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Data Collection &
Sampling
Dr. Guerette
Gathering Data

Three ways a researcher collects data:
By asking questions
 By direct observation
 By using written records
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The fundamental issue in data collection is its’
representativeness.
Logic of Probability Sampling
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Allows researchers to generalize to unobserved
cases.
Conscious and Unconscious Sampling Bias
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Bias in this case means that selections are not typical
or representative of the population from which they
are drawn. This could be result of personal beliefs or
many other reasons.
Logic of Probability Sampling
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Representativeness and Probability of Selection
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All members of a population have an equal chance
of being selected for a sample in probability
selections.
Probability Theory &
Sampling Distribution
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Probability theory permits inferences about how
sampled data are distributed around the value
found in a larger population.
Sample Element
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The unit about which information is collected and it
provides the basis of analysis. It is the grouping of
study elements.
Probability Theory &
Sampling Distribution
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Population parameter
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Sample statistic
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The summary description of a given variable in the
population.
The summary description of a given variable in the
sample.
Sampling distribution
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The range of sample statistics that would be
obtained when many samples are selected.
From Sampling Distribution to
Parameter Estimate
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Sampling frame
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Binomial variable
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A list of elements in the population
A variable that has only two values
Estimating sampling error
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When independent random samples are selected from a
population and sample statistics are calculated from those
samples they will be distributed around the population
parameter in a known way.
From Sampling Distribution to
Parameter Estimate

Standard error

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Is the way of estimating how closely the sample
statistics are clustered around the true value.
Confidence levels and Confidence Intervals

Use probability theory to indicate sample estimates
that fall within one, two or three standard errors of
the parameter.
Standard Error Diagram
Source: Jeremy Kemp (2005)
SE = √[p(1-p)/n]
Or
SE = √[p x q/n]
In Class Exercise –
Probability Sampling


You have selected a probability sample and want to
determine the sampling error in order to estimate the
population parameter. In your sample you compute that
70 percent of your sample opposes establishing a
hurricane relief fund derived from Miami-Dade tax
dollars, while 30 percent favor such a fund. You had a
sample of 400 (N = 400).
Report your confidence level and confidence interval.
Probability Sampling
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Simple random sampling
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Systematic sampling
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All elements in the population have an equal chance
of being selected for the sample.
Drawing a sample of every Nth element in the
population.
Stratified sampling

Based upon prior knowledge of a population, a
sample is drawn that will offer a greater degree of
representativeness.
Probability Sampling
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Disproportionate Stratified Sampling

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Specifically produce samples that are not
representative of a population on some variable.
Multistage Cluster Sampling
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Used when populations cannot easily be listed for
sampling purposes. Generally involves geographic
dispersion. While this technique increases efficiency
it decreases accuracy.
Probability Sampling
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Multistage Cluster Sampling with Stratification
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Used to increase the homogeneity of the sample.
Non-Probability Sampling
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Used when the likelihood of any given element
will be selected is not known (e.g. when random
probability sampling is not possible).
Purposive or Judgemental Sampling
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Selection based upon prior knowledge of the
population.
Quota sampling
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Uses a matrix or table to describe the characteristics
of the population and the sample is drawn to reflect
the cells of the matrix.
Non-Probability Sampling
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Reliance on available subjects
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Using people that are readily available seldom
produces data that have great value.
Snowball Sampling
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Begins by identifying a single member of a
population and then having that subject identify
others like him/her.