Question

Critically analyse the impact of sampling and non-sampling errors on the interpretation of research findings. Within your response you must describe each error (sampling and non- sampling error), evaluate these and detail how these can be mitigated. You should use examples to support your analysis and evaluation

Answer 1

SAMPLING ERROR

It is incurred when the statistical characteristics of a population are estimated from a subset, or sample, of that population. Since the sample does not include all members of the population, statistics on the sample, such as means and quartiles, generally differ from the characteristics of the entire population, which are known as parameters.

Sampling bias is a possible source of sampling errors, wherein the sample is chosen in a way that makes some individuals less likely to be included in the sample than others. It leads to sampling errors which either have a prevalence to be positive or negative. Such errors can be considered to be systematic errors.The main reason behind sampling error is that the sampler draws various sampling units from the same population but, the units may have individual variances. Moreover, they can also arise out of defective sample design, faulty demarcation of units, wrong choice of statistic, substitution of sampling unit done by the enumerator for their convenience. Therefore, it is considered as the deviation between true mean value for the original sample and the population.Five Types of Sampling Errors

1.Population Specification Error—This error occurs when the researcher does not understand who they should survey. For example, imagine a survey about breakfast cereal consumption. Who to survey? It might be the entire family, the mother, or the children. The mother might make the purchase decision, but the children influence her choice.

2.Sample Frame Error—A frame error occurs when the wrong sub-population is used to select a sample. .

3.Selection Error—This occurs when respondents self-select their participation in the study – only those that are interested respond. Selection error can be controlled by going extra lengths to get participation. A typical survey process includes initiating pre-survey contact requesting cooperation, actual surveying, and post-survey follow-up. If a response is not received, a second survey request follows, and perhaps interviews using alternate modes such as telephone or person-to-person.

4. Non-Response—Non-response errors occur when respondents are different than those who do not respond. This may occur because either the potential respondent was not contacted or they refused to respond. The extent of this non-response error can be checked through follow-up surveys using alternate modes.

Sampling Errors—These errors occur because of variation in the number or representativeness of the sample that responds. Sampling errors can be controlled by (1) careful sample designs, (2) large samples, and (3) multiple contacts to assure representative response.

Examole:

Imagine that you want to know the average height of men on earth. This average height exists but obviously you will never be able to know it (unless you're able to measure several millions men...). What you can do is measure hundreds or thousands of people and calculate the average height of these people. The average height among these people is probably not exactly equal to the average height of men on earth (because they are particular men in the whole population) but, if you did a good job (use a representative sample of the population), it should be close enough. The difference between the quantity that you want to know (average height of men on earth) and its estimation through your sample (average height of men in the sample) is the sampling error.

NON-SAMPLING ERROR

Sampling error can be contrasted with non-sampling error. Non-sampling error is a catch-all term for the deviations from the true value that are not a function of the sample chosen, including various systematic errors and any random errors that are not due to sampling. Non-sampling errors are much harder to quantify than sampling error. One of the two reasons for the difference between an estimate (from a sample) and the true value of a population parameter; the other reason being the error caused because data are collected from a sample rather than the whole population (sampling error). Non-sampling errors have the potential to cause bias in surveys or samples.

There are two types of non-sampling error:

1. Response Error: Error arising due to inaccurate answers were given by respondents, or their answer is misinterpreted or recorded wrongly. It consists of researcher error, respondent error and interviewer error which are further classified as under.

>Researcher Error

Surrogate Error

Sampling Error

Measurement Error

Data Analysis Error

Population Definition Error

>Respondent Error

Inability Error

Unwillingness Error

>Interviewer Error

Questioning Error

Recording Erro

Respondent Selection Error

Cheating Error

2. Non-Response Error: Error arising due to some respondents who are a part of the sample do not respond.

Some examples of non-sampling errors are:

• The sampling process is such that a specific group is excluded or under-represented in the sample, deliberately or inadvertently. If the excluded or under-represented group is different, with respect to survey issues, then bias will occur.

• The sampling process allows individuals to select themselves. Individuals with strong opinions about the survey issues or those with substantial knowledge will tend to be over-represented, creating bias.

• If people who refuse to answer are different, with respect to survey issues, from those who respond then bias will occur. This can also happen with people who are never contacted and people who have yet to make up their mind.

• If the response rate (the proportion of the sample that takes part in a survey) is low, bias can occur because respondents may tend consistently to have views that are more extreme than those of the population in general.

• The wording of questions, the order in which they are asked and the number and type of options offered can influence survey results.

• Answers given by respondents do not always reflect their true beliefs because they may feel under social pressure not to give an un