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Chapter 14 Population & Sampling Process - Coggle Diagram
Chapter 14 Population & Sampling Process
This aspect is important to manage systematically in any research activities because it involve the end product of the research activity which it influence the validity & reliability of the research findings.
Sampling Procedure
Select sampling procedure
Define the sample size
Identifying the sampling frame
Select the sample units
Define the population
Collect data from the samples
Population
Defined as including all people or items with the characteristic one wish to understand.
There is very rarely enough time or money to gather information from everyone or everything in a population, the goal becomes finding a representative sample of that population.
Non-probability sampling
Quota sampling
Type: Proportional & Non-proportional
A non probability sampling technique wherein the researcher ensures equal proportionate representation of subjects depending on which trait is considered as basis of the quota.
Heterogeneity sampling
Not to sampling people but the ideas.
When we want to include all opinions or views, & we are not concerned about representing these views proportionately.
Snowball sampling
Especially useful when you are trying to reach population that are inaccessible or hard to find.
Usually done when there is a very small size population.
Judgmental sampling
Subjects are chosen to be part of the of the sample with specific purpose in mind.
Commonly known as purposive sampling
Convenience sampling
With convenience sampling, the samples are selected because they are accessible to the researcher
This techniques is considered easiest, cheapest & least time consuming
The most common of all sampling techniques.
Purposive sampling
It can be very useful for situations where you need to reach a targeted sample quickly & where sampling for proportionality is not the primary concern.
A sample for specific groups or types of people as in modal instance, expert, or quota sampling.
We sample respondents with a purpose in mind.
Expert sampling
The best way to elicit views of respondent who have specific expertise.
To provide evidence for the validity of another sampling approach you have chosen.
Involves the assembling of a sample respondents with known or demonstrable experiences and expertise in some area.
Probability sampling
Simple random sampling
Disadvantages: A complete frame or list of all units in the whole population is needed.
Advantages: Easy to understand and apply in research activities.
Multi stage sampling
Combine the simple methods described earlier in a variety of useful ways that help us address our sampling needs in the most efficient & effective manner possible.
Four method samplings: Simple, Stratified, systemic & cluster are the simplest random sampling strategies.
Systematic sampling
Each element in the population has a known and equal probability of selection.
Often used instead of random sampling. this is random sampling with system.
Cluster sampling
One of the draw back in this sampling units is if there is a large variation between clusters in the variables to be examined the method may yield poor precision.
The population is divided into mutually exhaustive subsets. A random sample of the subsets is selected
Stratified random sampling
This method can be tricky for the uninitiated, as the researcher must decide what weight to assign to each stratification variable.
Involves dividing the population into subgroups based on variables known about those subgroups, & then taking a simple random sample of each subgroups.