Do implicitly measured math–anxiety associations play a role in math behavior?

Schmitz, Eva; Jansen, Brenda R.J.; Wiers, Reinout W.

doi:10.1016/j.jecp.2019.05.013

Cited by 11 publications

(12 citation statements)

References 79 publications

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“…An assumption shared by various reliability coefficients is a linear relation between task score and trial scores. Linearity is assumed by alpha (Green et al, 2016 ), Spearman-Brown-adjusted intraclass correlation coefficient (de Vet et al, 2017 ; Hedge et al, 2018 ; Warrens, 2017 ), and Spearman-Brown-adjusted Pearson correlation (Abacioglu et al, 2019 ; Chapman et al, 2019 ; Cooper et al, 2017 ; de Hullu et al, 2011 ; Enock et al, 2014 ; Lancee et al, 2017 ; MacLeod et al, 2010 ; Schmitz et al, 2019 ; Waechter et al, 2014 ). However, task-scoring algorithms may apply a range of non-linear transformations, thereby being incompatible with these coefficients by design.…”

Section: Introductionmentioning

confidence: 99%

Methods to split cognitive task data for estimating split-half reliability: A comprehensive review and systematic assessment

et al. 2021

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Estimating the reliability of cognitive task datasets is commonly done via split-half methods. We review four methods that differ in how the trials are split into parts: a first-second half split, an odd-even trial split, a permutated split, and a Monte Carlo-based split. Additionally, each splitting method could be combined with stratification by task design. These methods are reviewed in terms of the degree to which they are confounded with four effects that may occur in cognitive tasks: effects of time, task design, trial sampling, and non-linear scoring. Based on the theoretical review, we recommend Monte Carlo splitting (possibly in combination with stratification by task design) as being the most robust method with respect to the four confounds considered. Next, we estimated the reliabilities of the main outcome variables from four cognitive task datasets, each (typically) scored with a different non-linear algorithm, by systematically applying each splitting method. Differences between methods were interpreted in terms of confounding effects inflating or attenuating reliability estimates. For three task datasets, our findings were consistent with our model of confounding effects. Evidence for confounding effects was strong for time and task design and weak for non-linear scoring. When confounding effects occurred, they attenuated reliability estimates. For one task dataset, findings were inconsistent with our model but they may offer indicators for assessing whether a split-half reliability estimate is appropriate. Additionally, we make suggestions on further research of reliability estimation, supported by a compendium R package that implements each of the splitting methods reviewed here.

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Section: Introductionmentioning

confidence: 99%

Methods to split cognitive task data for estimating split-half reliability: A comprehensive review and systematic assessment

et al. 2021

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“…A total of 1549 Dutch secondary school students participated in three studies. The original study purposes were the introduction of a math anxiety questionnaire (Study 1: n = 998, Schmitz, 2020), comparing implicit and explicit math anxiety measures (Study 2: n = 189, Schmitz et al, 2019), and relating math anxiety to other motivational and affective factors (Study 3: n = 362, Sachisthal et al, 2021). 2 Combining the data of the studies allowed for a powerful test of measurement invariance.…”

Section: Methodsmentioning

confidence: 99%

Test of Measurement Invariance, and Evidence for Reliability and Validity of AMAS Scores in Dutch Secondary School and University Students

Schmitz

Salemink

Wiers

et al. 2022

Journal of Psychoeducational Assessment

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The Abbreviated Math Anxiety Scale (AMAS) is commonly used to compare groups on math anxiety. Group comparisons should however be preceded by a demonstration of metric and scalar measurement invariance, which is currently only available for undergraduate students in the USA. This study tested for metric and scalar measurement invariance of AMAS scores across sexes and age groups and investigated reliability and validity evidence. Dutch secondary school students ( N = 1504) and university students ( N = 629) completed the AMAS and reported their math grades. Subsamples also completed questionnaires on related (e.g., trait anxiety) and unrelated constructs (e.g., anxiety for learning a foreign language). Results of multi-group Confirmatory Factor Analyses showed the 2-factor structure that was detected in earlier studies and supported partial metric and scalar invariance, although cross-loadings for one item were included for the female university students group to improve model fit. Reliability ranged from acceptable to good and validity was supported.

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“…Math anxiety is characterized by an excessive and sometimes unreasonable fear of numerical related activities (e.g., math homework, comparing loan options), as well as fear of situations wherein the individual is concerned about possible negative evaluation (e.g., by teachers or classmates) of one’s numerical performance (e.g., during math exams; Richardson & Suinn, 1972). Thus, in terms of numerical and academic achievements, math anxiety is negatively linked to math performance (Chang & Beilock, 2016; Hembree, 1990; Ma, 1999), and math anxious individuals show lower performance on numerical tasks (Beilock et al, 2010; Choe et al, 2019; Maloney & Beilock, 2012; Passolunghi et al, 2020; Rolison et al, 2020; Schmitz et al, 2019) and poor cognitive abilities in mathematics (Rubinsten et al, 2012; Rubinsten & Tannock, 2010).…”

Section: Self-centeredness Well-being and Mindfulness: Theoretical Li...mentioning

confidence: 99%

“…For instance, it was shown that anxious participants typically rush through tasks (Ashcraft & Krause, 2007) and thus show faster and less carefully examined responses (which do not result from hampered working memory resources; see Morsanyi et al, 2014b). Additionally, math anxious individuals demonstrate reduced confidence in their performance (Jain & Dowson, 2009; Morsanyi et al, 2014a) and thus tend to select easy and low-rewarding math problems over hard and high-rewarding problems (Choe et al, 2019; Schmitz et al, 2019). Together with the shorter time they spend on math problems (Morsanyi et al, 2014a), this tendency is expected to hinder their corrective actions.…”

Section: Self-centeredness Well-being and Mindfulness: Theoretical Li...mentioning

confidence: 99%

Math anxiety, self-centeredness, and dispositional mindfulness.

David¹,

Rubinsten²,

Berkovich‐Ohana³

2022

Journal of Educational Psychology

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Math anxiety has received increasing focus in recent years, yet the causes for developing math-anxiety remain unclear. Whereas previous research focused on physiological/environmental causes, we examine the link between math-anxiety, dispositional mindfulness, and self-centeredness (operationalized as self-prioritization and decentering). The experiment was performed by 81 participants, and included the original perceptual shape-matching task, measuring the self-prioritization effect, and our novel perceptual number/ equation-matching tasks, developed to examine self-prioritization under math-anxiety activation. We also measured math-anxiety, dispositional mindfulness, and decentering (self-reports). We showed that (a) math anxiety was significantly and negatively correlated with dispositional mindfulness and decentering (though there was no correlation between self-prioritization and dispositional mindfulness); (b) self-prioritization was reduced among high math anxiety participants under math-anxiety activation only in the numbermatching task (main finding); and (c) decentering was significantly correlated with self-prioritization in the number-matching task, stemming from the low math anxiety group. Our study is the first to indicate a link between math-anxiety, dispositional mindfulness, and self-centeredness. Discussing the main findings, we suggest three interpretations: (a) Negative mood induction may reduce self-prioritization by turning attention to internal states rather than to the stimuli; (b) math-anxiety activation may reduce emotional valence, which in turn reduces the advantage of self-processing; and (c) disruption of self-prioritization by induced negative mood can be due to a breakdown of the integrated-self (previously conceptualized as a high degree of connectedness between the cognitive/affective/motivational/behavioral systems). Educational Impact and Implications StatementThis study links math anxiety to decentering, which is the ability to create distance from the inner experience and disidentify with it. It adds a crucial psychological dimension to math learning, emphasizing the way one interprets and internalizes math situations, particularly failures in math, in relation to one's self-awareness. The current study introduces a novel perspective for examining and understanding math anxiety, relating its origin to maladaptive self-awareness, and shows that such maladaptive self-awareness is related to one's dispositional level of mindfulness.

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Do implicitly measured math–anxiety associations play a role in math behavior?

Cited by 11 publications

References 79 publications

Methods to split cognitive task data for estimating split-half reliability: A comprehensive review and systematic assessment

Methods to split cognitive task data for estimating split-half reliability: A comprehensive review and systematic assessment

Test of Measurement Invariance, and Evidence for Reliability and Validity of AMAS Scores in Dutch Secondary School and University Students

Math anxiety, self-centeredness, and dispositional mindfulness.

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