Article Per Year (5 Year)
p-Index From 2019 - 2024
1.554
P-Index
This Author published in this journals
All Journal Jurnal Penelitian dan Evaluasi Pendidikan TAZKIYA JOURNAL OF PSYCHOLOGY Gadjah Mada Journal of Psychology Jurnal Pengukuran Psikologi dan Pendidikan Indonesia (JP3I) Islamic Guidance and Counseling Journal Indonesian Journal of Educational Assesment Jurnal Psikologi Universitas Gadjah Mada
Muhammad Dwirifqi Kharisma Putra
Faculty of Psychology, Universitas Gadjah Mada
Arts Humanities Decision Sciences, Operations Research & Management Education Environmental Science Languange, Linguistic, Communication & Media Mathematics Public Health Social Sciences Other

Published : 11 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : Jurnal Penelitian dan Evaluasi Pendidikan

 1 
Comparing item parameter estimates and fit statistics of the Rasch model from three different traditions Bahrul Hayat; Muhammad Dwirifqi Kharisma Putra; Bambang Suryadi
Jurnal Penelitian dan Evaluasi Pendidikan Vol 24, No 1 (2020)
Publisher : Graduate School, Universitas Negeri Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/pep.v24i1.29871

Abstract

Rasch model is a method that has a long history in its application in the fields of social and behavioral sciences, including educational measurement. Under certain circumstances, Rasch models are known as a special case of Item response theory (IRT), while IRT is equivalent to the Item Factor Analysis (IFA) models as a special case of Structural Equation Models (SEM), although there are other ‘tradition’ that consider Rasch measurement models not part of both. In this study, a simulation study was conducted using simulated data to explain how the interrelationships between the Rasch model as a constraint version of 2-parameter logistic (2-PL) IRT, Rasch model as an item factor analysis were compared with the Rasch measurement model using Mplus, IRTPRO and WINSTEPS program, each of which came from its own 'tradition'. The results of this study indicate that Rasch models and IFA as a special case of SEM are mathematically equal, as well as the Rasch measurement model, but due to different philosophical perspectives, people might vary in their understanding of this concept. Given the findings of this study, it is expected that confusion and misunderstanding between the three can be overcome.
Pengaruh ukuran sampel dan intraclass correlation coefficients (ICC) terhadap bias estimasi parameter multilevel latent variable modeling: studi dengan simulasi Monte Carlo Muhammad Dwirifqi Kharisma Putra; Jahja Umar; Bahrul Hayat; Agung Priyo Utomo
Jurnal Penelitian dan Evaluasi Pendidikan Vol 21, No 1 (2017)
Publisher : Graduate School, Universitas Negeri Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (96.024 KB) | DOI: 10.21831/pep.v21i1.12895

Abstract

Studi ini menggunakan simulasi Monte Carlo dilakukan untuk melihat pengaruh ukuran sampel dan intraclass correlation coefficients (ICC) terhadap bias estimasi parameter multilevel latent variable modeling. Kondisi simulasi diciptakan dengan beberapa faktor yang ditetapkan yaitu lima kondisi ICC (0.05, 0.10, 0.15, 0.20, 0.25), jumlah kelompok (30, 50, 100 dan 150), jumlah observasi dalam kelompok (10, 20 dan 50) dan diestimasi menggunakan lima metode estimasi: ML, MLF, MLR, WLSMV dan BAYES. Jumlah kondisi keseluruhan sebanyak 300 kondisi dimana tiap kondisi direplikasi sebanyak 1000 kali dan dianalisis menggunakan software Mplus 7.4. Kriteria bias yang masih dapat diterima adalah 10%. Hasil penelitian ini menunjukkan bahwa bias yang terjadi dipengaruhi oleh ukuran sampel dan ICC, penelitian ini juga menujukkan bahwa metode estimasi WLSMV dan BAYES berfungsi lebih baik pada berbagai kondisi dibandingkan dengan metode estimasi berbasis ML.Kata kunci: multilevel latent variable modeling, intraclass correlation coefficients, Metode Markov Chain Monte Carlo THE IMPACT OF SAMPLE SIZE AND INTRACLASS CORRELATION COEFFICIENTS (ICC) ON THE BIAS OF PARAMETER ESTIMATION IN MULTILEVEL LATENT VARIABLE MODELING: A MONTE CARLO STUDYAbstractA monte carlo study was conducted to investigate the effect of sample size and intraclass correlation coefficients (ICC) on the bias of parameter estimates in multilevel latent variable modeling. The design factors included (ICC: 0.05, 0.10, 0.15, 0.20, 0.25), number of groups in between level model (NG: 30, 50, 100 and 150), cluster size (CS: 10, 20 and 50) to be estimated with five different estimator: ML, MLF, MLR, WLSMV and BAYES. Factors were interegated into 300 conditions (4 NG  3 CS  5 ICC  5 Estimator). For each condition, replications with convergence problems were exclude until at least 1.000 replications were generated and analyzed using Mplus 7.4, we also consider absolute percent bias 10% to represent an acceptable level of bias. We find that the degree of bias depends on sample size and ICC. We also show that WLSMV and BAYES estimator performed better than ML-based estimator across varying sample sizes and ICC’s conditions.Keywords: multilevel latent variable modeling, intraclass correlation coefficients, Markov Chain Monte Carlo method
Co-Authors Agung Priyo Utomo Bahrul Hayat Bahrul Hayat Bambang Suryadi Bambang Suryadi, Bambang Diana Mutiah Heri Retnawati Iva Sarifah Jahja Umar Khairunesa Isa Melis Melis Nia Tresniasari Rahmat Hidayat Tina Deviana Valendra Granitha Shandika Puri Wardani Rahayu