scholarly journals A latent profile analysis of math achievement, numerosity, and math anxiety in twins.

2016 ◽  
Vol 108 (2) ◽  
pp. 181-193 ◽  
Author(s):  
Sara A. Hart ◽  
Jessica A. R. Logan ◽  
Lee Thompson ◽  
Yulia Kovas ◽  
Gráinne McLoughlin ◽  
...  
Keyword(s):  
Latent Profile Analysis ◽  
Math Achievement ◽  
Math Anxiety ◽  
Profile Analysis ◽  
Latent Profile

scholarly journals Anxiety is not enough to drive me away: A latent profile analysis on math anxiety and math motivation

PLoS ONE ◽  
2018 ◽  
Vol 13 (2) ◽  
pp. e0192072 ◽  
Author(s):  
Zhe Wang ◽  
Nicholas Shakeshaft ◽  
Kerry Schofield ◽  
Margherita Malanchini
Keyword(s):  
Latent Profile Analysis ◽  
Math Anxiety ◽  
Profile Analysis ◽  
Math Motivation ◽  
Latent Profile

scholarly journals Students’ Motivation and Affection Profiles and Their Relation to Mathematics Achievement, Persistence, and Behaviors

2021 ◽  
Vol 11 ◽  
Author(s):  
Feiya Xiao ◽  
Li Sun
Keyword(s):  
United States ◽  
Perceived Control ◽  
Latent Profile Analysis ◽  
Math Achievement ◽  
Math Anxiety ◽  
Profile Analysis ◽  
Self Concept ◽  
Affective Factors ◽  
Practical Support ◽  
Instrumental Motivation

ObjectiveWe aimed to explore profiles of subgroups of United States students based on their motivational and affective characteristics and investigate the differences in math-related behaviors, persistence, and math achievement across profiles.MethodWe used 1,464 United States students (male 743 51%, female 721 49%, age 15.82 ± 0.28) from PISA 2012 United States data in our study. First, we employed latent profile analysis and secondary clustering to identify subgroups of students based on motivational (math self-concept, interest in math, perceived control, and instrumental motivation) and affective factors (math anxiety). Next, we used regression to compare differences in math behavior, persistence, and achievement among all identified subgroups.ResultsWe found five distinct groups of students with different patterns of motivation and affection. The subgroup of students with the lowest math anxiety and the highest motivation levels showed the highest math achievement and levels of persistence. The groups with high math interest, math self-concept, and instrumental motivation showed the most frequent math-related behaviors.ConclusionsOur findings reveal the complexity of the students’ motivational and affective profiles. Our findings are significant for teachers and educators to understand the diversity of students and provide theoretical and practical support for individualized and differentiated instruction.

scholarly journals A latent profile analysis of math anxiety and core beliefs towards mathematics among children

2021 ◽  
Author(s):  
Lars Orbach ◽  
Annemarie Fritz
Keyword(s):  
Latent Profile Analysis ◽  
Math Anxiety ◽  
Profile Analysis ◽  
Behavioral Therapy ◽  
Careful Consideration ◽  
Intervention Programs ◽  
Core Beliefs ◽  
Cognitive Phenomenology ◽  
High Trait ◽  
Latent Profile

Recent findings on the negative impacts of math anxiety (MA) in children raised outstanding issues for educational and clinical research, regarding effective intervention programs. One basic approach to develop intervention programs in field of cognitive behavioral therapy is to gain an in-depth understanding of the cognitive beliefs (CB) of children with a specific mental problem. By applying latent profile analysis (LPA), the present study aimed at identifying different patterns of MA and to provide more insights into its cognitive phenomenology. For this purpose, trait-MA, state-MA, attitudes towards math, academic self-concepts (math, language, general), fixed/growth mindsets, executive functions and math performance of 475 fourth and fifth graders (48.2% girls) were assessed. LPA indicated seven distinct profiles characterized by different dimensions and patterns of state-MA, trait-MA and core beliefs towards math. Furthermore, the profiles showed clear different math performances. The weakest performances were found for a profile with highest state-MA, high trait-MA and negative CB towards math and a profile with average state-MA, high trait-MA and negative CB towards math, whereas the highest achieving profile had no state-MA, high trait-MA and very positive CB towards math. The findings underline the complexity of MA and emphasize the necessity to develop interventions with careful consideration of the heterogeneous patterns.

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