Response Options ◽  
Response Biases

The forced-choice (FC) item formats used for noncognitive tests typically develop a set of response options that measure different traits and instruct respondents to make judgments among these options in terms of their preference to control the response biases that are commonly observed in normative tests. Diagnostic classification models (DCMs) can provide information regarding the mastery status of test takers on latent discrete variables and are more commonly used for cognitive tests employed in educational settings than for noncognitive tests. The purpose of this study is to develop a new class of DCM for FC items under the higher-order DCM framework to meet the practical demands of simultaneously controlling for response biases and providing diagnostic classification information. By conducting a series of simulations and calibrating the model parameters with a Bayesian estimation, the study shows that, in general, the model parameters can be recovered satisfactorily with the use of long tests and large samples. More attributes improve the precision of the second-order latent trait estimation in a long test, but decrease the classification accuracy and the estimation quality of the structural parameters. When statements are allowed to load on two distinct attributes in paired comparison items, the specific-attribute condition produces better a parameter estimation than the overlap-attribute condition. Finally, an empirical analysis related to work-motivation measures is presented to demonstrate the applications and implications of the new model.

scholarly journals Direct Estimation of Diagnostic Classification Model Attribute Mastery Profiles via a Collapsed Gibbs Sampling Algorithm

2020 ◽  
Author(s):  
Kazuhiro Yamaguchi ◽  
Jonathan Templin
Keyword(s):  
Gibbs Sampling ◽  
Estimation Method ◽  
Real Data ◽  
Classification Model ◽  
Diagnostic Classification ◽  
Model Parameters ◽  
Sampling Algorithm ◽  
Diagnostic Classification Models ◽  
Gibbs Sampling Algorithm ◽  
Collapsed Gibbs Sampling

This paper proposes a novel collapsed Gibbs sampling algorithm that marginalizes model parameters and directly samples latent attribute mastery patterns in diagnostic classification models. This estimation method makes it possible to avoid boundary problems in the estimation of model item parameters by eliminating the need to estimate such parameters. A simulation study showed the collapsed Gibbs sampling algorithm can accurately recover the true attribute mastery status in various conditions. In a real data analysis, the collapsed Gibbs sampling algorithm indicated good classification agreement with results from a previous study.

Identification of Structural Parameters in Mistuned Bladed Disks

1997 ◽  
Vol 119 (3) ◽  
pp. 428-438 ◽  
Author(s):  
Marc P. Mignolet ◽  
Chung-Chih Lin
Keyword(s):  
Maximum Likelihood ◽  
Least Squares ◽  
Structural Model ◽  
Structural Parameters ◽  
Model Parameters ◽  
Disk Model ◽  
Response Data ◽  
State Response ◽  
Bladed Disk ◽  
True Model

The present investigation focused on the estimation of the parameters of a structural model to represent “at best” a set of measurements of the steady state response of a mistuned bladed disk. The applicability of the least squares and maximum likelihood approaches to the identification of the bladed disk model from this data is first investigated. The advantages and drawbacks of these techniques motivate the introduction of a new mixed least squares-maximum likelihood formulation which is shown to recover well the true model parameters from noisy simulated response data.

scholarly journals Pre-print #3

2018 ◽  
Author(s):  
D. Samuel Schwarzkopf ◽  
Nonie J Finlayson ◽  
Benjamin de Haas
Keyword(s):  
Response Bias ◽  
Forced Choice ◽  
Perceptual Bias ◽  
Confounding Factors ◽  
Decision Criteria ◽  
Response Modality ◽  
Multiple Alternative ◽  
The Mean ◽  
Response Biases ◽  
Perceptual Factors

Perceptual bias is inherent to all our senses, particularly in the form of visual illusionsand aftereffects. However, many experiments measuring perceptual biases may besusceptible to non-perceptual factors, such as response bias and decision criteria. Here wequantify how robust Multiple Alternative Perceptual Search (MAPS) is for disentanglingestimates of perceptual biases from these confounding factors. First our results show thatwhile there are considerable response biases in our four-alternative forced choice design,these are unrelated to perceptual biases estimates, and these response biases are notproduced by the response modality (keyboard versus mouse). We also show that perceptualbias estimates are reduced when feedback is given on each trial, likely due to feedbackenabling observers to partially (and actively) correct for perceptual biases. However, thisdoes not impact the reliability with which MAPS detects the presence of perceptual biases.Finally, our results show that MAPS can detect actual perceptual biases and is not adecisional bias towards choosing the target in the middle of the candidate stimulusdistribution. In summary, researchers conducting a MAPS experiment should use a constantreference stimulus, but consider varying the mean of the candidate distribution. Ideally,they should not employ trial-wise feedback if the magnitude of perceptual biases is ofinterest.

An Effective Method for Determining Finite Element Model Parameters of Rotor Elastic Support System

2011 ◽  
Vol 141 ◽  
pp. 191-197
Author(s):  
Yong Xing Wang ◽  
Jiang Yan ◽  
Sheng Ze Wang
Keyword(s):  
Finite Element ◽  
Finite Element Model ◽  
Support System ◽  
Structural Parameters ◽  
Element Model ◽  
Elastic Support ◽  
Asymmetric Structure ◽  
Model Parameters ◽  
Equivalent Stiffness ◽  
Rolling Ball

A finite element model of the elastic support rotor system based on the corresponding experimental model was established. According to the principle of two types of model with an equal first order critical speed, the equivalent stiffness and damping of a rolling ball bearing support system with rubber rings determined by experiment were transferred into the finite element model. Then, the dynamic behavior of rotor systems with symmetric and asymmetric structure, different support system stiffness and support span were calculated and analyzed respectively. At last, the influence of the rotor structural parameters on the equivalent stiffness of elastic bearing support system obtained by experiment was pointed out.

scholarly journals Implementation of Spiegler–Kedem and Steric Hindrance Pore Models for Analyzing Nanofiltration Membrane Performance for Smart Water Production

Membranes ◽  
2018 ◽  
Vol 8 (3) ◽  
pp. 78 ◽  
Author(s):  
Remya Nair ◽  
Evgenia Protasova ◽  
Skule Strand ◽  
Torleiv Bilstad
Keyword(s):  
Mass Transfer ◽  
Reflection Coefficient ◽  
Oil Recovery ◽  
Water Permeability ◽  
Structural Parameters ◽  
Pure Water ◽  
Model Parameters ◽  
Water Production ◽  
Solute Permeability ◽  
Smart Water

A predictive model correlating the parameters in the mass transfer-based model Spiegler–Kedem to the pure water permeability is presented in this research, which helps to select porous polyamide membranes for enhanced oil recovery (EOR) applications. Using the experimentally obtained values of flux and rejection, the reflection coefficient σ and solute permeability Ps have been estimated as the mass transfer-based model parameters for individual ions in seawater. The reflection coefficient and solute permeability determined were correlated with the pure water permeability of a membrane, which is related to the structural parameters of a membrane. The novelty of this research is the development of a model that consolidates the various complex mechanisms in the mass transfer of ions through the membrane to an empirical correlation for a given feed concentration and membrane type. These correlations were later used to predict ion rejections of any polyamide membrane with a known pure water permeability and flux with seawater as a feed that aids in the selection of suitable nanofiltration (NF) for smart water production.

scholarly journals Human listeners’ perception of behavioural context and core affect dimensions in chimpanzee vocalizations

2020 ◽  
Vol 287 (1929) ◽  
pp. 20201148
Author(s):  
Roza G. Kamiloğlu ◽  
Katie E. Slocombe ◽  
Daniel B. M. Haun ◽  
Disa A. Sauter
Keyword(s):  
Related Species ◽  
Acoustic Analysis ◽  
Forced Choice ◽  
Choice Task ◽  
Emotional States ◽  
Response Options ◽  
Core Affect ◽  
Forced Choice Task ◽  
Affective Information ◽  
Arousal And Valence

Vocalizations linked to emotional states are partly conserved among phylogenetically related species. This continuity may allow humans to accurately infer affective information from vocalizations produced by chimpanzees. In two pre-registered experiments, we examine human listeners' ability to infer behavioural contexts (e.g. discovering food) and core affect dimensions (arousal and valence) from 155 vocalizations produced by 66 chimpanzees in 10 different positive and negative contexts at high, medium or low arousal levels. In experiment 1, listeners ( n = 310), categorized the vocalizations in a forced-choice task with 10 response options, and rated arousal and valence. In experiment 2, participants ( n = 3120) matched vocalizations to production contexts using yes/no response options. The results show that listeners were accurate at matching vocalizations of most contexts in addition to inferring arousal and valence. Judgments were more accurate for negative as compared to positive vocalizations. An acoustic analysis demonstrated that, listeners made use of brightness and duration cues, and relied on noisiness in making context judgements, and pitch to infer core affect dimensions. Overall, the results suggest that human listeners can infer affective information from chimpanzee vocalizations beyond core affect, indicating phylogenetic continuity in the mapping of vocalizations to behavioural contexts.

On the Validity of Forced Choice Scores Derived From the Thurstonian Item Response Theory Model

Assessment ◽  
2019 ◽  
Vol 27 (4) ◽  
pp. 706-718 ◽  
Author(s):  
Kate E. Walton ◽  
Lina Cherkasova ◽  
Richard D. Roberts
Keyword(s):  
Item Response Theory ◽  
Item Response ◽  
Discriminant Validity ◽  
Criterion Validity ◽  
Theory Model ◽  
Forced Choice ◽  
Response Theory ◽  
Test Criterion ◽  
Convergent And Discriminant Validity ◽  
Response Biases

Forced choice (FC) measures may be a desirable alternative to single stimulus (SS) Likert items, which are easier to fake and can have associated response biases. However, classical methods of scoring FC measures lead to ipsative data, which have a number of psychometric problems. A Thurstonian item response theory (TIRT) model has been introduced as a way to overcome these issues, but few empirical validity studies have been conducted to ensure its effectiveness. This was the goal of the current three studies, which used FC measures of domains from popular personality frameworks including the Big Five and HEXACO, and both statement and adjective item stems. We computed TIRT and ipsative scores and compared their validity estimates. Convergent and discriminant validity of the scores were evaluated by correlating them with SS scores, and test-criterion validity evidence was evaluated by examining their relationships with meaningful outcomes. In all three studies, there was evidence for the convergent and test-criterion validity of the TIRT scores, though at times this was on par with the validity of the ipsative scores. The discriminant validity of the TIRT scores was problematic and was often worse than the ipsative scores.

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