Please enable JavaScript.
Coggle requires JavaScript to display documents.
Scale Reliability and Validity (Validity: often called construct validity,…
Scale Reliability and Validity
Reliability: is the degree to which the measure of a construct is consistent or dependable.
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.
Translational validity: is typically assessed using a panel of expert judges, who rate each item (indicator) on how well they fit the conceptual definition of that construct, and a qualitative technique called Q-sort
criterion-related validity examines whether a given measure behaves the way it should, given the theory of that construct
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
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.
Concurrent validity examines how well one measure relates to other concrete criterion that is presumed to occur simultaneously.
Theory of Measurement: to synthesize the understanding of reliability and validity in a mathematical manner using classical test theory, also called true score theory.
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
The statistical impact of these errors is that random error adds variability (e.g., standard deviation) to the distribution of an observed measure, but does not affect its central tendency (e.g., mean), while systematic error affects the central tendency but not the variability
An Integrated Approach to Measurement Validation: A complete and adequate assessment of validity must include both theoretical and empirical approaches.
The first step is conceptualizing the constructs of interest. This includes defining each construct and identifying their constituent domains and/or dimensions.
Next, we select (or create) items or indicators for each construct based on our conceptualization of these construct, as described in the scaling procedure
Following this step, a panel of expert judges (academics experienced in research methods and/or a representative set of target respondents) can be employed to examine each indicator and conduct a Q-sort analysis.
Inter-rater reliability is assessed to examine the extent to which judges agreed with their classifications.
Next, the validation procedure moves to the empirical realm
The integrated approach to measurement validation discussed here is quite demanding of researcher time and effort. Nonetheless, this elaborate multi-stage process is needed to ensure that measurement scales used in our research meets the expected norms of scientific research.