Sampling ExerciseESP178 Research Methods

Professor Susan Handy2/4/16

Types of Sampling

Type DefinitionProbability sampling i.e. random

Every element in the population has a none non-zero probability of being selected; sampling involves random selection (equal chance)

Non-probability sampling i.e. non-random

Do not know in advance how likely that any element of the population will be selected for the sample; non-random selection (not equal chance)

Sampling Error

Difference between characteristics of sample and characteristics of population

Random sampling error

Inherent in process of sampling! Measure it with confidence intervals (see below).

Systematic samplingerror

Depends on how good your sampling method is!Use good methods to minimize this.

Probability Sampling MethodsMethod DefinitionSimple Random Sampling Elements chosen completely by chanceSystematic Random Sampling

Select first element randomly, then pick every nth element

Stratified Random Sampling – Proportionate

Sort population into strata; randomly select within strata; proportionate to population

Stratified Random Sampling – Disproportionate

Sort population into strata; randomly select within strata; disproportionate to population

Cluster Sampling Draw random sample of clusters, then select elements within clusters

Matched-pairs Sampling Divide population into two groups by key characteristic; draw random sample in Group 1, then find matches in Group 2

Non-Probability Sampling

Method DefinitionAvailability or Convenience Sampling

Cases selected because they’re easy to find

Quota Sampling (proportional or not)

Groups defined by key characteristics; specified number of cases selected in each group.

Purposive or Expert Sampling

Individuals selected for sample because of their knowledge – “key informants”

Snowball Sampling Start with initial sample, ask them to recommend other participants

Used for exploratory and qualitative research.Must be very cautious about generalizing!

A few examples

My Cul-de-Sac Sampling Plan:

The household survey will provide the primary data for testing the hypothesis. We expect the design of the sampling plan for the survey and the design of the survey instrument itself to be particularly challenging for this study. The target population for this study is children living in houses located on cul-de-sacs and through streets in the Sacramento region. Because no sampling frame exists for this population, we will use a multi-stage cluster sampling strategy. First, residential neighborhoods throughout the region will be defined based on major roadways and other geographic features. Census data will be used to eliminate neighborhoods built before 1950 because of the infrequent use of cul-de-sacs in residential developments before this time. From the remaining post-1950 neighborhoods, a random sample of neighborhoods will be selected. Within these neighborhoods, cul-de-sacs will be identified using the 2000 Census street network and the capabilities of geographic information systems (GIS). A random sample of the cul-de-sacs within each neighborhood will be chosen. For each cul-de-sac in the sample, a segment of a nearby through street (defined as a street that links arterial streets and carries significant levels of through traffic) of otherwise similar characteristics and similar length will be chosen. This approach creates matched pairs of streets. Next, all addresses on the sample street pairs will be compiled to create a sample of households. The sample of streets will be used in the field observations, the sample of households will be used in the household survey, and a sub-sample of the sample of households will be used in the in-depth interviews, as described below.

Handy, et al., 2005

Handy, et al., 2005

Other things to think about!Goal Example 1

Child as unit of analysisExample 2Neighborhood as unit

Ensuring that the independent variable varies

Sample includes children who live on cul-de-sacs and children who don’t

Sample includes neighborhoods that have lots of cul-de-sacs and neighborhoods that have few cul-de-sacs

Ensuring that the control variables don’t vary

Sample includes only children from moderate-income households

Sample includes only neighborhoods with average income in the moderate range

After the mid-term: non-response bias

Probability Sampling MethodsMethod DefinitionSimple Random Sampling Elements chosen completely by chanceSystematic Random Sampling

Select first element randomly, then pick every nth element

Stratified Random Sampling – Proportionate

Sort population into strata; randomly select within strata; proportionate to population

Stratified Random Sampling – Disproportionate

Sort population into strata; randomly select within strata; disproportionate to population

Cluster Sampling Draw random sample of clusters, then select elements within clusters

Matched-pairs Sampling Divide population into two groups by key characteristic; draw random sample in Group 1, then find matches in Group 2

Non-Probability Sampling

Method DefinitionAvailability or Convenience Sampling

Cases selected because they’re easy to find

Quota Sampling (proportional or not)

Groups defined by key characteristics; specified number of cases selected in each group.

Purposive or Expert Sampling

Individuals selected for sample because of their knowledge – “key informants”

Snowball Sampling Start with initial sample, ask them to recommend other participants

Used for exploratory and qualitative research.Must be very cautious about generalizing!

Stratified vs. Cluster SamplingStratified Cluster

Example 1. Divide city into districts (strata).2. Draw random sample of

households from each district.

1. Divide city into districts (clusters).2. Draw random sample of districts.3. Draw random sample of households

from each district.Reason for use

To ensure desired number of households in each district.

To make it easier to do door-to-door surveys.

Another Example

1. Define strata for UCD students by year (e.g. 1st, 2nd, etc.).

2. Draw a random sample of students from within each year.

1. Define clusters as UCD classes.2. Draw a random sample of classes from

the course schedule.3. Draw random sample of students from

within those classes – or survey all of them.

Reason for use

To ensure desired number of students in each year.

To make it easier to distribute surveys in person.

Criteria for defining

Strata defined by selected characteristic – key independent variable (e.g. what year they’re in).

Cluster defined by spatial or social linkages – may not have anything to do with study (e.g. what class they’re in).

Notes Need to have sampling frame for each stratum. May be necessary when sampling frame for entire population is not available but separate sampling frames for strata are.

Useful when surveys to be administered in-person. Useful when sampling frame for clusters is available but not for elements within clusters.

To do

• Bring questions to midterm review in section tomorrow

• Study for midterm on Tuesday!!!