Araştırma Makalesi
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Karma Testlerde İç Tutarlılık Kestirimlerinin Farklı Benzetim Koşullarında İncelenmesi

Yıl 2020, Cilt: 20 Sayı: 87, 101 - 118, 20.05.2020

Öz

Problem Durumu: Alanyazın incelendiğinde karma testlerde güvenirlik üzerine yapılan az sayıda araştırma olduğu görülmüştür. Bu araştırmalarda farklı madde tipleri farklı oranlarda kullanılmıştır. Ancak madde tipi oranlarının ve örneklem büyüklüğünün birlikte güvenirlik üzerindeki etkisini inceleyerek bu yöntemlerin karşılaştırıldığı bir araştırmaya rastlanmamıştır. Karma testlerde kullanılacak madde tipleri ve bunların sayısı, ayrıca güvenirlik kestirimleri için gerekli örneklem büyüklüğü, önemli tartışma ve sorun alanları arasındadır. Bu bağlamda; karma testlerde iç tutarlılık anlamında güvenirlik hesaplamalarında kullanılan katsayıların, karma testlerin yapısını belirleyen önemli değişkenler olarak farklı madde tipi oranı ve örneklem büyüklüğü dikkate alındığında, bu katsayıların nasıl değiştiği, ideal/ daha uygun katsayıların hangileri olduğu bu araştırmanın problem durumunu oluşturmaktadır.

Araştımanın Amacı: Bu araştırmanın araştırmada, karma testlerde örneklem büyüklüğü (500, 1000 ve 2000) ve kullanılan madde tiplerinin oranı (2:1; 1:1 ve 1:2) değişimlendiğinde; α, Tabakalı α, Angoff-Feldt ve Feldt-Raju güvenirlik katsayılarının nasıl değiştiğinin incelenmesi ve bu güvenirlik katsayıları arasındaki betimsel ilişkinin ortaya konması amaçlanmıştır.

Araştırmanın Yöntemi: Araştırma için belirlenen koşullara uygun veri üretmek için; WinGen program kullanmıştır. Araştırma kapsamında oluşturulan koşullarda madde tipi sayısı, very türetmede kullanılan model, toplam madde sayısı, yanıt kategori sayısı, toplam puan alma yöntemi, madde ayırıcılığı ve madde güçlüğü sabit tutulurken; örneklem büyüklüğü ve madde tipi oranı için değişimlemeler yapılmıştır. Sabit tutulan ve üzerinde değişimleme yapılan değişkenler için ilgili alanyazın dikkate alınmıştır. Theta, her bir örneklem büyüklüğü için; ortalaması 0.00 ve standart sapmaları 1.00 olan normal dağılıma uygun olacak şekilde üretilmiştir. İki kategorili puanlanan maddeler İki Parametreli Lojistik Model'le, çok kategorili puanlanan maddeler Kısmi Puan Modeli ile üretilmiştir. Örneklem sayısı (500, 1000 ve 2000) ve madde oranları (2:1, 1:1 ve 1:2) olacak şekilde değişimlenmiş ve ilk beş adım bu koşulların her biri için tekrarlanmıştır. Veri üretiminde 25 tekrar(replikasyon) yapılmıştır. Böylelikle, 3x3=9 farklı deneysel koşul için 9x25=225 farklı veri seti üretilmiştir. Elde edilen very setlerine ait her bir koşul ve tekrar için α, Tabakalı α, Angoff-Feldt ve Feldt-Raju değerleri hesaplanmış ve tablolaştırılmıştır. Bu tablo değerleri, ortalama ve standart hatalar dikkate alınarak betimsel düzeyde değerlendirilmiş ve yorumlanmıştır.

Kaynakça

  • Baker, F. B. (1998). An investigation of the item parameter recovery characteristics of a Gibbs sampling procedure. Applied Psychological Measurement, 22(2), 153-169.
  • Bastari, B. (2000). Linking MCQ and CR Itemsto a common proficiency scale (Unpublished doctoral dissertation). University of Massachusetts Amherst, USA.
  • Berger, M. P. (1998). Optimal design of tests with dichotomous and polytomous items. Applied Psychological Measurement, 22(3), 248-258.
  • Cao, Y. (2008). Mixed-format test equating: Effects of test dimensionality and common-item sets(Doctoral dissertation). Retrived from https://drum.lib.umd.edu/handle/1903/8843
  • Charter, R. A. (1999). Sample size requirements for precise estimates of reliability, generalizability, and validity coefficients. Journal of Clinical and Experimental Neuropsychology, 21(4), 559-566.
  • Crocker, L., &Algina J. (2008). Introductiont a classical and modern test theory. N.Y.: Nelson Education.
  • Cronbach, L. J., Schönemann, P., &McKie, D. (1965). α coefficients for stratified-parallel tests. Educational and Psychological Measurement, 25(2), 291-312.
  • Cronbach, L. J., &Shavelson, R. J. (2004). My current thoughts on coefficient alpha and successor procedures. Educational and psychological measurement, 64(3), 391-418.
  • DeVellis, R. F. (2003). Scale development: Theory and application. Sage Publications: California.
  • Donoghue, J. R. (1993). An empirical examination of the IRT information in polytomously scored reading items. ETS Research Report Series, 1993(1).
  • Ercikan, K., Schwarz, R., Julian, M.W., Burket, G.R., Weber, M.W., &Link, V. (1998). Calibration and scoring of tests with multiplie-choice and constructed response test item type. Journal of Educational Measurement, 35(2), 137-154.
  • Eren, B. (2015). The comparison of student achievements, students' and teachers' views for multiple choice and mixed format test applications (Unpublished master’s dissertation). Ankara üniversitesi, Ankara.
  • Falk, C. F., & Savalei, V. (2011). The relationship between unstandardized and standardized alpha, true reliability, and the underlying measurement model. Journal of personality assessment, 93(5), 445-453.
  • Feldt, L. S., &Brennan, R. L. (1989). Reliability. In R. L. Linn (Ed.), Educational Measurement(3rd ed.,pp.105-146). New York: Macmillan.
  • Feldt, L. S. (2002). Estimating the internal consistency reliability of tests composed of test lets varying in length. Applied Measurement in Education, 15(1), 33-48.
  • Feldt, L. S., & Charter, R. A. (2003). Estimation of internal consistency reliability when test parts vary in effective length. Measurement and Evaluation in Counseling and Development, 36(1), 23-27.
  • Gao, F., & Chen, L. (2005). Bayesia nor non-Bayesian: A comparison study of item parameter estimation in the three-parameter logistic model. Applied Measurement in Education, 18(4), 351-380.
  • Gay, L. R. (1996). Educational research: competencies for analysis and application (5th ed). By Prentice-HallInc.: USA.
  • Gubes, N. Ö. (2014). The effects of test dimensionality, common item format, ability distribution and scale transformation methods on mixed - format test equating(Doctoral dissertation). Retrieved from https://tez.yok.gov.tr/UlusalTezMerkezi/. (Accession Number: 399465)
  • Gul, E. (2015). Examining multidimensional structure in view of unidimensional and multidimensional item response theory (Doctoral dissertation). Retrieved from https://tez.yok.gov.tr/UlusalTezMerkezi/. (Accession Number: 419288)
  • Gultekin, S. (2011). The evaluation based on Item Response Theory of the psychometric characteristics in multiple choice, constructed response and mixed format tests (Doctoral dissertation). Retrieved from https://tez.yok.gov.tr/UlusalTezMerkezi/. (Accession Number: 302033)
  • Guttman, L. (1945). A basis for analyzing test-retest reliability. Psychometrika, 10(4), 255-282.
  • Hambleton, R. K., Swaminathan, H., Rogers, H. (1991), Fundamentals of Item Response Theory. Newbury Park CA: Sage Publications.
  • Harwell, M., Stone, C. A., Hsu, T. C., &Kirisci, L. (1996). Monte Carlo studies in item response theory. Applied psychological measurement, 20(2), 101-125.
  • He, Q. (2009). Estimating the reliability of composite scores. Retrieved from https://pdfs.semanticscholar.org/0f54/d8c356f82fbca4fd2326239c1d21fbc9b778.pdf
  • He, Y. (2011). Evaluating equating properties formixed-format tests (Unpublished doctoral dissertation). University of Iowa, Iowa City.
  • Hu, B. (2018). Equating Errors and Scale Drift in Linked-Chain IRT Equating with Mixed-Format Tests. Journal of applied measurement, 19(1), 41-58.
  • Kim, S. H., & Lee, W.-C. (2006). An extension of four IRT linking methods for mixedformat tests. Journal of Educational Measurement, 43, 53-76.
  • Kim, S. Y., & Lee, W. C. (2018). Simple-Structure MIRT True-Score Equating for Mixed-Format Tests. Mixed-Format Tests: Psychometric Properties with a Primary Focus on Equating (Volume 5), 127.
  • Kim, S. Y., & Lee, W. C. (2019). Classification consistency and accuracy for mixed-format tests. Applied Measurement in Education, 32(2), 97-115.
  • Kinsey, T. L. (2003). A comparison of IRT and rasch procedures in a mixed-item format test (Doctoral dissertation).Retrieved from ProQuest Digital Dissertations.
  • Kirkpatrick, R. K. (2005). The effects of item format in common item equating(Unpublished doctoral dissertation). University of Iowa, Iowa City.
  • Lee, G., & Lee, W. C. (2016). Bi-factor MIRT observed-score equating for mixed-format tests. Applied Measurement in Education, 29(3), 224-241.
  • Li, Z., Chen, H., & Li, T. (2018). Exploring the Accuracy of MIRT Scale Linking Procedures for Mixed-format Tests. arXiv preprint arXiv:1805.00189.
  • Lord, F. M. (1980). Applications of item response theory topractical testing problems. London: Routledge.
  • Lord, F. M., & Novick, M. R. (1968). Statistical theories of mental test scores. Reading MA: Addison-Welsley Publishing Company.
  • Lucke, J. F. (2005a). The α and ω of congeneric test theory: An extension of reliability and internal consistency to heterogeneous tests. Applied Psychological Measurements. 29(1), 65-81.
  • Masters, G. N. (1982). A Rasch model forpartial credit scoring. Psychometrika, 47(2), 149-174.
  • Mehren, W.A. & Lehmann I.J. (1973). A measurement and evaluation in educationand psychology. New York: Holt. Rinehartand Winston.
  • Nunnally, J.C. (1964). Educational measurement and evaluation (6th ed.). New York: McGraw- Hill Book Company.
  • Odabas, M. (2016). The comparison of DINA model signed difference index, standardization and logistic regression techniques for detecting differential item functioning (Unpublished doctoral dissertation). Hacettepe Üniversitesi, Ankara.
  • Osbourn, H.G. (2000). Coefficient α and related internal consistency reliability coefficients. Psychological Methods, 5, 343-355.
  • Qualls, A. L. (1995). Estimating the reliability of a test containing multiple item formats. Applied Measurement in Education, 8(2), 111-120.
  • Raykov, T., &Shrout, P. E. (2002). Reliability of scaleswith general structure: Point andinterval estimation using a structural equation modeling approach. Structural Equation Modeling, 9(2), 195-212.
  • Saen-amnuaiphon, R., Tuksino, P.,& Nichanong, C. (2012). The Effect of Proportion of Mixed-Format Scoring: Mixed-Format Achievement Tests. Procedia-Social and Behavioral Sciences, 69, 1522-1528.
  • Sijtsma, K. (2009). On the use, the misuse, and the very limited usefulness of Cronbach’s α. Psychometrika, 74(1), 107.
  • Spray, J. A. (1990). Comparison of Two Logistic Multidimensional Item Response Theory Models. (Research Report ONR90-8). ACT, Inc., Iowa City, IA.
  • Sykes, R. C., Truskosky, D.,&White, H. (11-12 April 2001), Determining The Representation of Constructed Response Items in Mixed-Item-Format Exams. Paperpresented at Annual Meeting of the National Council on Measurement in Education, ABD: Seattle.
  • Tekin, H. (1991). Measurement and evaluation in education. Ankara: Yargı yayınevi.
  • Uysal, İ., & Kilmen, S. (2016). Comparison of Item Response Theory Test Equating Methods for Mixed Format Tests. International Online Journal of Educational Sciences, 8(2), 1-11.
  • Wainer, H. (1976). Estimating coefficients in linear models: It don't make nonever mind. Psychological Bulletin, 83(2), 213.
  • Wang, W., Drasgow, F., & Liu, L. (2016). Classification accuracy of mixed format tests: A bi-factor item response theory approach. Frontiers in psychology, 7, 270.
  • Warrens, M. J. (2016). A comparison of reliability coefficients for psychometric tests that consist of two parts. Advances in Data Analysis and Classification, 10(1), 71-84.
  • Young, M. J., &Yoon, B. (1998). Estimating the consistency and accuracy of classification in a standards-referenced assessment.Retrieved from https://cresst.org/wp-content/uploads/TECH475.pdf.
  • Zinbarg, R. E., Revelle, W., Yovel, I. & Li, W. (2005). Cronbach’s α, Revelle’s, β and McDonalds ω: their relations with each other and two alternative conceptualizations of reliability. Psychometrika, 70(1), 1-11.

Examining of Internal Consistency Coefficients in Mixed-Format Tests in Different Simulation Conditions

Yıl 2020, Cilt: 20 Sayı: 87, 101 - 118, 20.05.2020

Öz

Purpose: The present study aims to evaluate how the reliabilities computed using α, Stratified α, Angoff-Feldt, and Feldt-Raju estimators may differ when sample size (500, 1000, and 2000) and item type ratio of dichotomous to polytomous items (2:1; 1:1, 1:2) included in the scale are varied.

Research Methods: In this study, Cronbach’s α, Stratified α, Angoff-Feldt, and Feldt-Raju reliability coefficients were estimated on simulated datasets (sample sizes 500, 1000, 2000) and the number of dichotomous versus polytomous item ratios (2:1, 1:1, 1:2).


Findings:
In the simulation conditions of this research, in all sample size conditions, estimated Angoff-Feldt, and Feldt-Raju reliability coefficients were higher when the number of dichotomous items in the item-type ratio was higher than that of polytomous items. This was also the case for the estimated α and Stratified α reliability coefficients when the item-type ratio was reversed. While all different reliability estimators gave similar results in the large samples (n≥1000), there were some differences in reliability estimates depending on the item-type ratio in the small samples (n=500).

Implications for Research and Practice: In the light of the findings and conclusions obtained in this study, it may be advisable to use α and Stratified α for mixed-type scales when the number of polytomously scored items in the scale is higher than that of the dichotomously scored items. On the other hand, the coefficients Angoff-Feldt and Feldt-Raju are recommended when the number of items scored dichotomously is higher.

Kaynakça

  • Baker, F. B. (1998). An investigation of the item parameter recovery characteristics of a Gibbs sampling procedure. Applied Psychological Measurement, 22(2), 153-169.
  • Bastari, B. (2000). Linking MCQ and CR Itemsto a common proficiency scale (Unpublished doctoral dissertation). University of Massachusetts Amherst, USA.
  • Berger, M. P. (1998). Optimal design of tests with dichotomous and polytomous items. Applied Psychological Measurement, 22(3), 248-258.
  • Cao, Y. (2008). Mixed-format test equating: Effects of test dimensionality and common-item sets(Doctoral dissertation). Retrived from https://drum.lib.umd.edu/handle/1903/8843
  • Charter, R. A. (1999). Sample size requirements for precise estimates of reliability, generalizability, and validity coefficients. Journal of Clinical and Experimental Neuropsychology, 21(4), 559-566.
  • Crocker, L., &Algina J. (2008). Introductiont a classical and modern test theory. N.Y.: Nelson Education.
  • Cronbach, L. J., Schönemann, P., &McKie, D. (1965). α coefficients for stratified-parallel tests. Educational and Psychological Measurement, 25(2), 291-312.
  • Cronbach, L. J., &Shavelson, R. J. (2004). My current thoughts on coefficient alpha and successor procedures. Educational and psychological measurement, 64(3), 391-418.
  • DeVellis, R. F. (2003). Scale development: Theory and application. Sage Publications: California.
  • Donoghue, J. R. (1993). An empirical examination of the IRT information in polytomously scored reading items. ETS Research Report Series, 1993(1).
  • Ercikan, K., Schwarz, R., Julian, M.W., Burket, G.R., Weber, M.W., &Link, V. (1998). Calibration and scoring of tests with multiplie-choice and constructed response test item type. Journal of Educational Measurement, 35(2), 137-154.
  • Eren, B. (2015). The comparison of student achievements, students' and teachers' views for multiple choice and mixed format test applications (Unpublished master’s dissertation). Ankara üniversitesi, Ankara.
  • Falk, C. F., & Savalei, V. (2011). The relationship between unstandardized and standardized alpha, true reliability, and the underlying measurement model. Journal of personality assessment, 93(5), 445-453.
  • Feldt, L. S., &Brennan, R. L. (1989). Reliability. In R. L. Linn (Ed.), Educational Measurement(3rd ed.,pp.105-146). New York: Macmillan.
  • Feldt, L. S. (2002). Estimating the internal consistency reliability of tests composed of test lets varying in length. Applied Measurement in Education, 15(1), 33-48.
  • Feldt, L. S., & Charter, R. A. (2003). Estimation of internal consistency reliability when test parts vary in effective length. Measurement and Evaluation in Counseling and Development, 36(1), 23-27.
  • Gao, F., & Chen, L. (2005). Bayesia nor non-Bayesian: A comparison study of item parameter estimation in the three-parameter logistic model. Applied Measurement in Education, 18(4), 351-380.
  • Gay, L. R. (1996). Educational research: competencies for analysis and application (5th ed). By Prentice-HallInc.: USA.
  • Gubes, N. Ö. (2014). The effects of test dimensionality, common item format, ability distribution and scale transformation methods on mixed - format test equating(Doctoral dissertation). Retrieved from https://tez.yok.gov.tr/UlusalTezMerkezi/. (Accession Number: 399465)
  • Gul, E. (2015). Examining multidimensional structure in view of unidimensional and multidimensional item response theory (Doctoral dissertation). Retrieved from https://tez.yok.gov.tr/UlusalTezMerkezi/. (Accession Number: 419288)
  • Gultekin, S. (2011). The evaluation based on Item Response Theory of the psychometric characteristics in multiple choice, constructed response and mixed format tests (Doctoral dissertation). Retrieved from https://tez.yok.gov.tr/UlusalTezMerkezi/. (Accession Number: 302033)
  • Guttman, L. (1945). A basis for analyzing test-retest reliability. Psychometrika, 10(4), 255-282.
  • Hambleton, R. K., Swaminathan, H., Rogers, H. (1991), Fundamentals of Item Response Theory. Newbury Park CA: Sage Publications.
  • Harwell, M., Stone, C. A., Hsu, T. C., &Kirisci, L. (1996). Monte Carlo studies in item response theory. Applied psychological measurement, 20(2), 101-125.
  • He, Q. (2009). Estimating the reliability of composite scores. Retrieved from https://pdfs.semanticscholar.org/0f54/d8c356f82fbca4fd2326239c1d21fbc9b778.pdf
  • He, Y. (2011). Evaluating equating properties formixed-format tests (Unpublished doctoral dissertation). University of Iowa, Iowa City.
  • Hu, B. (2018). Equating Errors and Scale Drift in Linked-Chain IRT Equating with Mixed-Format Tests. Journal of applied measurement, 19(1), 41-58.
  • Kim, S. H., & Lee, W.-C. (2006). An extension of four IRT linking methods for mixedformat tests. Journal of Educational Measurement, 43, 53-76.
  • Kim, S. Y., & Lee, W. C. (2018). Simple-Structure MIRT True-Score Equating for Mixed-Format Tests. Mixed-Format Tests: Psychometric Properties with a Primary Focus on Equating (Volume 5), 127.
  • Kim, S. Y., & Lee, W. C. (2019). Classification consistency and accuracy for mixed-format tests. Applied Measurement in Education, 32(2), 97-115.
  • Kinsey, T. L. (2003). A comparison of IRT and rasch procedures in a mixed-item format test (Doctoral dissertation).Retrieved from ProQuest Digital Dissertations.
  • Kirkpatrick, R. K. (2005). The effects of item format in common item equating(Unpublished doctoral dissertation). University of Iowa, Iowa City.
  • Lee, G., & Lee, W. C. (2016). Bi-factor MIRT observed-score equating for mixed-format tests. Applied Measurement in Education, 29(3), 224-241.
  • Li, Z., Chen, H., & Li, T. (2018). Exploring the Accuracy of MIRT Scale Linking Procedures for Mixed-format Tests. arXiv preprint arXiv:1805.00189.
  • Lord, F. M. (1980). Applications of item response theory topractical testing problems. London: Routledge.
  • Lord, F. M., & Novick, M. R. (1968). Statistical theories of mental test scores. Reading MA: Addison-Welsley Publishing Company.
  • Lucke, J. F. (2005a). The α and ω of congeneric test theory: An extension of reliability and internal consistency to heterogeneous tests. Applied Psychological Measurements. 29(1), 65-81.
  • Masters, G. N. (1982). A Rasch model forpartial credit scoring. Psychometrika, 47(2), 149-174.
  • Mehren, W.A. & Lehmann I.J. (1973). A measurement and evaluation in educationand psychology. New York: Holt. Rinehartand Winston.
  • Nunnally, J.C. (1964). Educational measurement and evaluation (6th ed.). New York: McGraw- Hill Book Company.
  • Odabas, M. (2016). The comparison of DINA model signed difference index, standardization and logistic regression techniques for detecting differential item functioning (Unpublished doctoral dissertation). Hacettepe Üniversitesi, Ankara.
  • Osbourn, H.G. (2000). Coefficient α and related internal consistency reliability coefficients. Psychological Methods, 5, 343-355.
  • Qualls, A. L. (1995). Estimating the reliability of a test containing multiple item formats. Applied Measurement in Education, 8(2), 111-120.
  • Raykov, T., &Shrout, P. E. (2002). Reliability of scaleswith general structure: Point andinterval estimation using a structural equation modeling approach. Structural Equation Modeling, 9(2), 195-212.
  • Saen-amnuaiphon, R., Tuksino, P.,& Nichanong, C. (2012). The Effect of Proportion of Mixed-Format Scoring: Mixed-Format Achievement Tests. Procedia-Social and Behavioral Sciences, 69, 1522-1528.
  • Sijtsma, K. (2009). On the use, the misuse, and the very limited usefulness of Cronbach’s α. Psychometrika, 74(1), 107.
  • Spray, J. A. (1990). Comparison of Two Logistic Multidimensional Item Response Theory Models. (Research Report ONR90-8). ACT, Inc., Iowa City, IA.
  • Sykes, R. C., Truskosky, D.,&White, H. (11-12 April 2001), Determining The Representation of Constructed Response Items in Mixed-Item-Format Exams. Paperpresented at Annual Meeting of the National Council on Measurement in Education, ABD: Seattle.
  • Tekin, H. (1991). Measurement and evaluation in education. Ankara: Yargı yayınevi.
  • Uysal, İ., & Kilmen, S. (2016). Comparison of Item Response Theory Test Equating Methods for Mixed Format Tests. International Online Journal of Educational Sciences, 8(2), 1-11.
  • Wainer, H. (1976). Estimating coefficients in linear models: It don't make nonever mind. Psychological Bulletin, 83(2), 213.
  • Wang, W., Drasgow, F., & Liu, L. (2016). Classification accuracy of mixed format tests: A bi-factor item response theory approach. Frontiers in psychology, 7, 270.
  • Warrens, M. J. (2016). A comparison of reliability coefficients for psychometric tests that consist of two parts. Advances in Data Analysis and Classification, 10(1), 71-84.
  • Young, M. J., &Yoon, B. (1998). Estimating the consistency and accuracy of classification in a standards-referenced assessment.Retrieved from https://cresst.org/wp-content/uploads/TECH475.pdf.
  • Zinbarg, R. E., Revelle, W., Yovel, I. & Li, W. (2005). Cronbach’s α, Revelle’s, β and McDonalds ω: their relations with each other and two alternative conceptualizations of reliability. Psychometrika, 70(1), 1-11.
Toplam 55 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Bölüm Makaleler
Yazarlar

Hatice Gurdıl Ege Bu kişi benim 0000-0002-0079-3202

Ergul Demır Bu kişi benim 0000-0002-3708-8013

Yayımlanma Tarihi 20 Mayıs 2020
Yayımlandığı Sayı Yıl 2020 Cilt: 20 Sayı: 87

Kaynak Göster

APA Gurdıl Ege, H., & Demır, E. (2020). Examining of Internal Consistency Coefficients in Mixed-Format Tests in Different Simulation Conditions. Eurasian Journal of Educational Research, 20(87), 101-118.
AMA Gurdıl Ege H, Demır E. Examining of Internal Consistency Coefficients in Mixed-Format Tests in Different Simulation Conditions. Eurasian Journal of Educational Research. Mayıs 2020;20(87):101-118.
Chicago Gurdıl Ege, Hatice, ve Ergul Demır. “Examining of Internal Consistency Coefficients in Mixed-Format Tests in Different Simulation Conditions”. Eurasian Journal of Educational Research 20, sy. 87 (Mayıs 2020): 101-18.
EndNote Gurdıl Ege H, Demır E (01 Mayıs 2020) Examining of Internal Consistency Coefficients in Mixed-Format Tests in Different Simulation Conditions. Eurasian Journal of Educational Research 20 87 101–118.
IEEE H. Gurdıl Ege ve E. Demır, “Examining of Internal Consistency Coefficients in Mixed-Format Tests in Different Simulation Conditions”, Eurasian Journal of Educational Research, c. 20, sy. 87, ss. 101–118, 2020.
ISNAD Gurdıl Ege, Hatice - Demır, Ergul. “Examining of Internal Consistency Coefficients in Mixed-Format Tests in Different Simulation Conditions”. Eurasian Journal of Educational Research 20/87 (Mayıs 2020), 101-118.
JAMA Gurdıl Ege H, Demır E. Examining of Internal Consistency Coefficients in Mixed-Format Tests in Different Simulation Conditions. Eurasian Journal of Educational Research. 2020;20:101–118.
MLA Gurdıl Ege, Hatice ve Ergul Demır. “Examining of Internal Consistency Coefficients in Mixed-Format Tests in Different Simulation Conditions”. Eurasian Journal of Educational Research, c. 20, sy. 87, 2020, ss. 101-18.
Vancouver Gurdıl Ege H, Demır E. Examining of Internal Consistency Coefficients in Mixed-Format Tests in Different Simulation Conditions. Eurasian Journal of Educational Research. 2020;20(87):101-18.