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The Importance of Understanding Sampling In Research with a Focus on Business and Human Resource Development in Thailand

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Title: The Importance of Understanding Sampling In Research with a Focus on Business and Human Resource Development in Thailand


1
The 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

2
Outline
  • 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

3
What 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.

4
Sampling
  • 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.

5
Various 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.

6
Various 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?

7
Various 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

8
The 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.

9
The 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.

10
Example
  • 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.

11
Example (continued) SRS of size 3
12
Example (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.
13
Example (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.
14
Example (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.

15
Concluding 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.
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