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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.

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Presentation on theme: "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."— Presentation transcript:

1 Sampling

2 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

3 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

4 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

5 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

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

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

8 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

9 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

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

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

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

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

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

15 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 sub- sample from within the larger sample group –35 – 45 YO Women who actively invest in the stock market

16 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

17 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

18 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

19 Quota Sampling 65% 12%23%

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


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