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Sampling - Chapter 8 Outline (Sampling Process (The second step in the…
Sampling - Chapter 8 Outline
Sampling Process
The second step in the
sampling process
is to choose a sampling frame. This is an accessible section of the target population (usually a list with contact information) from where a sample can be drawn.
The last step in sampling is choosing a
sample
from the sampling frame using a well-defined sampling technique. Sampling techniques can be grouped into two broad categories: probability (random) sampling and non-probability sampling.
A
population
can be defined as all people or items (unit of analysis) with the characteristics that one wishes to study.
Probability Sampling
Probability sampling
is a technique in which every unit in the population has a chance (non-zero probability) of being selected in the sample, and this chance can be accurately determined.
Simple random sampling.
In this technique, all possible subsets of a population (more accurately, of a sampling frame) are given an equal probability of being selected.
Systematic sampling.
In this technique, the sampling frame is ordered according to some criteria and elements are selected at regular intervals through that ordered list.
Stratified sampling.
In stratified sampling, the sampling frame is divided into homogeneous and non-overlapping subgroups (called “strata”), and a simple random sample is drawn within each subgroup.
Cluster sampling.
If you have a population dispersed over a wide geographic region, it may not be feasible to conduct a simple random sampling of the entire population.
Matched-pairs sampling.
Sometimes, researchers may want to compare two subgroups within one population based on a specific criterion.
Multi-stage sampling.
The probability sampling techniques described previously are all examples of single-stage sampling techniques. Depending on your sampling needs, you may combine these single-stage techniques to conduct multi-stage sampling.
Non-Probability Sampling
Nonprobability sampling is a sampling technique in which some units of the population have zero chance of selection or where the probability of selection cannot be accurately determined.
Convenience sampling. Also called accidental or opportunity sampling, this is a technique in which a sample is drawn from that part of the population that is close to hand, readily available, or convenient.
Quota sampling. In this technique, the population is segmented into mutuallyexclusive subgroups (just as in stratified sampling), and then a non-random set of observations is chosen from each subgroup to meet a predefined quota.
In proportional quota sampling, the proportion of respondents in each subgroup should match that of the population.
Non-proportional quota sampling is less restrictive in that you don’t have to achieve a proportional representation, but perhaps meet a minimum size in each subgroup.
Expert sampling. This is a technique where respondents are chosen in a non-random manner based on their expertise on the phenomenon being studied.
Snowball sampling. In snowball sampling, you start by identifying a few respondents that match the criteria for inclusion in your study, and then ask them to recommend others they know who also meet your selection criteria.
Statistics of Sampling
When you measure a certain observation from a given unit, such as a person’s response to a Likert-scaled item, that observation is called a response (see Figure 8.2). In other words, a response is a measurement value provided by a sampled unit. Each respondent will give you different responses to different items in an instrument.
Responses from different respondents to the same item or observation can be graphed into a frequency distribution based on their frequency of occurrences.
For a large number of responses in a sample, this frequency distribution tends to resemble a bell-shaped curve called a normal distribution, which can be used to estimate overall characteristics of the entire sample, such as sample mean (average of all observations in a sample) or standard deviation (variability or spread of observations in a sample).
These sample estimates are called sample statistics (a “statistic” is a value that is estimated from observed data). Populations also have means and standard deviations that could be obtained if we could sample the entire population.
Since the entire population can never be sampled, population characteristics are always unknown, and are called population parameters (and not “statistic” because they are not statistically estimated from data).
Sample statistics may differ from population parameters if the sample is not perfectly representative of the population; the difference between the two is called sampling error.
If a sample is truly representative of the population, then the estimated sample statistics should be identical to corresponding theoretical population parameters. How do we know if the sample statistics are at least reasonably close to the population parameters? Here, we need to understand the concept of a sampling distribution.
The variability or spread of a sample statistic in a sampling distribution (i.e., the standard deviation of a sampling statistic) is called its standard error. In contrast, the term standard deviation is reserved for variability of an observed response from a single sample.
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