scholarly journals A Response-Time-Based Latent Response Mixture Model for Identifying and Modeling Careless and Insufficient Effort Responding in Survey Data

Psychometrika ◽  
2021 ◽  
Author(s):  
Esther Ulitzsch ◽  
Steffi Pohl ◽  
Lale Khorramdel ◽  
Ulf Kroehne ◽  
Matthias von Davier
Keyword(s):  
Response Time ◽  
Response Patterns ◽  
Response Behavior ◽  
Data Set ◽  
Parameter Recovery ◽  
Model Based ◽  
Proposed Model ◽  
Item Level ◽  
Different Types ◽  
Theoretical Considerations

AbstractCareless and insufficient effort responding (C/IER) can pose a major threat to data quality and, as such, to validity of inferences drawn from questionnaire data. A rich body of methods aiming at its detection has been developed. Most of these methods can detect only specific types of C/IER patterns. However, typically different types of C/IER patterns occur within one data set and need to be accounted for. We present a model-based approach for detecting manifold manifestations of C/IER at once. This is achieved by leveraging response time (RT) information available from computer-administered questionnaires and integrating theoretical considerations on C/IER with recent psychometric modeling approaches. The approach a) takes the specifics of attentive response behavior on questionnaires into account by incorporating the distance–difficulty hypothesis, b) allows for attentiveness to vary on the screen-by-respondent level, c) allows for respondents with different trait and speed levels to differ in their attentiveness, and d) at once deals with various response patterns arising from C/IER. The approach makes use of item-level RTs. An adapted version for aggregated RTs is presented that supports screening for C/IER behavior on the respondent level. Parameter recovery is investigated in a simulation study. The approach is illustrated in an empirical example, comparing different RT measures and contrasting the proposed model-based procedure against indicator-based multiple-hurdle approaches.

scholarly journals Modeling Conditional Dependence of Response Accuracy and Response Time with the Diffusion Item Response Theory Model

2021 ◽  
Author(s):  
Inhan Kang ◽  
Paul De Boeck ◽  
Roger Ratcliff
Keyword(s):  
Response Time ◽  
Item Difficulty ◽  
Theory Model ◽  
Cognitive Capacity ◽  
Extended Model ◽  
Response Accuracy ◽  
Parameter Recovery ◽  
Conditional Dependence ◽  
Proposed Model ◽  
Conditional Dependency

In this paper, we propose a model-based method to study conditional dependence be- tween response accuracy and response time (RT) with the diffusion IRT model. To this end, we extend the previously proposed model by introducing variability across persons and items in cognitive capacity and in the initial bias of the response processes. We show that the extended model can explain the behavioral patterns of conditional dependency found in the previous studies in psychometrics. The first variability component in cognitive capacity can predict positive and negative conditional dependency and their interaction with the item difficulty. The second variability in the initial bias can account for the early changes in the response accuracy as a function of RTs given the person and item effects, producing the curvilinear conditional accuracy functions. We also provide a simulation study to validate the parameter recovery of the proposed model and two empirical applications to describe how to implement the model to study conditional dependency underlying data response accuracy and RTs.

scholarly journals Estimating Model-Based Oral Reading Fluency: A Bayesian Approach

2020 ◽  
Vol 80 (5) ◽  
pp. 847-869
Author(s):  
Yusuf Kara ◽  
Akihito Kamata ◽  
Cornelis Potgieter ◽  
Joseph F. T. Nese
Keyword(s):  
Reading Fluency ◽  
Oral Reading ◽  
Latent Variable ◽  
Oral Reading Fluency ◽  
Assessment System ◽  
Read Aloud ◽  
Data Set ◽  
Model Based ◽  
Proposed Model ◽  
Fourth Grade Students

Oral reading fluency (ORF), used by teachers and school districts across the country to screen and progress monitor at-risk readers, has been documented as a good indicator of reading comprehension and overall reading competence. In traditional ORF administration, students are given one minute to read a grade-level passage, after which the assessor calculates the words correct per minute (WCPM) fluency score by subtracting the number of incorrectly read words from the total number of words read aloud. As part of a larger effort to develop an improved ORF assessment system, this study expands on and demonstrates the performance of a new model-based estimate of WCPM based on a recently developed latent-variable psychometric model of speed and accuracy for ORF data. The proposed method was applied to a data set collected from 58 fourth-grade students who read four passages (a total of 260 words). The proposed model-based WCPM scores were also evaluated through a simulation study with respect to sample size and number of passages read.

scholarly journals A Novel Spectral Clustering based on Local Distribution

2015 ◽  
Vol 5 (2) ◽  
pp. 361
Author(s):  
Jyotsna Kumar Mandal ◽  
Parthajit Roy
Keyword(s):  
Outlier Detection ◽  
Spectral Clustering ◽  
Second Phase ◽  
Local Distribution ◽  
Data Set ◽  
Benchmark Data ◽  
Model Based ◽  
Clustering Model ◽  
Proposed Model ◽  
Novel Variation

This paper proposed a novel variation of spectral clustering model based on a novel affinitymetric that considers the distribution of the neighboring points to learn the underlayingstructures in the data set. Proposed affinity metric is calculated using Mahalanobis distancethat exploits the concept of outlier detection for identifying the neighborhoods of the datapoints. RandomWalk Laplacian of the representative graph and its spectra has been consideredfor the clustering purpose and the first k number of eigenvectors have been consideredin the second phase of clustering. The model has been tested with benchmark data and thequality of the output of the proposed model has been tested in various clustering indicesscales.

scholarly journals Multi-energy load forecasting model based on bi-directional gated recurrent unit multi-task neural network

2021 ◽  
Vol 256 ◽  
pp. 02032
Author(s):  
Zhijie Zheng ◽  
Liang Feng ◽  
Xuan Wang ◽  
Rui Liu ◽  
Xian Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Input Data ◽  
Load Forecasting ◽  
Energy System ◽  
Forecasting Model ◽  
Data Set ◽  
Model Based ◽  
Different Types ◽  
Integrated Energy System ◽  
Gated Recurrent Unit

The complex coupling, coordination and complementarity of different energy in the integrated energy system puts forward higher requirements for the technology of multi-energy load forecasting. To this end, this paper proposes a novel multi-energy load forecasting model based on bi-directional gated recurrent unit (BiGRU) multi-task neural network. Firstly, through the correlation analysis, an effective multi-energy load input data set is constructed. Secondly, the input data set is utilized to train the BiGRU and master the evolution laws of multi-energy loads. Then, multi-task learning (MTL) is used to share the information learned by BiGRU from perspectives of different load forecasting tasks, so as to fully dig the coupling relations among various energy loads. Finally, different types of load forecasting results can be obtained. Simulation results show that BiGRU can simultaneously consider the known data of the past and the future, and it can learn more characteristic information effectively. At the same time, the proposed model utilizes MTL to carry out parallel learning and information sharing for forecasting tasks of various energy loads, which can dig the complex coupling relations among different types of loads more deeply, thus improving the forecasting accuracy of multi-energy loads.

scholarly journals A New Text Classification Model Based on Contrastive Word Embedding for Detecting Cybersecurity Intelligence in Twitter

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1527 ◽  
Author(s):  
Han-Sub Shin ◽  
Hyuk-Yoon Kwon ◽  
Seung-Jin Ryu
Keyword(s):  
Deep Learning ◽  
Text Classification ◽  
Area Under The Curve ◽  
Word Embedding ◽  
Classification Model ◽  
Data Set ◽  
Feature Vectors ◽  
Model Based ◽  
Proposed Model ◽  
The Difference

Detecting cybersecurity intelligence (CSI) on social media such as Twitter is crucial because it allows security experts to respond cyber threats in advance. In this paper, we devise a new text classification model based on deep learning to classify CSI-positive and -negative tweets from a collection of tweets. For this, we propose a novel word embedding model, called contrastive word embedding, that enables to maximize the difference between base embedding models. First, we define CSI-positive and -negative corpora, which are used for constructing embedding models. Here, to supplement the imbalance of tweet data sets, we additionally employ the background knowledge for each tweet corpus: (1) CVE data set for CSI-positive corpus and (2) Wikitext data set for CSI-negative corpus. Second, we adopt the deep learning models such as CNN or LSTM to extract adequate feature vectors from the embedding models and integrate the feature vectors into one classifier. To validate the effectiveness of the proposed model, we compare our method with two baseline classification models: (1) a model based on a single embedding model constructed with CSI-positive corpus only and (2) another model with CSI-negative corpus only. As a result, we indicate that the proposed model shows high accuracy, i.e., 0.934 of F1-score and 0.935 of area under the curve (AUC), which improves the baseline models by 1.76∼6.74% of F1-score and by 1.64∼6.98% of AUC.

Item Bias Analysis of the Young Schema-Questionnaire for Psychopathology, Gender, and Educational Level

2011 ◽  
Vol 27 (1) ◽  
pp. 65-70 ◽  
Author(s):  
Marleen M. Rijkeboer ◽  
Huub van den Bergh ◽  
Jan van den Bout
Keyword(s):  
Educational Level ◽  
Cross Validation ◽  
Item Bias ◽  
Response Patterns ◽  
Dutch Population ◽  
Bias Analysis ◽  
Young Schema Questionnaire ◽  
Item Functioning ◽  
Item Level ◽  
Schema Questionnaire

This study examines the construct validity of the Young Schema-Questionnaire at the item level in a Dutch population. Possible bias of items in relation to the presence or absence of psychopathology, gender, and educational level was analyzed, using a cross-validation design. None of the items of the YSQ exhibited differential item functioning (DIF) for gender, and only one item showed DIF for educational level. Furthermore, item bias analysis did not identify DIF for the presence or absence of psychopathology in as much as 195 of the 205 items comprising the YSQ. Ten items, however, spread over the questionnaire, were found to yield relatively inconsistent response patterns for patients and nonclinical participants.

A Modified Dugoff Tire Model for Combined-slip Forces

2010 ◽  
Vol 38 (3) ◽  
pp. 228-244 ◽  
Author(s):  
Nenggen Ding ◽  
Saied Taheri
Keyword(s):  
Vehicle Dynamics ◽  
Tire Model ◽  
Magic Formula ◽  
Model Based ◽  
Proposed Model ◽  
Road Friction ◽  
On Line ◽  
Double Lane Change ◽  
Tire Forces ◽  
Tire Models

Abstract Easy-to-use tire models for vehicle dynamics have been persistently studied for such applications as control design and model-based on-line estimation. This paper proposes a modified combined-slip tire model based on Dugoff tire. The proposed model takes emphasis on less time consumption for calculation and uses a minimum set of parameters to express tire forces. Modification of Dugoff tire model is made on two aspects: one is taking different tire/road friction coefficients for different magnitudes of slip and the other is employing the concept of friction ellipse. The proposed model is evaluated by comparison with the LuGre tire model. Although there are some discrepancies between the two models, the proposed combined-slip model is generally acceptable due to its simplicity and easiness to use. Extracting parameters from the coefficients of a Magic Formula tire model based on measured tire data, the proposed model is further evaluated by conducting a double lane change maneuver, and simulation results show that the trajectory using the proposed tire model is closer to that using the Magic Formula tire model than Dugoff tire model.

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.

Human Activity Recognition using Fourier Transform Inspired Deep Learning Combination Model

2019 ◽  
Vol 9 (1) ◽  
pp. 16-31
Author(s):  
Kyungkoo Jun
Keyword(s):  
Fourier Transform ◽  
Deep Learning ◽  
Short Term Memory ◽  
Window Size ◽  
Sensor Data ◽  
Data Sets ◽  
Data Set ◽  
Proposed Model ◽  
Testing Data ◽  
Labeling Scheme

Background & Objective: This paper proposes a Fourier transform inspired method to classify human activities from time series sensor data. Methods: Our method begins by decomposing 1D input signal into 2D patterns, which is motivated by the Fourier conversion. The decomposition is helped by Long Short-Term Memory (LSTM) which captures the temporal dependency from the signal and then produces encoded sequences. The sequences, once arranged into the 2D array, can represent the fingerprints of the signals. The benefit of such transformation is that we can exploit the recent advances of the deep learning models for the image classification such as Convolutional Neural Network (CNN). Results: The proposed model, as a result, is the combination of LSTM and CNN. We evaluate the model over two data sets. For the first data set, which is more standardized than the other, our model outperforms previous works or at least equal. In the case of the second data set, we devise the schemes to generate training and testing data by changing the parameters of the window size, the sliding size, and the labeling scheme. Conclusion: The evaluation results show that the accuracy is over 95% for some cases. We also analyze the effect of the parameters on the performance.

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