Dergiler / International Journal of Assessment Tools in Education / 2018 / Cilt: 5 - Sayı: 2

Effects of Various Simulation Conditions on Latent-Trait Estimates: A Simulation Study

Sayfa
263–273
Yayın Tarihi
2022-10-19
DOI
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Particularly in situations when the distribution is either negatively or positively skewed and when the sample size is small, these models display a rather good performance.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:The aim of this simulation study, determine the relationship between true latent scores and estimated latent scores by including various control variables and different statistical models.
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Temel Bulgular & Veri:The study also aimed to compare the statistical models and determine the effects of different distribution types, response formats and sample sizes on latent score estimations. 108 different data bases, comprised of three different distribution types (positively skewed, normal, negatively skewed), three response formats (three-, five- and seven-level likert) and four different sample sizes (100, 250, 500, 1000) were used in the present study.
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Sonuç & Katkı:Particularly in situations when the distribution is either negatively or positively skewed and when the sample size is small, these models display a rather good performance.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Item response theory · Classical test theory · Factor analysis

Özet

The aim of this simulation study, determine the relationship between true latent scores and estimated latent scores by including various control variables and different statistical models. The study also aimed to compare the statistical models and determine the effects of different distribution types, response formats and sample sizes on latent score estimations. 108 different data bases, comprised of three different distribution types (positively skewed, normal, negatively skewed), three response formats (three-, five- and seven-level likert) and four different sample sizes (100, 250, 500, 1000) were used in the present study. Results show that, distribution types and response formats, in almost all simulations, have significant effect on determination coefficients. When the general performance of the models are evaluated, it can be said that MR and GRM display a better performance than the other models. Particularly in situations when the distribution is either negatively or positively skewed and when the sample size is small, these models display a rather good performance.

Abstract

The aim of this simulation study, determine the relationship between true latent scores and estimated latent scores by including various control variables and different statistical models. The study also aimed to compare the statistical models and determine the effects of different distribution types, response formats and sample sizes on latent score estimations. 108 different data bases, comprised of three different distribution types (positively skewed, normal, negatively skewed), three response formats (three-, five- and seven-level likert) and four different sample sizes (100, 250, 500, 1000) were used in the present study. Results show that, distribution types and response formats, in almost all simulations, have significant effect on determination coefficients. When the general performance of the models are evaluated, it can be said that MR and GRM display a better performance than the other models. Particularly in situations when the distribution is either negatively or positively skewed and when the sample size is small, these models display a rather good performance.

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Bu Makaleye Atıf Yap

KOGAR, H. (2022). Effects of Various Simulation Conditions on Latent-Trait Estimates: A Simulation Study. International Journal of Assessment Tools in Education, Cilt: 5 - Sayı: 2, 263–273. https://ajindex.com/articles/540205

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Kaynakça (3)

  1. Allahyari, E., Jafari, P., & Bagheri, Z. (2016). A simulation study to assess the effect of the number of response categories on the power of ordinal logistic regression for differential ıtem functioning analysis in rating scales. Computational and mathematical methods in medicine, vol. 2016, Article ID 5080826. doi.org/10.1155/2016/5080826 Bartholomew, D. J., Knott, M., & Moustaki, I. (2011). Latent variable models and factor analysis: A unified approach (Vol. 904). John Wiley & Sons. doi.org/10.1002/9781119970583 Borsboom, D., & Mellenbergh, G. J. (2002). True scores, latent variables, and constructs: A comment on Schmidt and Hunter. Intelligence, 30(6), 505-514. doi.org/10.1016/S0160-2896(02)00082-X Brzezińska, J. (2016). Latent variable modelling and item response theory analyses in marketing research. Folia Oeconomica Stetinensia, 16(2), 163-174. doi.org/10.1515/foli-2016-0032
  2. Crocker, L., & Algina, J. (1986). Introduction to classical and modern test theory. Holt, Rinehart and Winston, 6277 Sea Harbor Drive, Orlando, FL 32887. Cyr, A., & Davies, A. (2005). Item response theory and latent variable modeling for surveys with complex sampling design: The case of the national longitudinal survey of children and youth in Canada. In conference of the Federal Committee on Statistical Methodology, Office of Management and Budget, Arlington, VA. Dawber, T., Rogers, W. T., & Carbonaro, M. (2009). Robustness of Lord's formulas for item difficulty and discrimination conversions between classical and item response theory models. Alberta Journal of Educational Research, 55(4), 512. DeCoster, J. (1998). Overview of factor analysis. Retrieved June 12, 2017 from http://www.stat-help.com/factor.pdf
  3. Dumenci, L., & Achenbach, T. M. (2008). Effects of estimation methods on making trait-level inferences from ordered categorical items for assessing psychopathology. Psychological assessment, 20(1), 55-62. doi.org/10.1037/1040-3590.20.1.55 Han, K. T. (2007). WinGen: Windows software that generates item response theory parameters and item responses. Applied Psychological Measurement, 31(5), 457-459. doi.org/10.1177/0146621607299271 Hauck Filho, N., Machado, W. D. L., & Damásio, B. F. (2014). Effects of statistical models and items difficulties on making trait-level inferences: A simulation study. Psicologia: Reflexão e Crítica, 27(4), 670-678. doi.org/10.1590/1678-7153.201427407 Jafari, P., Bagheri, Z., Ayatollahi, S. M. T., & Soltani, Z. (2012). Using Rasch rating scale model