Multiple Meanings of Calling: Next Steps for Studying an Evolving Construct

Research on work as a calling has rapidly increased in recent years, yet the lack of consensus regarding the construct’s definition presents key challenges to researchers, most notably the potential lack of coherence as research on calling accumulates. We begin with a brief overview of current definitions in the literature to illustrate the overlapping yet distinct conceptualizations of the construct, placing them along a continuum of “neoclassical” to “modern.” Next, we explore strengths and shortcomings of the two most commonly employed methodological strategies for studying calling, the “top-down” and “bottom-up” approaches. We invite researchers to adopt a third strategy, the typological approach (and the taxometric method in particular), to offer much-needed conceptual clarity by empirically investigating whether there are distinct types of calling or whether the construct is best conceptualized as dimensional in nature. Finally, we present recommendations to guide researchers, reviewers, and consumers of research related to work as a calling on a path that reduces its ongoing conceptual murkiness.

Taxometric Analysis

Whether individual differences are treated as categorical or continuous has consequences for theory, assessment, classification, and research in criminal justice. Paul Meehl's (1995) taxometric method allows investigators to test between these two competing structural models. This article provides an overview of the method's inferential framework and data-analytic procedures. Because guidelines for implementing taxometric analyses and interpreting their results have received little research attention, investigators are encouraged to adopt an empirically grounded approach to taxometric analysis rather than following conventions or relying on personal opinion. The guidance afforded by Monte Carlo studies, including the two reported here, can be supplemented by simulating comparison data. This empirically grounded approach, described and illustrated below, helps to implement the taxometric method effectively and to draw valid conclusions.

Robustness of Taxometric Analysis with Skewed Indicators: II. A Monte Carlo Study of the Maxcov Procedure

A small Monte Carlo study was conducted to determine whether MAXCOV analysis, a taxometric method for testing between discrete (“taxonic”) and continuous models of latent variables, is robust when indicators of the latent variable are skewed. Analysis of constructed data sets containing three levels of skew indicated that the MAXCOV procedure is unlikely to yield spurious findings of taxonicity even when skewness is considerable. However, care must be taken to distinguish low base-rate taxonic variables from skewed nontaxonic variables.

Robustness of Taxometric Analysis with Skewed Indicators: I. a Monte Carlo Study of the Mambac Procedure

A small Monte Carlo study was conducted to determine whether Meehl and Yonce's (1994) MAMBAC procedure—a taxometric method for testing between discrete and continuous models of latent variables—is robust when the latent variable and its manifest indicators are skewed Analysis of constructed data sets containing three levels of skew indicated that the MAMBAC procedure is highly unlikely to yield spurious findings of discreteness (“taxonicity”) even when skewness is considerable. MAMBAC appears to be a robust and promising addition to the family of taxometric procedures.