Sampling MethodsSampling Methods

Definitions of Important Definitions of Important TermsTerms

Population• The total group of people from whom

information is needed. Census

• Data obtained from or about every member of the population of interest.

Sample• A subset of the population of interest.

Sampling Units (elements) – • Person/object of interest for study

Sampling Frame –• All the elements that are available for

selection

Parameter vs StatisticParameter vs Statistic

Parameter: The “true” population data.

Statistic: The sample data.

Sampling DesignsSampling Designs

Nonprobability• Convenience• Judgment• Quota• snowball

Probability • Simple Random• Systematic

Random– Stratified – Cluster

Probability SamplingProbability Sampling

Each unit in the sampling frame has an equal, non-zero chance of being selected for the study. Implies random selection.

You can then say that the sample of representative of the target population (of interest)

Nonprobability SamplingNonprobability Sampling

Nonprobability sampling the probability of selection of each sampling unit is unknown.

Data results can’t be used to make predictions about the defined target population; it is limited to just the people who provided the raw data in the survey.

•Every sampling unit making up the defined target population has a known equal, non-zero, chance of being selected into the sample.

•Similar to simple random sampling, but requires that the defined target population be naturally ordered in some way, i.e customer list.

•Requires the separation of the defined target population into different subgroups (strata), and the selecting of samples from each stratum.

Simple Random Sampling

Systematic Random Sampling

Stratified Random Sampling

Types of Probability Types of Probability Sampling DesignsSampling Designs

•Drawn based on the convenience of the researcher or interviewer of when and where the study is being conducted.

•Participants are selected based on an experienced individual’s belief that the prospective respondent will meet the requirements of the study.

•Selection of prospective participants based on pre-specified quota requirements. Ie., males and females

Convenience Sampling(Accidental Samples)

Judgment Sampling

Quota Sampling

Types of Nonprobability Types of Nonprobability Sampling DesignsSampling Designs

Total ErrorTotal Error

the difference between the true value in the population of interest (the parameter) and the observed value in the sample (the statistic).

•sources of error:–Sampling error, sampling bias–Non-sampling error

Total Error – when a Total Error – when a sample is not sample is not representativerepresentative

Total Error

Poor LogicImproper Use of Statistics

Inadequate sample size

Inadequate sampledesign

Poor data collectionImproper design

Poor problemformulation

Poorly written report

Controlling Sampling ErrorControlling Sampling Error

Error due to sampling problems or sampling size.

controlled by increasing the sample size. • However, a larger sample can result in

less quality control…in other words, more non-sampling error

Controlling Sampling Controlling Sampling BBias - ias - systematic errorsystematic error

Sample differs from the population in a systematic way

• not controlled by sample size but by better sampling controls (ie., defining population better).

Controlling Non-sampling Controlling Non-sampling ErrorError

All other error sources such as measurement error• controlled by using good

measurement principles and good data entry/analysis– a census and a sample both have

non-sampling error