Types and Sources of Errors
in Statistical Data
SADC Course in Statistics
Types of Errors
• In general, there are two types of errors:
a. non-sampling errors and
b. sampling errors.
• It is important for a researcher to be aware of
these errors, in particular non-sampling errors, so
that they can be either minimised or eliminated
from the data collected.
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Non-sampling errors
– These are errors that arise during the course of
all data collection activities.
– In summary, they have the following
characteristics:
• exist in both sample surveys and censuses
data.
• difficult to measure .
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Sources of non-sampling errors
Non-sampling errors arise from:
• defects in the sampling frame.
• failure to identify the target population.
• non response.
• responses given by respondents.
• data processing and
• reporting, among others.
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Defects in the sampling frame
• This result in coverage errors.
• These occur when there is an omission,
duplication or wrongful inclusion of units in the
sampling frame.
• Omissions are referred to as ‘under coverage’
while duplications and wrongful inclusions are
called ‘over coverage’.
• These errors are caused by defects such as
inaccuracy, incompleteness, duplication,
inadequacy and out of date sampling frames.
• Coverage errors may also occur in field
operations, that is, when an enumerator misses
several households or persons during the
interviewing process.
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Failure to Identify Target Population
• This occurs when the target population is not
clearly defined through the use of imprecise
definitions or concepts or when the survey
population does not reflect the target population
due to an inadequate sampling frame and poor
coverage rules.
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Response
• They result from the data that have been
requested, provided, received or recorded
incorrectly.
• They may occur as a result of inefficiencies with
the questionnaire, the interviewer, the respondent
or the survey process.
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a.
Poor questionnaire design
• The content and wording of the questionnaire may
be misleading and the layout of the questionnaire
may make it difficult to accurately record
responses.
• As a rule, questions in questionnaire should not be
loaded, double-barrelled, misleading or
ambiguous, and should be directly relevant to the
objectives of the survey.
• It is essential to pilot test questionnaires to
identify questionnaire flow and question wording
problems, and allow sufficient time for
improvements to be made to the questionnaire.
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Poor questionnaire design – cont’d
• The questionnaire should then be re-tested to
ensure changes made do not introduce other
problems.
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b.
Interviewer bias
• An interviewer may influence the way a
respondent answers survey questions.
• To prevent this, interviewers must be trained to
remain neutral throughout the interviewing
process and must pay close attention to the way
they ask each question.
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c.
Respondent errors
• These arise through the respondent providing
inaccurate or wrong information.
• They occur because of memory biases or
respondents giving inaccurate or false information
when they believe that they are protecting their
personal interests or integrity.
• They can also arise from the way the respondent
interprets the questionnaire and the wording of
the answer that the respondent gives.
• Careful questionnaire design and effective
questionnaire testing can overcome these
problems to some extent.
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d.
Problems with the survey process
• Errors can also occur because of problems with
the actual survey process such as using proxy
responses, that is, taking answers from someone
other than the respondent or lacking control over
the survey procedure.
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Non-Response
• Non-response results when data is not collected
from respondents.
• The proportion of these non-respondents in the
sample is called the non-response rate.
• Non-response can be either total or partial.
• Total non-response or unit non-response can
arise if a respondent cannot be contacted
(because the sampling frame is incomplete or outof-dated) or the respondent is not at home or is
unable to respond because of language difficulties
or illness or out rightly refuses to answer any
questions or the dwelling unit is vacant.
• Other respondents may indicate that they simply
don't have the time to complete the interview or
survey form.
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Non-response - cont’d
• When conducting surveys it is important to
document information on why a respondent has
not responded.
• Partial non-response or item non-response
can occur when a respondent replies to some but
not all questions of the survey.
• This can arise due to memory problems,
inadequate information or an inability to answer a
particular question/section of the questionnaire.
• A respondent may refuse to answer if;
a.
they find questions particularly sensitive, or if
b.
they have been asked too many questions.
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Non-response - cont’d
• To reduce non-response, the following approaches
can be used:
– care should be taken in questionnaire design
through the use of simple questions.
– pilot testing of the questionnaire.
– explaining survey purposes and uses.
– assuring confidentiality of responses.
– public awareness activities including discussions
with key organisations and interest groups,
news releases, media interview and articles.
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Processing
• These occur at various stages of data processing
such as data cleaning, data capture and editing.
• Data cleaning involves taking preliminary checks
before entering the data onto the processing
system.
• Coder bias is usually a result of poor training or
incomplete instructions, variability in coder
performance and data entry errors.
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Processing – cont’d
• Inadequate checking and quality management at
this stage can introduce data loss (where data is
not entered into the system) and data duplication
(where the same data is entered into the system
more than once) thus introducing errors in data.
• To minimise these errors, processing staff should
be given adequate training, instructions and
realistic workloads.
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Time Period Bias
• This occurs when a survey is conducted during an
unrepresentative time period.
• Survey timing is thus important and failure to
recognise this introduces errors in data.
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Analysis and Estimation
• Analysis errors include any errors that occur when
using wrong analytical tools or when preliminary
results are used instead of the final ones.
• Errors that occur during the publication of the
data results are also considered as analysis errors.
• Estimation errors occur when inappropriate or
inaccurate weights are used in the estimation
procedure thus introducing errors to the data.
• They also occur when wrong estimators are
selected by the analyst.
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Reducing non-sampling errors
• Can be minimised by adopting any of the following
approaches:
– using an up-to-date and accurate sampling
frame.
– careful selection of the time the survey is
conducted.
– planning for follow up of non-respondents.
– careful questionnaire design.
– providing thorough training and periodic
retraining of interviewers and processing staff.
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Reducing non-sampling errors – cont’d
-
designing good systems to capture errors that
occur during the process of collecting data,
sometimes called Data Quality Assurance
Systems.
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Sampling error
• Refer to the difference between the estimate
derived from a sample survey and the 'true' value
that would result if a census of the whole
population were taken under the same conditions.
• These are errors that arise because data has been
collected from a part, rather than the whole of the
population.
• Because of the above, sampling errors are
restricted to sample surveys only unlike nonsampling errors that can occur in both sample
surveys and censuses data.
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Sampling errors – cont’d
• There are no sampling errors in a census because
the calculations are based on the entire
population.
• They are measurable from the sample data in the
case of probability sampling.
• More will be discussed in detail in more advanced
modules of the training programme.
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Factors Affecting Sampling Error
It is affected by a number of factors including:
a.
sample size.
• In general, larger sample sizes decrease the
sampling error, however this decrease is not
directly proportional.
• As a rough rule of the thumb, you need to
increase the sample size fourfold to halve the
sampling error but bear in mind that non sampling
errors are likely to increase with large samples.
b.
the sampling fraction.
• this is of lesser influence but as the sample size
increases as a fraction of the population, the
sampling error should decrease.
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Factors Affecting Sampling Error – cont’d
c.
the variability within the population.
• More variable populations give rise to larger
errors as the samples or the estimates calculated
from different samples are more likely to have
greater variation.
• The effect of variability within the population can
be reduced by the use of stratification that allows
explaining some of the variability in the
population.
d.
sample design.
• An efficient sampling design will help in reducing
sampling error.
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Characteristics of the sampling error
• generally decreases in magnitude as the sample
size increases (but not proportionally).
• depends on the variability of the characteristic of
interest in the population.
• can be accounted for and reduced by an
appropriate sample plan.
• can be measured and controlled in probability
sample surveys.
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Reducing sampling error
 If sampling principles are applied carefully within
the constraints of available resources, sampling
error can be kept to a minimum.
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Sources
– http://www.nss.gov.au/nss/home.nsf/S
urveyDesignDoc/4354A8928428F834CA2
571AB002479CE?OpenDocument
– http://www.statcan.ca/english/edu/pow
er/ch6/nonsampling/nonsampling.htm
– http://www.statcan.ca/english/edu/pow
er/ch6/sampling/sampling.htm
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