Non-Sampling Errors


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Non-Sampling Errors

Not all errors in a piece of research are a result of the sampling process Certain kinds of error may arise even if a complete census is taken. There are four main categories of such error:

• response errors,

• interviewer errors,

• non-response errors,

• processing errors.

 

Where research is based on asking people questions then response errors may arise where, for one reason or another, respondents give wrong answers. This may be through dishonesty, forgetfulness, faulty memories. unwillingness or misunderstanding of the questions being asked. Many of these errors arise as a result of poor or inadequate questionnaire design putting it the otherway round, the potential for such errors to arise can be minimized by careful design of question-wording, question formulation and questionnaire layout. In interview surveys, whether face-to-face or by telephone, interviewers may themselves misunderstand questions or the instructions for filling them in. they may be dishonest, inaccurate, make mistakes or ask questions in a non-standard fashion. Interviewer training, along with field supervision and con­trol can, to a large extent, remove the likelihood of such errors, but they will never be entirely eliminated, and there is always the potential for systematic differences between the results obtained by different interviewers.

 

In nearly all research there will be missing cases, but in survey research there will always be a degree of non-response because some people will refuse to he interviewed or to complete a questionnaire, some will be ineligible because they turn out not to be part of the survey population, some will terminate the interview or refuse to answer some of the questions, and some will be non-contactable, for example, because they have moved away, died, or are on holiday at the time of the survey. Even where a census is attempted, it will often remain incomplete. The extent of non-response will vary consider­ably according to the type of research, the topic of the research, and, where based on face-to-face interviews, on the experience and training of the inter­viewers. Calculating the amount of non-response can be confusing since some researchers will, for example, take the proportion of refusals in the sample drawn, others will take refusals and non-contacts as a proportion of those found eligible, and so on.

 

Processing errors can arise back at the office, particularly at the stage of entering answers to questions onto a computerized database via a keyboard and screen. Agencies sometimes validate these entries by, in effect, entering them twice, and the computer checks to see if the two entries are identical. Alternatively, some agencies check samples of the entries. It is possible, in addition, to apply range checks and logical checks.

 

There are, then, a number of sources of non-sampling error, and it is important to bear these in mind when interpreting survey results, whether based on a sample or not. The crucial point is that such errors can arise even if a census is taken.

Total survey error

Any research that is based on addressing questions to people and recording their answers risks error resulting from the respondents themselves and from interviewers where these are used in addition to those kinds of error that arise in any research from data handling, and from inadequacies of sampling. Total survey error is the addition of all these sources of error, both sampling and non-sampling It is difficult to estimate what the total survey error is in any one survey, and it will tend to vary from question to question. What is certainly true is that the error that results from random sampling fluctuations – which is the only kind of error that is taken into account when confidence intervals are calculated or tests are made against the null hypothesis – accounts for only a very small proportion of the total survey error.

 

Errors of various kinds can always be reduced by spending more money, for example, on more interviewer training and supervision, on random sampling techniques, on pilot testing or on getting a higher response rate. However, the reduction in error has to be traded off against the extra cost involved. Further­more errors are often interrelated so that attempts to reduce one kind of error may actually increase another, for example, minimizing the non-response errors by persuading more reluctant respondents may well increase response error Non-sampling errors tend to be pervasive, not well-behaved and do not decrease – indeed may increase – with the size of the sample. It is sometimes even difficult to see whether they cause under- or over-estimation of population characteristics. There is, in addition, the paradox that the more efficient the sample design is in controlling random sampling fluctuations, the more important in proportion become bias and non-sampling error.


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