A Signal Detection Model for Multiple-Choice Exams

2021 ◽  
pp. 014662162110145
Author(s):  
Lawrence T. DeCarlo
Keyword(s):  
Signal Detection ◽  
Choice Model ◽  
Multiple Choice ◽  
Multinomial Logit Model ◽  
Model Parameters ◽  
Data Set ◽  
Detection Model ◽  
Grade Of Membership ◽  
Random Mixing ◽  
Multiple Choice Exam

A model for multiple-choice exams is developed from a signal-detection perspective. A correct alternative in a multiple-choice exam can be viewed as being a signal embedded in noise (incorrect alternatives). Examinees are assumed to have perceptions of the plausibility of each alternative, and the decision process is to choose the most plausible alternative. It is also assumed that each examinee either knows or does not know each item. These assumptions together lead to a signal detection choice model for multiple-choice exams. The model can be viewed, statistically, as a mixture extension, with random mixing, of the traditional choice model, or similarly, as a grade-of-membership extension. A version of the model with extreme value distributions is developed, in which case the model simplifies to a mixture multinomial logit model with random mixing. The approach is shown to offer measures of item discrimination and difficulty, along with information about the relative plausibility of each of the alternatives. The model, parameters, and measures derived from the parameters are compared to those obtained with several commonly used item response theory models. An application of the model to an educational data set is presented.

Multiple-Choice Tests: Polytomous IRT Models Misestimate Item Information

2014 ◽  
Vol 17 ◽  
Author(s):  
Miguel A. García-Pérez
Keyword(s):  
Empirical Data ◽  
Choice Model ◽  
Multiple Choice ◽  
Data Sets ◽  
Data Set ◽  
Information Functions ◽  
Multiple Choice Tests ◽  
Test Information ◽  
Choice Tests ◽  
Polytomous Models

AbstractLikert-type items and polytomous models are preferred over yes–no items and dichotomous models for the measurement of attitudes, because a broader range of response categories provides superior item and test information functions. Yet, for ability assessment with multiple-choice tests, the dichotomous three-parameter logistic model (3PLM) is often chosen. Because multiple-choice responses are polytomous before they are categorized as correct or incorrect, a polytomous characterization might render more efficient tests. Early studies suggested that the nominal response model (NRM) is advantageous in this respect. We investigate the reasons for those results and the outcomes of a polytomous characterization based on the multiple-choice model (MCM). An empirical data set is used to compare polytomous (NRM and MCM) and dichotomous (3PLM) characterizations of a test. The results revealed superior item and test information functions from polytomous models. Yet, close inspection suggests that these outcomes are artifactual and two simulation studies confirmed this point. These studies revealed a structural inadequacy of the NRM for multiple-choice items and that the MCM characterization outperforms the 3PLM characterization only when distractor endorsement frequencies vary non-monotonically with ability, although this feature is rarely observed in empirical data sets.

Constructing and model-fitting receiver operator characteristics using continuous data.

2018 ◽  
Author(s):  
Josephine Ann Urquhart ◽  
Akira O'Connor
Keyword(s):  
Signal Detection ◽  
Least Squares Method ◽  
Model Fitting ◽  
Simulated Data ◽  
Continuous Variable ◽  
Continuous Data ◽  
Model Parameters ◽  
Operating Characteristics ◽  
Unequal Variance ◽  
Discrete Discrimination

Receiver operating characteristics (ROCs) are plots which provide a visual summary of a classifier’s decision response accuracy at varying discrimination thresholds. Typical practice, particularly within psychological studies, involves plotting an ROC from a limited number of discrete thresholds before fitting signal detection parameters to the plot. We propose that additional insight into decision-making could be gained through increasing ROC resolution, using trial-by-trial measurements derived from a continuous variable, in place of discrete discrimination thresholds. Such continuous ROCs are not yet routinely used in behavioural research, which we attribute to issues of practicality (i.e. the difficulty of applying standard ROC model-fitting methodologies to continuous data). Consequently, the purpose of the current article is to provide a documented method of fitting signal detection parameters to continuous ROCs. This method reliably produces model fits equivalent to the unequal variance least squares method of model-fitting (Yonelinas et al., 1998), irrespective of the number of data points used in ROC construction. We present the suggested method in three main stages: I) building continuous ROCs, II) model-fitting to continuous ROCs and III) extracting model parameters from continuous ROCs. Throughout the article, procedures are demonstrated in Microsoft Excel, using an example continuous variable: reaction time, taken from a single-item recognition memory. Supplementary MATLAB code used for automating our procedures is also presented in Appendix B, with a validation of the procedure using simulated data shown in Appendix C.

EXPONENTIATED HALF-LOGISTIC LOMAX DISTRIBUTION WITH PROPERTIES AND APPLICATION

2019 ◽  
Vol XVI (2) ◽  
pp. 1-11
Author(s):  
Farrukh Jamal ◽  
Hesham Mohammed Reyad ◽  
Soha Othman Ahmed ◽  
Muhammad Akbar Ali Shah ◽  
Emrah Altun
Keyword(s):  
Real Data ◽  
Continuous Model ◽  
Model Parameters ◽  
Data Set ◽  
Lomax Distribution ◽  
Mathematical Properties ◽  
Proposed Model ◽  
Probability Weighted Moment ◽  
Record Statistics ◽  
Maximum Likelihood Criterion

A new three-parameter continuous model called the exponentiated half-logistic Lomax distribution is introduced in this paper. Basic mathematical properties for the proposed model were investigated which include raw and incomplete moments, skewness, kurtosis, generating functions, Rényi entropy, Lorenz, Bonferroni and Zenga curves, probability weighted moment, stress strength model, order statistics, and record statistics. The model parameters were estimated by using the maximum likelihood criterion and the behaviours of these estimates were examined by conducting a simulation study. The applicability of the new model is illustrated by applying it on a real data set.

A dynamic simulator for the ergonomics evaluation of powered torque tools for human assembly

2017 ◽  
Vol 37 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Haluk Ay ◽  
Anthony Luscher ◽  
Carolyn Sommerich
Keyword(s):  
Risk Factors ◽  
Joint Torque ◽  
Human Hand ◽  
Model Parameters ◽  
Test Rig ◽  
Musculoskeletal Discomfort ◽  
Data Set ◽  
Content Type ◽  
Working Posture ◽  
Displacement Measurements

Purpose The purpose of this study is to design and develop a testing device to simulate interaction between human hand–arm dynamics, right-angle (RA) computer-controlled power torque tools and joint-tightening task-related variables. Design/methodology/approach The testing rig can simulate a variety of tools, tasks and operator conditions. The device includes custom data-acquisition electronics and graphical user interface-based software. The simulation of the human hand–arm dynamics is based on the rig’s four-bar mechanism-based design and mechanical components that provide adjustable stiffness (via pneumatic cylinder) and mass (via plates) and non-adjustable damping. The stiffness and mass values used are based on an experimentally validated hand–arm model that includes a database of model parameters. This database is with respect to gender and working posture, corresponding to experienced tool operators from a prior study. Findings The rig measures tool handle force and displacement responses simultaneously. Peak force and displacement coefficients of determination (R2) between rig estimations and human testing measurements were 0.98 and 0.85, respectively, for the same set of tools, tasks and operator conditions. The rig also provides predicted tool operator acceptability ratings, using a data set from a prior study of discomfort in experienced operators during torque tool use. Research limitations/implications Deviations from linearity may influence handle force and displacement measurements. Stiction (Coulomb friction) in the overall rig, as well as in the air cylinder piston, is neglected. The rig’s mechanical damping is not adjustable, despite the fact that human hand–arm damping varies with respect to gender and working posture. Deviations from these assumptions may affect the correlation of the handle force and displacement measurements with those of human testing for the same tool, task and operator conditions. Practical implications This test rig will allow the rapid assessment of the ergonomic performance of DC torque tools, saving considerable time in lineside applications and reducing the risk of worker injury. DC torque tools are an extremely effective way of increasing production rate and improving torque accuracy. Being a complex dynamic system, however, the performance of DC torque tools varies in each application. Changes in worker mass, damping and stiffness, as well as joint stiffness and tool program, make each application unique. This test rig models all of these factors and allows quick assessment. Social implications The use of this tool test rig will help to identify and understand risk factors that contribute to musculoskeletal disorders (MSDs) associated with the use of torque tools. Tool operators are subjected to large impulsive handle reaction forces, as joint torque builds up while tightening a fastener. Repeated exposure to such forces is associated with muscle soreness, fatigue and physical stress which are also risk factors for upper extremity injuries (MSDs; e.g. tendinosis, myofascial pain). Eccentric exercise exertions are known to cause damage to muscle tissue in untrained individuals and affect subsequent performance. Originality/value The rig provides a novel means for quantitative, repeatable dynamic evaluation of RA powered torque tools and objective selection of tightening programs. Compared to current static tool assessment methods, dynamic testing provides a more realistic tool assessment relative to the tool operator’s experience. This may lead to improvements in tool or controller design and reduction in associated musculoskeletal discomfort in operators.

scholarly journals The use of remote sensing satellite using deep learning in emergency monitoring of high-level landslides disaster in Jinsha River

2021 ◽  
Author(s):  
Leijin Long ◽  
Feng He ◽  
Hongjiang Liu
Keyword(s):  
Remote Sensing ◽  
Southwest China ◽  
Influence Factors ◽  
Classification Error ◽  
Model Parameters ◽  
Detection Accuracy ◽  
Remote Sensing Images ◽  
Jinsha River ◽  
Detection Model ◽  
High Level

AbstractIn order to monitor the high-level landslides frequently occurring in Jinsha River area of Southwest China, and protect the lives and property safety of people in mountainous areas, the data of satellite remote sensing images are combined with various factors inducing landslides and transformed into landslide influence factors, which provides data basis for the establishment of landslide detection model. Then, based on the deep belief networks (DBN) and convolutional neural network (CNN) algorithm, two landslide detection models DBN and convolutional neural-deep belief network (CDN) are established to monitor the high-level landslide in Jinsha River. The influence of the model parameters on the landslide detection results is analyzed, and the accuracy of DBN and CDN models in dealing with actual landslide problems is compared. The results show that when the number of neurons in the DBN is 100, the overall error is the minimum, and when the number of learning layers is 3, the classification error is the minimum. The detection accuracy of DBN and CDN is 97.56% and 97.63%, respectively, which indicates that both DBN and CDN models are feasible in dealing with landslides from remote sensing images. This exploration provides a reference for the study of high-level landslide disasters in Jinsha River.

scholarly journals Bayesian Inference of Species Trees using Diffusion Models

2020 ◽  
Vol 70 (1) ◽  
pp. 145-161 ◽  
Author(s):  
Marnus Stoltz ◽  
Boris Baeumer ◽  
Remco Bouckaert ◽  
Colin Fox ◽  
Gordon Hiscott ◽  
...  
Keyword(s):  
Bayesian Inference ◽  
Numerical Algorithms ◽  
Diffusion Models ◽  
Model Parameters ◽  
Data Sets ◽  
Species Trees ◽  
Computationally Efficient ◽  
Data Set ◽  
Snp Data ◽  
Binary Markers

Abstract We describe a new and computationally efficient Bayesian methodology for inferring species trees and demographics from unlinked binary markers. Likelihood calculations are carried out using diffusion models of allele frequency dynamics combined with novel numerical algorithms. The diffusion approach allows for analysis of data sets containing hundreds or thousands of individuals. The method, which we call Snapper, has been implemented as part of the BEAST2 package. We conducted simulation experiments to assess numerical error, computational requirements, and accuracy recovering known model parameters. A reanalysis of soybean SNP data demonstrates that the models implemented in Snapp and Snapper can be difficult to distinguish in practice, a characteristic which we tested with further simulations. We demonstrate the scale of analysis possible using a SNP data set sampled from 399 fresh water turtles in 41 populations. [Bayesian inference; diffusion models; multi-species coalescent; SNP data; species trees; spectral methods.]

scholarly journals Maximum Likelihood Estimation of the VAR(1) Model Parameters with Missing Observations

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Helena Mouriño ◽  
Maria Isabel Barão
Keyword(s):  
Stochastic Process ◽  
Missing Data ◽  
Maximum Likelihood ◽  
Moving Average ◽  
Practical Importance ◽  
Likelihood Estimation ◽  
Model Parameters ◽  
Missing Observations ◽  
Data Set ◽  
The Impact

Missing-data problems are extremely common in practice. To achieve reliable inferential results, we need to take into account this feature of the data. Suppose that the univariate data set under analysis has missing observations. This paper examines the impact of selecting an auxiliary complete data set—whose underlying stochastic process is to some extent interdependent with the former—to improve the efficiency of the estimators for the relevant parameters of the model. The Vector AutoRegressive (VAR) Model has revealed to be an extremely useful tool in capturing the dynamics of bivariate time series. We propose maximum likelihood estimators for the parameters of the VAR(1) Model based on monotone missing data pattern. Estimators’ precision is also derived. Afterwards, we compare the bivariate modelling scheme with its univariate counterpart. More precisely, the univariate data set with missing observations will be modelled by an AutoRegressive Moving Average (ARMA(2,1)) Model. We will also analyse the behaviour of the AutoRegressive Model of order one, AR(1), due to its practical importance. We focus on the mean value of the main stochastic process. By simulation studies, we conclude that the estimator based on the VAR(1) Model is preferable to those derived from the univariate context.

A new parameter set for anisotropic multiparameter full-waveform inversion and application to a North Sea data set

Geophysics ◽  
2016 ◽  
Vol 81 (4) ◽  
pp. U25-U38 ◽  
Author(s):  
Nuno V. da Silva ◽  
Andrew Ratcliffe ◽  
Vetle Vinje ◽  
Graham Conroy
Keyword(s):  
North Sea ◽  
Waveform Inversion ◽  
Real Data ◽  
Model Parameters ◽  
Full Waveform Inversion ◽  
Radiation Patterns ◽  
Data Set ◽  
Full Waveform ◽  
Seismic Image ◽  
Central North Sea

Parameterization lies at the center of anisotropic full-waveform inversion (FWI) with multiparameter updates. This is because FWI aims to update the long and short wavelengths of the perturbations. Thus, it is important that the parameterization accommodates this. Recently, there has been an intensive effort to determine the optimal parameterization, centering the fundamental discussion mainly on the analysis of radiation patterns for each one of these parameterizations, and aiming to determine which is best suited for multiparameter inversion. We have developed a new parameterization in the scope of FWI, based on the concept of kinematically equivalent media, as originally proposed in other areas of seismic data analysis. Our analysis is also based on radiation patterns, as well as the relation between the perturbation of this set of parameters and perturbation in traveltime. The radiation pattern reveals that this parameterization combines some of the characteristics of parameterizations with one velocity and two Thomsen’s parameters and parameterizations using two velocities and one Thomsen’s parameter. The study of perturbation of traveltime with perturbation of model parameters shows that the new parameterization is less ambiguous when relating these quantities in comparison with other more commonly used parameterizations. We have concluded that our new parameterization is well-suited for inverting diving waves, which are of paramount importance to carry out practical FWI successfully. We have demonstrated that the new parameterization produces good inversion results with synthetic and real data examples. In the latter case of the real data example from the Central North Sea, the inverted models show good agreement with the geologic structures, leading to an improvement of the seismic image and flatness of the common image gathers.

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