David Frank Feldon

Community college transfer students represent a diverse and talented group to recruit to PhD and other graduate programs. Yet, little is known about practical strategies to support community college transfer students’ access to graduate training. Focusing specifically on transfer students in computer science and guided by social cognitive career theory, this manuscript draws on survey data from over 200 community college transfer students and utilizes a staged innovation design to examine a new intervention designed to pique transfer students’ interests in PhD study. Findings suggest that brief targeted interventions can significantly predict transfer students’ perceptions about PhD study, but that more sustained efforts will likely be necessary to influence transfer students’ more tangible degree plans. In addition to highlighting implications for future research, we identify strategies for faculty and staff seeking to support community college transfer students and build access to graduate training.

Within this integrative review, cognitive load theory (CLT) is asserted as a powerful framework for conceptualizing human cognitive processes within learning. The relationship between cognition and emotion is then examined and further integrated within the scope of CLT. Emotion regulation strategies are discussed and adaptive strategies are proposed as being of particular relevance to broadening the theoretical and practical impacts of CLT. Central to the argument of this review is the use of cognitive reappraisal as a potential mitigator of cognitive load. Cognitive reappraisal involves reframing or reassessing understandings or beliefs that underlie an emotional response, which may mitigate cognitive load imposed by maladaptive emotion. It is proposed that effectively integrating adaptive emotion regulation strategies such as cognitive reappraisal in our pursuit of more effective cognitive functioning will aid in the development of a more integrated model of cognition and emotion within CLT.

To date, measurement has been a fully quantitative endeavor, lacking in strategies to engage idiographic meanings systematically in the representation of phenomena. In this chapter, I argue that reframing incommensurability to orient around the information lost between phenomena and observations of them provides a framework for linking idiographic and nomothetic representations in a meaningful manner. Specifically, because categorical variables are defined by qualitative criteria that entail specific levels of incommensurability, no information is inherently lost in the conversion of idiographic themes or constructed meanings into nominal variables. Further, this incommensurability can operate as a parameter in the estimation of measures that reflect or otherwise address idiographic meanings.

This chapter argues that the qualitative practice of member checking is a valuable approach to gauging idiographic aspects of sensemaking underlying responses to multiple choice assessments and similar instruments. By asking respondents to synchronously assign Likert-style values to the degree of alignment between their personal understanding and the phrasing of the item they selected, it is possible to quantify incommensurability. Including quantitative incommensurability estimates in a MIMIC model with structured residuals permitted differentiation between residual variance attributable to idiographic incommensurability of the items and unclassified variance. It also provided an evaluation of the validity of individual items in terms of their narrow ability to assess the target construct consistently across individuals. The viability of this approach demonstrates the ways in which idiographic phenomena are an intrinsic part of the evaluation of items that can be incorporated into measurement models through the modeling and interpretation of residuals.

This chapter argues that it is possible to utilize ideographically defined categorical variables as quantitative measures theoretically and mathematically. First, an analysis of multiple types of generalizability distills the logic applied to measurement, as distinct from generalizability for findings from an experimental study, and argues that the development of idiographic categories adheres to the same logic as measurement. Second, consistent with the assumptions of phenomenography in which idiographic conceptions can be conceptualized as a finite range of categories, applications of central limit theorem through Monte Carlo simulations offer a theoretical foundation for estimating population parameters for distributions of idiographic categories. Lastly, a strategy for deriving joint population distributions that normalize misclassification (error) variance and differentiate it from underlying variation in the frequencies of target categories.

This chapter argues that idiographic phenomena are fundamental to the development and application of measures. Their salience is demonstrated through reviews of research related to several well-known method effects, biases, and sources of differential item function (DIF). Despite the idiographic nature of these phenomena, most research has not attempted to incorporate the individual sensemaking they represent at the level of the individual. It is argued that this fundamentally limits the precision of measurement, as evidenced when individual-level idiographic data has been incorporated into measurement models. Additionally, some measurement theorists have taken the position that such idiographic effects cannot be meaningfully measured, because they are not ontologically quantitative phenomena. However, multiple arguments are introduced demonstrating that valid and robust measurement can take place without any specific claims or assumptions about the underlying nature of a phenomenon. Collectively, these positions justify a mixed method approach to measurement in which idiographic data can be collected using strategies consistent with a critical realist stance. Phenomenography represents one well-established approach that holds compatible assumptions and has been applied effectively in relevant applications.