Concepts, Measures, and Measuring Well

Abstract

Measurement theorists agree that one has measured well when one's measurement scheme faithfully represents the concept under investigation. Yet, the conventional wisdom on ''measurement validation'' pays surprisingly little attention to conceptual meaning and instead emphasizes measurement error and the pursuit of true scores. Researchers are advised to adopt an empiricist stance; treat data as objective facts; and confer validity through predictive correlations. This article offers an alternative outlook on ascertaining goodness in measurement. First, researchers must measure a concept's dimensional expanse. Second, they must contextualize their measures to ensure concept-measure congruence and categorial pertinence. Third, this approach hinges on dialogue among subject matter experts to craft disciplinary measurement norms. The article contrasts these dueling approaches through an extended example of how scholars measure the concept state capacity. Overall, this article argues that social scientists must reconceive what it means to have measured well.

Video Data Analysis: A Methodological Frame for a Novel Research Trend

“…Such criteria are a cornerstone of any analytic framework and help evaluate research. 13 Validity means that the concepts and indicators researchers use capture what they are intended to capture and that data used provide evidence for conclusions drawn (LeCompte and Goetz 2007:17; Saylor 2013:354). Extant studies that employ VDA follow criteria of validity but without an explicit discussion of how the use of visual data affects these criteria (e.g., Collins 2008; Klusemann 2009).…”

Motivated and Able to Make a Difference? The Reinforcing Effects of Democracy and State Capacity on Human Development

“…Nevertheless, as administrative capacity is such a disputed concept and difficult to measure, many of the measures are problematic (Hendrix, 2010;Saylor, 2013). A common reason is that they capture political decisions and not the capacity to implement them.…”

What Do Books in the Home Proxy For? A Cautionary Tale

“…Virtually without exception, methodological literature on proxy variables departs from some version of the "classical" model where error is treated as random noise, unrelated to all model variables and the regression residual (Bohrnstedt 2010, Saylor 2013. It is well known that such error will lead to a bias toward the null as a simple function of the ratio of noise to total variance.…”