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3.7 Data validity and reliability
In any research, whether the methodology is quantitative or qualitative, the methods
being used to gather data have to be reliable and valid (Jennings, 2001). Reliability
means whether or not a specific method, when applied repeatedly to a particular entity,
would produce the same result every time (Finn et al., 2000). Validity refers to the
representativeness of the measurements to the concepts being researched (Lewin, 2005).
Finn et al. (2000) go further and distinguish between external validity and internal
validity. External validity asks whether the research results can be generalized, while
internal validity refers to the extent to which the observations reflect the research
hypothesis (Denzin and Lincoln, 1998).
Jennings (2001) explained that validity and reliability are not the same. Research
findings can be reliable but not valid: i.e. the instrument used can generate the same
results consistently but this does not mean that the findings are the intended results of
the research (Lewin, 2005). However, perfect validity would signify perfect reliability
(Jennings, 2001).
Finn et al. (2000) refers to Decrop (1999) to propose four criteria for assessing both
quantitative and qualitative research: credibility, transferability, dependability and
conformability. Credibility questions the truthfulness of the research findings,
transferability is concerned with the application of the results to other populations,
dependability questions the consistency of the findings, and conformability assesses the
unbiased nature of the findings.
Saunders et al. (2000) cautioned that a high non-response rate may cause response bias.
Response rate is different from response bias. Dey (1997) claimed that:
It may be that a survey that yields a very low response rate, say 10%,
does a fairly good job of representing the population from which the
mail-out sample was originally drawn. This would be the case if the
10% who responded to this hypothetical survey were quite similar to the
90% who failed to respond. (p. 217)
A factor that strongly influences response rate was found to be the salience of an issue
to the sample population (Martin, 1994). According to Bean and Roszkowski, (1995),
salience has greater influence on the response rate than the length of the survey. The
low response rate received for the operator survey could have been caused due to
operators’ lack of interest in the survey topic.
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