Agreement on the Classification of Latent Class Membership Between Different Identification Constraint Approaches in the Mixture Rasch Model

Methodology ◽  
2018 ◽  
Vol 14 (2) ◽  
pp. 82-93
Author(s):  
Yi-Jhen Wu ◽  
Insu Paek
Keyword(s):  
Rasch Model ◽  
Latent Class ◽  
Model Identification ◽  
Real Data ◽  
Latent Classes ◽  
Class Membership ◽  
High Agreement ◽  
Common Scale ◽  
Constraint Approach

Abstract. When using the mixture Rasch model, the model identification constraints are either to set the equal means for all classes in the assumed normal ability distributions (equal ability mean constraint in short), or to set the sum of item difficulties to be zero for each class. In real data analysis, however, both constraints are not always sufficient to establish a common scale across latent classes unless some items are specified as anchor items in the estimation. If these two conventional constraint approaches recover the class membership as good as the anchor item constraint approach, the conventional constraint approaches may be considered useful for the purpose of class membership classification. This study investigated agreement on class membership between one conventional constraint (the equal ability mean) and the anchor item constraint approaches. Results showed high agreement between these two constraint approaches, indicating that the conventional constraint of the equal mean ability approach may be used to recover the latent class membership although item profiles are not correctly estimated across latent classes.

scholarly journals The Q-Matrix Anchored Mixture Rasch Model

2021 ◽  
Vol 12 ◽  
Author(s):  
Ming-Chi Tseng ◽  
Wen-Chung Wang
Keyword(s):  
Rasch Model ◽  
Latent Variable ◽  
Latent Class ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Latent Classes ◽  
Modeling Framework ◽  
Class Invariant ◽  
Common Scale ◽  
Q Matrix

Mixture item response theory (IRT) models include a mixture of latent subpopulations such that there are qualitative differences between subgroups but within each subpopulation the measure model based on a continuous latent variable holds. Under this modeling framework, students can be characterized by both their location on a continuous latent variable and by their latent class membership according to Students’ responses. It is important to identify anchor items for constructing a common scale between latent classes beforehand under the mixture IRT framework. Then, all model parameters across latent classes can be estimated on the common scale. In the study, we proposed Q-matrix anchored mixture Rasch model (QAMRM), including a Q-matrix and the traditional mixture Rasch model. The Q-matrix in QAMRM can use class invariant items to place all model parameter estimates from different latent classes on a common scale regardless of the ability distribution. A simulation study was conducted, and it was found that the estimated parameters of the QAMRM recovered fairly well. A real dataset from the Certificate of Proficiency in English was analyzed with the QAMRM, LCDM. It was found the QAMRM outperformed the LCDM in terms of model fit indices.

scholarly journals Associations Between Cytokine Genes and a Symptom Cluster of Pain, Fatigue, Sleep Disturbance, and Depression in Patients Prior to Breast Cancer Surgery

2014 ◽  
Vol 17 (3) ◽  
pp. 237-247 ◽  
Author(s):  
Sy-Huey Doong ◽  
Anand Dhruva ◽  
Laura B. Dunn ◽  
Claudia West ◽  
Steven M. Paul ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Sleep Disturbance ◽  
Latent Class ◽  
Symptom Cluster ◽  
Breast Cancer Surgery ◽  
Latent Classes ◽  
Breast Cancer Patients ◽  
Class Membership ◽  
Necrosis Factor Alpha ◽  
Cytokine Genes

Pain, fatigue, sleep disturbance, and depression are common and frequently co-occurring symptoms in oncology patients. This symptom cluster is often attributed to the release of proinflammatory cytokines. The purposes of this study were to determine whether distinct latent classes of patients with breast cancer ( n = 398) could be identified based on their experience with this symptom cluster, whether patients in these latent classes differed on demographic and clinical characteristics and whether variations in cytokine genes were associated with latent class membership. Three distinct latent classes were identified: “all low” (61.0%), “low pain and high fatigue” (31.6%), “all high” (7.1%). Compared to patients in the all low class, patients in the all high class were significantly younger, had less education, were more likely to be non-White, had a lower annual income, were more likely to live alone, had a lower functional status, had a higher comorbidity score, and had more advanced disease. Significant associations were found between interleukin 6 (IL6) rs2069845, IL13 rs1295686, and tumor necrosis factor alpha rs18800610 and latent class membership. Findings suggest that variations in pro- and anti-inflammatory cytokine genes are associated with this symptom cluster in breast cancer patients.

scholarly journals Comparison of Cognitive Diagnosis Models under Changing Conditions: DINA, RDINA, HODINA and HORDINA

2018 ◽  
Vol 11 (6) ◽  
pp. 119
Author(s):  
Omur K. Kalkan ◽  
Hulya Kelecioglu ◽  
Tahsin O. Basokcu
Keyword(s):  
Latent Class ◽  
Real Data ◽  
Cognitive Diagnosis ◽  
Item Parameter ◽  
Cognitive Diagnosis Models ◽  
Parameter Estimations ◽  
S Parameter ◽  
Parameter Values ◽  
Higher Order Models

The application of CDMs to fraction subtraction data revealed problems on the classification of examinees, latent class sizes, and the use of higher-order models. Additionally, selecting the most appropriate model assumes critical importance if there are several appropriate models available for the data. In the present study, DINA–RDINA and HODINA–HORDINA models were compared under changing conditions (i.e., number of attributes, g and s item parameter values, and number of items) with simulated and real data. The results show that for conditions where the g–s parameter values and the number of attributes were low (0.1 and 3, respectively), the reparameterized models generated values that were virtually identical to those obtained using DINA models. However, when the g–s parameter values and the number of attributes were increased (0.5 and 5, respectively), the parameter estimations obtained from the models, latent class estimates, AIC, and BIC show differences through the values from the models.

scholarly journals Classification of Physiologic Swallowing Impairment Severity: A Latent Class Analysis of Modified Barium Swallow Impairment Profile Scores

2020 ◽  
Vol 29 (2S) ◽  
pp. 1001-1011
Author(s):  
Jonathan Beall ◽  
Elizabeth G. Hill ◽  
Kent Armeson ◽  
Kendrea L. (Focht) Garand ◽  
Kate (Humphries) Davidson ◽  
...  
Keyword(s):  
Latent Class ◽  
Credible Interval ◽  
Barium Swallow ◽  
Latent Classes ◽  
Data Sets ◽  
Class Membership ◽  
Modified Barium Swallow ◽  
Level Data ◽  
Patient Database ◽  
Impairment Severity

Purpose Our objectives were to (a) identify oral and pharyngeal physiologic swallowing impairment severity classes based on latent class analyses (LCAs) of the Modified Barium Swallow Impairment Profile (MBSImP) swallow task scores and (b) quantify the probability of severity class membership given composite MBSImP oral total (OT) and pharyngeal total (PT) scores. Method MBSImP scores were collected from a patient database of 319 consecutive modified barium swallow studies. Because of missing swallow task scores, LCA was performed using 25 multiply imputed data sets. Results LCA revealed a three-class structure for both oral and pharyngeal models. We identified OT and PT score intervals to assign subjects to oral and pharyngeal impairment latent severity classes, respectively, with high probability (probability of class membership ≥ 0.9 given OT or PT scores within specified ranges) and high confidence (95% credible interval [CI] widths ≤ 0.24 for all total scores within specified ranges). OT scores ranging from 0 to 10 and from 14 to 18 yielded assignments in Oral Latent Classes 1 and 2, respectively, while OT = 22 was assigned to Oral Latent Class 3. PT scores ranging from 0 to 13 and from 18 to 24 yielded assignments in Pharyngeal Latent Classes 1 and 2, respectively, while PT = 26 was assigned to Pharyngeal Latent Class 3. Conclusions LCA of MBSImP task-level data revealed significant underlying oral and pharyngeal ordinal class structures representing increasingly severe gradations of physiologic swallow impairment. Clinically meaningful OT and PT score ranges were derived facilitating latent class assignment. Supplemental Material https://doi.org/10.23641/asha.12315677

Method of determination of the text direction on the image with the use of convolutional neural network

2020 ◽  
pp. 96-99
Author(s):  
P.L. Nikolaev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Deep Neural Network ◽  
Binary Classification ◽  
Synthetic Data ◽  
Real Data ◽  
Method Of Determination ◽  
Classification Of Images

This article deals with method of binary classification of images with small text on them Classification is based on the fact that the text can have 2 directions – it can be positioned horizontally and read from left to right or it can be turned 180 degrees so the image must be rotated to read the sign. This type of text can be found on the covers of a variety of books, so in case of recognizing the covers, it is necessary first to determine the direction of the text before we will directly recognize it. The article suggests the development of a deep neural network for determination of the text position in the context of book covers recognizing. The results of training and testing of a convolutional neural network on synthetic data as well as the examples of the network functioning on the real data are presented.

Index of suicide risk in Mexico using Twitter

2019 ◽  
pp. 1-13
Author(s):  
Luz Judith Rodríguez-Esparza ◽  
Diana Barraza-Barraza ◽  
Jesús Salazar-Ibarra ◽  
Rafael Gerardo Vargas-Pasaye
Keyword(s):  
Social Networks ◽  
Analytic Hierarchy Process ◽  
Suicide Risk ◽  
Opinion Mining ◽  
Real Data ◽  
Analytic Hierarchy ◽  
Specialized Care ◽  
The Analytic Hierarchy Process ◽  
Hierarchy Process

Objectives: To identify early suicide risk signs on depressive subjects, so that specialized care can be provided. Various studies have focused on studying expressions on social networks, where users pour their emotions, to determine if they show signs of depression or not. However, they have neglected the quantification of the risk of committing suicide. Therefore, this article proposes a new index for identifying suicide risk in Mexico. Methodology: The proposal index is constructed through opinion mining using Twitter and the Analytic Hierarchy Process. Contribution: Using R statistical package, a study is presented considering real data, making a classification of people according to the obtained index and using information from psychologists. The proposed methodology represents an innovative prevention alternative for suicide.

The Classification of Eating Disorders

2010 ◽  
pp. 7-24 ◽  
Author(s):  
Kathryn H. Gordon ◽  
Jill M. Holm-Denoma ◽  
Ross D. Crosby ◽  
Stephen A. Wonderlich
Keyword(s):  
Eating Disorders ◽  
Eating Disorder ◽  
Latent Class ◽  
Minority Groups ◽  
Statistical Manual ◽  
Text Revision ◽  
Diagnostic And Statistical Manual ◽  
Key Issues ◽  
Dsm Iv

The purpose of the chapter is to elucidate the key issues regarding the classification of eating disorders. To this end, a review of nosological research in the area of eating disorders is presented, with a particular focus on empirically based techniques such as taxometric and latent class analysis. This is followed by a section outlining areas of overlap between the current Diagnostic and Statistical Manual of Mental Disorders – Fourth Edition, Text Revision (DSM-IV-TR; American Psychiatric Association, 2000) eating disorder categories and their symptoms. Next, eating disorder classification models that are alternatives to the DSM-IV-TR are described and critically examined in light of available empirical data. Finally, areas of controversy and considerations for change in next version of the DSM (i.e., the applicability of DSM criteria to minority groups, children, males; the question of whether clinical categories should be differentiated from research categories) are discussed.

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