Sampling Census and Sample defined A census is

Sampling

Census and Sample (defined) • A census is based on every member of the population of interest in a research project • A sample is a subset of the population

Characteristics of a Sample • Representative of the larger population • Can be more efficient in terms of cost, time • Generalizable results • Can reflect animate or inanimate populations

The Sampling Frame • The list of elements from which a sample may be drawn • Defines the criteria on which elements will be selected • But, does not ensure that some elements will not be excluded or accurately represented – Sample frame error

Sampling Unit • A single element or group of elements subject to selection in a sample – Ex. An 18 to 24 year old male with senior academic classification – Ex. Grocery retailers that gross $30, 000 in revenues monthly

Sampling Methods Probability sampling • Elements each have a known, calculable non-zero probability of inclusion • The probability of inclusion is predictable across elements

Sampling Methods Non-probability sample • Sampling does not ensure a representative range of elements found in the larger population

Forms of Probability Sampling • Simple random sampling – Each member of the population has a known, equal chance of being selected – Allows comparable estimates without surveying the entire population

Forms of Probability Sampling • Systematic random sampling – Sampling occurs based on a skip interval system where every nth member is selected from the population – Each element at the skip level is selected and interviewed

Systematic Random Sampling • Directory of Physicians in the Gainesville, FL area

Forms of Probability Sampling • Stratified random sampling – Sampling based on applying weights to population stratas • Proportionate vs. disproportionate stratums – Appropriate when the population is nonhomogenous or has wide variations

Stratified Random Sampling Proportionate to their representation in the population 65% 12% 23%

Stratified Random Sampling Disproportionate to their representation in the population 34% 33%

Cluster Sampling • Segmenting the population to sample based on geography – Postal codes, electoral constituencies, states, regions

Multi-stage Sampling • Two-step process • Select a primary sample based on a pre-specified sampling method – 35 – 45 YO Women • Then, selecting a secondary subsample from within the larger sample group – 35 – 45 YO Women who actively invest in the stock market

Forms of Non-probability Sampling • Convenience sampling – Participants are selected based on convenience and accessibility – Quick, uncomplicated, low in cost – Useful for exploratory research or quick info

Forms of Non-probability Sampling • Judgment sampling – Participants are selected based on an expert’s judgment of the characteristics of a representative sample – Example: the “typical” customer

Forms of Non-probability Sampling • Quota sampling – Attempts to ensure demographic characteristics of interest are represented in the sample proportionately to their representation in the population – Sample based on population percentiles

Quota Sampling 65% 12% 23%

Forms of Non-probability Sampling • Snowball sampling – Initial respondents are selected by probability sampling techniques – Additional respondents are obtained by referral from initial respondents