Search for: All records

Objective: The present study aimed to better understand key conceptualizations and operationalizations of intraindividual variability (IIV). We expected that differing types and metrics of IIV would relate to one another and predict outcomes (academic achievement) similarly. Method: The sample comprised 238 young adults. IIV was computed within and across six measures – three related to math and three more generally cognitive; in each case, score was separated from response time. We computed three types of IIV (inconsistency, dispersion, and dispersion of inconsistency), across several metrics (standard deviation, coefficient of variability, residualized standard deviation), and assessed their interrelations, and their prediction of academic achievement. Results: Differing metrics of variability were related to one another, but variably so. For prediction, whether or not inconsistency IIV metrics were significant was highly dependent on the measure they were derived from, with or without the primary score for a given measure also included. For dispersion of inconsistency and dispersion, variability metrics were often significant, though this was eliminated in most cases when score was also included in models. Conclusions: By concurrently examining multiple metrics and types of IIV within the same set of measures, this study highlights the need to (a) clarify the type of IIV utilized and why; (b) clarify the rationale for the kinds of measures used to compute IIV, particularly dispersion; and (c) include score alongside timing. Doing so will likely improve the generalizability of IIV findings, and prompt future research avenues, both psychometric- (e.g., simulations) and clinical-related (e.g., across ages and populations). 

With a diverse sample of community college (CC) students (n = 94), we investigated how the working memory (WM)-math relation may be moderated by aspects of acculturation, including cultural adoption and cultural maintenance. We predicted that higher levels of each of the above acculturation factors would improve math performance by way of reducing WM load (via cognitive load). Conversely, we expected a weaker WM-math correlation at lower levels of acculturation due to the increased variability in cultural factors and the adverse effect of lower acculturation on WM through heightened cognitive load. In this cohort, WM correlated with math performance, but acculturation did not significantly influence this relationship. Neither cultural adoption nor cultural maintenance moderated the WM-math association. Results suggest that individualized educational interventions based on acculturation status alone may not be an effective strategy. Instead, institutions such as schools and governmental agencies may focus on providing a better foundation for educational success by enhancing academic and non-academic support systems to promote equitable educational opportunities for all students. Further research should explore additional individual and/or demographic factors (e.g. socioeconomic status, experiences of discrimination, cultural background) to better understand these complex relations. 

Motivational and cognitive factors are key determinants of academic achievement, but their combined effects are underexplored and show mixed results in the literature (Lu et al., 2011; Weber et al., 2013). Evidence indicates that motivational factors and working memory interact with academic settings. Gareau and Gaudreau (2017) found that working memory moderates the relationship between motivation and academic achievement, whereas Grogan et al. (2022) suggest that motivational factors may enhance working memory performance during academic testing. However, there is no comprehensive framework that integrates these motivational and cognitive elements in an academic context (Peckrun, 2024). The current study investigates whether motivational components, as defined by Pintrich’s theoretical model (1988), independently affect academic performance and whether such effects are moderated by working memory. Hypotheses include that: (1) both working memory and motivational factors would significantly relate to academic performance; and (2) motivation would impact academic performance primarily for individuals with low working memory capacity. Covariates were also considered. 

Objective There are many measures of health and/or financial literacy and/or numeracy, which vary widely in terms of length, content, and the extent to which numbers or math operations are involved. Although the literature is large, there is less considers what is known about math prediction and development, perhaps because the bulk of this literature is with adults. This is important because the literature on how math develops, what predicts it, and how to intervene with it, is very large (Cirino, 2022). To the extent that performance and prediction are similar, then information from the developmental literature of mathematics can be brought to bear with regard to health and financial numeracy. Here we assess math cognition variables (arithmetic concepts and number line estimation) alongside working memory, likely the most robust cognitive predictor of math, as well as sociodemographic covariates. We expect all predictors to relate to each type of outcome, though we expect reading to be more related to health and financial numeracy relative to symbolic math. Participants and Methods Participants were 238 young adults, diverse in language and race/ethnicity, enrolled in their first and entry-level college math class at either community college or university; approximately 30% were taking developmental coursework. For this study, participants were given three sets of analogous math problems: (a) pure symbolic; (b) health numeracy context; (c) financial numeracy context. Additional measures were of reading (KTEA-3 Reading Comprehension), math cognition (Arithmetic Concepts and Number Line Estimation), and complex span working memory (Symmetry Span and Reading Span). Correlations assessed relations, and multiple regression assessed prediction. Results All measures involving math correlated, though symbolic math less well than health and financial numeracy with one another. For symbolic math, math cognition and working memory together accounted for R2=56% variance, and all were unique predictors, with arithmetic concepts strongest (ηp2 = .19). For health numeracy, all predictors also accounted for R2=56% variance. Beyond symbolic math, math cognition and working memory were unique predictors (all p < .05); reading comprehension was not. The clearly strongest unique predictor was number line estimation (ηp2 = .06). For financial numeracy, all predictors accounted for R2=61% variance. Beyond symbolic math and reading comprehension, again math cognition and working memory were unique predictors (all p < .05), and again number line estimation was the strongest (ηp2 = .08). Results held with covariate control. Conclusions Math cognition and working memory are known important contributors to math skill. This study shows these to be equally important whether math is in a pure symbolic context, or a health and or financial context. This suggests that the utility of health and financial numeracy measures (and potentially the constructs themselves) needs to consider the underlying concomitants of math skill more generally, particularly as the extent to which numbers and/or specific math operations are used in such measures varies widely. Context is likely important, however, and future work will need to consider practical outcomes (e.g., risky health or financial behaviors and management) across a range of populations. 

Executive function (EF) is wrought with confusion around how it is conceptualized and measured. The roots of EF trace back to unusual neurological case studies beginning in the 19th century and war injuries in the early 20th century. Since then, EF has taken on quite a number of meanings and operationalizations. This presentation will compare a variety of current perspectives on EF, review key issues relevant to its understanding, and offer (at least one) path forward, to allow for greater coherence in this complex field. 

Objective Online surveys are a common method of data collection. The use of “attention-check” questions are an effective method of identifying careless responding in surveys (Liu & Wronski, 2018; Meade & Craig 2012; Ward & Meade, 2023), which occurs in 10-12% of undergraduate samples (Meade & Craig, 2012). Instructed response type attention checks are straightforward and the most recommended (Meade & Craig, 2012; Ward & Meade, 2023). This study evaluated the effect of instructed response attention check questions on the measurement of math ability and non-cognitive factors commonly related to math (self-efficacy and math anxiety). We evaluated both level differences as well as whether check questions alter the relationship of non-cognitive factors to math. We expected that incorrect responding to check questions would lower math performance but were unable to make hypotheses about level of self-report non-cognitive factors. We predicted that incorrect responding to check questions would moderate the relationship between both math anxiety and self-efficacy to math performance. Participants and Methods Participants were 424 undergraduates (age 20.4, SD=2.7) at a large southwestern university. The sample was majority female (74%) but diverse socioeconomically and in race/ethnicity. The non-cognitive measures were researcher developed Math Anxiety (MA) and Math Self-Efficacy (MSE; Betz & Hackett, 1993) scales, with items selected directly targeting the use/manipulation of math in everyday life; both showed good reliability (α=.95). The two math scales were also researcher developed; one was a pure symbolic computational measure (EM-A) and the other consisted of word problems in an everyday context (EM-B). These measures had good reliability (α=.80 and α=.73). The four check questions were embedded in the surveys and two groupings were formed – one consisting of those who provided the correct answer for all items versus those who did not, and a second consisting of those who got all correct or only one answer incorrect versus those with more items incorrect. Correlational, ANOVA, and ANCOVA models were utilized. Results Descriptively, check questions were skewed – 75% participants answered all check questions correctly, and 8% missed only one. Relations of both MA and MSE with EM-A and EM-B were modest though significant (|r|=.22 to .37) and in the expected directions (all p<.001). Check questions were related to level of all tasks (p<.001), with incorrect responses resulting in lower math performance, lower MSE, and higher MA. Check questions did not moderate the relation of MA or MSE to either math performance, with some suggestion that MA was more strongly related to EM-B in those who missed check questions, though only when failing several. Conclusions Check questions showed a clear relation to both self-report and math performance measures. However, check questions did not alter the relation of MA or MSE to math performance in general. These results affirm extant relations of key self-perceptions to math using novel measures and highlight the need to evaluate the validity of self-report measures, even outside of objective performance indicators. Future work could examine the effect of attention checks in domains other than math and investigate other types of attention checks. 

Objective Math and reading are related at the disability level and along the continuum of skill (Cirino, 2022). Cognitive correlates of math and reading in children are well-known separately, with a recent focus on the reason for their overlap. However, less is known about these issues in community college (CC) students despite more than half of post-secondary education occurring at this level. Here we assess cognitive predictors of math and reading (language, working memory, processing speed, nonverbal reasoning, attention) in CC students, outcome overlap, and the extent that predictors account for overlap. We expect all predictors to relate to achievement, with language and working memory as the strongest predictors, and accounting for the most overlap. We also expect more overlap and stronger prediction for complex outcomes (reading comprehension and math applications) relative to foundational skills (word reading and computations). Participants and Methods Participants were 94 CC students enrolled in their first math class. Approximately half the students were taking developmental coursework. Participants were administered four KTEA-3 measures: Letter-Word Reading, Reading Comprehension, Math Computation, and Math Concepts and Application. Language consisted of Vocabulary (K-BIT-2), and Elision and Rapid Naming subtests of the CTOPP-2. Working memory was assessed with two complex span measures (Symmetry Span and Reading Span). Processing speed was measured with the WAIS-IV, and nonverbal reasoning with the K-BIT-2. Attention was assessed via a researcher-designed continuous performance task and a self-rating scale. Multiple regression assessed cognitive prediction for each achievement measure; and partial correlation evaluated overlap. Results For computations, all predictors accounted for R2=53% variance; nonverbal reasoning and elision were unique predictors (p<.05). For math applications, R2=58%, with unique prediction for nonverbal reasoning, vocabulary, elision, and symmetry span. For word reading, R2=50%, with unique prediction for vocabulary, elision, and reading span. Finally, for reading comprehension, R2=47%, with unique prediction for vocabulary and nonverbal reasoning. Regarding overlap, computations and word reading correlated r=.50, and math applications and reading comprehension r=.57, which is higher than a recent meta-analysis (Unal et al., 2023). Language was the strongest contributor of overlap; these variables reduced the correlation for foundational achievement by 50%, and for complex achievement, by 32%. Other domains accounted for little overlap, despite significant zero-order correlations. Substantive results were generally similar when covariates were considered. Conclusions Individual prediction was dominated by language, nonverbal reasoning, and working memory variables. Math and reading performances were strongly related, and language was the strongest predictor of this overlap, which is only partially consistent with extant literature but adds context and generalization for CC students. Attention and processing speed were only weakly related to performance, which may reflect the overlearned nature of these skills at this level. Future work might need to include more malleable factors (e.g., motivation), as well as broader views of achievement (e.g., course grades). 

Objective Some studies have demonstrated evidence of a 'bilingual advantage' in domains such as working memory (WM), processing speed (PS), and attention. Less is known about whether similar patterns appear in students who have not yet mastered a second language, and available evidence is conflicting. For example, in Hansen et al. (2016), young students classified as limited English proficient (LEP) outperformed monolingual peers in WM; however, the opposite was found in Castillo et al. (2022). In both studies, the group differences did not persist into adolescence, and other studies at this age show no difference (Low & Siegel, 2005). Research on LEP students and PS is sparse, but most existing studies show no difference between monolinguals and bilinguals or LEP students (Barac et al., 2014). Visual attention (VA) has rarely been studied in this context. However, one study of adults found no difference in visual attention between monolinguals and bilinguals (Bouffier et al., 2020). Here, we compare groups of students who are classified as LEP or not; given prior research and the age and limited second-language proficiency of our subjects, we hypothesized that there would be no difference between groups in WM and PS; the limited research on VA does not allow for a directional hypothesis. Methods Participants were 199 students in from four diverse middle schools in Texas, whose mean age was 12.97 (0.86); 54% were male. Most (80%) students were Hispanic, and 54% were classified by their schools as LEP; 88% received lunch assistance. WM and PS were assessed via the respective indices of the WISC-V (Wechsler, 2014). Attention was evaluated with two versions of a visual attention span (VAS) measure, and two versions of a visual search (VSEARCH) measure. We considered covariates of age, nonverbal reasoning, and phonological processing. Analyses were ANCOVA, with a grouping variable of LEP status. Results Descriptively, on measures where standard scores were available, performances for the whole sample were in the low average range (SS equivalents range 85 to 88). Students designated as LEP had lower WM performances, p < .001. For PS, the reverse was true, with LEP students having stronger PS, p < .001. For attention, results were mixed; performance on VAS was similar between groups, p = .174, whereas for VSEARCH, LEP students had better performances, p < .001. All results held in the context of any combination of covariates. Discussion Results were interesting but differed from expectations. For WM, there was a disadvantage for students classified as LEP, whereas the opposite was true for PS and VSEARCH. The results highlight the need to consider these and similar cognitive individual differences in the context of second language learning, and a need to consider the balance of proficiency across languages. Supporting this possibility, a meta-analysis found that studies of balanced compared to unbalanced bilinguals are more likely to report an advantage in WM and attention (Yurtsever et al., 2023). Overall, this study adds to a limited body of evidence on cognitive processes in students with exposure to, but not mastery of, multiple languages.