# The Logic of Sampling Methods of Sampling Nonprobability

• Slides: 15

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 50 th 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 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 Inferences • Inferential Statistics • Sampling Error