scholarly journals Sample Size Requirements for Applying Diagnostic Classification Models

2021 ◽  
Vol 11 ◽  
Author(s):  
Sedat Sen ◽  
Allan S. Cohen
Keyword(s):  
Sample Size ◽  
Classification Accuracy ◽  
Base Rate ◽  
Item Parameter ◽  
Test Length ◽  
Diagnostic Classification Models ◽  
Parameter Recovery ◽  
Estimated Parameters ◽  
Item Parameters ◽  
Larger Sample

Results of a comprehensive simulation study are reported investigating the effects of sample size, test length, number of attributes and base rate of mastery on item parameter recovery and classification accuracy of four DCMs (i.e., C-RUM, DINA, DINO, and LCDMREDUCED). Effects were evaluated using bias and RMSE computed between true (i.e., generating) parameters and estimated parameters. Effects of simulated factors on attribute assignment were also evaluated using the percentage of classification accuracy. More precise estimates of item parameters were obtained with larger sample size and longer test length. Recovery of item parameters decreased as the number of attributes increased from three to five but base rate of mastery had a varying effect on the item recovery. Item parameter and classification accuracy were higher for DINA and DINO models.

scholarly journals The Estimates Item Parameter for Multidimensional Three-Parameter Logistics

2020 ◽  
Author(s):  
Ode Zulaeha ◽  
Wardani Rahayu ◽  
Yuliatri Sastrawijaya
Keyword(s):  
Sample Size ◽  
Item Parameter ◽  
Model Parameters ◽  
Test Length ◽  
Parameter Estimations ◽  
Item Parameters ◽  
Median Correlation ◽  
Item Parameter Estimation ◽  
Parameter Test ◽  
The Stability

The purpose of this study is to measure the accuracy of item parameters and abilities by using the Multidimensional Three-Parameter Logistics (M3PL) model. M3PL is a series of tests that measure more than one dimension of ability (θ). Item parameter estimation and the ability to model M3PL are reviewed based on a sample size of 1000 and test lengths of 15, 25, and 40. Parameter estimations are obtained using the Wingen software that is converted to BILOG. The results show that the estimate obtained with a test length of 15 displays a median correlation of 0.787 (high). The study therefore concludes that the level of difficulty of the questions is higher or the questions given to respondents are more difficult, so many respondents guessed the answers. The results of the estimated grain parameters and capabilities indicated that scoring based on sample size greatly affects the stability of the test length. By using the M3PL model, parameters can be measured pseudo-guessing, parameters b and parameters a. MIRT is able to explain interactions between the items on the test and the answers of the participants. The estimated results of the item parameters and the ability parameters of the participants also proved to be accurate and efficient. Keywords: Multidimensional Three-Parameter Logistics (M3PL), distribution parameter, test length

Detecting Noneffortful Responses Based on a Residual Method Using an Iterative Purification Process

2021 ◽  
pp. 107699862199436
Author(s):  
Yue Liu ◽  
Hongyun Liu
Keyword(s):  
True Positive Rate ◽  
Item Parameter ◽  
Parameter Estimates ◽  
Detection Accuracy ◽  
Purification Process ◽  
Parameter Recovery ◽  
Residual Method ◽  
Data Contamination ◽  
Item Parameters ◽  
The Impact

The prevalence and serious consequences of noneffortful responses from unmotivated examinees are well-known in educational measurement. In this study, we propose to apply an iterative purification process based on a response time residual method with fixed item parameter estimates to detect noneffortful responses. The proposed method is compared with the traditional residual method and noniterative method with fixed item parameters in two simulation studies in terms of noneffort detection accuracy and parameter recovery. The results show that when severity of noneffort is high, the proposed method leads to a much higher true positive rate with a small increase of false discovery rate. In addition, parameter estimation is significantly improved by the strategies of fixing item parameters and iteratively cleansing. These results suggest that the proposed method is a potential solution to reduce the impact of data contamination due to severe low test-taking effort and to obtain more accurate parameter estimates. An empirical study is also conducted to show the differences in the detection rate and parameter estimates among different approaches.

scholarly journals Sample Size and Test Length for Item Parameter Estimate and Exam Parameter Estimate

2021 ◽  
Vol 9 (1) ◽  
pp. 69-78
Author(s):  
Riswan Riswan
Keyword(s):  
Item Response Theory ◽  
Sample Size ◽  
Item Response ◽  
Parameter Estimate ◽  
Test Theory ◽  
Item Parameter ◽  
Parameter Estimates ◽  
Test Length ◽  
Response Theory ◽  
The Stability

The Item Response Theory (IRT) model contains one or more parameters in the model. These parameters are unknown, so it is necessary to predict them. This paper aims (1) to determine the sample size (N) on the stability of the item parameter (2) to determine the length (n) test on the stability of the estimate parameter examinee (3) to determine the effect of the model on the stability of the item and the parameter to examine (4) to find out Effect of sample size and test length on item stability and examinee parameter estimates (5) Effect of sample size, test length, and model on item stability and examinee parameter estimates. This paper is a simulation study in which the latent trait (q) sample simulation is derived from a standard normal population of ~ N (0.1), with a specific Sample Size (N) and test length (n) with the 1PL, 2PL and 3PL models using Wingen. Item analysis was carried out using the classical theory test approach and modern test theory. Item Response Theory and data were analyzed through software R with the ltm package. The results showed that the larger the sample size (N), the more stable the estimated parameter. For the length test, which is the greater the test length (n), the more stable the estimated parameter (q).

scholarly journals Estimation of ability and item parameters in mathematics testing by using the combination of 3PLM/ GRM and MCM/ GPCM scoring model

2015 ◽  
Vol 1 (1) ◽  
pp. 55
Author(s):  
Abadyo Abadyo ◽  
Bastari Bastari
Keyword(s):  
Sample Size ◽  
Root Mean Square ◽  
Statistical Testing ◽  
Accurate Estimation ◽  
Mean Square ◽  
Test Length ◽  
Item Format ◽  
Test Information ◽  
Item Parameters ◽  
The Impact

The main purpose of the study was to investigate the superiority of scoring by utilizing the combination of MCM/GPCM model in comparison to 3PLM/GRM model within a mixed-item format of Mathematics tests. To achieve the purpose, the impact of two scoring models was investigated based on the test length, the sample size, and the M-C item proportion within the mixed-item format test and the investigation was conducted on the aspects of: (1) estimation of ability and item parameters, (2) optimalization of TIF, (3) standard error rates, and (4) model fitness on the data. The investigation made use of simulated data that was generated based on fixed effects factorial design 2 x 3 x 3 x 3 and 5 replications resulting in 270 data sets. The data were analyzed by means of fixed effect MANOVA on Root Mean Square Error (RMSE) of the ability and RMSE and Root Mean Square Deviation (RNSD) of the itemparameters in order to identify the significant main effects at level of a = .05; on the other hand, the interaction effects were incorporated into the error term for statistical testing. The -2LL statistics were also used in order to evaluate the moel fitness on the data set. The results of the study show that the combination of MCM/GPCM model provide higher accurate estimation than that of 3PLM/GRM model. In addition, the test information given by the combination of MCM/GPCM model is three times hhigher than that of 3PLM/GRM model although the test information cannot offer a solid conclusion in relation to the sample size and the M-C item proportion on each test length which provides the optimal score of thest information. Finally, the differences of fit statistics between the two models of scoring determine the position of MCM/GPCM model rather than that of 3PLM/GRM model.

scholarly journals PENGARUH SAMPLE SIZE (N) DAN TEST LENGTH (n) TERHADAP ITEM PARAMETER ESTIMATE DAN EXAMINEE PARAMETER ESTIMATE, SUATU STUDI SIMULAS

2013 ◽  
Vol 12 (1) ◽  
pp. 25
Author(s):  
R. BUDIARTI
Keyword(s):  
Sample Size ◽  
Item Response ◽  
Classical Test Theory ◽  
Parameter Estimate ◽  
Test Theory ◽  
Item Parameter ◽  
Test Length ◽  
Item Response Model ◽  
Theory Test ◽  
Item Item

Studi yang mempelajari masalah pengukuran secara umum di bidang pendidikan dan mempelajari metode untuk menyelesaikannya, telah berkembang menjadi disiplin ilmu khusus yang dikenal dengan test theory. Test theory menyediakan kerangka kerja umum untuk melihat proses pembentukan instrumen tes (item test). Analisis item dapat dilakukan dengan pendekatan tes teori klasik (Classical Test Theory atau CTT) dan teori tes modern yang dikenal dengan  Item Respons Theory (IRT). Ada beberapa model respon item (item response model), yang berbeda banyaknya parameter dalam model.  Semua model IRT mengandung satu atau lebih parameter item dan satu atau lebih parameter examinee. Pada tulisan ini difokuskan pada model respon item dengan satu parameter examinee dengan dua parameter item. Parameter-parameter ini tidak diketahui, untuk itu perlu diduga. Agar hasil dugaan relatif stabil dan akurat, maka diperlukan sample size yang cukup. Tujuan dari paper ini adalah (1) menginvestigasi pengaruh sample size (N) terhadap kestabilan item  parameter estimate, (2) menginvestigasi pengaruh test length (n) terhadap kestabilan examinee parameter estimate. Kestabilan dugaan parameter item (a dan b) dipengaruhi oleh sample size, dan kestabilan parameter examinee (<em>Ө</em>) dipengaruhi oleh ukuran test length. Semakin besar sample size, maka pendugaan parameter item makin stabil, sedangkan semakin besar ukuran test length maka makin stabil dugaan parameter item.

scholarly journals Parameter estimation bias of dichotomous logistic item response theory models using different variables

2019 ◽  
Vol 8 (3) ◽  
pp. 425
Author(s):  
Alper Köse ◽  
C. Deha Doğan
Keyword(s):  
Parameter Estimation ◽  
Item Response Theory ◽  
Sample Size ◽  
Normal Distribution ◽  
Item Response ◽  
Relevant Literature ◽  
Item Parameter ◽  
Test Length ◽  
Item Response Model ◽  
Response Theory

The aim of this study was to examine the precision of item parameter estimation in different sample sizes and test lengths under three parameter logistic model (3PL) item response theory (IRT) model, where the trait measured by a test was not normally distributed or had a skewed distribution.In the study, number of categories (1-0), and item response model were identified as fixed conditions, and sample size, test length variables, and the ability distributions were selected as manipulated conditions. This is a simulation study. So data simulation and data analysis were done via packages in the R programming language. Results of the study showed that item parameter estimations performed under normal distribution were much stronger and bias-free compared to non-normal distribution. Moreover, the sample size had some limited positive effect on parameter estimation. However, the test length had no effect parameter estimation. As a result the importance of normality assumptions for IRT models were highlighted and findings were discussed based on relevant literature.

Planning the Size of Clinical Trials with Allowance for Patient Noncompliance

1990 ◽  
Vol 29 (03) ◽  
pp. 243-246 ◽  
Author(s):  
M. A. A. Moussa
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Sample Size ◽  
Event Rate ◽  
Survival Functions ◽  
Time Intervals ◽  
Patient Noncompliance ◽  
Event Rates ◽  
Larger Sample

AbstractVarious approaches are considered for adjustment of clinical trial size for patient noncompliance. Such approaches either model the effect of noncompliance through comparison of two survival distributions or two simple proportions. Models that allow for variation of noncompliance and event rates between time intervals are also considered. The approach that models the noncompliance adjustment on the basis of survival functions is conservative and hence requires larger sample size. The model to be selected for noncompliance adjustment depends upon available estimates of noncompliance and event rate patterns.

Augmentation Strategies for Clozapine-Resistant Patients with Schizophrenia

2020 ◽  
Vol 26 (2) ◽  
pp. 218-227
Author(s):  
Yi-Hang Chiu ◽  
Chia-Yueh Hsu ◽  
Mong-Liang Lu ◽  
Chun-Hsin Chen
Keyword(s):  
Sample Size ◽  
Repetitive Transcranial Magnetic Stimulation ◽  
Mood Stabilizers ◽  
Treatment Resistant ◽  
Double Blind ◽  
Beneficial Effects ◽  
Augmentation Strategies ◽  
Pubmed Database ◽  
Augmentation Strategy ◽  
Larger Sample

Background: Clozapine has been used in treatment-resistant patients with schizophrenia. However, only 40% of patients with treatment-resistant schizophrenia have response to clozapine. Many augmentation strategies have been proposed to treat those clozapine-resistant patients, but the results are inconclusive. In this review, we intended to review papers dealing with the augmentation strategies in the treatment of clozapineresistant patients with schizophrenia. Method: We reviewed randomized, double-blind, placebo- or sham-controlled trials (RCT) for clozapine-resistant patients with schizophrenia in Embase, PsycINFO, Cochrane, and PubMed database from January 1990 to June 2019. Results: Antipsychotics, antidepressants, mood stabilizers, brain stimulation, such as electroconvulsive therapy (ECT) and repetitive transcranial magnetic stimulation, and other strategies, were used as an augmentation in clozapine-resistant patients with schizophrenia. Except for better evidence in memantine with 2 RCTs and cognitive behavior therapy in 2 studies to support its effectiveness, we found that all the other effective augmentations, including sulpiride, ziprasidone, duloxetine, mirtazapine, ECT, sodium benzoate, ginkgo biloba, and minocycline, had only one RCT with limited sample size. Conclusion: In this review, no definite effective augmentation strategy was found for clozapine-resistant patients. Some potential strategies with beneficial effects on psychopathology need further studies with a larger sample size to support their efficacy.

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