Using Latent Profile Analysis to Identify Noncognitive Skill Profiles Among College Students

Noncognitive Skill Profiles and Their Relationships With Academic Outcomes at Hispanic‐Serving Institutions

“…In the current study, LPA was used to model unobserved heterogeneity in the CSUF sample to reveal previously unrecognized groups of students reporting similar levels of noncognitive skills as one another across the array of four SN domains. As described in Olivera-Aguilar et al (2017), LPA has advantages over standard person-centered clustering techniques in that it is both model-based (facilitating empirical vs. purely qualitative model selection) and probabilistic rather than deterministic (the former accounting for uncertainty in classification vs. presuming no ambiguity in profile assignment).…”

“…As described in an initial qualitative report, the project's primary objective “was to examine how noncognitive skill information could be infused into student success strategies at HSIs” (Holzman & Markle, 2018, p. 7). Collection and interpretation of information on student noncognitive skill levels at HSIs was aided by their administration of SuccessNavigator® (SN), an ETS assessment used in previous research to understand how inter‐ and intrapersonal factors relate to success in higher education (Markle et al, 2013; Olivera‐Aguilar et al, 2017).…”

“…Beyond coarse distinctions (e.g., higher scores vs. lower scores), such heterogeneity may also not be readily apparent or reliably distinguished given the large number of student‐level data points available to HSIs (student test scores, grades, application or registration information, advising logs, student surveys, etc.). Building off the points mentioned previously and following examples in past research (Olivera‐Aguilar et al, 2017; Pastor et al, 2007), in this study we sought to understand student noncognitive skill profiles at three HSIs using data from a standardized assessment instrument (Markle et al, 2013) and applying latent profile analysis (LPA), a person‐centered statistical clustering approach using categorical latent variables (Collins & Lanza, 2013). Where different types of outcomes data were available (typically grade point average [GPA] or persistence to subsequent semesters), we also examined relationships between those variables and noncognitive skill profile membership to determine whether such subgroupings also distinguished students from an academic perspective.…”

Development and initial validation of the life skills ability scale for higher education students

“…Moreover, given the growing levels of psychological distress reported in university students (Bewick et al 2010), studies assessing how certain life skills may impact upon students' mental health are warranted. In terms of educational outcomes, the growing interest in how non-cognitive or psychosocial skills can affect students' academic performance ( Olivera-Aguilar, Rikoon, and Robbins 2017) suggests that future research should assess how particular life skills may impact students' performance on different types of assessments. For example, do students with better problem solving skills perform better on case study assessments (e.g., a client case study in psychology); whereas, students with better leadership skills may perform better on practical assessments (e.g., a coaching practical in sport science)?…”

Anticipating College Enrollment: Adapting SuccessNavigator® for High School Students

“…As an example, latent profile analysis is expected to prove a useful strategy for developing typologies of late high school noncognitive skill expression. Such analyses should in turn enable the targeted delivery of student support services customized to the specific needs of empirically identified student subgroups (Olivera‐Aguilar, Markle, & Robbins, ; Pastor, Barron, Miller, & Davis, ). Further studies making use of multilevel modeling would also provide a more nuanced picture than is currently available of the extent to which both school‐ and student‐level noncognitive characteristics (in combination with other contextual factors) contribute to the prediction of student academic success in high school and beyond.…”