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LAMPIRAN
Lampiran 1. Output EASWESPOP
Lampiran 2. Model Null
. use "C:\Users\Lenovo\OneDrive\Desktop\semangat.dta"
. melogit jumlahanak || kabupaten:
Fitting fixed-effects model:
Iteration 0: log likelihood = -4820.2219 Iteration 1: log likelihood = -4758.3003 Iteration 2: log likelihood = -4758.299 Iteration 3: log likelihood = -4758.299 Refining starting values:
Grid node 0: log likelihood = -4728.3426 Fitting full model:
Iteration 0: log likelihood = -4728.3426 (not concave) Iteration 1: log likelihood = -4720.3555 (not concave) Iteration 2: log likelihood = -4714.2894
Iteration 3: log likelihood = -4714.2812 Iteration 4: log likelihood = -4714.2812
Mixed-effects logistic regression Number of obs = 15,358 Group variable: kabupaten Number of groups = 24 Obs per group:
min = 504 avg = 639.9 max = 852
Integration method: mvaghermite Integration pts. = 7 Wald chi2(0) = .
Log likelihood = -4714.2812 Prob > chi2 = . ---
jumlahanak | Coef. Std. Err. z P>|z| [95% Conf. Interval]
---+---
_cons | -2.304095 .0735601 -31.32 0.000 -2.44827 -2.15992 ---+---
kabupaten |
var(_cons)| .1098515 .0380683 .0556965 .2166626 ---
LR test vs. logistic model: chibar2(01) = 88.04 Prob >= chibar2 = 0.0000 . melogit jumlahanak || kabupaten:, or
Fitting fixed-effects model:
Iteration 0: log likelihood = -4820.2219 Iteration 1: log likelihood = -4758.3003 Iteration 2: log likelihood = -4758.299 Iteration 3: log likelihood = -4758.299 Refining starting values:
Grid node 0: log likelihood = -4728.3426 Fitting full model:
Iteration 0: log likelihood = -4728.3426 (not concave) Iteration 1: log likelihood = -4720.3555 (not concave) Iteration 2: log likelihood = -4714.2894
Iteration 3: log likelihood = -4714.2812 Iteration 4: log likelihood = -4714.2812
Mixed-effects logistic regression Number of obs = 15,358 Group variable: kabupaten Number of groups = 24 Obs per group:
min = 504 avg = 639.9 max = 852
Integration method: mvaghermite Integration pts. = 7 Wald chi2(0) = .
Log likelihood = -4714.2812 Prob > chi2 = . ---
jumlahanak | Odds Std. Err. z P>|z| [95% Conf. Interval]
---+---
_cons | .0998491 .0073449 -31.32 0.000 .086443 .1153343 ---+---
kabupaten |
var(_cons)| .1098515 .0380683 .0556965 .2166626 ---
Note: Estimates are transformed only in the first equation.
LR test vs. logistic model: chibar2(01) = 88.04 Prob >= chibar2 = 0.0000 . estat icc
Intraclass correlation
--- Level | ICC Std. Err. [95% Conf. Interval]
---+---
kabupaten | .0323119 .0108357 .0166479 .0617883 ---
. estat ic
Akaike's information criterion and Bayesian information criterion --- Model | N ll(null) ll(model) df AIC BIC ---+--- . | 15,358 . -4714.281 2 9432.562 9447.841 --- Note: BIC uses N = number of observations. See [R] BIC note.
Lampiran 3. Model 1
. melogit jumlahanak umurkawin1 tingkatpendidikan statusbekerja wiltempattinggal pemakaiankontrasepsi ln_kapita || kabupaten:
Fitting fixed-effects model:
Iteration 0: log likelihood = -4489.0459 Iteration 1: log likelihood = -4190.497 Iteration 2: log likelihood = -4182.7148 Iteration 3: log likelihood = -4182.5932 Iteration 4: log likelihood = -4182.593 Refining starting values:
Grid node 0: log likelihood = -4139.462 Fitting full model:
Iteration 0: log likelihood = -4139.462 (not concave) Iteration 1: log likelihood = -4132.8813 (not concave) Iteration 2: log likelihood = -4127.6645
Iteration 3: log likelihood = -4126.2661 Iteration 4: log likelihood = -4126.2655 Iteration 5: log likelihood = -4126.2655
Mixed-effects logistic regression Number of obs = 15,358 Group variable: kabupaten Number of groups = 24 Obs per group:
min = 504 avg = 639.9 max = 852
Integration method: mvaghermite Integration pts. = 7 Wald chi2(6) = 969.92
Log likelihood = -4126.2655 Prob > chi2 = 0.0000 ---
jumlahanak | Coef. Std. Err. z P>|z| [95% Conf. Interval]
---+---
umurkawin1 | 2.165077 .2161935 10.01 0.000 1.741345 2.588808 tingkatpendidikan | -.630171 .0642062 -9.81 0.000 -.7560127 -.5043292 statusbekerja | -.1306899 .0608551 -2.15 0.032 -.2499637 -.011416 wiltempattinggal | -.2986425 .0723354 -4.13 0.000 -.4404173 -.1568678 pemakaiankontrasepsi | -1.300157 .0599431 -21.69 0.000 -1.417644 -1.182671 ln_kapita | -1.11559 .0563135 -19.81 0.000 -1.225963 -1.005218 _cons | 12.0577 .7988497 15.09 0.000 10.49199 13.62342 ---+---
kabupaten |
var(_cons)| .1475586 .0497755 .0761783 .2858234 ---
LR test vs. logistic model: chibar2(01) = 112.65 Prob >= chibar2 = 0.0000 . melogit jumlahanak umurkawin1 tingkatpendidikan statusbekerja wiltempattinggal pemakaiankontrasepsi ln_kapita || kabupaten:, or
Fitting fixed-effects model:
Iteration 0: log likelihood = -4489.0459 Iteration 1: log likelihood = -4190.497 Iteration 2: log likelihood = -4182.7148 Iteration 3: log likelihood = -4182.5932 Iteration 4: log likelihood = -4182.593 Refining starting values:
Grid node 0: log likelihood = -4139.462 Fitting full model:
Iteration 0: log likelihood = -4139.462 (not concave) Iteration 1: log likelihood = -4132.8813 (not concave) Iteration 2: log likelihood = -4127.6645
Iteration 3: log likelihood = -4126.2661 Iteration 4: log likelihood = -4126.2655 Iteration 5: log likelihood = -4126.2655
Mixed-effects logistic regression Number of obs = 15,358 Group variable: kabupaten Number of groups = 24 Obs per group:
min = 504 avg = 639.9 max = 852
Integration method: mvaghermite Integration pts. = 7 Wald chi2(6) = 969.92
Log likelihood = -4126.2655 Prob > chi2 = 0.0000 ---
jumlahanak | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval]
---+---
umurkawin1 | 8.71527 1.884185 10.01 0.000 5.705013 13.31389 tingkatpendidikan | .5325008 .0341898 -9.81 0.000 .4695349 .6039105 statusbekerja | .8774899 .0533997 -2.15 0.032 .7788291 .9886489 wiltempattinggal | .7418245 .0536602 -4.13 0.000 .6437677 .8548171 pemakaiankontrasepsi | .2724889 .0163338 -21.69 0.000 .2422842 .3064591 ln_kapita | .3277218 .0184552 -19.81 0.000 .293475 .3659649 _cons | 172422.3 137739.5 15.09 0.000 36025.61 825231 ---+---
kabupaten |
var(_cons)| .1475586 .0497755 .0761783 .2858234 ---
Note: Estimates are transformed only in the first equation.
Note: _cons estimates baseline odds (conditional on zero random effects).
LR test vs. logistic model: chibar2(01) = 112.65 Prob >= chibar2 = 0.0000 . estat icc
Residual intraclass correlation
--- Level | ICC Std. Err. [95% Conf. Interval]
---+---
kabupaten | .0429271 .0138588 .0226314 .0799351 ---
. estat ic
Akaike's information criterion and Bayesian information criterion --- Model | N ll(null) ll(model) df AIC BIC ---+--- . | 15,358 . -4126.266 8 8268.531 8329.646 --- Note: BIC uses N = number of observations. See [R] BIC note.
Lampiran 4. Model 2
. melogit jumlahanak umurkawin1 tingkatpendidikan statusbekerja pemakaiankontrasepsi wiltempattinggal ln_kapita bukanlistrik praktikbidan kelaspengasuha
> n adaposyandu jamkesbawahduta || kabupaten:, or Fitting fixed-effects model:
Iteration 0: log likelihood = -4472.0416 Iteration 1: log likelihood = -4161.371 Iteration 2: log likelihood = -4153.0016 Iteration 3: log likelihood = -4152.8712 Iteration 4: log likelihood = -4152.871 Refining starting values:
Grid node 0: log likelihood = -4137.9989 Fitting full model:
Iteration 0: log likelihood = -4137.9989 (not concave) Iteration 1: log likelihood = -4131.3153 (not concave) Iteration 2: log likelihood = -4124.9265 (not concave) Iteration 3: log likelihood = -4122.6211
Iteration 4: log likelihood = -4121.7929 Iteration 5: log likelihood = -4121.7124 Iteration 6: log likelihood = -4121.7118 Iteration 7: log likelihood = -4121.7118
Mixed-effects logistic regression Number of obs = 15,358 Group variable: kabupaten Number of groups = 24 Obs per group:
min = 504 avg = 639.9 max = 852
Integration method: mvaghermite Integration pts. = 7 Wald chi2(11) = 977.54
Log likelihood = -4121.7118 Prob > chi2 = 0.0000 ---
jumlahanak | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval]
---+---
umurkawin1 | 8.711252 1.883241 10.01 0.000 5.702479 13.30753 tingkatpendidikan | .5322672 .0341669 -9.82 0.000 .4693427 .603628 statusbekerja | .8761324 .0532653 -2.18 0.030 .7777145 .9870049 pemakaiankontrasepsi | .2719718 .0163039 -21.72 0.000 .2418225 .3058799 wiltempattinggal | .7505493 .0545571 -3.95 0.000 .6508873 .8654713 ln_kapita | .3275061 .01842 -19.85 0.000 .2933224 .3656736 bukanlistrik | 1.012566 .0053184 2.38 0.017 1.002196 1.023044 praktikbidan | .9829808 .0068165 -2.48 0.013 .9697111 .996432 kelaspengasuhan | 1.014773 .0079638 1.87 0.062 .9992837 1.030503 adaposyandu | 1.021104 .0073715 2.89 0.004 1.006758 1.035655 jamkesbawahduta | .9822314 .0088572 -1.99 0.047 .9650241 .9997456 _cons | 188825.5 153380.3 14.96 0.000 38427.42 927854.5 ---+---
kabupaten |
var(_cons)| .093847 .0339925 .0461429 .1908692 ---
LR test vs. logistic model: chibar2(01) = 62.32 Prob >= chibar2 = 0.0000
. melogit jumlahanak umurkawin1 tingkatpendidikan statusbekerja pemakaiankontrasepsi wiltempattinggal ln_kapita bukanlistrik praktikbidan kelaspengasuha
> n adaposyandu jamkesbawahduta || kabupaten:
Fitting fixed-effects model:
Iteration 0: log likelihood = -4472.0416 Iteration 1: log likelihood = -4161.371 Iteration 2: log likelihood = -4153.0016 Iteration 3: log likelihood = -4152.8712 Iteration 4: log likelihood = -4152.871 Refining starting values:
Grid node 0: log likelihood = -4137.9989 Fitting full model:
Iteration 0: log likelihood = -4137.9989 (not concave) Iteration 1: log likelihood = -4131.3153 (not concave) Iteration 2: log likelihood = -4124.9265 (not concave) Iteration 3: log likelihood = -4122.6211
Iteration 4: log likelihood = -4121.7929 Iteration 5: log likelihood = -4121.7124 Iteration 6: log likelihood = -4121.7118 Iteration 7: log likelihood = -4121.7118
Mixed-effects logistic regression Number of obs = 15,358 Group variable: kabupaten Number of groups = 24 Obs per group:
min = 504 avg = 639.9 max = 852
Integration method: mvaghermite Integration pts. = 7 Wald chi2(11) = 977.54
Log likelihood = -4121.7118 Prob > chi2 = 0.0000 ---
jumlahanak | Coef. Std. Err. z P>|z| [95% Conf. Interval]
---+---
umurkawin1 | 2.164616 .2161848 10.01 0.000 1.740901 2.58833 tingkatpendidikan | -.6306096 .0641912 -9.82 0.000 -.7564222 -.5047971 statusbekerja | -.132238 .0607959 -2.18 0.030 -.2513958 -.0130802 pemakaiankontrasepsi | -1.302057 .0599471 -21.72 0.000 -1.419551 -1.184563 wiltempattinggal | -.2869499 .0726895 -3.95 0.000 -.4294188 -.1444811 ln_kapita | -1.116249 .0562431 -19.85 0.000 -1.226483 -1.006014 bukanlistrik | .0124879 .0052524 2.38 0.017 .0021935 .0227824 praktikbidan | -.0171657 .0069345 -2.48 0.013 -.0307571 -.0035744 kelaspengasuhan | .014665 .0078479 1.87 0.062 -.0007165 .0300466 adaposyandu | .0208846 .0072192 2.89 0.004 .0067353 .035034 jamkesbawahduta | -.0179283 .0090175 -1.99 0.047 -.0356022 -.0002545 _cons | 12.14858 .8122862 14.96 0.000 10.55653 13.74063 ---+---
kabupaten |
var(_cons)| .093847 .0339925 .0461429 .1908692 ---
LR test vs. logistic model: chibar2(01) = 62.32 Prob >= chibar2 = 0.0000
. estat icc
Residual intraclass correlation
--- Level | ICC Std. Err. [95% Conf. Interval]
---+---
kabupaten | .0277349 .0097673 .0138318 .0548359 ---
. estat ic
Akaike's information criterion and Bayesian information criterion --- Model | N ll(null) ll(model) df AIC BIC ---+--- . | 15,358 . -4121.712 13 8269.424 8368.736 ---
Note: BIC uses N = number of observations. See [R] BIC note.
.
Lampiran 5. Model 3
. melogit jumlahanak umurkawin1 tingkatpendidikan statusbekerja wiltempattinggal pemakaiankontrasepsi ln_kapita praktikbidan kelaspengasuhan adaposyand
> u jamkesbawahduta bukanlistrik ekonomi || kabupaten:
Fitting fixed-effects model:
Iteration 0: log likelihood = -4464.4588 Iteration 1: log likelihood = -4148.9333 Iteration 2: log likelihood = -4140.2453 Iteration 3: log likelihood = -4140.1092 Iteration 4: log likelihood = -4140.1089 Refining starting values:
Grid node 0: log likelihood = -4137.3915 Fitting full model:
Iteration 0: log likelihood = -4137.3915 (not concave) Iteration 1: log likelihood = -4130.6763 (not concave) Iteration 2: log likelihood = -4124.0899 (not concave) Iteration 3: log likelihood = -4118.9177
Iteration 4: log likelihood = -4118.4219 Iteration 5: log likelihood = -4118.4218
Mixed-effects logistic regression Number of obs = 15,358 Group variable: kabupaten Number of groups = 24 Obs per group:
min = 504 avg = 639.9 max = 852
Integration method: mvaghermite Integration pts. = 7 Wald chi2(12) = 984.78
Log likelihood = -4118.4218 Prob > chi2 = 0.0000 ---
jumlahanak | Coef. Std. Err. z P>|z| [95% Conf. Interval]
---+---
umurkawin1 | 2.163812 .2161808 10.01 0.000 1.740105 2.587519 tingkatpendidikan | -.6316 122 .064168 -9.84 0.000 -.7573791 -.5058452 statusbekerja | -.135164 .0607381 -2.23 0.026 -.2542084 -.0161196 wiltempattinggal | -.2925544 .0723429 -4.04 0.000 -.4343438 -.1507649 pemakaiankontrasepsi | -1.29925 .0599372 -21.68 0.000 -1.416725 -
1.181776 ln_kapita | -1.116661 .0561652 -19.88 0.000 -1.226742 -1.006579 praktikbidan | -.0139181 .0062086 -2.24 0.025 -.0260868 -.0017494 kelaspengasuhan | .0166459 .0069843 2.38 0.017 .002957 .0303348 adaposyandu | .0200643 .0063626 3.15 0.002 .0075939 .0325348 jamkesbawahduta | -.0212797 .0080698 -2.64 0.008 -.0370963 -.0054631 bukanlistrik | .0116302 .0046439 2.50 0.012 .0025282 .0207321 ekonomi | -.089776 .0331257 -2.71 0.007 -.1547012 -.0248509 _cons | 12.56957 .8243608 15.25 0.000 10.95385 14.18528 ---+---
kabupaten |
var(_cons)| .0682517 .0259274 .032416 .1437036
---
LR test vs. logistic model: chibar2(01) = 43.37 Prob >= chibar2 = 0.0000 . melogit jumlahanak umurkawin1 tingkatpendidikan statusbekerja wiltempattinggal pemakaiankontrasepsi ln_kapita praktikbidan kelaspengasuhan adaposyand
> u jamkesbawahduta bukanlistrik ekonomi || kabupaten:, or Fitting fixed-effects model:
Iteration 0: log likelihood = -4464.4588 Iteration 1: log likelihood = -4148.9333 Iteration 2: log likelihood = -4140.2453 Iteration 3: log likelihood = -4140.1092 Iteration 4: log likelihood = -4140.1089 Refining starting values:
Grid node 0: log likelihood = -4137.3915 Fitting full model:
Iteration 0: log likelihood = -4137.3915 (not concave) Iteration 1: log likelihood = -4130.6763 (not concave) Iteration 2: log likelihood = -4124.0899 (not concave) Iteration 3: log likelihood = -4118.9177
Iteration 4: log likelihood = -4118.4219 Iteration 5: log likelihood = -4118.4218
Mixed-effects logistic regression Number of obs = 15,358 Group variable: kabupaten Number of groups = 24 Obs per group:
min = 504 avg = 639.9 max = 852
Integration method: mvaghermite Integration pts. = 7 Wald chi2(12) = 984.78
Log likelihood = -4118.4218 Prob > chi2 = 0.0000 ---
jumlahanak | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval]
---+---
umurkawin1 | 8.704255 1.881693 10.01 0.000 5.697944 13.29674 tingkatpendidikan | .5317339 .0341203 -9.84 0.000 .4688938 .6029957 statusbekerja | .8735726 .0530591 -2.23 0.026 .7755301 .9840096 wiltempattinggal | .7463547 .0539935 -4.04 0.000 .6476895 .8600499
pemakaiankontrasepsi | .2727362 .016347 -21.68 0.000 .2425069 .3067336 ln_kapita | .3273712 .0183869 -19.88 0.000 .2932463 .3654672 praktikbidan | .9861783 .0061228 -2.24 0.025 .9742505 .9982521 kelaspengasuhan | 1.016785 .0071015 2.38 0.017 1.002961 1.0308 adaposyandu | 1.020267 .0064915 3.15 0.002 1.007623 1.03307
jamkesbawahduta | .9789451 .0078999 -2.64 0.008 .9635834 .9945518 bukanlistrik | 1.011698 .0046983 2.50 0.012 1.002531 1.020948 ekonomi | .9141359 .0302814 -2.71 0.007 .8566711 .9754554 _cons | 287669 237143.1 15.25 0.000 57173.65 1447406 ---+---
kabupaten |
var(_cons)| .0682517 .0259274 .032416 .1437036
---
LR test vs. logistic model: chibar2(01) = 43.37 Prob >= chibar2 = 0.0000 . estat icc
Residual intraclass correlation
--- Level | ICC Std. Err. [95% Conf. Interval]
---+---
kabupaten | .0203244 .0075639 .0097572 .0418525 ---
. estat ic
Akaike's information criterion and Bayesian information criterion --- Model | N ll(null) ll(model) df AIC BIC ---+--- . | 15,358 . -4118.422 14 8264.844 8371.795 ---
Note: BIC uses N = number of observations. See [R] BIC note.
Lampiran 6. Output Analisis Klaster
Case Processing Summarya,b
Cases
Valid Missing Total
N Percent N Percent N Percent
15358 100.0 0 .0 15358 100.0
a. Squared Euclidean Distance used b. Average Linkage (Between Groups)
Number of Cases in each Cluster
Cluster 1 2.000
2 5.000
3 1.000
4 2.000
5 14.000
Valid 24.000
Missing .000
Final Cluster Centers
Cluster
1 2 3 4 5
umurkawin 94.31 91.86 93.49 92.89 91.16
pendidikan 56.12 68.18 40.33 40.82 68.80
bekerja 28.24 57.61 53.35 55.62 56.04
pakaikb 69.38 65.19 64.56 69.48 67.19
wiltempattinggal 80.28 68.94 17.36 4.90 77.47
lnkapita 13.58 13.66 13.82 14.01 13.59
bidan 87.93 96.08 12.50 69.02 94.60
bukanlistrik 29.14 23.28 100.00 2.28 25.37
posyandu 96.74 99.83 66.67 98.37 98.68
kelaspengasuhan 63.97 75.77 33.33 80.72 59.75
jamkesmasbaduta 26.16 32.21 5.41 11.75 12.21
ekonomi 4.62 4.33 5.41 4.44 5.20
Lampiran 7. Pedoman Wawancara dan Kuesioner
PEDOMAN WAWANCARA WANITA USIA 15-65 TAHUN
Pedoman wawancara ini digunakan dalam penelitian yang berjudul “Kajian Fertilitas Wanita Usia 15-65 Tahun di Provinsi Sulawesi Selatan Tahun 2021.”
1. Berapa jumlah anak yang diinginkan? Berapa perempuan dan berapa laki- laki?
2. Bagaimana pandangan sendiri dan lingkungan tentang anak laki-laki dan perempuan? Bagaimana pandangan lingkungan terhadap jumlah anak yang dimiliki?
3. Pada umur berapa menikah? Apakah memang sudah siap menikah diumur tersebut dan bagaimana pandangan keluarga terhadap umur tersebut?
4. Apakah menggunakan KB? Pemakaian KB atas keputusan suami atau istri?
5. Bagaimana pandangan sendiri, keluarga dan lingkungan terhadap KB?
6. Bagaimana akses KB dari tempat tinggal? Jauhkah? Jika jauh, berapa biaya yang harus dikeluarkan?
7. Jika tidak menggunakan KB, apa kendala sehingga tidak menggunakan KB?
8. Apa tingkat pendidikan sendiri, suami, dan anak-anak?
9. Setinggi apa tingkat pendidikan yang diharapkan pada anak laki-laki dan perempuan?
10. Apa status pekerjaan sendiri dan suami? Berapa upah yang diterima? Apakah
ada pendapatan lain?
1 R E P U B L IK IN D O N E S IA SURVEI SOSIAL EKONO M I NASI O NAL 20 21 KET ERA N GAN PO KO K A N GG O T A RU M AH T AN G GA SIA M A R E T
BLOK I . KETE RA NGAN TE M PAT
Provinsi
upaten/Kota*)
matan
ahan*)
si Desa/Kelurahan1. Perkotaan 2. Perdesaan
or Blok Sensus
or Kode Sampel
Nomor Urut Bangunan Fisikdi Sketsa Peta WBNomor Urut Sampel Rumah Tangga
a Kepala Rumah Tangga ...
at (Nama Jalan/Gang, /RW/Dusun) ...
...
rdinat Lokasi Rumah Tangga Latitude (lintang) :
° ’ ”
Longitude (bujur) :
° ’ ”
g tidak perlu SELAMAT PAGI/SIANG/SORE/MALAM.KAMI/SAYA DARI BPS SEDANG MENGUMPULKAN DATA/INFORMASI KEADAANSOSIAL EKONOMI RUMAHTANGGA SEPERTI PENDIDIKAN,KESEHATAN,PEKERJAAN,PERUMAHANDANPENGELUARAN RUMAH TANGGA.UNTUK ITUKAMI/SAYA AKAN MEWAWANCARAI BAPAK/IBU BESERTA ANGGOTA
RUMAH TANGGA (ART) LAINNYA.SELURUH DATA YANGBAPAK/IBU BERIKAN KEPADA KAMI, AKAN DIRAHASIAKAN
DANHANYAAKANDIGUNAKANUNTUK KEPERLUANPERENCANAANPEMBANGUNAN.BOLEHSAYAMULAI
WAWANCARA SEKARANG?
Ya bersedia→ Mulai wawancara
Bersedia dengan perjanjian di lain waktu → Blok XXIII. Catatan
Tidak bersedia→ Lengkapi isian Blok I, Blok II, dan Blok XXIII Catatan. Lampirkan Berita Acara Nonrespon. Selesai dan segera laporkan ke pengawasBLOK II. KETE RA NGAN P ENC AC AH AN
Uraian Nama dan Kode/NIPJabatanWaktu Tanda Tangan
201. Pencacah…………...…
Staf BPS Provinsi ... 1 Staf BPS Kab/Kota... 2 KSK ... 3 Mitra ... 4 Tgl
Bln
202. Pengawas
…………
...…
Staf BPS Provinsi ... 1 Staf BPS Kab/Kota... 2 KSK ... 3 Mitra ... 4 Tgl
Bln
203. Hasil pencacahan rumah tangga Terisi lengkap ... 1 Terisi tidak lengkap ... 2 Tidak ada ART/responden yang dapat memberi jawaban sampai akhir masa pencacahan .... 3 Responden menolak ... 4 Rumah tangga pindah/bangunan sensus sudah tidak ada. ... 5
BLOK I II. RINGKASAN
301 Banyaknya anggota rumah tangga
302 Banyaknya anggota rumah tangga berumur 0-4 tahun
303 Banyaknya anggota rumah tangga berumur 5 tahun ke atas
304Banyaknya anggota rumah tangga berumur 10 tahun ke atas
305Banyaknya perempuan berumur 10-54 tahun berstatus pernah kawin U ji C ob a 2 V S E N 21 .K Dibua t 1 set unt uk B P S Ka b/ K ota
Blok XXIII.Catatan
T
2 PE TUN JUK P ENGIS IAN
engisian daftar, perlu diperhatikan tata tertib sebagai berikut: onsep, definisi, maksud, dan tujuan survei.s isian sejelas-jelasnya dengan pensil hitam pada tempat yang disediakan, agar mudah dibaca. akan blok catatan untuk mencatat hal-hal penting yang perlu diketahui oleh pengawas dan pengolah. gian kosong dari kuesioner juga dapat digunakan untuk mencatat hal-hal yang ditemui saat wawancara berlangsung. harus meneliti/memeriksa seluruh isian daftar dan memperbaiki setiap kesalahan, sebelumr isian diserahkan ke pengawas.tikan dan patuhi tanda-tanda atau alur pertanyaan yang tertera pada daftar isian. nyaan atau pilihan jawaban yang dicetak dengan huruf kapital harus dibacakan, sedangkan nyaan atau pilihan jawaban yang dicetak menggunakan huruf kecil tidak perlu dibacakan. pilihan jawaban yang menggunakan huruf kapital seperti A, B, C, dan seterusnya, boleh dilingkari dari satu pilihan jawaban. Kode pilihan jawaban yang menggunakan angka seperti 1, 2, 3, danusnya, hanya boleh dilingkari salah satu. tentang keterangan tempat diisi sebelum ke lapangan. an Blok IV terlebih dahulu sampai selesai sebagai panduan untuk mengisi pertanyaan dalam format roster. at bagian kertas yang ada tanda garis putus-putus dan tulisan lipat disini pada Blok IV halaman 2 gai panduan mengisi pertanyaan-pertanyaan yang terdapat pada halaman genap. Sementara itu, untukan mengisi pada halaman ganjil, kertas pada halaman 2 tidak perlu dilipat (dilebarkan saja). nyaan dalam format roster (nama anggota rumah tangga (ART) per baris) seperti pada Blok IVpai dengan Blok XIII diselesaikan dahulu dalam satu roster kemudian lanjut ke roster berikutnya. garis tebal pada pertanyaan roster menunjukkan batas pertanyaan untuk ART, isikan jawabannyaan di dalam tanda garis tebal untuk seluruh ART, lalu berpindah ke pertanyaan selanjutnya. garis dua pada pertanyaan roster menunjukkan perbedaan tema pertanyaan dari setiap blok.toh cara penulisan informasi penerimaan Bantuan Pangan adalah menggunakan format rata kanan:
ulasi umur responden yang sudah berulang tahun pada bulan Maret 2021: un lahir 2020 2019 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006ur 1 2 3 4 5 6 7 8 9 101112131415un lahir 2005 2004 2003 2002 2001 2000 1999 1998 1997 1996 1995 1994 1993 1992 1991ur 161718192021222324252627282930un lahir 1990 1989 1988 1987 1986 1985 1984 1983 1982 1981 1980 1979 1978 1977 1976ur 313233343536373839404142434445un lahir 1975 1974 1973 1972 1971 1970 1969 1968 1967 1966 1965 1964 1963 1962 1961ur 464748495051525354555657585960
BLOK IV . K ETERAN GAN DEMOGRAFI
No. urutART NAMA ANGGOTARUMAHTANGGA (ART), SEBUTKAN SIAPA SAJA YANG BIASA TINGGALDI RUMAH TANGGA INI DANKEPENGURUSAN MAKANNYA DIKELOLA DARI SATU DAPUR.MULAI DARI KEPALARUMAH TANGGA, PASANGANNYA,ANAK YANG BELUM MENIKAH,ANAK YANG SUDAH MENIKAH,MENANTU, CUCU,ORANG TUA/MERTUA,PEMBANTU/SOPIR, FAMILI LAIN,DAN LAINNYA. APAKAHHUBUNGAN(nama) DENGANKEPALARUMAHTANGGA?
(Kode) APAKAH STATUSPER-KAWINAN(nama)?
1.Belumkawin2.Kawin3.Cerai hidup4.Cerai mati APAKAH (nama) LAKI-LAKI ATAUPEREM-PUAN?
1. Laki-laki 2.Perem-puan KAPAN(nama)DILAHIRKAN? Tgl/Bln/Thn(DD/MM/YYYY) BERAPA-KAHUMUR(nama)?Umur harusdiisi. Jika≥97tahun, tulis ‘97’(Dalamtahun) Jika berstatus kawin(404= 2) APAKAH SUAMI/ ISTRI (nama)BIASANYA TINGGALDIRUMAHTANGGA INI? 1.Ya5.Tidak Jika berstatuspernahkawin(404 = 2, 3, atau 4) PADA UMURBERAPA(nama)MELANG-SUNGKANPER-KAWINANPERTAMA? No. urut ART
pem-beri infor-masi
401402403404405406407 408 4094101
_ _ / _ _ / _ _ _ _ _ _
2 _ _ / _ _ / _ _ _ _ _ _
3 _ _ / _ _ / _ _ _ _ _ _
4 _ _ / _ _ / _ _ _ _ _ _
5 _ _ / _ _ / _ _ _ _ _ _
6 _ _ / _ _ / _ _ _ _ _ _
7 _ _ / _ _ / _ _ _ _ _ _
8 _ _ / _ _ / _ _ _ _ _ _
9 _ _ / _ _ / _ _ _ _ _ _
10
_ _ / _ _ / _ _ _ _ _ _
Pastikan seluruh anggota rumah tangga tercatat dan tidak ada yang terlewat.Cek sekali lagi, apakah kepengurusan makan seluruh anggota rumah tangga di kolom 402 dikelola dari satu dapur.Jika terdapat ART yang kepengurusan makannya tidak dari satu dapur, maka keluarkan dari daftar.Kode 403: Hubungan dengan Kepala Rumah Tangga (KRT) 1. KRT3. Anak kandung/tiri 5. Menantu 7. Orang tua/mertua 9. Lainnya(famili lain,orang yang tidak ada 2. Istri/suami4. Anak angkat6. Cucu 8. Pembantu/sopir hubunganfamilidenganKRT)
