Title: The Importance of Understanding Sampling In Research with a Focus on Business and Human Resource Development in Thailand
1The Importance of Understanding SamplingIn
Research with a Focus on Business andHuman
Resource Development in Thailand
- By
- Arthur Dryver, Wasita Boonsathorn,
- and Kanogporn Narktabtee
- National Institute of Development Administration
2Outline
- What is sampling?
- A representative sample
- Various sampling designs
- The role of sampling in quantitative research
- Example of the dangers of convenience sampling in
relations to quantitative research - With a focus on Business and Human Resource
Development in Thailand - Concluding remarks
3What is sampling?
- A sample in the very general sense is a set of
units observed from the all possible units. - The desire in taking a sample is to learn about a
larger group, the population. - The sampling frame is the set of units the
researcher will take the sample from. - Ideally the sampling frame is the same as the
population of interest. - In reality this is often not possible.
- The sampling design is the methodology in which
the data is collected. - The sampling design can aid in obtaining a
representative sample of the population. That is
a sample thats attributes are similar to the
population of interest.
4Sampling
- More important than sample size is how the sample
was taken. Example - Imagine if a survey of the 10,000 people and
their attitude on the sky train and how often
they take the sky train was taken from people as
they were entering or exiting from different
locations of the sky train. - Imagine the same survey taken of 10,000 people
living in Bang Na. - Imagine if the same survey taken of 2,000 people
from various randomly chosen locations throughout
Bangkok. - From the latter examples it is clear that how the
data is collected will have a great impact on the
findings - Which survey results would you trust to represent
people living in Bangkok.
5Various sampling designs - Simple Random Sampling
(SRS)
- Simple Random Sampling (SRS)
- A simple random sample is a sample in which all
units in the sampling frame have an equal
probability of selection. - Many statistical tests have certain assumptions
that they rely on and these assumptions are often
met when a simple random sample is taken. - If the researcher wanted to collect a simple
random sample of people in Bangkok, the
researcher would need a list of all people in
Bangkok. - Where would this list come from?
- A telephone list, is only a list of all people in
Bangkok with a telephone.
6Various sampling designs - Stratified Sampling
- Stratified Sampling
- The population is separated in groups or strata
and from within each strata a SRS is taken. - Again where would this list come from for each
strata to perform a SRS within each strata?
7Various sampling designs - Convenience Sampling
- Convenience Sampling
- A sample collected by what is convenient
- For example, collecting surveys from a shopping
mall, yielding a lot of data at a low price. - Note statistical tests are inappropriate when
performed on a convenience sample
8The role of sampling in quantitative research
- Statistics is at the heart of quantitative
research and sampling is a very important part of
statistics. - There is an old saying Garbage in garbage out
(G.I.G.O.). - For understanding G.I.G.O. in reference to
statistics and sampling the reader can think of
how a garbage sample would yield garbage
statistical results.
9The role of sampling in quantitative research
- For many research projects collecting data takes
a large portion of the overall time of the
project. - After collecting and entering the data using
statistical software packages, such as SPSS or
Minitab, the statistics can be calculated within
minutes. - A very important fact though is that getting an
answer and getting the right answer are not the
same thing. - Most evident when thinking of exams.
- Think about G.I.G.O. before deciding how to
collect the data.
10Example
- The authors have created a fictitious population
consisting of 6 companies with large, medium, and
small market capitals and varying annual revenue. - The example research question is to estimate the
average annual revenue of all companies in the
population. - The population mean annual revenue, µ, equals
5,283,333 for this example.
11Example (continued) SRS of size 3
12Example (continued) SRS of size 3 excluding large
Market Cap Companies
A bad sampling frame
A convenience sample often results in giving
many units in the population a probability of
selection equal to 0. In a true convenience
sample (which this is not) the researcher does
not know the probability of selection.
13Example (continued) Stratified sample of size 3,
Strata is Market Cap (s.,m.,l.)
With a different sampling design a different
formula for estimating the population mean is
required.
14Example (continued) Comparison of the sampling
strategies (Pop. Mean, µ 5,283,333)
Unbiased
The maximum is less than the population mean.
- SRS is unbiased but has a large standard
deviation. - Stratified is unbiased with a much smaller
standard deviation. - SRS Excluding large market cap. is very biased,
more than 50 and has the smallest standard
deviation, adding to how misleading the results
are.
15Concluding Remarks
- In real life often sampling is driven by funding
monetary concerns. - It is easy to understand the cost and the sample
size but not as easy to understand the importance
of proper sampling versus convenience sampling. - In real life only a single sample is taken and
the difference from the estimate and that of the
truth cant be quantified. - Another reason many people go for quantity.
- Before collecting data think G.I.G.O. - quality
over quantity - Statistical tests p-values may often be
performed/calculated using convenience samples
but they truly have no meaning when calculated on
a convenience sample - Finally, the researchers note that much is easier
said than done. That is, to take a proper sample
is much easier said than done.