Are individual characteristics and behaviors necessary-but-not-sufficient conditions for academic success?: A demonstration of Dul's (2016) necessary condition analysis

Abstract

Claiming that high levels of an independent variable represent a necessary-but-not-sufficient condition for an outcome suggests that the outcome is only possible -but not guaranteed -with high levels of that variable. Necessary condition analysis (NCA) allows researchers to determine if an observed relation between an independent variable and a dependent variable is consistent with such a necessary-but-notsufficient relation. Using both archival and primary data, we apply Dul's ( 2016) necessary condition analysis techniques to common correlates of academic success in college. We find data patterns that are consistent with necessary-but-not-sufficient conditions for academic success for a variety of variables including class attendance, grit-perseverance, growth mindset, prior achievement, and admissions test scores. Our findings imply that some individual characteristics and behaviors may constrain the level of grades possible in college and that researchers may benefit from considering necessity models of academic performance. We discuss further applications of necessary condition analysis in educational research as a supplement to traditional data analysis.

From Autonomy Support and Grit to Satisfaction With Life Through Self-Determined Motivation and Group Cohesion in Higher Education

“…It is striking that both dimensions, consistency and perseverance, also predict basic psychological needs and intrinsic motivation, as this predictive relationship is usually related to the teacher’s interpersonal style. In this same direction, Ka and Zhoun (2019) and Tynan et al (2020) also place them at the same level as a predictor of academic success.…”

The Importance of Well-Being on Resiliency of Filipino Adults During the COVID-19 Enhanced Community Quarantine: A Necessary Condition Analysis

“…On the other hand, if there is a lack of empty space in the scatter plot then well-being elements are not contingents of resiliency. The effect size of a necessary condition can take the values between 0 and 1 where 0–0.1 corresponds to a small effect, 0.1–0.3 a medium effect, 0.3–0.5 a large effect, and d that is greater than 0.5 a very large effect ( Tynan et al, 2020 ). An R package that allows the calculation of various effect size indicators and inferential statistics useful for hypothesis testing is provided by Dul (2016) .…”

“…The resultant value of p is interpreted using traditional thresholds such as α = 0.05 or α = 0.01. Depending on the context of the research, both significance testing and effect size are useful in determining the theoretical and practical importance of an observed outcome ( Tynan et al, 2020 ). We focus our attention on conditions with both d > 0.5 and p < 0.05.…”

“SEW” What Do We Know and Where Do We Go? A Review of Socioemotional Wealth and a Way Forward

“…As such, following the guidance of necessary condition analysis (Dul, 2016a(Dul, , 2016b, we analyze the theoretical assumptions of the SEW construct. Necessary condition analysis allows researchers to analyze the theoretical assumptions of a construct and determine if a relation between two variables is consistent (Dul, 2016a(Dul, , 2016bTynan et al, 2020). Specifically, necessary condition analysis refers to causality statements regarding the logics of necessary and/or sufficient determinants on various outcomes, making it a useful tool for untangling the dimensionality of SEW and enabling us to augment our understanding of the construct (Podsakoff et al, 2016).…”