Selecting SamplesMarch2012

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Selecting Samples

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Selecting SamplesPopulation, Samples and IndividualCases

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The need to sample

Sampling- a valid alternative to a censuswhen

A survey of the entire population is

impracticable

Budget constraints restrict data collection

Time constraints restrict data collection

Results from data collection are neededquickly

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Overview of sampling techniques

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Probability (representative)sampling

The four stage process

1. Identify sampling frame from research objectives

2. Decide on a suitable sample size

3. Select the appropriate technique and select thesample

4. Check that the sample is representative of thepopulation under study.

Note: for population of less than 50 cases avoidprobability sampling

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Identifying a suitable sampling frame

Problems of using existing databases-Individual databases are often incomplete-Information held by organisations in databases

is inaccurate

-Information held in databases soon becomesoutdated Extent of possible generalisation from the

sample

Validity and reliability

Avoidance of bias

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Sample size

Choice of sample size is influenced by

Confidence needed in the data

Margin of error that can be tolerated

Types of analyses to be undertaken

Size of the sample population anddistribution

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The importance of response rate Non- respondents and analysis of refusals

Refusal to respondIneligibility to respondInability to locate respondentRespondent located but unable to make contact

Obtaining a representative sample

Calculating the active response rateTotal response rate = total no. of responses

total no. in sample - ineligible

Active response rate = total no. of responsestotal no. in sample (ineligible + unreachable)

Estimating response rate and sample size

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Sample Size Determination forthe MeanThe sample size n is equal to the

product of the Z value squared andthe variance , divided by the

sampling errore squared.n = Z e

Description

n = sample size neededZ = desired confidence interval

e = acceptable sampling error

= standard deviation 9


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Sample Size Calculation

The sample size refers to the number of cases to be included ina survey.

Base Sample-size Calculation

The appropriate sample size for a population-based survey isdetermined largely by three factors: (i) the estimatedprevalence of the variable of interest, (ii) the desired level of

confidence and (iii) the acceptable margin of error (samplingerror).

For a survey design based on a simple random sample, thesample size required can be calculated according to thefollowing formula.

n= z x p(1-p)

eDescription:

n = required sample sizez = confidence level at 95% (standard value of 1.96)p = estimated prevalence of variablee = margin of error at 5% (standard value of 0.05)

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Applying the Concepts

A survey is planned to determine themean annual family medical expenses ofemployees of a large company. Themanagement of the company wishes to

be at 95% confident that the samplemean is correct within + $50 of the truepopulation mean annual family medicalexpenses. A pilot study indicates thatthe standard deviation can be estimatedas $400.

a. How large a sample size isnecessary?

b. If management wants to be correct towithin + $ 25, what sample size is 11


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Sampling Techniques

Probability (representative) samplingThe chance or probability of each case beingselected from the population is known and isusually equal for all cases. Applicable in casesstatistical estimation of the characteristics of the

population from the sample is needed. Non- probability (judgemental) samplingThe probability of each case being selected from the

population is not known and it is impossible to

answer research questions or to addressobjectives that require statistical inferences aboutthe characteristics of the population. You may stillbe able to generalise but not on statisticalgrounds.

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Probability Sampling (1)

Stages in the Probability Sampling process:1. Identify a suitable sampling frame based

in your research question (s) or

objectives.2. Decide on a suitable sample size.

3. Select the most appropriate sampling

technique and select the sample.4. Check that the sample is representative

of the population.

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Probability Sampling (2) Simple random

Involves the selection of sample at random from the sampling frameusing either random number tables or computer/online system.

SystematicSystematic sampling involves you selecting the sample at regular

intervals from the sampling frame Stratified random

Stratified random sampling is a modification of random sampling inwhich you divide the population into two or more relevant andsignificant strata based on one or a number of attributes.

ClusterCluster sampling is on the surface similar to stratified sampling as you

need to divide the population into discrete groups prior to sampling.

For cluster sampling the sampling frame is the complete list ofclusters rather than a list of individual cases within the population. Multi-stageMulti-stage (cluster) sampling is a development of cluster sampling in

that it relies in a series of different sampling frames.

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Non- probability sampling (1)

Key considerations

Deciding on a suitable sample size

Selecting the appropriate technique

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Non- probability sampling (2)Sampling techniques

Quota sampling (larger populations)

Quota sampling is entirely non-random and is normally usedfor interview surveys. To select a quota sample you:1. Divide the population into specific groups2. Calculate a quota for each group based on relevant and

available data.3. Give each interviewer an assignment, which states the

number of cases in each quota from which they mustcollect data.

4. Combine the data collected by interviewers to provide fullsample.

Purposive sampling

Purposive or judgemental sampling enables you to use yourjudgement to select cases that will best enable you to

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Non- probability sampling (3) Snowball sampling

Snowball sampling is commonly used when it is difficult to identifymembers of the desired population. You need to:

1. Make contact with one or two cases in the population

2. Ask theses cases to identify further cases

3. Ask these new cases to identify further new cases (and so on)

4. Stop when either no new cases are given or the sample is as large

as is manageable. Self-selection sampling

Self selection sampling occurs when you allow each case, usuallyindividuals, to identify their desire to take part in the research. Youneed to:

1. Publicise your need for cases

2. Collect data for those who respond.

Convenience sampling

Involves selecting haphazardly those cases that are easiest to obtainfor your sample such as the person interviewed at random.

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All choices depend on the ability to

gain access to organisations

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Selecting SamplesMarch2012

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