Characterizing the Mathematics Anxiety Literature Using Confidence Intervals as a Literature Review Mechanism

Zientek, Linda Reichwein; Yetkiner, Z. Ebrar; Thompson, Bruce

doi:10.1080/00220670903383093

Cited by 29 publications

(29 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Negative affects in the academic context including anger, anxiety, and depression have profound influences on academic performance through both motivational and cognitive mechanisms (Eysenck et al, 2007;Pekrun et al, 2011), highlighting the importance of cognitive-affective interplay in academic development (Prevatt, Welles, Li, & Proctor, 2010;Putwain, Connors, & Symes, 2010). Mathematical anxiety (MA) is one particular example of such academic-related affects that has attracted recent research attention, and its debilitating impacts on the development of mathematics skills are well rep-licated (Ma, 1999;Zientek, Yetkiner, & Thompson, 2010). The overarching aim of this study was to investigate the etiology of MA in the context of cognitive-affective interplay.…”

Section: Introductionmentioning

confidence: 99%

Who is afraid of math? Two sources of genetic variance for mathematical anxiety

Wang

Hart²,

Kovas

et al. 2014

Child Psychology Psychiatry

155

117

View full text Add to dashboard Cite

Background Emerging work suggests that academic achievement may be influenced by the management of affect as well as through efficient information processing of task demands. In particular, mathematical anxiety has attracted recent attention because of its damaging psychological effects and potential associations with mathematical problem-solving and achievement. The present study investigated the genetic and environmental factors contributing to the observed differences in the anxiety people feel when confronted with mathematical tasks. In addition, the genetic and environmental mechanisms that link mathematical anxiety with math cognition and general anxiety were also explored. Methods Univariate and multivariate quantitative genetic models were conducted in a sample of 514 12-year-old twin siblings. Results Genetic factors accounted for roughly 40% of the variation in mathematical anxiety, with the remaining being accounted for by child-specific environmental factors. Multivariate genetic analyses suggested that mathematical anxiety was influenced by the genetic and non-familial environmental risk factors associated with general anxiety and additional independent genetic influences associated with math-based problem solving. Conclusions The development of mathematical anxiety may involve not only exposure to negative experiences with mathematics, but also likely involves genetic risks related to both anxiety and math cognition. These results suggest that integrating cognitive and affective domains may be particularly important for mathematics, and may extend to other areas of academic achievement.

show abstract

Section: Introductionmentioning

confidence: 99%

Who is afraid of math? Two sources of genetic variance for mathematical anxiety

Wang

Hart²,

Kovas

et al. 2014

Child Psychology Psychiatry

155

117

View full text Add to dashboard Cite

show abstract

“…However, when comparing the metric of one variable to another, overlapping CIs do not necessarily indicate a statistically nonsignificant difference between parameter estimates (see Cumming & Finch, 2001;Zientek, Yetkiner, & Thompson, 2010). One has to examine the distribution of differences between the two bootstrapped estimates of interest across replications.…”

Section: Confidence Intervalsmentioning

confidence: 99%

Understanding the Results of Multiple Linear Regression

Nimon

Oswald

2013

Organizational Research Methods

284

226

View full text Add to dashboard Cite

Multiple linear regression (MLR) remains a mainstay analysis in organizational research, yet intercorrelations between predictors (multicollinearity) undermine the interpretation of MLR weights in terms of predictor contributions to the criterion. Alternative indices include validity coefficients, structure coefficients, product measures, relative weights, all-possible-subsets regression, dominance weights, and commonality coefficients. This article reviews these indices, and uniquely, it offers freely available software that (a) computes and compares all of these indices with one another, (b) computes associated bootstrapped confidence intervals, and (c) does so for any number of predictors so long as the correlation matrix is positive definite. Other available software is limited in all of these respects. We invite researchers to use this software to increase their insights when applying MLR to a data set. Avenues for future research and application are discussed. Keywords multiple regression, quantitative research, exploratory, research designA continued goal of organizational researchers conducting regression analysis is to make inferences about the relative importance of predictor variables (cf. Nimon, Gavrilova, & Roberts, 2010;Zientek, Capraro, & Capraro, 2008), yet it is all too common to rely heavily (if not solely) on the regression coefficients from the analysis which optimize sample-specific prediction (minimize sum of squared errors). Instead, other metrics that operationalize relative importance in ways that are consistent with such researchers' goals would seem more appropriate, and a range of metrics and approaches exists. In addition to regression weights and zero-order correlation coefficients that researchers likely report, MLR interpretation may be further informed by considering structure

show abstract

“…This hypothesis is supported by the finding by Zientek et al (2013) that many of these same prospective teachers had difficulty solving fraction operations. Future research could also investigate if anxiety with fractions also hampered mathematics achievement given that elementary teachers tend to have higher mathematics anxiety levels than the general population (Zientek et al, 2010).…”

Section: Abandoned or Left Blankmentioning

confidence: 99%

“…Newton (2009) found prospective teachers in classrooms with reform methods exhibited less anxiety for fractions. Reducing mathematics anxiety should be important because mathematics anxiety has been linked to student achievement, and elementary teachers have been identified as having high levels of mathematics anxiety (Hembree, 1990;Ma, 1999;Zientek, Yetkiner, & Thompson, 2010).…”

Section: Introductionmentioning

confidence: 99%

Solving equations with fractions: An analysis of prospective teachers’ solution pathways and errors

Jones

Zientek

Sharon³

et al. 2020

School Sci & Mathematics

Self Cite

View full text Add to dashboard Cite

We analyzed the solution pathways and errors found in the written responses of 469 prospective teachers solving an equation containing fractions. The majority (332, or 70%) used an algebraic method; 141 of the 332 (42%) were correct, and 22% of the algebraic methods were abandoned before a solution was obtained. We identified the steps in the written solutions, determined which solution pathways led to the correct solution, and identified common errors in the solution pathways of respondents who incorrectly solved the equation. Respondents initially attempted different methods. The most common method was solving by using fractions, but the majority of respondents who solved by using mixed numbers were able to correctly solve the problem. Common errors related to fraction arithmetic and the distributive property.Nearly all of the abandoned pathways contained no errors, but ended with a step that likely would precede an operation with fractions. Our findings suggest that the ability to solve an arithmetic equation with no fractions was necessary, but not sufficient, to solve an arithmetic equation involving fractions, and that the problem of solving equations with fractions was more closely tied to one's difficulties with rational number arithmetic and less with one's understanding of algebra. K E Y W O R D S algebra, fractions, prospective teachers, solving equations | 233 JONES Et al.

show abstract

Characterizing the Mathematics Anxiety Literature Using Confidence Intervals as a Literature Review Mechanism

Cited by 29 publications

References 41 publications

Who is afraid of math? Two sources of genetic variance for mathematical anxiety

Who is afraid of math? Two sources of genetic variance for mathematical anxiety

Understanding the Results of Multiple Linear Regression

Solving equations with fractions: An analysis of prospective teachers’ solution pathways and errors

Contact Info

Product

Resources

About