The Logic of Sampling

Methods of SamplingMethods of Sampling • Nonprobability samples Nonprobability samples

– Used often in Qualitative ResearchUsed often in Qualitative Research

• Probability or random samples Probability or random samples

– Every person has an equal chance Every person has an equal chance of being included in the sampleof being included in the sample

Sampling of ParticipantsSampling 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 TerminologySampling Terminology

• Populations

• Sampling Element

• Target Population

• Sampling Frame

• Parameters and Statistics

Non-Probability SamplingNon-Probability Sampling

• Convenience or Accidental or Haphazard

• Quota

• Purposive or Judgmental

• Snowball

Non-Probability SamplingNon-Probability Sampling

• Deviant cases

• Sequential

• Theoretical

• Use of Informants

Theory & Logic of Probability SamplingTheory & Logic of Probability Sampling

• Sampling Distribution

• Central Limit Theorem

• Sampling Error

The Normal DistributionThe 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 DistributionCharacteristics 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 The Standard Deviation and the Normal DistributionNormal 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 SamplingProbability Sampling

• Simple Random Sample

• Systematic Sampling

• Stratified Sampling

Probability SamplingProbability Sampling

• Cluster Sampling

– Within Household Sampling

– Probability Proportionate to Size (PPS)

• Random-Digit Dialing

Hidden PopulationsHidden Populations

• Targeted Sampling

• Respondent Drive Sampling

Sample SizeSample 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

Drawing InferencesDrawing Inferences

• Inferential Statistics

• Sampling Error