Types of Sampling

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SAMPLING

By: Chukwuebuka Ejeckam

Preston Healthcare Consulting

Friday, 20th January 2017

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OutlineDefinition of Concept

Definition of Terms

Why Sample

Sampling Technique

Considerations in Sampling

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DefinitionsA sample can be thought of as a model of a larger population.

In research terms a sample is a group of people, objects, or items that are taken from a larger population for measurement. The sample should be representative of the population to ensure that we can generalise the findings from the research sample to the population as a whole.

A sample is usually a subunit within the universe- the universe consists of all the members of a distinct group. The universe or population is also the total collection of units from which a sample can be selected.

Sampling is a process used in statistical analysis in which a predetermined number of observations are taken from a larger population

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Definitions Cont’d

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Definition of Some Terms Respondent: A person or subject from whom information/data is gathered in an interview survey or questionnaire survey

Case: A more general term that may be used to describe the unit studied, regardless of the type of study or type of unit being studied.

Population/Universe- consist of all members of a group. The population is also the total collection of units from which you select your sample.

Census- A survey of the entire universe ( gives real estimate not sample estimate)

Sampling Units- are the elements into which a population is divided.

Sampling Interval: the nth unit of sample in a stratified technique. Derived by dividing the population by the desired sample size.

SAMPLING UNIT- are the elements into which a population is divided. E.g.- Married vs. Unmarried Preston staff, ENGINE- Rural School, Vulnerability Index

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Why do we Sample Enables research/ surveys to be done more quickly/ timely

Less expensive and often more accurate than large CENSUS ( survey of the entire population)

Given limited research budgets and large population sizes, there is no alternative to sampling.

Sampling also allows for minimal damage or lost

Sample data can also be used to validate census data

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Sample Technique

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Sampling Technique – Probability SamplingProbability Sampling - in this technique, each element of the larger population or universe has a KNOWN, NON-ZERO probability of being selected.

This is achieved through random selection of units for the sample from a list or sampling frame- which guards against introduction of bias into the sample by researcher and against other types of systematic bias.

Sampling Frame contains the list of all possible/probable or eligible subjects that can be selected or sampled.

Probability sampling maximizes external validity or generalizability of the result of the study.

Probability Sampling is the recommended gold- standard in most scientific research endeavors. It engenders OBJECTIVITY.

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Simple Random SamplingIn Simple Random Sampling, each element of the larger population is assigned a unique ID number, and a table of random numbers or a lottery technique is used to select elements, one at a time, until the desired sample size is reached.

Simple random sampling is usually reserved for use with relatively small populations with an easy-to-use sampling frame ( very tedious when drawing large samples).

Bias is avoided because the person drawing the sample does not manipulate the lottery or random number table to select certain individuals.

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Systematic SamplingSystematic sampling is a type of probability sampling method in which sample members from a larger population are selected according to a random starting point and a fixed periodic interval.

In this approach, the estimated number of elements in the larger population is divided by the desired sample size to yield a SAMPLNG INTERVAL, n). The sample is then drawn by listing the population in an arbitrary order and selecting every nth case, starting with a randomly selected number between 1 & n.

This is less time consuming and easier to implement.

Systematic sampling is useful when the units in your sampling frame are not numbered or when the sampling frame consists of very long list.

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Stratified SamplingPopulations often consist of strata or groups that are different from each other and that consist of very different sizes.

Stratified Sampling ensures that all relevant strata of the population are represented in the sample.

Stratification treats each stratum as a separate population- arranging the sampling frame first in strata before either a simple random technique or a systematic approach is used to draw the sample.

One can draw either a proportionate or disproportionate stratified sample.

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Disproportionate Vs. Proportionate Stratified Samples

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Cluster SamplingIn Cluster Sampling, samples are selected in two or more stages.

A cluster is a group of sampling units rather an individual unit

It is mainly used when it is not possible to get an adequate sampling frame for the individuals or unit you wish to study or when a SR technique would result in a list of individuals so dispersed that it would be too costly to visit each one.

When only two phases are involved , it is called TWO-Stage Cluster Sampling but when more than two phases are involved, it is called MULTI-Stage Cluster Sampling.

With extremely large populations, Multi-stage cluster sampling is the ideal.

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Sampling Technique:Non-Probability Sampling

Non-probability sampling involves a technique where samples are gathered in a process that does not give all the individuals in the population equal chances of being selected.

A core characteristic of non-probability sampling techniques is that samples are selected based on the subjective judgement of the researcher, rather than random selection (i.e., probabilistic methods), which is the cornerstone of probability sampling techniques

Because subjective Judgement of the researchers plays a key role in selecting the sample,almost always, nonprobability sampling tend to over-select some population elements and under-select others.

Nonprobability sampling procedures are not valid for obtaining a sample that is truly representative of a larger population – the external validity of intervention studies that employ nonprobability sampling technique depends on replication of the study results in different populations/enviroments.

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Convenience Sampling Convenience sampling is a non-probability sampling technique where subjects are selected because of their convenient accessibility and proximity to the researcher.

Convenience Sampling involves the selection of samples from whatever cases/subjects or respondents that happens to be available at a given place or time.

Also known as Incidental/Accidental, Opportunity or Grab Sampling.

Snow- ball Sampling is a special type of convenience sampling where individuals or persons that have agreed or showed up to be interviewed in the study serially recommend their acquaintances.

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Purposive Sampling Purposive sampling, also known as judgmental, selective or subjective sampling, is a type of non-probability sampling technique.

In this non-probability approach, samples consist of units deliberately selected to provide specific information about a population.

Purposive samples are commonly used in qualitative operation research studies (FGD with adolescent girls on a sanitary pad brand that is most appealing ).

Quota sampling is a type/variation of the purposive sampling method.

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Basic Considerations

Sample Size

Universe Size & Mix ( Homogeneity vs. Heterogeneity)

Related Resources (Time, cost, personnel, etc.)

Accesibility

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‘’ for education is the progressive discovery of our ignorance, for the MORE I know; that is the MORE I know that I know NOTHING”

Confucius

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