{"id":9674,"date":"2025-12-12T22:19:00","date_gmt":"2025-12-12T15:19:00","guid":{"rendered":"https:\/\/populicenter.org\/?p=9674"},"modified":"2026-06-19T11:20:25","modified_gmt":"2026-06-19T04:20:25","slug":"mengapa-pembobotan-penting-dalam-analisis-data-survei","status":"publish","type":"post","link":"https:\/\/populicenter.org\/en\/2025\/12\/12\/mengapa-pembobotan-penting-dalam-analisis-data-survei\/","title":{"rendered":"Mengapa Pembobotan Penting dalam Analisis Data Survei?"},"content":{"rendered":"<style>\n.pm-article{max-width:760px;margin:0 auto;font-family:Georgia,'Times New Roman',serif;\n  font-size:1.05rem;line-height:1.8;color:#222;}\n.pm-article h1{font-family:'Helvetica Neue',Arial,sans-serif;font-size:1.9rem;line-height:1.3;\n  font-weight:700;margin:.2em 0 .1em;color:#111;text-align:center;}\n.pm-article .pm-byline{font-family:'Helvetica Neue',Arial,sans-serif;margin:.1em 0;color:#555;text-align:center;}\n.pm-article .pm-role{font-family:'Helvetica Neue',Arial,sans-serif;font-style:italic;\n  color:#777;margin:0 0 1.4em;border-bottom:2px solid #e3e3e3;padding-bottom:1em;text-align:center;}\n.pm-article h2{font-family:'Helvetica Neue',Arial,sans-serif;font-size:1.4rem;font-weight:700;\n  margin:2.8em 0 .8em;color:#111;text-align:left;}\n.pm-article h3{font-family:'Helvetica Neue',Arial,sans-serif;font-size:1.15rem;font-weight:700;\n  margin:2.2em 0 .6em;color:#333;text-align:center;}\n.pm-article p{margin:0 0 1.1em;text-align:justify;}\n.pm-article ol,.pm-article ul{margin:0 0 1.1em 1.4em;}\n.pm-article li{margin:.25em 0;}\n.pm-article img{max-width:100%;height:auto;display:block;margin:1.4em auto;border-radius:6px;}\n.pm-article figcaption,.pm-article em.pm-caption{display:block;text-align:center;font-size:.92rem;\n  color:#666;margin-top:-1em;margin-bottom:1.6em;}\n.pm-article .math{overflow-x:auto;}\n.pm-references{font-size:.95rem;}\n\n.pm-article .pm-math-display{display:block;overflow-x:auto;overflow-y:hidden;text-align:center;margin:1.4em 0;}\n.pm-article .pm-math-display svg{max-width:100%;height:auto;}\n.pm-article .pm-math-inline svg{vertical-align:-0.25ex;height:1.05em;width:auto;}\n.pm-article .pm-math-inline svg,.pm-article .pm-math-display svg{fill:currentColor;}\n<\/style>\n\n<div class=\"pm-article\">\n<p class=\"pm-byline\">Afrimadona<\/p>\n<p class=\"pm-role\"><em>Direktur Eksekutif, Populi Center<\/em><\/p>\n<h2 id=\"survei-dan-janji-representasi\">Survei dan Janji Representasi<\/h2>\n<p>Survei merupakan salah satu alat paling mendasar dalam ilmu sosial dan kebijakan publik. Prinsipnya sederhana: karena tidak mungkin bertanya kepada seluruh populasi, kita mengambil sebagian kecil orang \u2014 sebuah sampel \u2014 dan dari jawaban mereka, kita menarik kesimpulan tentang populasi secara keseluruhan. Jika 60 persen responden menyatakan puas terhadap kinerja pemerintah, kita ingin bisa mengatakan bahwa &#8220;sekitar 60 persen masyarakat Indonesia puas terhadap kinerja pemerintah.&#8221;<\/p>\n<p>Namun kesimpulan semacam itu hanya sah jika sampel benar-benar mencerminkan populasi. Dalam praktiknya, hal ini jarang terjadi secara sempurna. Beberapa kelompok kerap terlalu banyak terwakili (overrepresented) dan yang lain terlalu sedikit (underrepresented). Perempuan mungkin lebih bersedia menjawab survei daripada laki-laki; responden berpendidikan tinggi lebih mudah dijangkau; penduduk perkotaan lebih sering diwawancarai dibanding mereka di pedesaan terpencil.<\/p>\n<p>Ketika ketimpangan ini terjadi, data mentah \u2014 tanpa koreksi apa pun \u2014 bisa memberikan gambaran yang menyesatkan. Di sinilah pembobotan (weighting) memainkan peran krusial: ia menjadi jembatan statistik antara sampel yang tidak sempurna dan kesimpulan yang lebih mendekati kenyataan populasi (Valliant &amp; Dever, 2018).<\/p>\n<h2 id=\"apa-itu-pembobotan\">Apa Itu Pembobotan?<\/h2>\n<p>Pembobotan pada dasarnya adalah memberi &#8220;nilai penting&#8221; yang berbeda-beda kepada setiap responden. Bayangkan sebuah musyawarah RT: jika hanya 2 dari 10 ibu rumah tangga yang hadir, sementara 8 dari 10 bapak hadir, maka suara dua ibu itu seharusnya dihitung lebih berat agar keputusan rapat mencerminkan keinginan seluruh warga \u2014 bukan hanya mereka yang kebetulan hadir.<\/p>\n<p>Dalam istilah teknis, setiap responden diberi angka yang disebut bobot survei (survey weight). Kelompok yang kurang terwakili mendapat bobot lebih besar; kelompok yang berlebihan mendapat bobot lebih kecil. Ketika kita menghitung rata-rata atau menjalankan model statistik, bobot ini diikutsertakan sehingga hasilnya lebih mendekati gambaran populasi sesungguhnya (Lumley, 2010).<\/p>\n<p>Secara matematis, rata-rata berbobot dihitung sebagai:<\/p>\n<p style=\"text-align: center;\"><em>\u0233w = \u03a3(w\u1d62 \u00d7 y\u1d62) \/ \u03a3(w\u1d62)<\/em><\/p>\n<p>di mana w\u1d62 adalah bobot responden ke-i dan y\u1d62 adalah nilai jawabannya. Bandingkan dengan rata-rata biasa tanpa bobot:<\/p>\n<p style=\"text-align: center;\"><em>\u0233 = \u03a3(y\u1d62) \/ n<\/em><\/p>\n<p>Perbedaannya: rata-rata biasa memperlakukan semua responden setara, sedangkan rata-rata berbobot memberikan kontribusi proporsional sesuai dengan seberapa banyak populasi yang diwakili masing-masing responden.<\/p>\n<h2 id=\"kapan-pembobotan-dibutuhkan\">Kapan Pembobotan Dibutuhkan?<\/h2>\n<p>Ada tiga kondisi utama yang menjadikan pembobotan sebuah keharusan, bukan sekadar pilihan (Heeringa, West, &amp; Berglund, 2017).<\/p>\n<p>Pertama, desain sampling yang tidak proporsional. Banyak survei nasional sengaja mengambil lebih banyak responden dari wilayah berpopulasi kecil \u2014 misalnya Papua \u2014 agar analisis lokal cukup kuat secara statistik. Tanpa pembobotan, responden Papua akan berpengaruh terlalu besar pada estimasi nasional.<\/p>\n<p>Kedua, nonresponse atau ketidakikutsertaan. Tidak semua orang yang terpilih bersedia diwawancarai. Jika yang menolak bukan kelompok acak \u2014 misal, pekerja penuh waktu lebih sulit dihubungi \u2014 maka sampel akhir sudah tidak lagi cerminan populasi.<\/p>\n<p>Ketiga, kesalahan cakupan (coverage error). Survei telepon tidak menjangkau mereka yang tak punya telepon; survei daring melewatkan mereka tanpa akses internet \u2014 yang di Indonesia masih merupakan proporsi signifikan, terutama di pedesaan dan wilayah timur.<\/p>\n<h2 id=\"lima-metode-pembobotan-utama\">Lima Metode Pembobotan Utama<\/h2>\n<p>Literatur metodologi survei mengenal beberapa jenis pembobotan. Berikut ringkasan kelima metode yang paling lazim digunakan.<\/p>\n<h2 id=\"base-weight-bobot-dasar\">1. Base Weight (Bobot Dasar)<\/h2>\n<p>Ini adalah pembobotan paling mendasar. Jika seorang responden memiliki peluang terpilih sebesar \u03c0\u1d62, maka bobotnya adalah kebalikan dari peluang itu:<\/p>\n<p style=\"text-align: center;\"><em>w\u1d62 = 1 \/ \u03c0\u1d62<\/em><\/p>\n<p>Logikanya: responden yang peluang terpilihnya kecil (misal 1 dari 1.000) secara alami &#8220;mewakili&#8221; lebih banyak orang di populasi dibanding responden dengan peluang besar (misal 1 dari 100). Dalam desain sampling bertingkat \u2014 memilih kecamatan, lalu desa, lalu rumah tangga \u2014 peluang di setiap tahap dikalikan sehingga menghasilkan satu bobot gabungan (Valliant &amp; Dever, 2018).<\/p>\n<h2 id=\"nonresponse-adjustment-weight\">2. Nonresponse Adjustment Weight<\/h2>\n<p>Metode ini mengoreksi ketimpangan akibat perbedaan tingkat respons antarkelompok. Responden dikelompokkan berdasarkan karakteristik tertentu (misalnya wilayah dan gender), lalu bobot disesuaikan:<\/p>\n<p style=\"text-align: center;\"><em>w\u1d62(adj) = w\u1d62(base) \u00d7 (1 \/ rg)<\/em><\/p>\n<p>di mana rg adalah tingkat respons di kelompok tersebut. Jika hanya 60 persen yang merespons, bobot dikalikan 1\/0,6 \u2248 1,67 \u2014 responden yang hadir &#8220;membawa suara&#8221; rekan mereka yang tidak berpartisipasi (Heeringa et al., 2017).<\/p>\n<h2 id=\"post-stratification-weight\">3. Post-Stratification Weight<\/h2>\n<p>Teknik ini menyesuaikan bobot agar distribusi sampel sesuai dengan data populasi yang sudah diketahui, misalnya dari sensus. Jika sensus menunjukkan 50,5 persen laki-laki namun sampel berisi 55 persen laki-laki, maka bobot laki-laki diturunkan dan bobot perempuan dinaikkan dengan faktor koreksi:<\/p>\n<p style=\"text-align: center;\"><em>fh = Ph \/ p\u0302h<\/em><\/p>\n<p>di mana Ph adalah proporsi kelompok h di populasi dan p\u0302h proporsinya di sampel.<\/p>\n<h2 id=\"raking-iterative-proportional-fitting\">4. Raking (Iterative Proportional Fitting)<\/h2>\n<p>Raking adalah perluasan post-stratifikasi yang menangani beberapa variabel sekaligus \u2014 gender, usia, wilayah \u2014 tanpa memerlukan distribusi gabungannya. Bobot disesuaikan secara iteratif terhadap setiap variabel bergiliran hingga seluruh distribusi marginal cocok dengan target populasi (Deming &amp; Stephan, 1940). Teknik ini menjadi standar dalam polling politik dan survei opini publik.<\/p>\n<h2 id=\"propensity-score-weighting\">5. Propensity Score Weighting<\/h2>\n<p>Metode ini digunakan terutama untuk survei daring non-probabilitas (opt-in). Probabilitas seseorang masuk ke sampel dimodelkan menggunakan regresi logistik, lalu kebalikan dari probabilitas tersebut dijadikan bobot. Metode ini memungkinkan survei daring yang murah dan cepat menghasilkan estimasi yang lebih representatif (Valliant &amp; Dever, 2018).<\/p>\n<h2 id=\"hal-hal-yang-perlu-diperhatikan\">Hal-Hal yang Perlu Diperhatikan<\/h2>\n<p>Pembobotan bukan obat mujarab. Ada beberapa hal yang perlu dicermati (Gelman, 2007; Solon, Haider, &amp; Wooldridge, 2015). Pertama, bobot yang terlalu ekstrem bisa berbahaya. Satu responden dengan bobot sangat besar akan mendominasi hasil estimasi. Praktik standar adalah melakukan weight trimming \u2014 memotong bobot yang melampaui ambang tertentu.<\/p>\n<p>Kedua, pembobotan selalu meningkatkan variansi. Rata-rata berbobot memiliki standard error lebih besar karena ukuran sampel efektif berkurang. Ini adalah trade-off yang harus diterima: kita menukar presisi demi akurasi.<\/p>\n<p>Ketiga, bobot hanya bisa mengoreksi bias yang kita ketahui. Jika ada sumber bias yang tidak tertangkap oleh variabel pembobotan, koreksi tidak akan terjadi.<\/p>\n<p>Keempat, transparansi wajib dijaga. Peneliti harus melaporkan desain survei dan metode pembobotan agar pembaca dapat menilai kredibilitas temuan.<\/p>\n<h2 id=\"simulasi-monte-carlo\">Simulasi Monte Carlo<\/h2>\n<p>Untuk membuktikan secara empiris bahwa pembobotan benar-benar memperbaiki estimasi, kita akan coba uji dengan menjalankan simulasi Monte Carlo \u2014 sebuah eksperimen komputasional yang mengulang proses sampling ribuan kali secara artifisial. Dengan cara ini, kita bisa mengamati perilaku statistik estimator dalam kondisi terkontrol (Robert &amp; Casella, 2004).<\/p>\n<p>Simulasi dirancang dengan parameter berikut:<\/p>\n<table>\n<colgroup>\n<col style=\"width: 50%\" \/>\n<col style=\"width: 50%\" \/>\n<\/colgroup>\n<thead>\n<tr>\n<th>Parameter<\/th>\n<th>Nilai<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Ukuran populasi (N)<\/td>\n<td>100.000 individu<\/td>\n<\/tr>\n<tr>\n<td>Ukuran sampel (n)<\/td>\n<td>1.000 per iterasi<\/td>\n<\/tr>\n<tr>\n<td>Jumlah iterasi<\/td>\n<td>5.000 kali<\/td>\n<\/tr>\n<tr>\n<td>Komposisi populasi<\/td>\n<td>Perkotaan 57%, Pedesaan 43%<\/td>\n<\/tr>\n<tr>\n<td>Rata-rata populasi<\/td>\n<td>Perkotaan: \u03bc=65; Pedesaan: \u03bc=45; \u03bc total \u2248 56.4<\/td>\n<\/tr>\n<tr>\n<td>Bias seleksi<\/td>\n<td>P(terpilih|Perkotaan) = 3\u00d7 P(terpilih|Pedesaan)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>Skenario ini mensimulasikan kondisi nyata di Indonesia: responden perkotaan tiga kali lebih mudah dijangkau dibanding responden pedesaan \u2014 sebuah bias coverage dan nonresponse yang sangat lazim terjadi.<\/p>\n<p>Gambar 1 menampilkan distribusi 5.000 estimasi rata-rata dari kedua metode. Histogram oranye (tidak berbobot) secara konsisten bergeser ke kanan dari garis merah putus-putus yang menandai rata-rata populasi sesungguhnya. Histogram biru (berbobot) terpusat tepat di sekitar garis tersebut.<\/p>\n<p><img decoding=\"async\" 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FBfI9XX33VDuGroX7Tcejbzp07m3vuuccOMx7O2rVrzccff2zOP\/98X\/MeMWKE5zTt2rUzRYsWzfZvTz75pOnbt68dkj5If\/75Z+DzhDFz5841LVu2tOvX73521113mR9\/\/NG88847ga5KzTtoGzZssPvM6NGjzUcffWTKli1rEkn76pVXXmnefvttz2mLFy9uxo0bZ04\/\/XSTiuLZ5tm+fbvp3bu3baPEatasWaZp06bmpptuskO0axj5eFi6dKn9nMWLF5t44hzuvy16zjnnmB9++MH3un7jjTfMzJkzbdvnqKOO8v3+JUuW2PbW1KlTTaw2b95snnrqKXvu1kvt63Sn387rd6tfv77Jly9f3JZhx44ddhmmTZtm9+1169aZLVu22LaRjuF6Va9e3Zx22mmmdOnSJtlMnjzZabpTTjnFeZ61atXyvLbTNc23335rWrVqle22\/PPPP3t+TpUqVUyRIkWclkm\/R+XKlc0ff\/wRcTp9rn6\/woULH\/Q3La\/LNbbfdRXk7wRjnnvuOXv9oXZCLFatWmW6detm27YjR440JUuWjMvq3bVrl71G03kk2vcHfV2ktuprr71m26C699CkSRPn92pZ1J565plnTNCWLVsW+DwzwcqVK825555rz1N+ffbZZ6Zx48bm66+\/dj7eBk3XTC7XTfHWs2dP06xZs5xejLQTr3aI62frXm0il0v3IFyWq1q1atn+7ZtvvvF8f4ECBXzdi9V3+PzzzyNOs3r1ajNv3jzf93h1\/pg4cWLYv+tcrb\/rWgwAAADRIeAOAAAAIFC\/\/fabc8D6vPPO8zVv3RRWQEYPIYOkh7kLFy604djDDz888AenCtHHO9wuemihgHMsAZrWrVvbB3s1a9b0FVzS++bMmWOCtHv3btOnTx\/74EMP+\/QAIygKbrRt29b+5tFSQLZ58+bm+++\/N+XKlXN+34wZM+wD\/uXLl5sgKajSvn17+zBIoYfcuXObdHHkkUfaB9ZjxoyJOJ3Cbn4C7trvXcJ+11xzTbb\/rjDILbfc4vx5yFlZ++zff\/8d9TzUIeaiiy7y9ZA3JymUquOzAmvZBdniQcfuyy+\/3OnBtsKyCpVEE\/5OBnpQ\/f7778elzaOOUG3atLGdKoI0cOBAG35UmCvI82pWYFPbW7zD7ZzD\/dm6das99qn9Ea1ffvnFtGjRwnboKFasmPP71BFQncTU6SZI\/\/zzjw0N33\/\/\/bbzUTrTccYrcKyAezypE6VeLo455hjToUMH0717d1OmTBmT07TudC70onOknw4cVatWdZpO14DZBdy1TC5BctfP2X96r4C7rk\/V6alBgwYH\/c01HOpnubQdaP3qWiWSrHVyyCGHOM8706hDu\/atl19+OdD5fvHFF7ajocJ\/FStWNEHTtVS04fZ4U3tLbbTvvvvOdtRx8fjjj8cl3I7o\/PXXX\/Z4pgIW0VLHHxVWCHrfcqVQ7dixY01Ou+CCC3J6EdJSPM6tftohqbRcumejzlcuHQD93PPUcnkF3LOWy2\/A3aWjvj6bgDsAAED00udpNwAAAIAco+D57NmzzYMPPmhv7Ho9vBY9PPRzczcrKB50uD2LAmR6sKkKhUF6+OGHzVdffWXiTaHLWMLtWTZt2mSDia7rQQ8ezjzzzMDD7fvTg75LLrnEPtAPigLgsYTb93+Y2qlTJ+eKcnpwqYBY0OH2\/amDyQ033GDSTbiQ+f5UqXrjxo2BVm8\/8cQTsw2OKTDYq1cv589CztI+qlETYgm3Zxk\/frx5\/vnnTapQMFWjICSCKmzqXO0SbtdDaQXvUy3crn1fD75vvfVWG1p0OTedffbZvr6nguKad9Dh9ixqS6nCftDVUFVtXh0d441zuD8azSGWcHsWtfX9nPfU4U6dzoIOt2fRvnf33XfbEQnSmUZZ8lKjRg2TLNTB5YEHHjAVKlQwXbt2tdcWOUmdGXVu8uKns6qf6TVyjJ9\/T9flchlJRiNzxLuDVKrr0aNH3AK4CxYssKMcaVSsIA0fPtyOBJLM1O5SZ2qX+1gKX\/bv3z8hywU3\/fr1iyncnuWVV15xOucCfsXj3HrEEUc4dVbWeTW7EeN0z1fFSrwULFjQlChRIuXbIbEuVyQu1\/kcWwAAAGJDwB0AAACAM1Xky5Ur10GvQw891N7QVcjEJcSgm\/AaDtpPdTZVZwwikByJgnYa6jtICngkgqqvB0Uh7DvvvNNzOgXjVDE8iIeJLmE8VelMxvWlypqDBw926gii4GfQoYHsDBkyxO5j6USBi\/Lly0ecRg\/uVJXY1Ztvvhl1sF7bpIZCR2oYNGiQmTRpUmDzW7dunUklCpzHeyQR7X8XXnih7XDlRaOEqBK02hXJKrv2jl6qQlunTh1bvdMlNKnwgY7Jfui4E+\/f67333jNPPfVUYPNT55GhQ4eaROAcnnPr6\/XXX3fax1VhXaMuBd1xNDt33HFHQjqT5pSpU6emVMB9\/87Jr776ql02He9zimtgumTJkr7mq2N7LJ\/PcvlbX\/jf9d1LL70U11Xx+++\/m6uuuiol74fESkFPl4IBGv1P1\/VIHi6jYbhKpU7MSB05ec7X\/rFs2bKD\/n3p0qVOHbVpH3krXbq0KVWqlOcoEWobAwAAIDp5onwfAAAAAETl8MMPt0G72rVrO79Hw6YqSOaiWLFitippkyZNzNFHH21vIM+fP9+8\/fbbduhtL88++6ytYO4yxKiL3bt3m0RTxwGFilSFTNUTtQwaqv6dd95xGpI1KwyqKp16fzjDhg0zX375pdP88ubNa9erqtgqpKzfRcukgJ3rcOUPPfSQueyyy3wPF+vS4ULbjALU2ma2bdtmK9Krurdr9dx7773XdOnSxRQqVChiNX\/XyrYKUGqZmjdvbsqUKWOXSZWE9EBdw6e70BDbbdq0OeihlyrIK3i2\/7Ds6tzhRcN+u4Z54kHDD1999dWeFfNUJVAVQ7388MMPdht02TbCVfF2+R21zZ5xxhl2u9d\/K+ynKvNa79oe9NIxzmUYZkRH1Ri1j7rSCCOqeH7yySebIkWKmBUrVtjzh4KdLpUdY6UqaRo14IQTTrAvVT3VtqNlUcBaxwOFiXVMmDBhgvNxSscgHXPjQUEfVWp2Ocdof1BAVufrdKdzqDrDRDqXHuj99983o0aNcn6YrlFEdHw+8sgj\/x1RR9uqSyhWnQcvuugiU7FiRZOK7Z1MPIfH6rDDDrPbTLNmzez2o46pM2fOtJ3iXEfjueWWW2wbU8ejSNO4dgJTcCar7a5wiJZp1qxZtuLvr7\/+6vl+hXOuvfZau+3nz5\/\/P3\/TcWl\/CvgoYOLlrLPOsh14I\/EKsgRF68KrfaQRMZKVAlRanzo\/NGzYMOGf79q+0r7hh+v04T6f5fK3vpKJtuP9f38d413aP6eccopnZ13J7tij0b90XHU9L+q6W\/udPk\/HCHVIV\/tizJgxnqO3aESst956y84jCPFoH+g7aX2qA43aypUqVbLtZL3y5ctn20OrV6+2VeknT55s7ze4BAvV8e\/GG2+08wjH5RpQ1Y5VCEDtebXj1bbXdqJrQG3jak\/onKVrwCA7oeH\/2oUaCUltMI0io0IZ6rzg0jFV98zUkSTSNgD4oXaqjkfxaou4jA6p486xxx570L\/Fa5lc6J6kzkcHXk+kajtE96n3v896IJ2X1ImsWrVqUc0fAAAg44UAAAAAZDwV4473q2DBgqEePXqEVq1a5Xt9N23a1Okz2rRpE1q7dm3Y+YwcOTKUL18+z\/mcccYZnsvUr1+\/qNZD\/vz5Q82bN7fr4q677grddNNNoQsuuCB05JFH2r8PHDjwoM+qUKGC8\/xLlSoVmjp1atjlHjVqVChv3rxO8+rbt2\/Y+ezevTtUtmxZp\/kcc8wxod9++y3svMaOHRsqUKCA07zatWvn+dv4+T2qVq0a+uOPP7Kdz759++zv4Tqv5557Luwyabs89NBDneZTp06d0LJly8LOa8iQIaHcuXM7zeu2227zXF8TJ050mpemy2laL17fPVeuXKElS5Z4zuv666\/3\/M7t27cP+\/6GDRtGfG\/JkiVDCxcudP5us2bNCj3xxBN2vvoOmof2s2SyePFi5\/0hkZ83bNiwiPN59tlnnZf79ttvD+3Zsyfb+SxdujRUo0YNX8cYF3369LHHwKuuuir08ccfh7Zt2+ZrPX3zzTeho48+2un8s2PHjsDOb1m2bNnifJ4+99xzQ9u3bw+lwjYcy+uwww6z29KmTZt8Ld\/evXtDVapUcfqMrl27RtxWnnzyyX+PJZFeHTt29FyuTp06RbUeChcuHGrdunXohhtusG2enj17hs4555xQ8eLF7d\/HjBlz0Gf5mX+mn8P9bs+nn356aMWKFdnOS+cbHYtc5zV+\/PiwyzVnzhynbU+vs88+O2zbXcfie+65x3mZtG696HzhMi+t22SgY4jXspYpUybmz4n2usbvcTHS9UC8PPXUU07Ld9lll\/mar7Zz1++dnfPOO8\/p\/S+88IKv5Xr++eed5qvrz+wULVrU6f3z5s3ztVyXXnqp03yffvrpUCL4beskor0aidqILp9x2mmn2fZqOF9++aXdJl2u38O1h\/0eTw985cmTx17vXHvttaE777wzdMstt4QuvvjiUMWKFe3fe\/XqddBnffTRR\/a80qJFi9CIESNCq1ev9rX+dF5u1KhRINe8XvdAjjvuOOflU7vvxx9\/DA0YMCBUs2ZN+3616V24fBdt56nMbztH7cL58+dnO6+ffvopVKhQIaf5TJs2LeHfNRHn4XgfpxLB9Zokmaxbt855\/btcq++vbt26TvP94IMPDnrv+++\/73xe8UPXp67fd8OGDQe9X9eLLu\/VtbYfus\/iMl8di6Nx+eWXe8573LhxUc0bAAAAoVDujE\/4AwAAAEgIVcxSxVnX6pdZVFVy4sSJntM1bdrUViVThfhwVIXs6aef9pyXqny5VHf0QxWgVHlalXtU4U1DHw8YMMBWCVMlNVUIVkVpVfmKpXK7KorVqVMn7DSq7K4q9S5eeeWVsBXXPvjgA6dKQaqGqsrHkarUnHfeebZyqAtVH3atCOqlaNGi9rcIV\/VSlYR69+5t7rjjDudh48MZOnSordrmRRXePvvsM1OuXLmw03Tv3t1Ws3eh6mMulcpShdaLqrFFouf9b775pufxyKVC8jXXXBP2b5GqM2Udb1RN0JUqEPbp08d88803ZuHCheb++++31QkRuxdeeMFpuo4dO9oqzTqWZkdVMLV\/Fi9ePNCfpV27dubPP\/+0xwmNcqEqj34riWpkFC87d+50qurtx+bNm+0yu5yntX51nlZlxXSn465+U1UO9UOV7V3e06FDB\/Pyyy9H3FZUAbxv376e89IIN0FXzFV1frW31q5dayvYDx482LZ5nnnmGdtOyWoLHXfccVF\/BudwfzSKgCqYqtJ\/dvLkyWOeeOIJ54q9kdo8zz33nGeFYFH13Uhtdx2LdS5Uu8eFtrN0s2TJEs9pNHJBKlAVXdffMkg697mONuWH6\/QauYflin19ZTJdc3hd24iutzWyT6Qq8Rq9Q6OJeFm8eLFtkwRJ19Y9e\/a094R0vaP2+YMPPmhH6nv33XftZ2rUDo0QcqATTzzR3kdSO1yV0f2ObqJrSFWm18glXqZMmRLx717XgBpRxHX5dK1Xt25dO6KO7j+pqrvuP8A\/VWdW27Jq1arZ\/v3UU091vq+jyvpAotshiW6L5HT7KFmXK9p2iEt73KVdDwAAgOzxpBQAAABAQigoPXbsWPtQVSFrheJcuDyA1YNBDTnscsNaDxzDPfQ6MPAVFA0zrmGx+\/XrZ4fNDqdevXr2Fa2uXbtGDLfvH9pVmNaLhstWB4Ps6CG0i9tuu80p5KuAZ5MmTTyn27t3rw3XB0EPOF2Gib\/77rudHobrgXS4B96u60vB9UidNLIoCH3gEMPhfsOvvvrKpJNIofMsI0aMiPh37Y9r1qyJOI2OE40bNw779\/z580d8fyzDzR9zzDHmnnvuIeAeAAV3tW96UehaARsvCofqmBAkBS78BnUOpHOHyzHhu+++M0HZtGmTadmypQ0peVHYWh2ZFKLNBOrQpDCazsk33nhj2M5i0bR5FOweNGiQ0\/x0HPEajl1hfIWMg1KqVCm7TfTq1ct27gsXXG7evLk54YQTov4czuH+PPLII7bjgZcnn3wy7O+2v6+\/\/tq2yQ6kYLtLhxtRhweXDi\/qeOTSsWjOnDlm3rx5Jp24dOqM9fyRHbVFdb2mTg8KlC5atMisW7fOHsvUvlHQVJ2yzjrrLBtadaVjQ1DteFeuHT39dioM1xnO9fNZLn\/rK5Opg7fLelEnn0j3GrKoY6LOwYm8H6L9S20cHfdLliwZdrrq1aubM888M9tro1jaDKJzoDrWx9pWjuc1oIL8t9xyS9Tvz2S61+Z1b6d169ZO81InjERTMQy1oXL61blz54R\/93Tn57yWyLZITrdDknW5om2HuLTHgyrWAgAAkIkIuAMAAABIOIV9zzjjDBuO8\/LJJ594TqMHtC7Bvqyb4G3atAnkc10pLKQK8\/GmgLsLBVFcH1x9++232f67S7VeresuXbqYIIPLrp\/t8nDDdR2og4IC+C6yeyCuzhwuVcD00F1hIhcKiboufxDrK5lo\/w1XgTbL3LlzzfTp06MOwLvsT5HCGVmhkOHDhztVsUX8uAa6FXhQMNfFlVdemZRB7aOOOspzGlUVD4rOvd9\/\/73ndKrOqfOgnxBkuti3b58dNUXVSL1C7ppWQVIvl112mfMoAoULF7YdCxPZ5lFHhpNOOsnEE+dwf7S9tG3b1vk44jVSSlbbRpV2swuZu4Q3VL2\/UaNGTsukThoXX3xxRrZ5tm7d6jmN31E\/IgVzVF1Zx3WNsqCRbtShskWLFjZcqu1I5z79HqoUfdVVV9lq0ap27GfEGgVcE8n1fK1jsB+u04f7fJbL3\/rKZC7naHXMdTnfZ3EJeqtNEtR1jEaUUfslHdrKXteAGsHGpT2H4Kiz3NVXX+05nWsniY0bNwawVID\/81oi2yI53Q5J1uWKth3i0h53adcDAAAge9wtAgAAAOBMVfoU9j1QVjU\/hUo1\/LwLVQbv0KFDxKGvt2zZYoMyXvw8zJXKlSt7TqPKhPp8hcNioc9S1fh4U9jEpXq733WWXTBbD30VfPGiCmxlypRxXiaXQFW4ZfJLy+YVkj5wfblUzNWyXXDBBQdt6y4PU1Qt3Ksi3IHrS8OZuyxTOtEDJ4WqVNE1EoXLa9eufdC\/q2NNpOOOaDSITp06eVbdjlQdX8fFjh07mttvv92G+BQG0\/GgSpUqtkOOS6V+xM51+\/dzHlGwRceQcCNcxErbzo8\/\/mjD+QqQLl682FYS1HlWlcH192gDR7FUlTzQ1KlTPTs5DRkyxHTr1s2kovPPPz9sVbe1a9fa9onaCS4UdFJQNNJ5RJWnXTr+xaPNo+0tCOrMp8qw8cY53B91KnWpyr7\/NuZ1nsw6vtasWfM\/\/zZjxgynz9DoD36ozfPyyy87LVM60THfS3bXZn6pU5\/alC6jYR3o9NNPt5XZGzRoYJYsWeLUeVYdJFwqTQfBtW3tOtKG3+nDjVKQzMvlMo9EL1cmczlHx6NtoJCv2iaxVk7XyDNBj36Udb9Go4nMmjXLLFy40N6j0EgTapupreY3\/OjaVtY1oEa1CEfHN52z1PGnYcOGtkOX1reu\/3QdqPWBYKmd47JedXwrVKiQZ8jUpT0OuPJzj0\/nSr\/TR3tuzel2SLIuV7TtEJf2OAF3AACA6BFwBwAAAODspZdeMhUrVow4jQIOeoA5efJkz\/l99NFHdpj6AwPBWf744w+nB5MKkOoVJH3uggULDgru+KUq24moWquwlx96UK0KqHv37o043T\/\/\/HPQv0V6oLs\/vxVcFfhVIN5rSGg9vNbv43dI2liWzXX9JnJ9KTCtdeC1j7iEjVKNgliPPPJIxJDvW2+9ZatGHzgU8ejRo8327ds9g61e1bxVSf7RRx\/1XFZtz6qCml2lVP3m6pii8IOCoYkKe2WS7PbJII6h8Qi4z58\/3wwcONC88847gQbR9xev+R5IYdqRI0eaiy66yKQqtU8i0flTwXW1P7KrZH2g5557zrYJatWqFfb3d6EKqEFXQdV+og6K6iwXC5fqmUHgHJ5568t1+nRr87h0ZgqiwnLZsmVjer\/a7y+++KJTxwUFjFRp36WCdBBcj2sunQmimT7c5yfzcrl03kr0cmUqtdtcOpa\/8MIL9hU0tU1iDbhfcsklMRcNyKIAu9pTw4YNsx1Ac6KtrGvA7K7tsjsfZndOVBX5GjVq2KC8OkErnE3Hjtj4uWfnEnCPtnMEEG6kRt0Tdmmv6VzpJ+Aey7k1p9shupeZXceUnF6uaNshiWqzAwAAZKro0wAAAAAAkA09pFNoIVxo\/UBPPfVU2L8tX748R9fxX3\/9FfM8FFxNBK+hsrMLH7qEafUQ+UBr1qxx+gw\/FdKzlC5d2ikY4zpSQFDrS2FkF4lcX3oQrodlXlxCEalGFfG8KhUqeDdhwoSD\/n3EiBGe87\/mmmuc9m1V3Y+WtgsdKx9\/\/HEbqC9RooQNTKjjDw++gpPdPpnIY4ILhSjuuOMOGypVQDCeIXS\/D4Kj1aJFC9O2bVuTztR55pxzzrGVVU877TSn3\/npp58O+3faPO44h2fe+nJpH6Zjm8elGqRXp71E0XFfFYpduHaECILrtuO3Yq\/rtUi4z2e5\/K2vTEXb4P8oVK4K6Pfee2\/cwu0ubWV1MtRyRGvFihW2g+SAAQNspyB18tc8XQpEIHvqNOAqmpFKgFhHIHQdvS+RbZGcbofo\/teBxShSuR3i0h5XBxsAAABEhwruAAAAAAKnSixDhgwx48eP9xwGVBXf9ZAvu4dSLtXj4knDS8fq+OOPN4kQzY1yVVLzusmvocmjDUhGU43adchuLYPrQ6Ig1pdr1bmcWF9eYdhEBVoTTSH0L7\/8MuI0w4cPN2efffZ\/Oq1MmjQp4nuOOeYYc9ZZZzktw6uvvmoaNGhgVq1aZWKlY6WOmXrVrVvXvPHGG3ZIe8TG9cGnS4hwf0FVoty1a5fp0KGDeffdd00iJKrzxLhx40yPHj1sWyARo5jkpIIFC5pnn33Wjsbg5f3337dVR7N7mJ\/qbR5VG\/Qa5SconMMzb31pepfql+nW5nH57ZIl4C6qRKwRuIIaXSUI5cuXd5rOtXOG3+nDfX4yL9f06dOTbrkyVaq3DYK6H6IRjvr06ZMUnYAVVlWH6TPPPNOzErgLHcMV3tdL1626vvQT2Ia\/NkUsowDG09tvv21fOa1nz56ehQTgn85ta9eudTpXVqhQIfBza7ly5bJdpiA\/I93bR0G0x\/3ecwIAAMD\/IeAOAAAAIC5UmVEhzW+\/\/dZz2p9++slWMD7Qnj17cvTX2blzZ8zzSNQw69GEW1zek13ISQ91XYOb8VrnrssQ1PpynT4Z11es6ypZXXjhhbbKbKQHVWPHjrXBkKzfZeTIkZ5Djnfp0sU5kFu5cmVbhV0B5RkzZpig6JiokNh3331nPwPxD3bu2LHD13yDChSqemOiwu2Jpmr0CpE899xzaR9yr127tjn66KM9R35REGr27Nnm5JNPTrs2T6LaO8I5PPPWl6Z3CTamW5unVKlSgQd8cnp5E73MVatWtecir\/af30rZriN9nXDCCb7+PRmWa8yYMTmyXOr85ToKQKZI9bZBEO2Dr776ytx8880mmdSrV8+OFNaxY0ezcOHCwOb7ySefmKZNm5opU6YEOlpUusuu42iqBdznzZtn713kNNeROOGPzq0\/\/\/yz07lV15UuVOjCpc1etmzZbDuBqFhGmTJlzN9\/\/x3x\/bqfpk6tLqNHpnP7KIhRpBilBgAAIHrJeSUHAAAAIC1kVyUmO8uWLQtbFTUnBVEhrECBAiYRvKp4H0ghD5fKxtlVSXetkJVdZc+gqi1HU+08lvXlOn0yrq9Y11Wyypcvnw0VeFVyHT169H8quns9HL\/qqqt8LYcegP3444\/mlVdeMaeccooJiqqbarh6xKZ48eI5ekyIRFVuH330UZOqXELrquB+ww03mEyQ6W2eRLV3hHN45q2vRLUPk43LqAiugZ1EcP1d8+bNaxJFxyaXCtIa1cpP8P733393mi67Dk2R\/v1ALhXxo1muGjVqZPvvNWvWDHy5NNKRyz6s30mjgSB92gaxtg\/27t1rrr32WpOMTj\/9dPPLL7+Yp556KtCOGfPnzzfdu3cPbH4Acl48zq2xtkOSdbkqVark1J5fsGCB8zIFsVxBtMcTNdoZAABAOiLgDgAAACBuXCvihhv6ukSJEgEvUfqaM2eOr+n1MGD37t2e02X3G7iG+FwfIOz\/AHvRokVOgVXXqsxBra+5c+c6TZfI9aUHKApwe3H9\/FTUtWtXz2myQu0KIPz6668Rp23durWtYuWXKsaq8ruquKs688CBA825554b87qfNm2a+eijj2KaR6ZzrX7ouo9HO312RowY4VTFWB0vrrjiCvPBBx+YxYsX2yrgChwd+GrcuLFJJG3nLp5\/\/vmMCLnT5kkczuGZt75cp0+3No8qH3tVP165cmWOV3nO4tXOypLoysSNGjVymm7WrFnO83SdNtxnq9r9cccd57Ttu55fdF3gEvhSkLxkyZIps64yWabfD5k8ebJziFHbz9ChQ81vv\/1m7y\/p3sKBbeV+\/foFunyHHnqouemmm2woferUqeahhx4yLVq0sKMZxkIdtHXtCiA9JOu5NRmXS\/c+1IHIiyrL+xlBw2W5NDpVtEUrXCrKH3PMMVHNGwAAAATcAQAAAMSRHi66CBfccK1u8vbbb2cb9ov11blzZ5MqFG7xU8Hm22+\/jbp6jSqUuQwDrbCvHiz7CcW4BDhcqjB6mTlzpn0gkoj15bq8eijuh+v0QayvZKXq6Q0bNow4zcSJE21nAIWJvVxzzTUxL9OJJ55oevfubT788ENbqVkVK1XhXcephx9+2FaIr169uvP8NB9EL1yF0mj3cVHA3M9D3nBchqFXldtPPvnEvPnmm+b888+350WFacItVyL16tXLDBgwwGna5557zvTs2dOkK3VUcK1sF2ub54cffohLm6dJkyYmVXAO90fHNz9VeGnzpNZ5TKMy+a1imUWdXf\/++28TBHXA+u6775ymDbLasYuWLVs6Tff11187TadriOnTpztdF5QuXTqm5dJv5Lpete+6dHaI9LkK5rq0ndQJ06WjrZ\/16vo7ZRJ12nG57n7kkUfi0jbo37+\/yUkubWVRcF1heF1nVatWzYYUc+fOnbC2skY1qlOnjrnjjjvMZ599ZlasWGHWrl1r90ldgz7wwAOmffv2pkqVKs7zpJMzkD50fMhu5KNoz5dBnVuDbh+5TqvOW7Vr107Ycuk6XcdlL2eeeabTOTc76ugUiUaocenYCAAAgOxRwR0AAABAXHzzzTfOYa9wD\/4V9ipWrJjn+xUexf9VqnbhEvaV7CrnKFzpEnzQQ92vvvrKeZneeeedqJfJLwXp3333XadpFRx66623ol62qlWrOlXfU2DPz9C\/Qa0v12HjXatHJppXKF2\/n7Z3r9+wbNmy5uyzzw546YwdXrlu3bqmXbt25vbbb7eVBdWZQw\/AXKpDuYaakL0GDRo4rZr333\/fbN++3Xla11BXJPPmzfOcplOnTuass87ynE4hJIULE+2uu+6yYR4XgwcPtqH4dDRq1Cjn0FS4No\/rcOi0eTiH+\/Xnn3+aSZMmOU2r7VijRXjJly+fDescqF69etmGCg\/06aef2g5grjK5zZPdeo62cnp2v3flypXNzTffbP755x8TLbW1rr\/+eueOrV6dE2XJkiU2NOr1eu211zzn1apVKxt49eJ6baDQrcsILJdccknEv1988cVOn+e6XK77SRDLpX3EJYCr9onL8qu9rN8pFcXzuKJRotR5NlPbBi5tZV1ru1Zm91PtN1YKs+qcpGD73Xffba9Hda2vziHly5dPqmtAdWRwOd4CmUYdgL32C5dOwjqWX3DBBU5tdpfjue5FjBs3znM6daqpWbNm2L\/rfpTagV70WS73StSpx6Xj5IUXXmjXSTgXXXSR0zEn0e2jSAVnVq9eHXEaXevr+gkAAADRIeAOAAAAIHCLFi0yHTt2dJ5eVbayoxvaLgEIBVddQgau\/FS5TCYDBw50epgwYcIEp+C5KswomJudc88912mZ7r\/\/fqfp9DBgyJAhTtO2adPGBPUg1yWQ+MorrzgFzxWQzm7IWW3HrVu3dlqm++67z2m6uXPnOj\/M8VpfRYsWdZrP7NmzTTLSQ6hwFZGzaLh4ryGDr7766qirNUVDYQyXUNaqVasSsjzpqlKlSnbf9LJ+\/Xpb\/dIlnBRENcuNGzeanTt3BhJslClTpthORTlB+5drdfZBgwaZm266yaQTVfF1De6rIn+46p0KQrmE2F5\/\/XUTpFRt83AO9+e2226zIWQvDz74oFPwXO1DtRMPpPOxS9tdx9LHHnvMuPjiiy\/M999\/7zmdOmA2a9Ys7do84driQQTcs34LXUPofKmQukZg8kMhK133abQRFwpauY5YEZSCBQvakKlLmNarE7DO3S7XNzreq5p0JNpXNBqRF7UXvar0q+PkG2+84TkvnWe8Ov9puSMFz7JoPXhdg+uc5XIddeWVVzoHxZNNvI8rjRo18pzm448\/NmvWrDHp1j5wuQ6qVauWUxBSIy98+eWXJqeparE6fXrhGhBIL66jBapDjBe1oV3a6926dfOcpmvXrk73Sp588slAlt1ludQJqEWLFp7z0YgZKq4Tic6Naud6UVGStm3bmmi4tMNd2vMAAAAIj4A7AAAAgJhpKHQFlFUdUhUAVd1bVfdcQ56RHuy73GBWtRQFcmKlB\/TDhg0zJ510kklFmzdvtmHmSCFH3Xh3CXjI5ZdfbgoVKhQ2hOBSoVOhS6\/Qtta75rdhwwbP+SkQ4\/KQ38WyZctsZaBIlZjVEaB3795O84v0YKhz585O83jzzTc9Q88Kxl5xxRV2v\/OideUVIjriiCOclu2ZZ55xqqKXaC6hJa+Hf9qWFXD3Y+nSpTYoEQuXYer9VLhF9rp06eK0anQeUSXuSKE2VeJXJ65YuVaA18NkL6qYe+edd5qc9PTTTzuvZ02rtkKq0jlL7Q49UFdQoX79+k6\/kzRt2jTiyDQubR6FT9VWiZWOXzqua5lSEedwf6ZOnWrbKZHaDmqDPProowlr8zz++OPm888\/96xk6XpsUZtOIfcg2jwDBgwwf\/31l0kGCu17tbldOgB4UafP559\/3oYvVdHzgQceMF9\/\/XXYqtM67r3wwgv2uknbjiuF6HOCzjsuHRnVCUujKmVHnURuvPFG8\/vvvztdR5UpUybiNArl9unTx3Ne+g3U1l23bl22f9e\/d+jQwanDt8vnqWPgZZdd5jndnDlz7PoI13lG69Hl8\/S7uF5vJSMdd3Q94kUdEKLZV13aBmqjuqxrL\/otNVKR2ja61slpLu1l1zaYOmQGcV2lziaxFlfgGhDZddxUp5Kcfrm24eDfaaed5tQJVB071Q4NR21nl4756nzlEqq\/9tprnTpq6V6Jli0ctRtdRow644wzzKmnnuo5Xd++fT2n0TarTnnhzldqP2mbdikEoPapy7k8XOV6L82bN49q3gAAAPj\/QgAAAAAyni4Ncur1wAMPRFz\/mzdvDhUrVsxzPrly5Qo9\/fTTUf2Wv\/32W6hfv36ho4466t\/5edH0Lt8vCBUqVPC1TkuWLBl6\/PHHQ\/PmzQtt27YttHHjxtC0adNCffv2DeXPn995PtOnT4+4XJdcconzvNq3bx9atGjRQfP48ccfQ6eddprzfJ599lnP9eV3G6xYsWLo+eeft8u3Y8eO0Lp160JTpkwJdevWLZQ7d26neeTLly+0YsWKiMt16qmnOs1Ln9mrV6+D5rd3797QhAkTQscff7zzdxs3blzIxf7bvtd+dsIJJ4RatmwZOv\/88w96ZfcbJ8LMmTNjOg61atXK92cOHjw4VKRIkdB1110XmjFjRlTL\/dZbb3kum36bZLF48WLndZrIzxs2bFjE+fz999+hvHnzOi\/75ZdfHvryyy9Da9asCe3cuTO0dOnS0CuvvBKqWrWq720rnF27djm9v1q1anbacPbs2WOPVa7L07hx47id33SM0rpzXZabb745lMzbcDxew4cP91y+Qw45xHM+Ope\/8847Ua2DqVOnhvr06RM67LDD7LzUxvDSqVMnz2VymY8Lv+s0k8\/h0WzPNWrUCL3++uuhP\/\/80x7fVq9eHfr8889Dl156qa92ptZ1OPpb2bJlneZVoECB0P333x9av379f+axe\/fu0Lvvvhs6+uijndf7L7\/84rl9bd261Wkf00vTaX2dffbZ2bZ5tmzZEkqUunXrRlzWQw89NOK5Ihytd5f9o3LlyqH69euHWrduHWrSpIk9H6pN6Hf70zlNv20i2wD76969u9M81b679957Q3PmzLHXUqtWrQqNHz\/enkNd3l+wYEHbdnCh8\/iJJ57oNN9KlSqFXnrppdCSJUvsfpbVPtG\/u7z\/pJNOsp\/nQp+h\/dNlvmeccUboo48+Cq1cudKur7lz59q2hNajy\/vVlk60oK\/ltX+47gf6vc4666xsjys\/\/PDDQfPW+ePYY491mreu911\/4wP3N90\/qFKlyr\/z0r9Fon3PZZm85hPJ6aef7tQmWrhwYcT56Lzn55gVidpQOg\/edtttofnz50f1vR5++GH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alt=\"\" \/><\/p>\n<p><em>Gambar 1. Distribusi sampling estimator berbobot (biru) versus tidak berbobot (oranye). Garis merah putus-putus = rata-rata populasi sesungguhnya.<\/em><\/p>\n<p>Secara kuantitatif, estimator tidak berbobot menghasilkan rata-rata estimasi sebesar 60.98 \u2014 meleset 4.59 poin dari nilai populasi sesungguhnya (56.39). Estimator berbobot menghasilkan rata-rata 56.35, dengan bias nyaris nol (-0.04).<\/p>\n<p>Mengapa estimasi tanpa bobot meleset ke atas? Karena sampel didominasi oleh responden perkotaan (rata-rata 80% dalam sampel, padahal populasinya hanya 57%). Penduduk kota memiliki skor rata-rata lebih tinggi (\u03bc=65 vs \u03bc=45), sehingga &#8220;menarik&#8221; rata-rata keseluruhan ke atas.<\/p>\n<p>Gambar 2 memperlihatkan boxplot bias dari kedua estimator. Seluruh distribusi bias estimator tidak berbobot berada jauh di atas garis nol, mengonfirmasi bahwa bias ini bersifat sistematis \u2014 bukan kebetulan dari satu sampel tertentu.<\/p>\n<p><img decoding=\"async\" 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alt=\"\" \/><\/p>\n<p><em>Gambar 2. Boxplot bias: estimator tidak berbobot (oranye) memiliki bias positif yang konsisten, sementara estimator berbobot (biru) terpusat di nol.<\/em><\/p>\n<p>Tabel berikut merangkum perbandingan kinerja kedua estimator:<\/p>\n<table>\n<colgroup>\n<col style=\"width: 33%\" \/>\n<col style=\"width: 33%\" \/>\n<col style=\"width: 33%\" \/>\n<\/colgroup>\n<thead>\n<tr>\n<th>Metrik<\/th>\n<th>Tidak Berbobot<\/th>\n<th>Berbobot<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Rata-rata estimasi<\/td>\n<td>60.98<\/td>\n<td>56.35<\/td>\n<\/tr>\n<tr>\n<td>Bias<\/td>\n<td>+4.59<\/td>\n<td>-0.04<\/td>\n<\/tr>\n<tr>\n<td>RMSE<\/td>\n<td>4.62<\/td>\n<td>0.68<\/td>\n<\/tr>\n<tr>\n<td>Std. Deviasi<\/td>\n<td>0.54<\/td>\n<td>0.68<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>Perhatikan bahwa standar deviasi estimator berbobot sedikit lebih besar \u2014 ini adalah trade-off klasik bias\u2013variansi. Namun dalam hal RMSE (Root Mean Squared Error), yang mengukur akurasi keseluruhan, estimator berbobot tetap jauh lebih unggul karena:<\/p>\n<p style=\"text-align: center;\"><em>RMSE\u00b2 = Bias\u00b2 + Variansi<\/em><\/p>\n<p>Pada estimator tidak berbobot, komponen bias mendominasi RMSE. Pada estimator berbobot, bias mendekati nol sehingga RMSE hanya ditentukan oleh variansi \u2014 yang jauh lebih kecil.<\/p>\n<p>Untuk memahami sensitivitas temuan ini, kita coba mengulangi simulasi dengan lima tingkat bias seleksi yang berbeda: rasio probabilitas perkotaan terhadap pedesaan dari 1x (tanpa bias) hingga 5x (bias sangat besar). Gambar 3 menampilkan hasilnya.<\/p>\n<p><img decoding=\"async\" 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alt=\"\" \/><\/p>\n<p><em>Gambar 3. Bias estimasi pada lima tingkat bias seleksi. Semakin besar rasio, semakin besar keunggulan estimator berbobot.<\/em><\/p>\n<p>Pola yang muncul sangat jelas. Pada rasio 1,0x (tanpa bias seleksi), kedua estimator menghasilkan kinerja yang setara \u2014 keduanya terpusat di nol. Ini masuk akal: jika sampel sudah representatif, pembobotan tidak mengubah apa-apa. Namun begitu bias seleksi muncul (rasio &gt; 1), estimator tidak berbobot langsung menunjukkan bias positif yang meningkat secara proporsional, sementara estimator berbobot tetap terpusat di nol pada semua tingkat bias.<\/p>\n<h2 id=\"implikasi-dan-penutup\">Implikasi dan Penutup<\/h2>\n<p>Simulasi Monte Carlo dengan 5.000 iterasi mengonfirmasi lima hal penting.<\/p>\n<p>Pertama, bias dari estimasi tidak berbobot bersifat struktural, bukan stokastik. Memperbesar ukuran sampel tidak akan menghilangkannya \u2014 ia berasal dari ketimpangan komposisi sampel, bukan dari keberuntungan penarikan.<\/p>\n<p>Kedua, pembobotan berbasis inverse probability secara efektif mengeliminasi bias ini, menghasilkan estimasi yang mendekati parameter populasi.<\/p>\n<p>Ketiga, ada harga yang harus dibayar: variansi meningkat. Namun secara keseluruhan (diukur dengan RMSE), estimator berbobot tetap lebih akurat ketika bias seleksi moderat hingga besar.<\/p>\n<p>Keempat, interval kepercayaan dari estimasi tidak berbobot bersifat menyesatkan \u2014 ia tampak sempit dan presisi, namun secara konsisten gagal mencakup nilai populasi yang sesungguhnya. Inferensi yang didasarkan padanya bisa mengarah pada kesimpulan yang salah.<\/p>\n<p>Kelima, ketika tidak ada bias seleksi, kedua metode menghasilkan hasil yang setara. Pembobotan tidak merugikan, tetapi manfaatnya paling terasa ketika ada ketimpangan representasi.<\/p>\n<p>Pembobotan bukanlah prosedur teknis yang hanya relevan bagi analis atau peneliti survey. Ia adalah komponen etis dari riset survei yang bertanggung jawab. Tanpa pembobotan, kita berisiko menyajikan potret masyarakat yang hanya menggambarkan siapa yang mudah dijangkau atau bersedia menjawab \u2014 bukan siapa yang sebenarnya ingin kita pelajari. Bagi jurnalis yang mengutip hasil survei, bagi peneliti yang merancangnya, dan bagi publik yang membacanya, memahami peran pembobotan berarti memahami perbedaan antara angka yang jujur dan angka yang menyesatkan.<\/p>\n<h2 id=\"daftar-pustaka\">Bibliography<\/h2>\n<p>Deming, W. E., &amp; Stephan, F. F. (1940). On a least squares adjustment of a sampled frequency table when the expected marginal totals are known. The Annals of Mathematical Statistics, 11(4), 427\u2013444.<\/p>\n<p>Gelman, A. (2007). Struggles with survey weighting and regression modeling. Statistical Science, 22(2), 153\u2013164.<\/p>\n<p>Heeringa, S. G., West, B. T., &amp; Berglund, P. A. (2017). Applied Survey Data Analysis (2nd ed.). Chapman &amp; Hall\/CRC.<\/p>\n<p>Lumley, T. (2010). Complex Surveys: A Guide to Analysis Using R. John Wiley &amp; Sons.<\/p>\n<p>Robert, C. P., &amp; Casella, G. (2004). Monte Carlo Statistical Methods (2nd ed.). Springer.<\/p>\n<p>Solon, G., Haider, S. J., &amp; Wooldridge, J. M. (2015). What are we weighting for? Journal of Human Resources, 50(2), 301\u2013316.<\/p>\n<p>Valliant, R., &amp; Dever, J. A. (2018). Survey Weights: A Step-by-Step Guide to Calculation. Stata Press.<\/p>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>Afrimadona Direktur Eksekutif, Populi Center Survei dan Janji Representasi Survei merupakan salah satu alat paling mendasar dalam ilmu sosial dan kebijakan publik. Prinsipnya sederhana: karena tidak mungkin bertanya kepada seluruh populasi, kita mengambil sebagian kecil orang \u2014 sebuah sampel \u2014 dan dari jawaban mereka, kita menarik kesimpulan tentang populasi secara keseluruhan. Jika 60 persen responden [&hellip;]<\/p>\n","protected":false},"author":35,"featured_media":9680,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[331],"tags":[78,109,191,151,110,98,113],"class_list":["post-9674","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-pojok-metodologi","tag-indonesia","tag-jajak-pendapat","tag-pemerintah","tag-pemilu","tag-politik","tag-populi-center","tag-survei"],"_links":{"self":[{"href":"https:\/\/populicenter.org\/en\/wp-json\/wp\/v2\/posts\/9674","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/populicenter.org\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/populicenter.org\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/populicenter.org\/en\/wp-json\/wp\/v2\/users\/35"}],"replies":[{"embeddable":true,"href":"https:\/\/populicenter.org\/en\/wp-json\/wp\/v2\/comments?post=9674"}],"version-history":[{"count":5,"href":"https:\/\/populicenter.org\/en\/wp-json\/wp\/v2\/posts\/9674\/revisions"}],"predecessor-version":[{"id":9736,"href":"https:\/\/populicenter.org\/en\/wp-json\/wp\/v2\/posts\/9674\/revisions\/9736"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/populicenter.org\/en\/wp-json\/wp\/v2\/media\/9680"}],"wp:attachment":[{"href":"https:\/\/populicenter.org\/en\/wp-json\/wp\/v2\/media?parent=9674"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/populicenter.org\/en\/wp-json\/wp\/v2\/categories?post=9674"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/populicenter.org\/en\/wp-json\/wp\/v2\/tags?post=9674"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}