Sampling for Research Green 2005 Presentation

7/31/2019 Sampling for Research Green 2005 Presentation

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Sampling for ResearchNon-Probability and Probability Techniques

John J. Green, Ph.D.Institute for Community-Based Research

Division of Social Sciences/Center for Community and Economic Development

Delta State University

Fall 2005


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Why Sample?

Population: The group of cases that the researcher isstudying and wants to generalize to. It includes all relevantcases sharing some common characteristic.

Sample:A number of individual cases selected (drawn orpulled) from a larger population. In reality, we actuallyselect the sample cases from what is known as the

sampling frame.We sample as a means to an end. To study a group and be

able to say something about it without having to studyevery case in the population, we must sample.

It is often the case that attempting to study every case in theentire group will be too overwhelming and/or costly.Furthermore, given the totality of constraints, we may endup with more errors than we would through sampling.(Note that this aspect of social measurement is highlightedin the debate over sampling for the Census).


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Types of Samples: Non-Probability

Non-Probability Sample: A sample that has been drawn in a way thatdoesnt give every member of the population a known chanceofbeing selected. (Adler and Clark 2001, p. 550, emphasis added).

Convenience samples are obtained from a pre-existing group of people

or other units of analysis that are thought to represent the targetpopulation.

Purposive samples consist of people whom you feel are important to the

study because of specific personal traits, where they live, the work

they do, or their involvement in a particular issue. Sometimes it is

helpful to use quotacriteria.

Snow-ball samplinginvolves a process of chain referrals. You begin witha small group of people and ask them who else you might want to

speak with.


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Non-Probability Sample When to Use

Non-probability samples can be used effectively in a wide

variety of circumstances. . .

When a group that represents the target population already

exists.

When it is impossible or overly difficult to obtain a list of

names for sampling. (Example homeless.)

When research is exploratory in nature and all of the cases

of interest may not be identified ahead of time.

It is critical to recognize that you cannotgeneralize with anyknowndegree of accuracy from a non-probabilitysample. In other words, the data represent those units ofanalysis you actually studied.


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Types of Samples: Probability

Probability Sample: A sample selected in such a way that everycase has a known chanceof being selected.

Probability samples are based on probability theory and the ability

to later use inferential statistics to compute the likelihood that

sample characteristics are representative of the population.

Probability samples allow for computation of the confidence that

the sample and findings drawn from it are representative of

the larger population. It is on this basis that we often refer to

confidence intervals and confidence levels. These are whatwe use to account for the error between our sample and the

population.


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Probability Sampling (contd)

Simple Random Sample: A probability sample in which every member ofa study population has been given an equal chance of selection.

Stratified Random Sample: A probability sample in which the studypopulation is divided into smaller groups or strata on the basis of

some important characteristic. Simple random samples are then

selected from each stratum.

Cluster Sample: A probability sample where clusters of elements withinthe study population are sampled. Then, every case within the

selected element is chosen for study.

Multi-stage Sample: A probability sample that involves two or morestages, typically combining different sampling strategies. For

example, clusters of elements from a study population may be

sampled, followed by a sample of cases within each element.


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Comparing a random sample to the population (Delta Rural Poll).

Total population

distribution, by

county.

Total sampledistribution, by

county.


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0.00 1.00 2.00 3.00 4.00 5.00

Recoded - In general, would you say yourhealth is excellent, good, fair or poor?

0

100

200

300

400

Frequency

Mean = 2.1578Std. Dev. = 0.86948N = 797

Cases weighted by Weight by County, Race & Gender

Self-Rated Health (Entire Sample, n = 797)

0.00 1.00 2.00 3.00 4.00 5.00


0

1

2

3

4

5

Frequency

Mean = 1.80Std. Dev. = 1.0328N = 10

Self-Rated Health, Small Sample (n = 10)

0.00 1.00 2.00 3.00 4.00 5.00


0

1

2

3

4

5

Frequency

Mean = 2.50

Std. Dev. = 0.97183N = 10

Self-Rated Health, Another Small Sample (n = 10)

0.00 1.00 2.00 3.00 4.00 5.00


0

2

4

6

8

10

12

14

Frequency

Mean = 2.50

Std. Dev. = 0.93772N = 30

Self-Rated Health, Moderate Sample (n = 30)

Entire sampling

frame, n = 787

Random sample

of 10 from 787

Different sample

of 10 from 787

Random sample

of 30 from 787

Illustration of variablevalues from a

random sample(Delta Rural Poll).


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Illustration of a multi-stage sample (Quality of Life Assessment).

Map of town is divided into blocks.

Each block is numbered.

A random sample of blocks ischosen using a random numbergenerator.

Every fifth or seventh house ischosen for the study, based on therelative number of houses in theblock.

0 1 2 3 4 5

In general, would you say that your health isexcellent, good, fair or poor?

0

5

10

15

20

25

30

Frequency

Mean = 2.92

Std. Dev. = 0.759

N = 49

Self-Rated Health (Ruleville, n = 49)

A total of fifty face-to-face interviewswere completed.

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Sampling for Research Green 2005 Presentation