Click on the following links. Please note these will open in a new window.

Research That Matters: Jang, Bohyun Joy, Megan E. Patrick, and Megan S. Schuler. 2017. "Substance Use Behaviors and the Timing of Family Formation during Young Adulthood." Journal of Family Issues, Online first.

Journal Article 1: Saylor, R. (2013). Concepts, measures, and measuring well: An alternative outlook. Sociological Methods & Research, 42(3), 354–391.

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.

Journal Article 2: Brenner, P. S. & DeLamater, J. (2016). Lies, damned lies, and survey self-reports? Identity as a cause of measurement bias. Social Psychology Quarterly, 79(4), 333–354.

Abstract: Explanations of error in survey self-reports have focused on social desirability: that respondents answer questions about normative behavior to appear prosocial to interviewers. However, this paradigm fails to explain why bias occurs even in self-administered modes like mail and web surveys. We offer an alternative explanation rooted in identity theory that focuses on measurement directiveness as a cause of bias. After completing questions about physical exercise on a web survey, respondents completed a text message–based reporting procedure, sending updates on their major activities for five days. Random assignment was then made to one of two conditions: instructions mentioned the focus of the study, physical exercise, or not. Survey responses, text updates, and records from recreation facilities were compared. Direct measures generated bias—overreporting in survey measures and reactivity in the directive text condition—but the nondirective text condition generated unbiased measures. Findings are discussed in terms of identity.

Journal Article 3: Howell, J., & Emerson, M. O. (2016). So what “should” we use? Evaluating the impact of five racial measures on markers of social inequality. Sociology of Race and Ethnicity, 3(1), 14–30.

Abstract: In recent years, researchers have increasingly noted the malleability of racial boundaries across time, context, and life course. Although this research has advanced our knowledge of the maintenance and perceptions of racial groups, it has introduced a new question: If we are attempting to best capture the actual variation in racial inequality, how should we operationalize race? Using the 2006 wave of the Portraits of American Life Study, a national-level, in-home survey with extensive race measures and oversamples of Blacks, Hispanics, and Asians, the authors identify five ways that race can be and to varying degrees is operationalized: census, combined race/ethnic, pentagon, triracial, and skin tone measures. Using the Vuong non-nested model tests, the authors compare the effectiveness of these five measurements in predicting three measures of social inequality: household income, education, and self-rated health. The authors find that overall, Hollinger’s ethnoracial pentagon is best able to capture existing inequality. Thus, for scholars attempting to understand variation in contemporary racial inequality, this research suggests that scholars should use five monoracial categories: White, Black, Hispanic, Native American and Asian.