Transcript Chapter7

The Logic of Sampling
Methods of Sampling
• Nonprobability samples
– Used often in Qualitative Research
• Probability or random samples
– Every person has an equal chance of
being included in the sample
Sampling of Participants
• Try to obtain a representative sample
– Representative samples allow us to generalize
findings to the larger group
• Sampling is often not under the control of
the researcher in low-constraint (field)
research
– Therefore, caution is required in interpreting
the results
– Generalize only to similar participants and NOT
to the general population
Sampling Terminology
• Populations
• Sampling Element
• Target Population
• Sampling Frame
• Parameters and Statistics
Non-Probability Sampling
• Convenience or Accidental or Haphazard
• Quota
• Purposive or Judgmental
• Snowball
Non-Probability Sampling
• Deviant cases
• Sequential
• Theoretical
• Use of Informants
Theory & Logic of Probability Sampling
• Sampling Distribution
• Central Limit Theorem
• Sampling Error
The Normal Distribution
• Represents the actual distribution of
naturally occurring data
• Real distributions do not conform
completely to the normal distribution
• Inferential statistics takes a set of data
and “normalizes” it so comparisons can
be made
Characteristics of the Normal
Distribution
• Bell shape
• Unimodal
• Mean is located at the center of the bell
curve
• Area under the curve is 100% of the data
• The 50th percentile or the median, is the
same value as the mean
The Standard Deviation and the
Normal Distribution
• Direct relationship between the standard
deviation and the curve
• The same number of observations will
always fall within the same standard
deviation units from the mean of the
distribution
– 68% lie within -1 to +1 s.d.’s from the mean
– 95% lie within -2 to +2 s.d.’s from the mean
– 99.8% lie within -3 to +3 s.d.’s from the mean
Probability Sampling
• Simple Random Sample
• Systematic Sampling
• Stratified Sampling
Probability Sampling
• Cluster Sampling
– Within Household Sampling
– Probability Proportionate to Size
(PPS)
• Random-Digit Dialing
Hidden Populations
• Targeted Sampling
• Respondent Drive Sampling
Sample Size
• Degree of precision or accuracy needed
– Larger samples will provide more
estimates of population parameters
precise
• Variability or diversity in the population
• Number of different variables
• Costs and time constraints
• The larger the sample, the more narrow
the confidence intervals
Drawing Inferences
• Inferential Statistics
• Sampling Error