Select Page

A consultant had administered a questionnaire to some 285 employees using a simple random sampling procedure. As she looked at the responses, she suspected that two questions might not have been clear to the respondents. Based on what you have learned in this session, Explain what should she do to find out if her suspicion is correct?
s7_chapter_13.pd.pdf

Unformatted Attachment Preview

Chapter 13
Sampling
Slide 13-2
Sampling
 Sampling: the process of selecting a sufficient
number of elements from the population, so that
results from analyzing the sample are
generalizable to the population.
 The reasons for using a sample are self-evident.
In research involving hundreds or even
thousands of elements, it would be practically
impossible to collect data from every element.
Even if it were possible, it would be prohibitive in
terms of time, cost, and other human resources.
Slide 13-3
Relevant Terms – 1
 Population refers to the entire group of
people, events, or things of interest that the
researcher wishes to investigate.
 An element is a single member of the
population.
 A sample is a subset of the population. It
comprises some members selected from it.
Slide 13-4
Relevant Terms – 2
 Sampling unit: the element or set of elements
that is available for selection in some stage
of the sampling process.
 A subject is a single member of the sample,
just as an element is a single member of the
population.
Slide 13-5
Relevant Terms – 3
 The characteristics of the population such as
µ (the population mean), σ (the population
standard deviation), and σ2 (the population
variance) are referred to as its parameters.
The central tendencies, the dispersions, and
other statistics in the sample of interest to the
research are treated as approximations of
the central tendencies, dispersions, and
other parameters of the population.
Slide 13-6
Statistics versus Parameters
Slide 13-7

Less costs
Less errors due to less fatigue
Less time
Destruction of elements avoided
Slide 13-8
The Sampling Process
 Major steps in sampling:
 Define the population.
 Determine the sample frame
 Determine the sampling design
 Determine the appropriate sample size
 Execute the sampling process
Slide 13-9
Sampling Techniques
 Probability versus nonprobability sampling
 Probability sampling: elements in the
population have a known and non-zero
chance of being chosen
Slide 13-10
Sampling Techniques
 Probability Sampling
 Simple Random Sampling
 Systematic Sampling
 Stratified Random Sampling
 Cluster Sampling
 Nonprobability Sampling
 Convenience Sampling
 Judgment Sampling
 Quota Sampling
Slide 13-11
Simple Random Sampling

Procedure
 Each element has a known and equal
chance of being selected

Characteristics
 Highly generalizable
 Easily understood
 Reliable population frame necessary
Slide 13-12
Systematic Sampling

Procedure
 Each nth element, starting with random
choice of an element between 1 and n

Characteristics
 Idem simple random sampling
 Easier than simple random sampling
 Systematic biases when elements are not
randomly listed
Slide 13-13
Cluster Sampling

Procedure
 Divide of population in clusters
 Random selection of clusters
 Include all elements from selected
clusters

Characteristics
 Intercluster homogeneity
 Intracluster heterogeneity
 Easy and cost efficient
 Low correspondence with reality
Slide 13-14
Stratified Sampling

Procedure
 Divide of population in strata
 Include all strata
 Random selection of elements from
strata
 Proportionate
 Disproportionate

Characteristics
 Interstrata heterogeneity
 Intrastratum homogeneity
 Includes all relevant subpopulations
Slide 13-15
Overview
Slide 13-16