Errors in Data Collection

7/29/2019 Errors in Data Collection

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TYPES OF ERRORS INDATA COLLETION

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ProblemsThere are many sources of error in data collection.

Here are a few: Transient personal factors such as health, fatigue,motivation

Situational factors such as relations with colleagues,distractions

Instrumentation problems such as lack of clarity in aninterview schedule, troublesome arrangement/organization on a questionnaire, or response factorssuch as respondents selecting "no" rather than "yes"because "yes" leads to more questions

Analysis factors such as errors in scoring, tabulation, orthe use of an inappropriate statistical test.

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Example Here is an example of a data collection

problem that was overcome. A questionnaire with a low response rate

on a pilot test was redesigned to include anendorsement cover letter, reduced length,

and a better benefit/rationale statement. If problems cannot be overcome, it may be

necessary to change the research designor delete a troublesome variable.

You are responsible for convincing thereader that your data collection methodwas appropriate and free from error.

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Reliability and ValidityBy: Eng. Luteganya Lucius RUniversity of Dar es salaam

Computing Centre

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Introduction As a researcher, we need to be able to

understand the usefulness of the data wecollect: How accurate a picture of social life we

are getting Whether or not the conclusions we draw

are applicable to everyone or simply thegroup of people we have studied("representativeness").

Can our research be repeated by others (a

process known as "replication") and wouldthey get similar results if they did repeatour research?

Two concepts that we use to test the

usefulness of the data we collect are:


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1. Reliability

2. Validity

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1. Reliability

The reliability of the data wecollect must, of course, be animportant consideration, since if

the data we use is not reliable,then the conclusions we draw onthe basis of such data are going

to be fairly useless.

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Data reliability, therefore, is concerned with ideas suchas:

The consistency of the data collected. The precision (or lack of same) with which it is

collected For example, how systematic is a form of data

collection that relies upon asking people questionsabout something about they may have little directknowledge?

The repeatability of the data collection method For example, if another researcher attempted torepeat my research "down the pub", would similarresults be achieved?

In simple terms, data can be considered broadly

reliable if the same results (or broadly similar) can begained by different researchers asking the samequestions to the same (or broadly similar) people.

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For example, a researcher may attempt tocheck the reliability of a response within aquestionnaire by asking basically the samequestion in a slightly different way:How old are you...When were you born?

In this (very simple) example, theresearcher attempts to cross-check thereliability of an answer - if they get twodifferent answers, then it is likely that thedata being collected is not going to be very

reliable (this, incidentally, is a form of datatriangulation)

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2. Validity

Data is only useful if it actuallymeasures what it claims to bemeasuring and, in this respect, theconcept of validity refers to theextent to which the data we collectgives a true measurement /description of "social reality" (what

is "really happening" in society).

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What causes data validity errors?

Data validity errors are usually caused byincorrect data entries, when a large volumeof data is entered in a short period of time.For example, a data entry operator enters12/25/2010 as 13/25/2010, by mistake,

and this data is therefore invalid. How can you reduce data validity errors?

You can use one of the following two, simplefield validation techniques.

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Technique 1:If the date field in a database usesthe MM/DD/YYYY format, then youcan use a program with the following

two data validation rules: "MM"should not exceed "12", and "DD"should not exceed "31".

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Technique 2:

If the original figures do not seem tomatch the ones in the database, thenyou can use a program to validate data

fields. You can compare the sum of thenumbers in the database data field tothe original sum of numbers from thesource. If there is a difference

between the two figures, it is anindication of an error in at least onedata element.

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As should be clear, the concepts ofreliability and validity go hand-in-hand in sociological research:

If data is reliablebut not valid,

then it may have limited use. We canmake general statements about theworld, but such statements may notactually apply to any one social group

(such as the "unemployed").

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If data is valid, but notreliable, we may not be able touse it to make generalstatements about the world (forexample, we may be able tounderstand something about onegroup of unemployed people that

doesn't necessarily apply to allunemployed people).

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Finally, therefore, a general rule to

follow whenever you are presentedwith data to analyse / interpret(whether it be data collected fromprimary sources such as interviews,

experiments, observation and thelike, or secondary sources such asnovels, Official Statistics and soforth), is that you should always seek

to apply the concepts of reliabilityand validity to the data.

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Triangulation. Various methods of data collection

have different advantages anddisadvantages and, given this fact, itwould seem to make sense for theresearchers to make use of a number

of different methods in theirresearch since: A weakness in one method could beavoided by using a second method

that is strong in the area that thefirst is weak.

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For example, when we interview people ageneral weakness here is that we have totake it on trust that the respondent istelling us the truth. In this instance, wemight use an observational method totry and check we are getting the truthabout someone's behaviour. By observingthem in their everyday life, forexample, we might be able to check theyactually do what they tell us they do...

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Errors in Data Collection