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Agung Priyo Utomo

Sekolah Tinggi Ilmu Statistik

Author-ID : 2674864

Agriculture, Biological Sciences & Forestry Decision Sciences, Operations Research & Management Education Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences Other

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KAJIAN TENTANG PENGARUH TWO STAGE CLUSTER SAMPLING TERHADAP STATISTIK UJI-F Agung Priyo Utomo
Jurnal Matematika Sains dan Teknologi Vol. 8 No. 2 (2007)
Publisher : LPPM Universitas Terbuka

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In regression analysis we make several assumptions about the error term. The following assumptions are often made: 1) the error terms are random variables with mean 0; 2) nonautocorrelation; 3) homoscedasticity, and 4) normality. The assumption of identically and independently distributed (iid) observations that underlies regression procedures is called into question when analyzing complex survey data. Particularly the existence of clusters in two stage samples usually exhibit positive intracluster correlation. If we use Ordinary Least Squares (OLS) procedures to make inferences in regression analysis for two stage cluster samples, we will be faced with a problem. This study aims to know the effect of two stage least squares on the F-Statistic. In general, although OLS procedures are unbiased but not fully efficient for estimation of the regression coefficients. Variance of the OLS estimators for the regression coefficients can be larger than the usual OLS variance expression would indicate. Failure to consider this possibility leads to underestimation of variances, with consequences for confidence intervals and the F-Statistic. The effect of intracluster correlation on the F-Statistic is the distortion of its distribution. The F-Statistic will not follow the Central F distribution anymore. Consequently, the hypothesis testing procedure is invalid.

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

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

Kesejahteraan Rumah Tangga dalam Pengaruh Wanita Kepala Rumah Tangga Agung Priyo Utomo; Rini Rahani
Jurnal Ilmu Sosial dan Ilmu Politik Vol 17, No 2 (2013): NOVEMBER (Korporasi dan Tanggung Jawab Sosial)
Publisher : Faculty of Social and Political Sciences, Universitas Gadjah Mada

This study focused on the welfare of the household headed by a working widow, viewed through the assets and the quality of their residence. Household welfare level is based on the wealth index. Ordinal logistic regression analysis show that level of education, age and employment status significantly influence the level of wealth of households headed by them. The higher the educational level of a widow, the household will tend to be richer. The older widows, the smaller tendency to have poor household. Households headed by widows who work in the agricultural sector, have a greater tendency to get a lower level of wealth status compared with who work in the non-agricultural sector.

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