Selection of samples for observation and questioning

One of the major aims of the survey is to select samples which will provide data representative of the population about which the researcher wishes to make general statements and inferences.

In order to make their studies more sensitive to various effects, sociologists often survey hundreds or even thousands of people.

This is because the effects of random errors will tend to cancel each other out but the effects of particular variables will be aggregated over all subjects.

Probability sampling: In this type of sampling, each member of the population has a known probability of being included. It is used to select samples so that results can be applied as widely as possible beyond the specific context of the researcher (Mayntz et al, 1976; Phillips, 1976).

The simple random sample, in which each member of the population has an equal chance of being selected, is the most widely used of the procedures of probability sampling. More elaborate procedures

are also used. For example, a stratified sample may be drawn by dividing the population into strata which are meaningful to the research. A simple random sample may be drawn from each stratum;

or quotas, which are proportional to the numbers in specific groups in the whole population, may be randomly drawn. Whatever method is used, the aim of this type of sampling is to calculate the probability that the result from the data provided by the sample differs from a result based on a survey of the whole population.

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-Like all statistical tests and the methods of scaling discussed previously, certain underlying assumptions need to be met for a method to be valid. For probability sampling, the main assumption is that the population is distributed normally and is completely characterised by its mean and standard deviation (Blalock, 1974).

The normal distribution is a theoretical curve which is symmetrical about its mean. Although an opinion or an ability or any other variable may be distributed normally in very large populations, such as all physiotherapists or all hemiplegic patients in the world, a normal distribution cannot be assumed for any

smaller popu l a t i o n under study (Senders, 1958),

For some surveys, data from censuses of populations can be used to test if the results obtained from one or more sub-samples differ significantly. Data of this type is not available from hemiplegic^.patients or from the physiotherapists who treat them.

Therefore, each member of these populations cannot have an equal chance, or known probability, of being represented in the sample;

and no calculation can be made to test if results obtained from samples of them differ from results based on censuses or surveys of them.

The extent to which results can be generalised is evaluated by the. statistic .of sampling error (Weisberg and Bowen, 1977).

Researchers who are concerned about this "external validity"

use large samples in order to minimise sampling error. However, large samples may create problems with "internal validity", or

the extent to which the data apply to the phenomenon under study.

For example, if probability is being trusted to provide a random sample in which the significant variations in the populations

of hemiplegic patients and physiotherapists would be represented, less may be known by the researcher about variables such as the method of treatment used by each physiotherapist .and the setting in which each patient is treated. Consequently, internal

validity might be doubtful, and it would be worthless to try to generalise from the data and the findings. Phillips (1976) considers that external validity becomes important after useful internally valid results have been obtained.

Non-probability sampling: The problem with large samples is that internal validity could be compromised: the problem with small samples, or samples for which the statistic of sampling error cannot be calculated, is a question of the worth of research for which the extent of external validity cannot be estimated. Fortunately, non-parametric statistical tests for samples as small as six members do not specify conditions about the parameters of the population from which the sample is drawn

(Siegel, 1956; Daniel,1978).

Firstly, they can be used to detect relationships

among variables, e.g. the compatibility of an assessment with the different methods of physiotherapy.

Secondly, theoretical distributions of test statistics allow probability statements to be made about numerical values which may be calculated from the data provided by the samples.

Therefore, in some circumstances, it may be more appropriate to use non-probability sampling which allows the researcher to select samples having particular characteristics. Inferences and general statements based on the findings might be considered less valid than those based on probability samples; but lack of a statistic of sampling error may be offset by a gain in internal

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validity. Consequently, inferences based on data from non­

probability samples may be equally or more valid than inferences made when the extent to which the findings can be generalised is vested in the precision of external validity.

A final comparison between probability and non-probability

sampling may be based on the use of random selection to minimise potential bias of the researcher. Conversely, it can be argued, especially when there is no alternative, that non-probability sampling, raises these biases to the surface in the criteria used to select the samples. In this way, characteristics are demonstrated which may be important when inferences and

general statements are made.

Biased samples: A non-probability sample may also result when data are collected from only a proportion of a designated probability sample. It is inevitable that some members of a selected sample of either type will not respond to a postal survey. Consequently, the structure of the responding sample can be a major problem (Reuss, 1943). A low rate of response is almost certain to produce a biased sample, but a high rate of response is not proof that no bias exists. Specific groups may have responded; and lack of response from specific groups may affect the representativeness of the samples and the validity of the findings of the research.

Researchers in the social sciences have been criticised for reporting results when respondents have represented only a small proportion of the selected sample. Fifty per cent is suggested as the lower limit of acceptability, and a proportion

less than sixty per cent of the selected sample is said to

produce "fragile data" (Fox, 1976). Goode and Hatt (1952) suggest that non-responding members of selected samples should be

interviewed to determine the direction of their biases; to allow a clear picture' of them to emerge; and to investigate whether lack of response is due to dissatisfaction with, say, the physio­

therapeutic assessment or to other factors. It would appear more feasible to try to follow up these people with letters of enquiry if the sample is drawn from all over the United Kingdom, and overseas, as is possible for the proposed project.

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