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Scale Reliability and Validity - Coggle Diagram
Scale Reliability and Validity
We also must test these scales to ensure that: (1) these scales indeed measure the unobservable construct that we wanted to measure. (2) They measure the intended construct consistently precisely.
A measure can be reliable but not valid, if it is measuring something very consistently but is consistently measuring the wrong construct.
A measure that is valid but not reliable will consist of shots centered on the target but not clustered within a narrow range, but rather scattered around the target.
Finally, a measure that is reliable but not valid will consist of shots clustered within a narrow range but off from the target.
Reliability is the degree to which the measure of a construct is consistent or dependable.
A more reliable measurement may be to use a weight scale, where you are likely to get the same value every time you step on the scale, unless your weight has actually changed between measurements.
Note that reliability implies consistency but not accuracy.
Reliability may be improved by using quantitative measures, for instance, by counting the number of grievances filed over one month as a measure of (the inverse of) morale.
A second source of unreliable observation is asking imprecise or ambiguous questions.
A third source of unreliability is asking questions about issues that respondents are not very familiar about or care about.
Inter-rater reliability, also called inter-observer reliability, is a measure of consistency between two or more independent raters (observers) of the same construct.
Test-retest reliability is a measure of consistency between two measurements (tests) of the same construct administered to the same sample at two different
points in time.
Split-half reliability is a measure of consistency between two halves of a construct measure.
Internal consistency reliability is a measure of consistency between different items of the same construct.
Validity, often called construct validity, refers to the extent to which a measure adequately represents the underlying construct that it is supposed to measure.
Validity can be assessed using theoretical or empirical approaches, and should ideally be measured using both approaches.
Theoretical assessment of validity focuses on how well the idea of a theoretical construct is translated into or represented in an operational measure.
This type of validity is called translational validity (or representational validity), and consists of two subtypes: face and content validity.
Empirical assessment of validity examines how well a given measure relates to one or more external criterion, based on empirical observations.
This type of validity is called criterion-related validity, which includes four sub-types: convergent, discriminant,
concurrent, and predictive validity.
Face validity refers to whether an indicator seems to be a reasonable measure of its underlying construct "on its face".
Content validity is an assessment of how well a set of scale items
matches with the relevant content domain of the construct that it is trying to measure.
Convergent validity refers to the closeness with which a measure relates to (or
converges on) the construct that it is purported to measure, nd discriminant validity refers to the degree to which a measure does not measure (or discriminates from) other constructs
that it is not supposed to measure.
Predictive validity is the degree to which a measure successfully predicts a future
outcome that it is theoretically expected to predict.
Assessing such validity requires creation of a "nomological network" showing how constructs are theoretically related to each other.
Concurrent validity examines how well one measure relates to other concrete criterion that is presumed to occur simultaneously.
Theory of Measurement
Random
error is the error that can be attributed to a set of unknown and uncontrollable external factors
that randomly influence some observations but not others.
Systematic error is an error that is introduced by factors that systematically affect all
observations of a construct across an entire sample in a systematic manner.