A fundamental question facing clinical scientists is whether the constructs they are studying are categorical or dimensional in nature. The taxometric method was developed expressly to answer this question and is being used by a growing number of investigators to inform theory, research, and practice in psychopathology. The current paper provides a practical introduction to the method, updating earlier tutorials based on the findings of recent methodological studies. We offer revised guidelines for data requirements, indicator selection, parameter estimation, and procedure selection and implementation. We illustrate our recommended approach to taxometric analysis using idealized data sets as well as data sets representative of those found in clinical research. We close with advice to help newcomers get started on their own taxometric analyses.
Determining whether a construct is more appropriately conceptualized and assessed in a categorical or a dimensional manner has received considerable research attention in recent years. There are a variety of statistical techniques to address this empirically, and Meehl's (1995) taxometric method has been among the most widely used methods applied to constructs in the areas of personality and psychopathology. In taxometric analysis, the comparison curve fit index (CCFI; Ruscio, Ruscio, & Meron, 2007) is an objective measure of whether parallel analysis of categorical or dimensional comparison data better reproduce empirical data results. The development and use of the CCFI helps to reduce the subjectivity involved in performing taxometric analyses and interpreting the results. In a series of simulation studies, we examine the use of the CCFI to flesh out some empirically supported guidelines. We find that a panel of curves should be averaged to calculate a single CCFI value (rather than calculating the CCFI for each curve and averaging these values), that an ambiguous range of CCFI values should be defined using a fixed-width interval (rather than a multiple of the estimated standard error), and that constructing a CCFI profile can help to differentiate categorical and dimensional data and provide a less biased and more precise estimate of the taxon base rate than conventional methods. Implications of these findings for taxometric research relevant to psychological assessment are discussed along with ways to perform analyses consistent with these recommendations. (PsycINFO Database Record
Despite near universal acceptance in the value of higher education for individuals and society, college persistence rates in 4‐year and community colleges are low. Only 57% of students who began college at a 4–year institution in 2001 had completed a bachelor's degree by 2007, and only 28% of community college students who started school in 2005 had completed a degree 4 years later (National Center for Education Statistics, 2011). To address this problem, this paper identified 3 goals. The first was to review the extant literature on persistence in higher education. The second was to develop a working model of persistence informed by our literature review. This resulted in a model centered on 3 basic categories of variables: those that put you on track towards persistence, those that push you off track, and those that keep you on track. The final goal was to outline a research agenda to develop student‐centered assessments informed by our model, and we conclude with a discussion of this agenda.
The present study tests predictions from the Tripartite Integration Model of Social Influences (TIMSI) concerning processes linking social interactions to social integration into science, technology, engineering, and mathematics (STEM) communities and careers. Students from historically overrepresented groups in STEM were followed from their senior year of high school through their senior year in college. Based on TIMSI, we hypothesized that interactions with social influence agents (operationalized as mentor network diversity, faculty mentor support, and research experiences) would promote both short- and long-term integration into STEM via social influence processes (operationalized as science self-efficacy, identity, and internalized community values). Moreover, we examined the previously untested hypothesis of reciprocal influences from early levels of social integration in STEM to future engagement with social influence agents. Results of a series of longitudinal structural equation model-based mediation analyses indicate that, in the short term, higher levels of faculty mentorship support and research engagement, and to a lesser degree more diverse mentor networks in college promote deeper integration into the STEM community through the development of science identity and science community values. Moreover, results indicate that, in the long term, earlier high levels of integration in STEM indirectly influences research engagement through the development of higher science identity. These results extend our understanding of the TIMSI framework and advance our understanding of the reciprocal nature of social influences that draw students into STEM careers.
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