Sampling Techniques

19th and 20th

Learning Outcomes

• Students should be able to design the source, the type and the technique of collecting data

Outlines

• Population and sampling• Determining sample size• Sampling design• Appropriate sampling design for different

research purposes

Population, Element, Population Frame

• Population:– The entire group of people, events, or things of interest that needs to

be investigated• Ex: if the CEO of a computer firm wants to know the kinds of advertising

strategies adopted by the computer firms in the Silicon Valley, then all computer firms located there will be the population.

• Element:– A single member of the population

• Ex: if 1000 blue-collar workers in a particular organization happen to be the population of interest to a researcher, then each blue-collar worker is an element.

• Population frame:– A listing of all the elements in the population from which the sample is

drawn• Ex: the payroll of an organization would serve as a population frame if its

members are to be studied

Sample and Subject

• Sample: – a subset of population comprises some members selected

from it (elements of the population would form the sample)• Ex: if there are 145 in-patients in a hospital and 40 of them are to

be surveyed by the hospital administrator to assess their level of satisfaction with the treatment received, then these 40 members will be the sample.

• Subject: – is a single member of the sample, just as an element of

single member of population• Ex: if a sample of 50 machines from a total of 500 machines is to

inspected, then every one of the 50 machines is a subject.

Sampling

• Selection of sufficient number of items or elements from the population so that the properties of the sample (statistic) could be generalized to the population (parameter)

Reasons for Sampling

• Why sampling?– Less costs (cheaper than studying whole

population)– Less errors due to less fatigue (better results)– Less time (quicker)– Destruction of elements avoided (e.g., bulbs)

• However biases in sampling might happen, either overestimation or underestimation in determining sample size

Probability vs Nonprobability Sampling

• Probability samplingwhen elements in the population have a known equal chance of being selected as subjects in the sample, either unrestricted (simple random sampling) or restricted (complex probability sampling)

• Nonprobability samplingthe elements in the population do not have any probabilities attached to their being chosen as sample subjects (the findings of the study from the sample cannot be confidently generalized to the population)

Simple Random Sampling• Every element in the population has a known and

equal chance of being selected as a subject.• It is the most representative for most research

purposes, has the least bias and offers the most generalizability

• Disadvantages are:– Most cumbersome and tedious– The entire listing of elements in population frequently

unavailable– Very expensive– Not the most efficient design

Complex Probability Sampling

• Systematic sampling• Stratified random sampling• Cluster sampling• Area sampling• Double sampling

Systematic Sampling

• Every nth element in the population is sampled, starting from a randomly chosen element

• Example:– Want a sample of 35 household from a total of 240

houses. Could sample every 7th house starting from a randomly chosen number from 1 to 10. If that random number is 7, sample 35 houses, starting with 7th house (14th house, 21st house, etc).

– Possible problem: there could be systematic bias, e.g., every 7th house could be a corner house with different characteristics of both house and dwellers

Stratified Random Sampling• Comprises sampling from populations segregated into a

number of mutually exclusive strata• Example:

– University students divided into sophomore, junior, senior, – Employees stratified into clerks, supervisors, managers

• Stratified random sampling can be proportionate or disproportionate

• Advantages:– Homogeneity between stratum and heterogeneity between

strata– Statistical efficiency is greater– Sub-group can be analyzed– Different methods of analysis can be used for different sub-group

• Cluster Sampling:– Take cluster or chunks of elements for study

• Example: sample all students in 07 PAJ and 07 PBJ to study the characteristics of Computer Science major

• Area Sampling:– Cluster sampling confined to a particular area

• Example: sampling residents of particular region, district, etc

• Double Sampling: – Collect preliminary data from a sample, and choose a

sub-sample of that sample for more detailed investigation• Example: Conduct unstructured interviews with a sample of

50. Repeat a structured interview with 30 out of 50 originally sampled

Nonprobability Sampling• Convenience Sampling (collection of information from

members of the population who are conveniently to provide it)– Survey whoever is easily available– Used for quick diagnosis situations– Simplest and cheapest– Weak representativeness (least reliable)

• Purposive Sampling (confined to specific types of people who can provide the desired information) consist of:

• Judgment sampling: experts’ opinion could be sought ,e.g., doctor surveyed for cancer causes

• Quota sampling: establish quotas for numbers or proportion of people to be sampled, e.g., survey for research on dual career families: 50% working men and 50% working women surveyed

• Generalizations of the findings depend on the representativeness of the sample (the sophistication of the sampling technique used)

How Big is Big

• The goal is to select a representative sample—– Larger samples are usually more representative– But larger samples are also more expensive – And larger samples ignore the power of scientific

inference

Estimating Sample Size

• Generally, larger samples are needed when– Variability within each group is great– Differences between groups are smaller

• Because– As a group becomes more diverse, more data points are

needed to represent the group– As the difference between groups becomes smaller, more

participants are needed to reach “critical mass” to detect the difference

Points for Consideration in Sampling

• What is the relevant population?• What type of sample should we draw?• What sampling frame do we use?• What are the parameters of interest?• How much accuracy and precision are desired?• What is the sample size needed? Sampling

costs?

Points for Consideration in Sampling

• What is the relevant population?• What type of sample should we draw?• What sampling frame do we use?• What are the parameters of interest?• How much accuracy and precision are desired?• What is the sample size needed? Sampling

costs?