What is scale coarseness? What are the consequences of scale coarseness and what can be done about it?

Question

What will be an ideal response?

Answer 1

Scale coarseness is related to measurement error, but it is a distinct phenomenon that also results in lack of measurement precision (Aguinis, Pierce, & Culpepper, 2009). A measurement scale is coarse when a construct that is continuous in nature is measured using items such that different true scores are collapsed into the same category. In these situations, errors are introduced because continuous constructs are collapsed. Although this fact is seldom acknowledged, HR researchers and practitioners use coarse scales every time, continuous constructs are measured using Likert-type or ordinal items. We are so accustomed to using these types of items that we seem to have forgotten they are intrinsically coarse. What are the consequences of scale coarseness? Although seldom recognized, the lack of precision introduced by coarse scales has a downward biasing effect on the correlation coefficient computed using data collected from such scales for the predictor, the criterion, or both variables. For example, consider the case of a correlation computed based on measures that use items anchored with 5-scale points. In this case, a population correlation of .50 is attenuated to a value of .44. A difference between correlations of .06 indicates that the correlation is attenuated by about 14%. As is the case with other statistical and methodological artifacts that produce a downward bias in the correlation coefficient, elimination of the methodological artifact via research design and before data are collected is always preferred in comparison to statistical corrections after the data have been collected (Hunter & Schmidt, 2014). Thus, one possibility regarding the measurement of continuous constructs is to use a continuous graphic-rating scale (i.e., a line segment without scale points) instead of Likert-type scales. However, this type of data-collection procedure is not practically feasible in most situations unless data are collected electronically (Aguinis, Bommer, & Pierce, 1996). The second best solution is to use a statistical correction procedure after data are collected.