Research methods for business 5th ch7

Slide 7.5Saunders, Lewis and Thornhill, Research Methods for Business Students, 5th Edition, © Mark Saunders, Philip Lewis and Adrian Thornhill 2009 Selecting samples Population, sample

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Saunders, Lewis and Thornhill, Research Methods for Business Students, 5th Edition, © Mark Saunders, Philip Lewis and Adrian Thornhill 2009

Chapter 7 Selecting Samples

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Selecting samples

Population, sample and individual cases

Source: Saunders et al (2009)

Figure 7.1 Population, sample and individual cases

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The need to sample

Sampling- a valid alternative to a census when

• A survey of the entire population is

impracticable

• Budget constraints restrict data collection

• Time constraints restrict data collection

• Results from data collection are needed quickly

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Overview of sampling techniques

Sampling techniques

Source: Saunders et al (2009)

Figure 7.2 Sampling techniques

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The sampling frame

• The sampling frame for any probability

sample is a complete list of all the cases in the population from which your sample will

be drown.

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Probability 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 the

sample

4 Check that the sample is representative

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Identifying a suitable sampling frame

Key points to consider

• Problems of using existing databases

• 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 and distribution

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) p=q=0.5 ( n’= n_adjusted Population(N

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The importance of response rate

Key considerations

• Non- respondents and analysis of refusals

• Obtaining a representative sample

• Calculating the active response rate

• Estimating response rate and sample size

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Selecting a sampling technique

Five main techniques used for a probability sample

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Systematic sampling

• Systematic sampling involves you selecting the

sample at regular intervals from the sampling frame.

1 Number each of the cases in your sampling frame

with a unique number The first is numbered 0, the second 1 and so on.

2 Select the first case using a random number.

3 Calculate the sample fraction.

4 Select subsequent cases systematically using the

sample fraction to determine the frequency of selection

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Stratified random sampling

• Stratified random sampling is a modification of

random sampling in which you divide the population into two or more relevant and significant strata based in a one or a number of attributes In effect, your sampling frame is

divided into a number of subsets A random sample (simple or systematic) is then drown from each of the strata Consequently stratified

sampling shares many of the advantages and disadvantages of simple random or systematic sampling

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Cluster Sampling

• Is on the surface, similar to stratified as you

need to divide the population into discrete groups prior to sampling The groups are termed clusters in this form of sampling and can be based in any naturally occurring

grouping For example, you could group your data by type of manufacturing firm or

geographical area

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Cluster Sampling

• For cluster sampling your sampling frame is

the complete list of clusters rather than complete list of individual cases within population, you then select a few cluster normally using simple random sampling,

Data are then collected from every case within the selected clusters

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Multi-stage sampling

(multi-stage cluster sampling

• It is a development of cluster sampling, it is

normally used to overcome problems associated with a geographically dispersed population when face to face contact is needed or where it is

expensive and time consuming to construct a sampling frame for a large geographical area

However, like cluster sampling you can use it for any discrete groups, including those not are

geographically based The technique involves taking a series of cluster samples, each involving some from of random sampling

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Quota sampling

• It is entirely non random and it is normally

used for interview surveys It is based on the premise that your sample will

represent the population as the variability

in your sample for various quota variables

is the same as that in population Quota sampling is therefore a type of stratified sample in which selection of cases within strata is entirely non-random

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Quota sampling

• Divide the population into specific groups.

• Calculate a quota for each group based on

relevant and available data.

• Give each interviewer an ‘assignment', which

states the number of cases in each quota from which they must collect data.

• Combine the data collected by interviewers to

provide the full sample.

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Quota sampling

• Quota sampling has a number of advantages over the

probabilistic techniques In particular, it is less costly and can be set up very quickly If, as with television audience research surveys, your data collection needs to be

undertaken very quickly then quota sampling frame and, therefore may be the only technique you can use if one is not available Quota sampling is normally used for large population For small population , it is usually possible to obtain a sampling frame Decisions on sample size are

governed by the need to have sufficient responses in each quota to enable subsequent statistical analyses to be

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