The Logic of Sampling

1
The Logic of Sampling
2
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

3
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

4
Sampling Terminology

• Populations
• Sampling Element
• Target Population
• Sampling Frame
• Parameters and Statistics

5
Non-Probability Sampling

• Convenience or Accidental or Haphazard
• Quota
• Purposive or Judgmental
• Snowball

6
Non-Probability Sampling

• Deviant cases
• Sequential
• Theoretical
• Use of Informants

7
Theory Logic of Probability Sampling

• Sampling Distribution
• Central Limit Theorem
• Sampling Error

8
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

9
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

10
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

11
Probability Sampling

• Simple Random Sample
• Systematic Sampling
• Stratified Sampling

12
Probability Sampling

• Cluster Sampling
• Within Household Sampling
• Probability Proportionate to Size (PPS)
• Random-Digit Dialing

13
Hidden Populations

• Targeted Sampling
• Respondent Drive Sampling

14
Sample Size

• Degree of precision or accuracy needed
• Larger samples will provide more precise
  estimates of population parameters
• Variability or diversity in the population
• Number of different variables
• Costs and time constraints
• The larger the sample, the more narrow the
  confidence intervals

15
Drawing Inferences

• Inferential Statistics
• Sampling Error