df_svy22

A dataset from a survey conducted at the province level in Indonesia in 2022.

datasets

Combines information from two independent surveys using a model-assisted projection method. Designed for survey sampling scenarios where a large sample collects only auxiliary information (Survey 1) and a smaller sample provides data on both variables of interest and auxiliary variables (Survey 2). Implements a working model to generate synthetic values of the variable of interest by fitting the model to Survey 2 data and predicting values for Survey 1 based on its auxiliary variables (Kim & Rao, 2012) <doi:10.1093/biomet/asr063>.

Ridson Al Farizal P

sae.projection

Small Area Estimation Using Model-Assisted Projection Method

Azka Ubaidillah

Silvi Ajeng Larasati

Amelia Rahayu

df_svy22 function

A data frame with 74.070 rows and 11 variables.
<dl>
 <dt>PSU</dt>
<dd>Primary Sampling Unit</dd> <dt>WEIGHT</dt>
<dd>Weight from survey</dd> <dt>PROV</dt>
<dd>province code</dd> <dt>REGENCY</dt>
<dd>regency/municipality code</dd> <dt>STRATA</dt>
<dd>Strata</dd> <dt>income</dt>
<dd>Income</dd> <dt>neet</dt>
<dd>Not in education employment or training status</dd> <dt>sex</dt>
<dd>sex (1: male, 2: female)</dd> <dt>age</dt>
<dd>age</dd> <dt>disability</dt>
<dd>disability status (0: False, 1: True)</dd> <dt>edu</dt>
<dd>last completed education</dd>
</dl>

Format

df_svy22 — df_svy22

A data frame with 74.070 rows and 11 variables.
<dl>
 <dt>PSU</dt>
<dd>Primary Sampling Unit</dd>

 <dt>WEIGHT</dt>
<dd>Weight from survey</dd>

 <dt>PROV</dt>
<dd>province code</dd>

 <dt>REGENCY</dt>
<dd>regency/municipality code</dd>

 <dt>STRATA</dt>
<dd>Strata</dd>

 <dt>income</dt>
<dd>Income</dd>

 <dt>neet</dt>
<dd>Not in education employment or training status</dd>

 <dt>sex</dt>
<dd>sex (1: male, 2: female)</dd>

 <dt>age</dt>
<dd>age</dd>

 <dt>disability</dt>
<dd>disability status (0: False, 1: True)</dd>

 <dt>edu</dt>
<dd>last completed education</dd>


</dl>

df_svy22: df_svy22

Description

Usage

Arguments

Format