Using STATA in Survey Data Analysis - Niveen El Zayat

Using STATA in Survey Data AnalysisNIVEEN EL ZAYAT

ASSISTANT LECTURER

DEPARTMENT OF STATISTICS

FACULTY OF ECONOMICS AND POLITICAL SCIENCE

Outlines

Aspects of survey data

Survey Designs

Setting survey design in STATA: svyset

Describing survey design: svydes

Estimation commands using prefix svy: (In STATA do-file)

Estimation for subpopulations (In STATA do-file)

Extracting survey-weighted tables and graphs (In STATA do-file)


What is Survey?

A research technique to draw information about well-defined population through selecting a

sample that systematically questioned, hence, results are analyzed and generalized to the

population.

Surveys necessary for providing decision-makers with information that serves the planning,

monitoring and evaluation purposes

Survey data is collected through either “complete enumeration” or “sampling”.

Complete enumeration indicates to collecting data from all units exist in the population.

Sampling indicates to collecting data from a subset of the population and designed to be

hopefully representative to that population and used to determine truths about it.

Censuses are examples of complete enumeration while Demographic Health Survey (DHS) and

Labor Force Sample Survey are examples of sample surveys.


Populations in research

Target population:

1. Refers to the ENTIRE group of individuals that researchers are interested in.

2. It is known as the theoretical population.

Accessible population:

1. Refers to the one that researchers can get access to their individuals.

2. It is a subset of the target population and also known as the study population.

3. Researchers actually draw their samples form it thus it is define by the sample frame.


Why Sample Survey Data?

Assessing entire population may be impossible:

Impractical; in case of infinite population and population with destructive observations.

Data are wasteful if they are not collected within time limit.

Expensive; a sample survey will be less costly than complete enumeration.

Limited recourses (money and human) and extremely large workload.

Cause a lot of errors to control and monitor.

Cause destructiveness of the observations.

Lists (frame) are rarely up to date.

Due to the above reasons, samples are the wonderful option.


Probability (random) Samples

Every unit of the population at any stage is drawn with known probability. Inference

about population parameter can be easily drawn based on “probability theory”.

There are two sampling procedures:

1. Sampling with replacement (with finite population)

2. Sampling without replacement (with infinite population)

Survey Designs

► Simple Survey Design: SRS

Data from a single-round survey analyzed with limited reference to other information (aka “flat” or

“rectangle”)

Every unit in the population has an equal chance of being part of the sample.

Data collection is very simple, it just needs sample frame to the study population.

Often lead to inaccurate point estimates and/or inaccurate standard errors if population is not

homogeneous.

Most standard statistical methods assume simple random sampling

Complex or multi-stage designSimple survey design

Stratified Sample

Simple Random Sample (SRS) Cluster Sample

Stratified-cluster sample

► Complex (multi-stages/ hierarchical) designs

Drawing SRS is sometimes impossible (e.g., there is indicative frame to the population elements).

Different elements may have different probability of selection into the sample due to population

nature.

Used with hierarchical data (HH surveys).

Many statistical procedures assume i.i.d. , moreover, many statistical packages treat data as SRS.

Elements are not samples independently in most surveys as one may need to select groups of

individual.

Survey Designs

Survey Designs

► Stratification

The entire studied population is divided into well-defined groups called “strata” based

on a relevant characteristic often based on geographically (region) or demographic

variables (gender, level of education or SES).

Sampling units are independently and randomly sampled from within each stratum with

different probability of selection.

It is usually results in smaller variance and standard errors than that of SRS.

An example is to stratify the population by locality (urban/rural).

Survey Designs

► Clustering

Set of individuals (regions, districts, city blocks or households) are sampled as a group (cluster)

then population elements are drawn from the selected cluster.

Further subsamples within cluster may be drawn (often called multi-stage design).

The highest level of cluster is referred to as Primary Sampling Unit (PSU)

The lower level of cluster is referred to as Secondary Sampling Unit (SSU)

An example when geographical regions, such as local government areas, are selected in the

first stage. In the second stage schools were selected. In the third stage, the unit of analysis -

perhaps teachers or students are sampled. Regions represent PSUs in this example.

Different sample techniques may be applied at different stages which increase sample-to-

sample variability and lead to higher variance and standard errors.

Survey Designs

► Exercise

Survey Designs

► Exercise (Name each of the following design)

Survey Designs

► Features of survey designs

Probability (sample) weights

The most common is the sampling weight (aka probability weight) which is used to weight the

sample back to the population from which the sample was drawn.

By definition, this weight is the inverse of the sample fraction (N/n).

In a two-stage design, the probability weight is calculated as f1f2, which means that the inverse of

the sampling fraction for the first stage is multiplied by the inverse of the sampling fraction for the

second stage.

In actual survey data sets, the "final weight" usually starts with the inverse of the sampling fraction,

then several adjustments may be applied to account for sampling design problems such as unit

non-response, errors in the sampling frame (aka non-coverage) or post-stratification.

Survey Designs


Finite population correction (FPC)

It is an adjustment applied to the variance due to sampling without replacement from finite

population. Based on central limit theorem, FPC is calculated as:

FPC = [ (N-n)/(N-1) ]1/2.

PC is usually applied when sample fraction (n/N) is large otherwise when n is small relative to

the population size N, the FPC is almost close to 1, it will have a little impact and can be

safely ignored.

For multi-stage survey design, one may apply FPC at one or more stages.

Survey Designs


Design effect (DEFF)

Standard errors under different sample designs are compared using design effect statistics. For complex

samples, this is typically carried out by drawing comparisons to a hypothetical simple random sample (SRS)

of the same size.

It is computed as the ratio of the variance of an estimate θ (based on complex design) to the variance of

an estimate θ from a simple random sample (SRS) of the same size; DEEF=Var(θDesign)/Var(θSRS).

Design factor (DEFT)

The square root of the design effect; DEFT=(D.EEF)½ which sets things back to the scale of standard errors.

DEFT=1 (No effect of sample design on standard errors).

DEFT>1 (Sample design increase/ inflate standard errors).

DEFT<1 (Sample design reduces standard errors).


STATA Syntax

The command svyset (declare data as survey data) is used to identify the sample design features of

your data to STATA. It allows us to identify a wide range of complex sampling designs.

Single-stage design:

svyset [psu_varname] [weight_var] [, strata(varname) fpc(varname) options]

Multiple-stage design

svyset psu_var [weight_var] [, design_options options] [|| ssu_var , design_options]

Once the data saved with the survey design it will be a part of dataset until they are cleared or

changed or a new dataset is loaded into memory.


One-stage design

Example 1: The National Maternal and Infant Health Survey (NMIHS) 1988:

Data file name: NMIHS.dta (represents a sample of 9,953 live births)

population was divided into 6 strata according to the subdomains of two birth demographic variables; race

(black & non black) and birth weight (<1,500 g, 1,500-2,499 g & 2,500+ g), a systematic samples of live birth that

were restricted to women 15+ years of age and that were registered in 48 States.Svyset

no survey characteristics are set

svyset [pw=finwgt], strata(stratan)

pweight: finwgt

VCE: linearized

Single unit: missing

Strata 1: stratan

SU 1: <observations>

FPC 1: <zero>

This is an example of stratified systematic design. The weight variable was adjusted to get “finwgt” (see Table N, look at p23 in the report pdf file).


Two-stage design

Example 2: Oman World Health Survey (OWHS) 2008:

Data file name: OWHS_chronic.dta [40% of the original sample], the design of the survey was

set as a part of the data set

svyset

pweight: adjINDweight

VCE: linearized

Single unit: missing

Strata 1: v022

SU 1: v021

FPC 1: <zero>

This is an example of stratified cluster design. Strata is represented by “v022”, clusters by “v021”

and the weight variable by “adjINDweight”.


Exercises 1: We want to know the income per household in a certain city, and we don’t have a list of households.

Instead of trying to create a list of households, it would be more practical to sample blocks. Each block would be

considered a sampling unit. Assuming a FPC variable exist in the data set, write the STATA command declaring

such survey design?

Exercises 2: In above example, instead of having sampling clusters from the city, we first divided the city into

regions and then, within each region, we sampled blocks (eventually with different criteria among regions).

Assuming a FPC variable exist in the data set, write the STATA command declaring such survey design?

► More survey design (Hypothetical Exercises)

svyset block [pw = pwvar], fpc(fpcvar)

svyset block [pw = pwvar], strata(region) fpc(fpcvar)


Exercises 3: We want to perform a survey on the eating habits of children attending elementary schools. A possible

design would be: perform samples independently on each state. For each state, perform a random sample of

counties. Within each county, perform a random sample of schools, and interview each student for the selected

schools. Assuming a FPC variables exist at each stage, write the STATA command declaring such design?

Exercises 4: In above example, if within each school we stratify per grade and sample students independently on

each grade, then we need to add another level. Assuming a FPC variables exist at each stage, write the STATA

command declaring such design?

► More survey design (Hypothetical Exercises)

svyset county [pw = pwvar], strata(state) fpc(fpcvar) || school, fpc(fpcvar2)

svyset county [pw = pwvar], strata(state) fpc(fpcvar) || school, fpc(fpcvar2) || student, fpc(fpcvar3) strata(grade)


STATA Syntax

The command svydes describe the survey design that was previously declared to the data set by

svyset.

svyset [varlist], [stage(#) finalstage single option]

For multistage design it describe the design for each stage by determining the number of the

stage by option [stage(#)]. Option [single] used to only list strata with single PSUs (singleton).

Generally, It adds “*” at the strata id variable to show that it has a single PSU.


Two-stage design

use data set: OWHS_chronic.dta , STATA command

Svydes

Stratum is the stratum id number given by the strata

variable;

#units is the number of PSUs in the strata and

#Obs the number of PSUs in a given stratum.

The other columns give some summary statistics on the

number of observations.

The important thing to note here: if strata have singleton

PSUs then #units will =1. This means they only include one

PSU which also indicated by a "*"

Using STATA in Survey Data Analysis - Niveen El Zayat

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