Imperfect reliability and
validity of research tools is a big reason that different studies
examining the same research question come up with different conclusions.
Some measurement procedures are just more reliable and valid than
others, and for some phenomena there is no 100% reliable/valid measurement
tool, so inferences/extrapolations must be made from the data available.
to the repeatability or consistency of a measure. If a variable
is measured more than once using the same tool, will the results
be the same? For example, if you are measuring vitamin C content
of a urine sample, do repeated measures using the same sample yield
the same vitamin C content? If so, the measurement technique is
To determine if a measure
is reliable, you can compare your measurement technique to a "gold
standard" or repeat the measure to determine if the same results
are obtained. And, if more than one person is performing the measurement,
make sure they obtain the same results with the same measure.
Validity refers to
measuring what you thing you are measuring. For example, is your
nutrition knowledge test a true indicator of nutrition knowledge?
If you ask questions about car engines only, then your nutrition
knowledge test is certainly not a valid indicator of true nutrition
Types of validity include:
FACE VALIDITY: Does the measurement
instrument seem relevant to the study to the study subjects?
CONTENT VALIDITY: Does the
measurement instrument a good way to assess according to experts
in the field?
CRITERION VALIDITY: Does your
measuring instrument predict or agree with parallel indicators of
the same phenomenon?
CONSTRUCT VALIDITY: Do two
tests of the same measure agree?
A study can be reliable (repeatable)
without being valid. When you are evaluating a research article,
it is important to evaluate the reliability/validity of the measurement
tools. Hopefully, the authors of the article address these issues
in the article text.