Reliability and validity evidence of the Swedish shortened mathematics anxiety rating scale elementary (MARS-E)

Reliability and validity evidence of the Swedish shortened mathematics anxiety rating scale elementary (MARS-E) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Reliability and validity evidence of the Swedish shortened mathematics anxiety rating scale elementary (MARS-E) Jonatan Finell, Hanna Eklöf, Bert Jonsson, Johan Korhonen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4360120/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract The current study assessed reliability and validity evidence of the shortened Swedish Mathematics Anxiety Rating Scale – Elementary (MARS-E), using data from three time points. After initial pilot tests, a total of 429 students participated in the study, completing the MARS-E twice during grade 4 and once during grade 5. Confirmatory factor analyses supported a one-factor structure at each timepoint. The scale displayed both longitudinal and gender measurement invariance across timepoints, ensuring both stability and fairness across gender and time. Factor correlations with criterion variables were examined, revealing a strong correlation between math anxiety (MA) and test anxiety (TA) ( r = 0.707). However, the two constructs had distinct relationships to other criterion variables, such as math performance ( r ma = -0.343, r ta = -0.110) and self-concept in math ( r ma = -0.580, r ta = -0.273). Gender correlations provided evidence of girls being more strongly associated with higher math anxiety scores compared to boys. The current paper provides evidence of the Swedish MARS-E as a valid, easily interpreted, unidimensional instrument for measuring math anxiety in Swedish primary school students. Additionally, the study highlights the gender disparities concerning math anxiety in a longitudinal study conducted in primary schools. Figures Figure 1 1 Introduction For many, math is the most demanding subject in school [ 1 ] and might be interconnected with math anxiety (MA) as early as from grade 1 [ 2 ]. MA is a phenomenon that can be described as feelings of apprehension, tension, and fear that one may experience when engaging with math-related tasks [ 3 ]. MA can, in addition to affecting one's emotional well-being, also reduce one’s performance in math-related activities, potentially impacting academic achievement and future educational choices [ 4 ]. In populations stemming from secondary education or older, there is certainly strong evidence marking a significant negative relationship between math achievement and MA [ 5 – 7 ]. This is of concern, as reports have shown that up to 25% of US college populations experience MA (see review by Ramirez [ 8 ]. In a review, Dowker et al. [ 9 ] present reports that conclude various estimates regarding the prevalence of MA. The prevalence fluctuates depending on group identity, though, in general, it is likely that MA peaks around the end of secondary school [ 10 ]. Furthermore, studies have found differences in MA as a function of gender, although its relationship with math is not significantly affected by gender [ 5 , 11 ]. There are several theories that explain the relationship between math anxiety and math performance [ 12 ]. (I) The Deficit Theory suggests that poor math performance can affect emotions towards math. In contrast (II) The Debilitating Anxiety Model posits that anxiety in math will ultimately affect math performance. (III) A Reciprocal Model accepts both theories, acknowledging the interplay between the two variables. A further explanation relating to The Debilitating Anxiety Model can be fetched from the Attentional Control Theory [ 13 ] which suggests that the underlying mechanisms for the anxiety-performance link lie in anxiety depleting attentional resources required for calculation. Evidence for such a model has been synthesized in two separate meta-analyses [ 14 , 15 ], though in Caviola’s [ 15 ] study, significant results were only found through Monte Carlo simulations on their data. Ma and Xu’s [ 16 ] longitudinal study provides some support for a reciprocal model, though stronger relations were found from math achievement to MA, indicating favour to the deficit model. In other words, the relationship between math and MA is likely complex. Reciprocity is probably the name of the game, yet exactly how, when, and to what extent each variable causes change in the other is still worth researching. With previous research predominantly centred around secondary school and university populations [ 5 ], the present study aims to evaluate a MA instrument tailored for younger age groups. In the current study, a newly translated instrument measuring MA in Swedish primary school students was examined. Based on Henschel and Roick’s [ 17 ] MARS-E, the current study aimed to investigate the dimensionality of the translated and adapted Swedish version of the instrument. Henschel and Roick’s [ 17 ] MA scale consisted of two components, one being an affective component relating to feelings of nervousness, the other being a cognitive component relating to worries about math tasks. As part of the validation process the factor dimensionality was assessed, as well as invariance testing across time and gender. Further, both reliability and criterion validity were evaluated through assessing relationships between MA and math performance, as well as test anxiety, math interest, and math self-concept. The literature was scanned for reference-values as part of assessing the MARS-E’s criterion validity. The results consistently reveal significant small to medium effect sizes between MA and math achievement. Hembree [ 5 ] reported a correlation of r = -0.34 for populations in grade 5–12. In younger populations, both Zhang et al. [ 18 ] and Namkung et al. [ 7 ] reported a correlation of r = − 0.27 for students in primary schools. However, the spread in effect sizes were wide, as seen in Zhang et al.’s data, the distribution of effect sizes ranged from + 0.21 (primary students) to -0.75 (secondary school students). Based on these results, expectations for the current correlation between MA and math performance lied around r = -0.27, though awareness of the widespread distribution of effect sizes remains important to consider. Hembree [ 5 ] also documented the correlation between MA and math self-concept as r = -0.7, and MA and test anxiety as r = 0.52. It is worth noting that these were effect sizes for the whole sample, which consisted of a large variety of age groups, which were, on average, older than the sample studied in the current study. Regarding the relationship between test anxiety and MA, a more recent meta-analysis by Robson et al. [ 19 ], focused solely on participants in primary school, found a correlation of 0.57 ( k = 4), which aligns well with Hembree’s findings. Although the positive correlation for MA and test anxiety is high, there is support for the two constructs being separate from each other [ 20 ]. As for the relationship between MA and self-concept in math, Ahmed et al. [ 21 ] found the correlation in grade 7 students to be around r = -0.43 under three occasions within one school year. An even larger contrast from Hembree’s results is found in Justicia-Galiano et al.’s [ 22 ] study on children aged 8–12, where the correlation was estimated at r = -0.27. Gender differences occur both in generalized and social anxiety disorders, as women have a higher prevalence than men. These gender differences can be observed around mid-adolescence and pre-puberty for generalized- and social anxiety disorders respectively (see review by Altemus [ 23 ]). Regarding the more specific form of anxiety and the main study-variable, namely math anxiety, the highest levels of MA are often observed in grades 9 or 10 [ 10 ]. Mixed results are found in the younger populations, as Hill et al. [ 24 ] did indeed find gender differences in grade 3–5, while Gierl and Bisanz’s [ 25 ] study did not. How these differences in MA translate into broader disparities later in life remains unclear. Though notably, studies have found correlations between MA and avoidance behaviour towards math, manifesting in both secondary school and college settings [ 5 ]. This tendency to avoid math has potential long-term consequences, particularly in the context of STEM careers. A substantial gender gap exists in these fields, where females are underrepresented [ 26 ]. The relationship between early math anxiety, gender differences, and subsequent avoidance of math-related courses, could potentially affect the eventual choice of career paths. Current study This study is part of a larger research project investigating the longitudinal effects of MA, math performance, learning strategies and working memory. The purpose of this study was to evaluate the shortened translated MARS-E scale by assessing reliability, dimensionality and its relations to performance and self-concept in math. The MARS (Math Anxiety Rating Scale) which was originally developed as a 98-item scale [ 27 ], has since been adapted and shortened for different uses and target groups (e.g., MARS-R [ 28 ]; MARS-E [ 29 ]). Henschel and Roick [ 17 ] introduced a more practical group administration instrument by creating a shortened version of the Mathematics Anxiety Rating Scale – Elementary (MARS-E). The MARS-E scale [ 17 ] consists of 36 items and was designed as a more convenient tool for administration purposes. Given the age and diversity of the sample, a shorter and simpler instrument was required to ensure that all participants could both comprehend the questions and maintain focus for answering the questionnaire. Thus, based on the groundwork laid by Henschel and Roick [ 17 ], the instrument was translated and adapted to a functional 16-item instrument (Swedish MARS-E). In evaluating the Swedish MARS-E, the current paper addressed three critical questions: To what extent can the Swedish MARS-E reliably measure math anxiety among primary school students? How does the Swedish MARS-E perform in terms of structural validity? What associations does the Swedish MARS-E demonstrate with criterion variables related to math anxiety? 2 Method The Swedish version of the MARS-E was translated from German by a translation company. Content validity was assessed by experts from the research project, and schoolteachers who participated in the two pilot tests. Corrections and clarifications on minor issues were made. As part of evaluating the content the instrument was piloted at three occasions in two schools in Sweden (N = 118). From the original 36 items, the Swedish MARS-E was reduced to 16 items, with 8 items originating from the cognitive component and 8 from the affective component. The target population for the current study were students in Swedish primary schools. 2.1 Participants and Procedure The sample included grade 4 students from Sweden, selected from 18 schools across 6 different municipalities, spanning from the northern to the southern regions of the country. The participants were n = 429, 9–10-year-old students, 213 girls and 214 boys (2 preferred not to answer). The project underwent ethical review by the Swedish Ethical Review Authority and consent was collected by the student’s legal guardians. Participating teachers, students and guardians were informed about the project’s aim, procedure, and data handling. Data was collected in autumn 2022, spring 2023 and autumn 2023. Two research assistants visited participating schools and carried out the measurements one class at a time (ca 18/class). The tests were split over two sessions with the questionnaire and math test administered on separate lessons, to a) reduce student fatigue, and b) reduce the risk of inducing awareness of anxiety. Verbal and written instructions were provided, with the option for students to request clarification or assistance. Though no specific guidance was given for solving math tasks. Each session was supervised by at least one research assistant in addition to their own teachers. All assessments were administered digitally. 2.2 Assessments Previous research offers guidelines for how MA correlates with other anxieties, specifically test anxiety [ 3 , 20 ]. This is also true for the relationship between math achievement and MA [ 5 , 7 , 11 ]. As part of assessing the validity of the Swedish MARS-E, criterion variables were employed to determine relations between the MA-measure and math achievement, as well as test anxiety, math interest, and math self-concept. A high positive correlation between math anxiety and test anxiety as well as a negative correlation with math self-concept would indicate criterion validity. 2.2.1 Swedish MARSE The Swedish MARS-E was measured with 16 items where participants took a stand on statements describing scenarios about math on a 4-point likert scale. Out of the 16 items, 8 targeted the worry (cognitive) component of MA, where the answer options ranged from “Not at all worried” to “Very worried”. The remaining 8 items targeted the nervous (affective) component, with answer-options ranging from “Not at all nervous” to “Very nervous”. All items are presented in Appendix A. 2.2.2 Test anxiety Test anxiety was assessed using the Swedish version of the STAS (School Test Anxiety Survey [ 25 ]), comprising 6 items rated on a 4-point likert scale. Participants responded to statements concerning their emotions in test-related situations. The answer options ranged from "Not at all nervous" to "Very nervous". The internal consistency across all 6 items was robust, with alpha levels ranging from α T1−T3 = 0.85–0.89, indicating good reliability. 2.2.3 Math self-concept and interest In the past, self-beliefs have been a subject of debate regarding whether they have a significant relationship with actual academic performances. However, when isolating self-beliefs into domain-specific constructs and matching them to specific academic subjects, the relationship becomes clearer, indicating significant links [ 30 ]. Shavelson et al.’s [ 31 ] model of self-concept was established as a multidimensional hierarchy with a top tier consisting of a general self-concept. From the general self-concept, the model then branches out into academic- and non-academic self-concepts, and further into more domain-specific branches. Drawing on Shavelson et al.’s model, Marsh et al. [ 32 ] published the SDQ (I) containing 7 blocks of self-concept measures. The authors reported good reliability regarding the mathematics block (α public schools = 0.92; α private schools = 0.94). In the current study, the mathematics block measuring both math interest and math self-concept [ 33 ] are used. Each variable consisting of 3 statements on a 5-point likert scale ranging from 1 = False to 5 = True. Cronbach’s alpha for each variable indicated good reliability on all three measurement occasions (α math self−concept = 0.83–0.85; α math interest = 0.92–0.94). 2.2.4 Math performance Math performance was measured with a digital test battery (FUNA-DB) which consists of six different sub-tests: (1) Number Comparison, (2) Digit Dot Matching, (3) Number Series, (4) Single Digit Addition, (5) Single Digit Subtraction, and (6) Multiple Digit Calculation. Räsänen et al. [ 34 ] found that the test battery was reliable and valid and was best described as a 2-factor model based on a large sample of 9–15-year-old Finnish students (invariant across Finnish speaking and Swedish speaking students). In the current study, four subtests were collapsed and used as a composite score of arithmetic fluency, providing a stable measurement (sub-test: 3, 4, 5 & 6, corresponding to one of Räsänen et al.’s factors). Response-consistency for each subtest was analysed on item-level with the KR-20 method, which resulted in acceptable estimates of 0.856, 0.957, 0.964, 0.934, for sub-tests 3, 4, 5 and 6, respectively. Prior to every actual sub-test, there were two practice rounds, one where the student received feedback on how he or she had succeeded and one that mimicked the actual test more closely. 2.3 Analysis R (version 4.3.0) was used for computing descriptives, t-tests, reliability, and Little’s MCAR test, utilising the r-base package ‘stats’ [ 35 ], the ‘psych’-package [ 36 ], and the ‘naniar’-package [ 37 ]. Cronbach's alpha estimates were reported for all three occasions, demonstrating the consistent reliability of the instruments used in the current study. Beyond invariance and reliability analysis, the focus of the current study was on the T1 measurement occasion with respect to criterion validity. Mplus (version 8.5) was used to estimate latent measurement models of the Swedish MARS-E. Applying Confirmatory Factor Analysis (CFA), models were assessed based on a set of model fit indices, structural loadings between the latent factors and items, and previous theory. The final model underwent a series of constraints, effectively evaluating invariance across gender and time. This method (invariance testing) tests the hypothesis that the model with the constrained parameters is close to equal in the two samples [ 38 ]. Lastly, the measurement model of MA was extended by adding correlations to math performance, test anxiety, math self-concept, math interest and gender. The relations were assessed and juxtaposed to previous research. Various patterns of missing data were identified in the dataset. To determine the nature of the missingness, Little’s MCAR test (Missing Completely At Random) was carried out on all the current study’s included variables. Little’s MCAR test examines if missing data are systematically missing [ 39 , 40 ]. The resulting non-significant p -value suggested that the missingness was completely at random (χ 2 = 77.4, df = 73, p = 0.341). Table 1 Means, standard deviations and t-tests on sum score variables. Variable M (SD) Boys M (SD) Girls M (SD) Total T-test N total (N boys , N girls ) MARS-E T1 23.78 (7.47) 26.30 (8.49) 25.01 (8.07) t = 2.98** 360 (180, 178) MARS-E T2 22.11 (6.33) 24.91 (8.05) 23.50 (7.36) t = 3.74** 376 (190, 184) MARS-E T3 21.63 (5.95) 25.99 (8.7) 23.75 (7.73) t = 5.63** 375 (190, 183) Tax T1 11.05 (3.37) 12.35 (3.68) 11.70 (3.57) t = 3.49** 360 (178, 180) Interest T1 10.01 (3.88) 10.10 (3.88) 10.06 (3.88) t = 0.22 357 (178, 177) Self T1 11.96 (2.68) 10.91 (2.73) 11.43 (2.76) t = 3.69** 360 (178, 180) Arith T1 0.24 (1.10) -0.22 (0.85) 0.00 (1.00) t = 4.31** 367 (179, 186) **p < 0.001. Tax = test anxiety, Interest = math interest, Self = math self-concept, Arith = standardized arithmetic fluency. 2.4 MARS-E Dimensionality and Invariance Testing Two MA-models were tested on the data: the original 2-factor model, used in Henschel and Roick’s study [ 17 ], and a unidimensional model. The models were evaluated based on model fit indices and factor correlations. Model fit indices for all models were satisfactory by standard guidelines, recommended by Hu and Bentler [ 41 ]. CFI and TLI were close to 0.95, SRMR-values close to 0.08, and RMSEA-values close to 0.06. Since the data for the Swedish MARS-E was categorical, the models were estimated with the Weighted Least Squares estimator with adjusted means and variances (WLSMV). A study of potential gender differences in MA was carried out. Ensuring an unbiased comparison between genders, necessitates that the scale used is invariant across groups. Accordingly, the MA invariant model was subjected to a series of constraints to rigorously test for invariance. These constraints were measured as (1) configural, (2) metric, (3) scalar and (4) strict invariance. The configural invariance is the least restrictive model, maintaining same model specification for each group. In the case of the Swedish MARS-E the number of factors was determined by the dimensionality investigation, with other model parameters freely estimated. The second model, metric invariance, assumes configural invariance is met, with factor loadings additionally constrained to equality across groups. The third model, scalar invariance, requires metric invariance in addition to equal intercepts across groups [ 38 ]. Given that the data for the Swedish MARS-E consisted of categorical ordered variables, scalar invariance tested the equality of thresholds across groups instead of intercepts. Lastly, strict invariance is achieved if the model remains stable even when residuals are constrained to equality in addition of scalar invariance [ 42 ]. A common cutoff value for assessing change in model fit during measurement invariance testing is a change in the CFI of less than or equal to 0.01 and a change in the RMSEA of less than or equal to 0.015 [ 43 ]. Accordingly, each model was evaluated both independently and juxtaposed against the preceding less restrictive model, to determine invariance. 3 Results 3.1 Measurement Model Two separate factor models were specified and tested on the data. A 2-factor model, which aimed to separate the cognitive and the affective factor from each other, and a 1-factor model, conceptualising the construct as unidimensional. The 2-factor model resulted in extremely high factor correlations ( r = 0.95), questioning the model’s divergent validity as a distinct 2-factor construct. Such a high correlation suggests that the cognitive and affective components of MA might not be as separate as previously thought. Despite previous research finding that a 2-factor model fit their German sample [ 17 ], the Swedish MARS-E appeared more sensible as a unidimensional construct. The 1-factor model was both conceptually and statistically viable in the Swedish context as model fit indices were within the acceptable range (see Table 2 ) and all factor loadings were considered strong. Appendix B contains information for all factor loadings at each timepoint. The accumulated evidence led to the decision of proceeding with the 1-factor model. Table 2 CFA: math anxiety dimensionality and construct associations - T1. Models χ² (DF) CFI TLI RMSEA [90% C.I.] SRMR 1-factor model 262* (104) 0.969 0.964 0.065 [0.055; 0.075] 0.048 2-factor model 259* (103) 0.970 0.965 0.065 [0.055; 0.075] 0.047 Criterion model 899* (454) 0.960 0.956 0.051 [0.047, 0.056] 0.059 * p < 0.05. All models were estimated with the WLSMV-estimator. Criterion model comprised: math anxiety, test anxiety, math self-concept, math interest, math performance, and gender. 3.2 Reliability The internal consistency of the 16 MA items was robust, as indicated by Cronbach’s alpha (α T1-T3 = 0.91–0.92). This level of reliability aligns with α-estimates reported by Henschel and Roick [ 17 ], who recorded alpha levels of α = 0.94 and α = 0.89 for the worry and affective components, respectively. Additionally, test-retest factor correlations obtained from the longitudinal strict invariance measurement model revealed strong associations between measurements at different time points: r = 0.76 between T1 and T2, and r = 0.681 between T1 and T3 (see Table 3 ), underscoring the test’s reliability over time. Table 3 Latent factor correlations and reliability estimates for math anxiety. T1 T2 T3 α T1 - 0.92 T2 0.732 - 0.91 T3 0.656 0.731 - 0.92 Note. All factor correlations significant at p < 0.001. Cronbach’s α estimates ( k = 16). 3.3 Measurement Invariance and differences Measurement invariance across gender was assessed by comparing model fit between a less constrained model and an increasingly stricter constrained model, using data from T1, T2 and T3 measurements. Nested models for all three occasions were examined and changes in model fit were recorded. Notably, all gender invariance measurement models for each measurement occasion demonstrated acceptable model fit. The strictest model in the series was the strict model which displayed acceptable model fit (Strict T1 χ² = 387, DF = 266, CFI = 0.976, RMSEA = 0.05; Strict T2 χ² = 457, DF = 262, CFI = 0.962, RMSEA = 0.063; Strict T3 χ² = 476, DF = 262, CFI = 0.957, RMSEA = 0.077). Furthermore, the model fit change, specifically regarding the CFI and the RMSEA, in each increasingly stricter model was minimal – less than 0.01 and 0.015, respectively (see Table 4 ). Regarding the longitudinal invariance measurement over three occasions, each model resulted in acceptable model fit. The results indicate that the strict model fitted the data marginally better than the scalar model, leaving the scalar model as the worst fitting but still within an acceptable range (Scalar time χ² = 1561, DF = 1119, CFI = 0.968, RMSEA = 0.031). The change in model fit was marginal when transitioning from less to more constrained models, as change in CFI and RMSEA was less than 0.01 and 0.015 for each transition, respectively. A state of strict invariance implies that any differences between the group’s variances, covariances, and means are due to variations in the latent common factors [ 42 ]. These measurement invariance results indicate the robustness of the measurement invariance assessment and allow for meaningful and fair gender and time comparisons. Table 4 Invariance test for gender T1 - T3. Models χ² DF CFI RMSEA Δ χ² Δ DF ΔCFI ΔRMSEA Gender Invariance T1 Configural 335.809 208 0.975 0.059 Metric 352.884 223 0.974 0.057 17.075 15 -0.001 0.002 Scalar 362.389 250 0.978 0.050 9.505 27 0.004 0.007 Strict 386.929 266 0.976 0.050 24.54 16 -0.002 0 Gender Invariance T2 Configural 421.794 208 0.958 0.074 Metric 449.107 223 0.956 0.074 27.313 15 -0.002 0 Scalar 455.817 246 0.959 0.067 6.71 23 0.003 0.007 Strict 457.269 262 0.962 0.063 1.452 16 0.003 0.004 Gender Invariance T3 Configural 430.988 208 0.954 0.076 Metric 459.156 223 0.951 0.075 28.168 15 -0.003 0.001 Scalar 469.448 246 0.954 0.070 10.292 22 0.003 0.005 Strict 475.939 262 0.957 0.066 10.79 16 0.001 0.003 Time Invariance T1-T3 Configural 1445.950 1029 0.970 0.031 Metric 1487.731 1059 0.969 0.031 41.781 30 -0.001 0 Scalar 1561.058 1119 0.968 0.031 73.327 60 -0.001 0 Strict 1555.161 1151 0.971 0.029 -5.897 32 0.003 0.002 Note: Stricter – liberal for χ², DF, RMSEA. Liberal – stricter for CFI. Comparison is between the current and preceding model. 3.4 Factor correlations The criterion model (measurement model) included MA, test anxiety, math self-concept, math interest, math performance, and gender, and displayed acceptable model fit (see ‘criterion model’ in Table 2 ). Factor correlations are presented in Table 5 . Table 5 Factor correlations. MA TAX SELF INT ARITH TAX 0.707** Self -0.580** -0.273** Interest -0.333** -0.148** 0.614** ARITH -0.343** -0.110* 0.484** 0.209** Gender -0.176** -0.204** 0.225** -0.010 0.204** ** p < 0.001, * p < 0.05. MA = math anxiety, TAX = test anxiety, Self = math self-concept, Interest = math interest, Arith = arithmetic fluency. MA and test anxiety correlated highly, however the two variables behaved differently in their relations to the other variables. MA correlated noticeably higher with the other math domain-specific variables than test anxiety. To further investigate the separateness of the two constructs a general factor model was estimated, consisting of all MA and test anxiety items. This model was significantly worse than a two-construct model (χ² = 115, p < 0.001) and resulted in an overall bad model fit (CFA/TLI 0.1, SRMR > 0.8). Gender correlated with all factors except with the math interest factor. Since gender was dummy coded 1 for boys and 0 for girls, the small negative correlation that gender exhibited with both MA and test anxiety, implies that boys were associated with lower levels of anxiety compared to girls. The small positive correlations indicate higher levels of self-concept in math and slightly higher scores of arithmetic fluency in favour of boys. There were no gender differences in math interest (see Table 5 ). These gender effects were also observed when employing t-tests on sum score variables (Table 1 ). 4 Discussion This paper provides valuable insights into how the Swedish MARS-E functions over time and gender, and how it relates to other attitude variables and math performance. The instrument was tested on a young Swedish population over 3 semesters, from grade 4 to 5, and displayed invariance over time as well as across gender. As reliability and measurement invariance was established, meaningful and fair comparisons over time and gender can be tested and reported with the Swedish MARS-E. The initial version of the MARS-E was developed by Henschel and Roick [ 17 ] and comprised 36 items that assessed two different factors of the MA construct: a cognitive and an affective factor. This instrument was translated into Swedish, condensed to 16 items (8 from the cognitive and 8 from the affective factor) and underwent three pilot tests in two different schools prior to the longitudinal project. The Swedish MARS-E showed good model fit as a 2-factor model, resembling the original model well. However, the unidimensional model proved equally effective (Table 2 ). In the interest of parsimony, in addition to the high factor correlations produced by the 2-factor model, the 1-factor model was adopted, which in turn allows a simpler interpretation of the results. Factor loadings for all three CFA’s are presented in Appendix B. Factor loadings for each CFA are considered strong (λ T1−T3 = 0.614–0.873), implying that each item is highly related to the construct. The inclusion of all latent variables in one model utilizing CFA ( criterion model in Table 2 ) resulted in adequate model fit, allowing for further analyses of the relationships with the MA-construct, providing evidence of criterion validity. 4.1 Construct associations MA displayed significant correlations with all measured criterion variables, with especially large relations to test anxiety and self-concept in math. Large correlations between these variables have been documented in previous studies [ 5 , 19 ]. The association between math and test anxiety has been a topic of prior discussion, with doubts raised about its divergent validity, given previous reported effect sizes ranging from r = 0.65 to 0.75 [ 20 ]. The present correlation of 0.71 is substantial, indicating a significant degree of overlap between these two constructs. However, 1) the correlation between the two factors were not perfect, and 2) the constructs had different relationships with the other factors, indicating discriminant validity. For example, the correlation with math performance and MA was r = -0.34 ( p < 0.001) but only r = -0.11 and marginally significant ( p = 0.039) between MA and test anxiety. The differences were also evident when relating the two constructs with math self-concept ( Δ r = 0.308) and math interest ( Δ r = 0.185), where the stronger correlation was found with MA. Finally, 3) a comprehensive model employing items from both the MA and the test anxiety scale was estimated. However, this model exhibited poor fit indices, indicating a lack of evidence that the items measured a unified construct. The current results demonstrates that gender differences do exist in the lower grades. As mentioned in the introduction chapter, previous reports have shown mixed results regarding potential gender differences in the lower grades [ 24 , 25 ]. Looking further down the age span, studies have reported more consistent findings that indicate an absence of gender differences in MA [ 44 , 45 ]. The results from this study suggest weak, but clear gender effects on MA, where girls are significantly more prone to feeling MA and perform worse on the arithmetic fluency test. 4.2 Limitations This study makes significant contributions to the measurement and understanding of MA among Swedish primary school students. However, several limitations are to be considered. The translation and adaption of the MARS-E from German to Swedish and its condensation from 36 to 16 items may have implications for the instrument’s content validity and comparability to the German MARS-E. The scale used for measuring MA is a self-report questionnaire lacking defence against certain biases, such as social desirability or non-serious responses. The sample in the current study is limited to Swedish speaking primary school students, which could be a limiting factor in generalizability for other linguistic or educational contexts. Further research is required to validate these findings across linguistic and educational settings. 4.3 Conclusion The Swedish MARS-E can be considered a valid and reliable tool for measuring math anxiety in a school-aged population. The MA construct was closely related to both test anxiety and self-concept in math. Importantly, MA also exhibited a negative association to arithmetic fluency and gender. The current study does not dissect the underlying causes for these differences, and future research has yet to address this question. The longitudinal stability of the Swedish MARS-E, as suggested by the invariance findings, provides opportunities for longitudinal studies, such as exploring the trajectory of MA over a more extended period. This could in turn reveal how early intervention strategies can alter the course of the MA development. The potential for early detection and intervention, maybe even before grade 4, could contribute to mitigating the long-term consequences of MA. The Swe MARS-E instrument not only provides researchers with a powerful tool to delve into the intricacies of math anxiety. It also simplifies the interpretation of the MA construct with its unidimensional nature, thus empowering educators, or other professions to assess MA in a very straightforward fashion. Investigating the cross-cultural application of the Swedish MARS-E would further develop our understanding of MA across different educational and cultural contexts. Declarations 5 Statements and declaration Swedish Ethical Review Authority All protocols associated with the current study were approved by the Swedish Ethical Review Authority. Reference number: 2020–05982. Ethical guidelines/ Accordance The present study was conducted in accordance with the Declaration of Helsinki and was fully approved by the Swedish Ethical Review Authority. Reference number: 2020–05982. Declarations Swedish Ethical Review Authority All protocols associated with the current study were approved by the Swedish Ethical Review Authority. Reference number: 2020-05982. Ethical guidelines/ Accordance The present study was conducted in accordance with the Declaration of Helsinki and was fully approved by the Swedish Ethical Review Authority. Reference number: 2020-05982. Informed Consent Informed consent was obtained from the participants legal guardians. Funding Funding was received from the Swedish Research Council, VR, Grant (2019–03928). Conflict of interest: The authors declare that they have no conflict of interest. Author Contribution BJ, HE and JK contributed to the study’s design. JF collected data, performed the statistical analysis and wrote the first draft of the manuscript. All authors critically revised the manuscript for important intellectual content. All authors read and approved the final manuscript. Data Availability The data analysed for the current study are available from the corresponding author upon reasonable request. References Ashcraft, M. H. and Ridley, K. S., Math anxiety and its cognitive consequences: A tutorial review, in Handbook of mathematical cognition, J. I. D. Campbell Ed. Psychology Press, 2005, pp. 315–327. Mononen, R., Niemivirta, M., Korhonen, J., Lindskog, M., and Tapola, A., Developmental relations between mathematics anxiety, symbolic numerical magnitude processing and arithmetic skills from first to second grade, Cognition and Emotion . 2022; vol. 36, no. 3, pp. 452-472, doi: 10.1080/02699931.2021.2015296. Ashcraft, M. H., Math anxiety: personal, educational, and cognitive consequences, Curr. Dir. Psychol. Sci. 2002; vol. 11, pp. 181–185, doi: 10.1111/1467-8721.00196. Beilock, S. L. and Maloney, E. A., Math anxiety: A factor in math achievement not to be ignored, Policy Insights from the Behavioral and Brain Sciences . 2015; vol. 2, no. 1, pp. 4-12, doi: 10.1177/2372732215601438. Hembree, R., The nature, effects, and relief of mathematics anxiety, J. Res. Math. Educ. 1990; vol. 21, pp. 33–46, doi: 10.2307/749455. Ma, X., A meta-analysis of the relationship between anxiety toward mathematics and achievement in mathematics, J. Res. Math. Educ. 1999; vol. 30, pp. 520–540, doi: 10.2307/749772. Namkung, J. M., Peng, P., and Lin, X., The relation between mathematics anxiety and mathematics performance among school-aged students: a meta-analysis, Rev. Educ. Res. 2019; vol. 89, pp. 459–496, doi: 10.3102/0034654319843494. Ramirez, G., Shaw, S. T., and Maloney, E. A., Math anxiety: Past research, promising interventions, and a new interpretation framework, Educational psychologist . 2018; vol. 53, no. 3, pp. 145-164, doi: 10.1080/00461520.2018.1447384. Dowker, A., Sarkar, A., and Looi, C. Y., Mathematics anxiety: What have we learned in 60 years?, Frontiers in psychology . 2016; vol. 7, p. 508, doi: 10.3389/fpsyg.2016.00508. Ashcraft, M. H. and Moore, A. M., Mathematics anxiety and the affective drop in performance, J. Psychoeduc. Assess. 2009; vol. 27, pp. 197–205, doi: 10.1177/0734282908330580. Barroso, C., Ganley, C. M., McGraw, A. L., Geer, E. A., Hart, S. A., and Daucourt, M. C., A meta-analysis of the relation between math anxiety and math achievement, Psychological Bulletin . 2021; vol. 147, no. 2, p. 134, doi: 10.1037/bul0000307. Carey, E., Hill, F., Devine, A., and Szücs, D., The chicken or the egg? The direction of the relationship between mathematics anxiety and mathematics performance, Front. Psychol. 2016; vol. 6, p. 1987, doi: 10.3389/fpsyg.2015.01987. Eysenck, M. W., Derakshan, N., Santos, R., and Calvo, M. G., Anxiety and cognitive performance: attentional control theory, Emotion . 2007; vol. 7, p. 336, doi: 10.1037/1528-3542.7.2.336. Finell, J., Sammallahti, E., Korhonen, J., Eklöf, H., and Jonsson, B., Working Memory and its mediating role on the relationship of math anxiety and math performance: A meta-analysis, Frontiers in Psychology . 2022; vol. 12, p. 798090, doi: 10.3389/fpsyg.2021.798090. Caviola, S., Toffalini, E., Giofrè, D., Ruiz, J. M., Sz?cs, D., and Mammarella, I. C., Math performance and academic anxiety forms, from sociodemographic to cognitive aspects: A meta-analysis on 906,311 participants, Educational Psychology Review . 2022; pp. 1-37, doi: 10.1007/s10648-021-09618-5. Ma, X. and Xu, J., The causal ordering of mathematics anxiety and mathematics achievement: a longitudinal panel analysis, Journal of adolescence . 2004; vol. 27, pp. 165–179, doi: 10.1016/j.adolescence.2003.11.003. Henschel, S. and Roick, T., Relationships of mathematics performance, control and value beliefs with cognitive and affective math anxiety, Learning and Individual Differences . 2017; vol. 55, pp. 97-107, doi: 10.1016/j.lindif.2017.03.009. Zhang, J., Zhao, N., and Kong, Q. P., The relationship between math anxiety and math performance: A meta-analytic investigation, Frontiers in psychology . 2019; vol. 10, p. 1613, doi: 10.3389/fpsyg.2019.01613. Robson, D. A., Johnstone, S. J., Putwain, D. W., and Howard, S., Test anxiety in primary school children: A 20-year systematic review and meta-analysis, Journal of School Psychology . 2023; vol. 98, pp. 39-60, doi: 10.1016/j.jsp.2023.02.003. Kazelskis, R., Reeves, C., Kersh, M. E., Bailey, G., Cole, K., and Larmon, M., Mathematics anxiety and test anxiety: separate constructs?, J. Exp. Educ. 2000; vol. 68, pp. 137–146, doi: 10.1080/00220970009598499. Ahmed, W., Minnaert, A., Kuyper, H., and Van der Werf, G., Reciprocal relationships between math self-concept and math anxiety, Learning and individual differences . 2012; vol. 22, no. 3, pp. 385-389, doi: 10.1016/j.lindif.2011.12.004. Justicia-Galiano, M. J., Martín-Puga, M. E., Linares, R., and Pelegrina, S., Math anxiety and math performance in children: The mediating roles of working memory and math self-concept, British Journal of Educational Psychology . 2017; vol. 87, no. 4, pp. 573-589, doi: 10.1111/bjep.12165. Altemus, M., Sarvaiya, N., and Epperson, C. N., Sex differences in anxiety and depression clinical perspectives, Frontiers in neuroendocrinology . 2014; vol. 35, no. 3, pp. 320-330, doi: 10.1016/j.yfrne.2014.05.004. Hill, F., Mammarella, I. C., Devine, A., Caviola, S., Passolunghi, M. C., and Sz?cs, D., Maths anxiety in primary and secondary school students: Gender differences, developmental changes and anxiety specificity, Learning and individual differences . 2016; vol. 48, pp. 45-53, doi: 10.1016/j.lindif.2016.02.006. Gierl, M. J. and Bisanz, J., Anxieties and attitudes related to mathematics in grades 3 and 6, The Journal of experimental education . 1995; vol. 63, no. 2, pp. 139-158, doi: 10.1080/00220973.1995.9943818. Dasgupta, N. and Stout, J. G., Girls and women in science, technology, engineering, and mathematics: STEMing the tide and broadening participation in STEM careers, Policy Insights from the Behavioral and Brain Sciences . 2014; vol. 1, no. 1, pp. 21-29, doi: 10.1177/23727322145494. Richardson, F. C. and Suinn, R. M., The mathematics anxiety rating scale: psychometric data, Journal of counseling Psychology . 1972; vol. 19, no. 6, p. 551, doi: 10.1037/h0033456. Plake, B. S. and Parker, C. S., The development and validation of a revised version of the Mathematics Anxiety Rating Scale, Educational and psychological measurement . 1982; vol. 42, no. 2, pp. 551-557, doi: 10.1177/0013164482042002. Suinn, R. M., Taylor, S., and Edwards, R. W., Suinn mathematics anxiety rating scale for elementary school students (MARS-E): Psychometric and normative data, Educational and Psychological Measurement . 1988; vol. 48, no. 4, pp. 979-986. Valentine, J. C., DuBois, D. L., and Cooper, H., The relation between self-beliefs and academic achievement: A meta-analytic review, Educational psychologist . 2004; vol. 39, no. 2, pp. 111-133, doi: 10.1207/s15326985ep3902_3. Shavelson, R. J., Hubner, J. J., and Stanton, G. C., Self-concept: Validation of construct interpretations, Review of educational research . 1976; vol. 46, no. 3, pp. 407-441. Marsh, H. W., Relich, J. D., and Smith, I. D., Self-concept: The construct validity of interpretations based upon the SDQ, Journal of Personality and social psychology . 1983; vol. 45, no. 1, p. 173, doi: 10.1037/0022-3514.45.1.173. Marsh, H. W., Self description questionnaire-I, Cultural Diversity and Ethnic Minority Psychology . 1990, doi: 10.1037/t01843-000. Räsänen, P., Aunio, P., Laine, A., Hakkarainen, A., Väisänen, E., and Korhonen, J., Effects of gender on basic numerical and arithmetic skills: Pilot data from third to ninth grade for a large-Scale online dyscalculia screener, Frontiers in education . 2021; vol. 6, p. 683672, doi: 10.3389/feduc.2021.683672. Team, R. C., R: A Language and Environment for Statistical Computing. [Online]. Available: https://www.R-project.org/ Revelle, W., psych: Procedures for psychological, psychometric, and personality research. [Online]. Available: https://CRAN.R-project.org/package=psych Tierney, N. and Cook, D., Expanding Tidy Data Principles to Facilitate Missing Data Exploration, Visualization and Assessment of Imputations, Journal of Statistical Software . 2023; vol. 105, no. 7, pp. 1–31, doi: 10.18637/jss.v105.i07. Kline, R. B., Principles and practice of structural equation modeling, 4th ed. New York: Guilford publications; 2016. Little, R. J., A test of missing completely at random for multivariate data with missing values, Journal of the American statistical Association . 1988; vol. 83, no. 404, pp. 1198-1202. [Online]. Available: https://www.jstor.org/stable/pdf/2290157. Heymans, M. W. and Eekhout, I., Applied missing data analysis with SPSS and (R)studio, 2019. [Online]. Available: https://bookdown.org/mwheymans/bookmi/. Hu, L. T. and Bentler, P. M., Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives, Structural equation modeling: a multidisciplinary journal . 1999; vol. 6, no. 1, pp. 1-55, doi: 10.1080/10705519909540118. Liu, Y., Millsap, R. E., West, S. G., Tein, J. Y., Tanaka, R., and Grimm, K. J., Testing measurement invariance in longitudinal data with ordered-categorical measures, Psychological methods . 2017; vol. 22, no. 3, p. 486, doi: 10.1037/met0000075. Chen, F. F., Sensitivity of goodness of fit indexes to lack of measurement invariance, Structural equation modeling: a multidisciplinary journal . 2007; vol. 14, no. 3, pp. 464-504, doi: 10.1080/10705510701301834. Harari, R. R., Vukovic, R. K., and Bailey, S. P., Mathematics anxiety in young children: An exploratory study, The Journal of experimental education . 2013; vol. 81, no. 4, pp. 538-555, doi: 10.1080/00220973.2012.727888. del-Río, M. F., Susperreguy, M. I., Morales, M. F., Peake, C., and Angulo, M., Kindergarten children’s math anxiety and its relationship with mathematical performance (Ansiedad matemática en niños y niñas de kínder y su relación con el rendimiento matemático), Studies in Psychology . 2023; vol. 44, no. 2-3, pp. 542-561, doi: 10.1080/02109395.2023.2254158. Additional Declarations No competing interests reported. Supplementary Files AppendixABDisc.Ed.pdf Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 23 Aug, 2024 Reviews received at journal 20 Aug, 2024 Reviewers agreed at journal 13 Aug, 2024 Reviewers agreed at journal 12 Aug, 2024 Reviews received at journal 29 May, 2024 Reviewers agreed at journal 24 May, 2024 Reviewers invited by journal 16 May, 2024 Editor assigned by journal 16 May, 2024 Submission checks completed at journal 16 May, 2024 First submitted to journal 02 May, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4360120","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":306739547,"identity":"e3566a30-b06e-40bb-bee2-fb8f734c0290","order_by":0,"name":"Jonatan Finell","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIiWNgGAWjYBACxgYog70ZSDwosCFSywEg5jkMJBIM0oi0CqzlAFjLYcKqmdt7D37+UHOHgYed9+CDBIPziWtnJDB/+IDPYT3nkiUOHHvGwMPMl2yQYHA7cduNBDbJGfi0zMgxkDjAdpjBnpnHTAKmhZkHvxbjHwf+HQbawmP+I8HgHEgL8+c/+LWYSRxsA2sxA3r/AEgLgzQ+7zP2nDGzONt3mAeoxRjosGTjbWcetkn24NFi2N5jfKPi22E5Hv4zhh8+VNjJbjuefPjDD3xaGiA0sn/hKQI7kMcrOwpGwSgYBaMABABb50+bQ4W0IQAAAABJRU5ErkJggg==","orcid":"","institution":"Umeå University","correspondingAuthor":true,"prefix":"","firstName":"Jonatan","middleName":"","lastName":"Finell","suffix":""},{"id":306739548,"identity":"53efc988-03df-43f7-93a6-94895ec341e1","order_by":1,"name":"Hanna Eklöf","email":"","orcid":"","institution":"Umeå University","correspondingAuthor":false,"prefix":"","firstName":"Hanna","middleName":"","lastName":"Eklöf","suffix":""},{"id":306739550,"identity":"21a4c2b0-8770-4d21-8fa8-9d322544a091","order_by":2,"name":"Bert Jonsson","email":"","orcid":"","institution":"Umeå University","correspondingAuthor":false,"prefix":"","firstName":"Bert","middleName":"","lastName":"Jonsson","suffix":""},{"id":306739551,"identity":"52941131-6695-4c50-90cb-c8419a4c5db1","order_by":3,"name":"Johan Korhonen","email":"","orcid":"","institution":"Åbo Akademi University","correspondingAuthor":false,"prefix":"","firstName":"Johan","middleName":"","lastName":"Korhonen","suffix":""}],"badges":[],"createdAt":"2024-05-02 15:54:47","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4360120/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4360120/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57224494,"identity":"ad7f30e1-64bd-4467-886a-9bd9f0d75112","added_by":"auto","created_at":"2024-05-27 16:50:22","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":42468,"visible":true,"origin":"","legend":"\u003cp\u003eMath anxiety as a unidimensional construct at T1. Standardized factor loadings.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4360120/v1/d68fa79a223ea01f40def4e7.jpg"},{"id":57224665,"identity":"4717afaa-f372-4900-b72f-a716654e34bc","added_by":"auto","created_at":"2024-05-27 16:58:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":758504,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4360120/v1/4f514ce4-52cf-4f8f-8ef3-2c711c882cc9.pdf"},{"id":57224495,"identity":"12c6044c-ac5a-48b7-b80f-409b4c9d8071","added_by":"auto","created_at":"2024-05-27 16:50:23","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":278300,"visible":true,"origin":"","legend":"","description":"","filename":"AppendixABDisc.Ed.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4360120/v1/d9ceba8071db54bd2a483f3b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eReliability and validity evidence of the Swedish shortened mathematics anxiety rating scale elementary (MARS-E)\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eFor many, math is the most demanding subject in school [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] and might be interconnected with math anxiety (MA) as early as from grade 1 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. MA is a phenomenon that can be described as feelings of apprehension, tension, and fear that one may experience when engaging with math-related tasks [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. MA can, in addition to affecting one's emotional well-being, also reduce one’s performance in math-related activities, potentially impacting academic achievement and future educational choices [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In populations stemming from secondary education or older, there is certainly strong evidence marking a significant negative relationship between math achievement and MA [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e–\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. This is of concern, as reports have shown that up to 25% of US college populations experience MA (see review by Ramirez [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In a review, Dowker et al. [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] present reports that conclude various estimates regarding the prevalence of MA. The prevalence fluctuates depending on group identity, though, in general, it is likely that MA peaks around the end of secondary school [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Furthermore, studies have found differences in MA as a function of gender, although its relationship with math is not significantly affected by gender [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThere are several theories that explain the relationship between math anxiety and math performance [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. (I) The Deficit Theory suggests that poor math performance can affect emotions towards math. In contrast (II) The Debilitating Anxiety Model posits that anxiety in math will ultimately affect math performance. (III) A Reciprocal Model accepts both theories, acknowledging the interplay between the two variables. A further explanation relating to The Debilitating Anxiety Model can be fetched from the Attentional Control Theory [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] which suggests that the underlying mechanisms for the anxiety-performance link lie in anxiety depleting attentional resources required for calculation. Evidence for such a model has been synthesized in two separate meta-analyses [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], though in Caviola’s [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] study, significant results were only found through Monte Carlo simulations on their data. Ma and Xu’s [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] longitudinal study provides some support for a reciprocal model, though stronger relations were found from math achievement to MA, indicating favour to the deficit model. In other words, the relationship between math and MA is likely complex. Reciprocity is probably the name of the game, yet exactly how, when, and to what extent each variable causes change in the other is still worth researching.\u003c/p\u003e \u003cp\u003eWith previous research predominantly centred around secondary school and university populations [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], the present study aims to evaluate a MA instrument tailored for younger age groups. In the current study, a newly translated instrument measuring MA in Swedish primary school students was examined. Based on Henschel and Roick’s [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] MARS-E, the current study aimed to investigate the dimensionality of the translated and adapted Swedish version of the instrument. Henschel and Roick’s [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] MA scale consisted of two components, one being an affective component relating to feelings of nervousness, the other being a cognitive component relating to worries about math tasks. As part of the validation process the factor dimensionality was assessed, as well as invariance testing across time and gender. Further, both reliability and criterion validity were evaluated through assessing relationships between MA and math performance, as well as test anxiety, math interest, and math self-concept.\u003c/p\u003e \u003cp\u003eThe literature was scanned for reference-values as part of assessing the MARS-E’s criterion validity. The results consistently reveal significant small to medium effect sizes between MA and math achievement. Hembree [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] reported a correlation of \u003cem\u003er\u003c/em\u003e = -0.34 for populations in grade 5–12. In younger populations, both Zhang et al. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] and Namkung et al. [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] reported a correlation of \u003cem\u003er\u003c/em\u003e = − 0.27 for students in primary schools. However, the spread in effect sizes were wide, as seen in Zhang et al.’s data, the distribution of effect sizes ranged from + 0.21 (primary students) to -0.75 (secondary school students). Based on these results, expectations for the current correlation between MA and math performance lied around \u003cem\u003er\u003c/em\u003e = -0.27, though awareness of the widespread distribution of effect sizes remains important to consider.\u003c/p\u003e \u003cp\u003eHembree [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] also documented the correlation between MA and math self-concept as \u003cem\u003er\u003c/em\u003e = -0.7, and MA and test anxiety as \u003cem\u003er\u003c/em\u003e = 0.52. It is worth noting that these were effect sizes for the whole sample, which consisted of a large variety of age groups, which were, on average, older than the sample studied in the current study. Regarding the relationship between test anxiety and MA, a more recent meta-analysis by Robson et al. [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], focused solely on participants in primary school, found a correlation of 0.57 (\u003cem\u003ek\u003c/em\u003e = 4), which aligns well with Hembree’s findings. Although the positive correlation for MA and test anxiety is high, there is support for the two constructs being separate from each other [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. As for the relationship between MA and self-concept in math, Ahmed et al. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] found the correlation in grade 7 students to be around \u003cem\u003er\u003c/em\u003e = -0.43 under three occasions within one school year. An even larger contrast from Hembree’s results is found in Justicia-Galiano et al.’s [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] study on children aged 8–12, where the correlation was estimated at \u003cem\u003er\u003c/em\u003e = -0.27.\u003c/p\u003e \u003cp\u003eGender differences occur both in generalized and social anxiety disorders, as women have a higher prevalence than men. These gender differences can be observed around mid-adolescence and pre-puberty for generalized- and social anxiety disorders respectively (see review by Altemus [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]). Regarding the more specific form of anxiety and the main study-variable, namely math anxiety, the highest levels of MA are often observed in grades 9 or 10 [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Mixed results are found in the younger populations, as Hill et al. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] did indeed find gender differences in grade 3–5, while Gierl and Bisanz’s [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] study did not. How these differences in MA translate into broader disparities later in life remains unclear. Though notably, studies have found correlations between MA and avoidance behaviour towards math, manifesting in both secondary school and college settings [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. This tendency to avoid math has potential long-term consequences, particularly in the context of STEM careers. A substantial gender gap exists in these fields, where females are underrepresented [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The relationship between early math anxiety, gender differences, and subsequent avoidance of math-related courses, could potentially affect the eventual choice of career paths.\u003c/p\u003e \u003cp\u003eCurrent study\u003c/p\u003e \u003cp\u003eThis study is part of a larger research project investigating the longitudinal effects of MA, math performance, learning strategies and working memory. The purpose of this study was to evaluate the shortened translated MARS-E scale by assessing reliability, dimensionality and its relations to performance and self-concept in math. The MARS (Math Anxiety Rating Scale) which was originally developed as a 98-item scale [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], has since been adapted and shortened for different uses and target groups (e.g., MARS-R [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]; MARS-E [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]). Henschel and Roick [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] introduced a more practical group administration instrument by creating a shortened version of the Mathematics Anxiety Rating Scale – Elementary (MARS-E). The MARS-E scale [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] consists of 36 items and was designed as a more convenient tool for administration purposes. Given the age and diversity of the sample, a shorter and simpler instrument was required to ensure that all participants could both comprehend the questions and maintain focus for answering the questionnaire. Thus, based on the groundwork laid by Henschel and Roick [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], the instrument was translated and adapted to a functional 16-item instrument (Swedish MARS-E). In evaluating the Swedish MARS-E, the current paper addressed three critical questions:\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo what extent can the Swedish MARS-E reliably measure math anxiety among primary school students?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eHow does the Swedish MARS-E perform in terms of structural validity?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eWhat associations does the Swedish MARS-E demonstrate with criterion variables related to math anxiety?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003cp\u003e\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003c/div\u003e \u003c/div\u003e "},{"header":"2 Method","content":"\u003cp\u003eThe Swedish version of the MARS-E was translated from German by a translation company. Content validity was assessed by experts from the research project, and schoolteachers who participated in the two pilot tests. Corrections and clarifications on minor issues were made. As part of evaluating the content the instrument was piloted at three occasions in two schools in Sweden (N = 118). From the original 36 items, the Swedish MARS-E was reduced to 16 items, with 8 items originating from the cognitive component and 8 from the affective component. The target population for the current study were students in Swedish primary schools.\u003c/p\u003e\u003ch2\u003e2.1 Participants and Procedure\u003c/h2\u003e\u003cp\u003eThe sample included grade 4 students from Sweden, selected from 18 schools across 6 different municipalities, spanning from the northern to the southern regions of the country. The participants were n = 429, 9–10-year-old students, 213 girls and 214 boys (2 preferred not to answer). The project underwent ethical review by the Swedish Ethical Review Authority and consent was collected by the student’s legal guardians. Participating teachers, students and guardians were informed about the project’s aim, procedure, and data handling.\u003c/p\u003e\u003cp\u003eData was collected in autumn 2022, spring 2023 and autumn 2023. Two research assistants visited participating schools and carried out the measurements one class at a time (ca 18/class). The tests were split over two sessions with the questionnaire and math test administered on separate lessons, to a) reduce student fatigue, and b) reduce the risk of inducing awareness of anxiety. Verbal and written instructions were provided, with the option for students to request clarification or assistance. Though no specific guidance was given for solving math tasks. Each session was supervised by at least one research assistant in addition to their own teachers. All assessments were administered digitally.\u003c/p\u003e\u003ch2\u003e2.2 Assessments\u003c/h2\u003e\u003cp\u003ePrevious research offers guidelines for how MA correlates with other anxieties, specifically test anxiety [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. This is also true for the relationship between math achievement and MA [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. As part of assessing the validity of the Swedish MARS-E, criterion variables were employed to determine relations between the MA-measure and math achievement, as well as test anxiety, math interest, and math self-concept. A high positive correlation between math anxiety and test anxiety as well as a negative correlation with math self-concept would indicate criterion validity.\u003c/p\u003e\u003ch2\u003e2.2.1 Swedish MARSE\u003c/h2\u003e\u003cp\u003eThe Swedish MARS-E was measured with 16 items where participants took a stand on statements describing scenarios about math on a 4-point likert scale. Out of the 16 items, 8 targeted the worry (cognitive) component of MA, where the answer options ranged from “Not at all worried” to “Very worried”. The remaining 8 items targeted the nervous (affective) component, with answer-options ranging from “Not at all nervous” to “Very nervous”. All items are presented in Appendix A.\u003c/p\u003e\u003ch2\u003e2.2.2 Test anxiety\u003c/h2\u003e\u003cp\u003eTest anxiety was assessed using the Swedish version of the STAS (School Test Anxiety Survey [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]), comprising 6 items rated on a 4-point likert scale. Participants responded to statements concerning their emotions in test-related situations. The answer options ranged from \"Not at all nervous\" to \"Very nervous\". The internal consistency across all 6 items was robust, with alpha levels ranging from α\u003csub\u003eT1−T3\u003c/sub\u003e = 0.85–0.89, indicating good reliability.\u003c/p\u003e\u003ch2\u003e2.2.3 Math self-concept and interest\u003c/h2\u003e\u003cp\u003eIn the past, self-beliefs have been a subject of debate regarding whether they have a significant relationship with actual academic performances. However, when isolating self-beliefs into domain-specific constructs and matching them to specific academic subjects, the relationship becomes clearer, indicating significant links [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Shavelson et al.’s [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] model of self-concept was established as a multidimensional hierarchy with a top tier consisting of a general self-concept. From the general self-concept, the model then branches out into academic- and non-academic self-concepts, and further into more domain-specific branches. Drawing on Shavelson et al.’s model, Marsh et al. [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] published the SDQ (I) containing 7 blocks of self-concept measures. The authors reported good reliability regarding the mathematics block (α\u003csub\u003epublic schools\u003c/sub\u003e = 0.92; α\u003csub\u003eprivate schools\u003c/sub\u003e = 0.94). In the current study, the mathematics block measuring both math interest and math self-concept [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] are used. Each variable consisting of 3 statements on a 5-point likert scale ranging from 1 = False to 5 = True. Cronbach’s alpha for each variable indicated good reliability on all three measurement occasions (α\u003csub\u003emath self−concept\u003c/sub\u003e = 0.83–0.85; α\u003csub\u003emath interest\u003c/sub\u003e = 0.92–0.94).\u003c/p\u003e\u003ch2\u003e2.2.4 Math performance\u003c/h2\u003e\u003cp\u003eMath performance was measured with a digital test battery (FUNA-DB) which consists of six different sub-tests: (1) Number Comparison, (2) Digit Dot Matching, (3) Number Series, (4) Single Digit Addition, (5) Single Digit Subtraction, and (6) Multiple Digit Calculation. Räsänen et al. [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] found that the test battery was reliable and valid and was best described as a 2-factor model based on a large sample of 9–15-year-old Finnish students (invariant across Finnish speaking and Swedish speaking students). In the current study, four subtests were collapsed and used as a composite score of arithmetic fluency, providing a stable measurement (sub-test: 3, 4, 5 \u0026amp; 6, corresponding to one of Räsänen et al.’s factors). Response-consistency for each subtest was analysed on item-level with the KR-20 method, which resulted in acceptable estimates of 0.856, 0.957, 0.964, 0.934, for sub-tests 3, 4, 5 and 6, respectively. Prior to every actual sub-test, there were two practice rounds, one where the student received feedback on how he or she had succeeded and one that mimicked the actual test more closely.\u003c/p\u003e\u003ch2\u003e2.3 Analysis\u003c/h2\u003e\u003cp\u003eR (version 4.3.0) was used for computing descriptives, t-tests, reliability, and Little’s MCAR test, utilising the r-base package ‘stats’ [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], the ‘psych’-package [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], and the ‘naniar’-package [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Cronbach's alpha estimates were reported for all three occasions, demonstrating the consistent reliability of the instruments used in the current study. Beyond invariance and reliability analysis, the focus of the current study was on the T1 measurement occasion with respect to criterion validity. Mplus (version 8.5) was used to estimate latent measurement models of the Swedish MARS-E. Applying Confirmatory Factor Analysis (CFA), models were assessed based on a set of model fit indices, structural loadings between the latent factors and items, and previous theory. The final model underwent a series of constraints, effectively evaluating invariance across gender and time. This method (invariance testing) tests the hypothesis that the model with the constrained parameters is close to equal in the two samples [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Lastly, the measurement model of MA was extended by adding correlations to math performance, test anxiety, math self-concept, math interest and gender. The relations were assessed and juxtaposed to previous research.\u003c/p\u003e\u003cp\u003eVarious patterns of missing data were identified in the dataset. To determine the nature of the missingness, Little’s MCAR test (Missing Completely At Random) was carried out on all the current study’s included variables. Little’s MCAR test examines if missing data are systematically missing [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The resulting non-significant \u003cem\u003ep\u003c/em\u003e-value suggested that the missingness was completely at random (χ\u003csup\u003e2\u003c/sup\u003e = 77.4, df = 73, \u003cem\u003ep\u003c/em\u003e = 0.341).\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMeans, standard deviations and t-tests on sum score variables.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM (SD) Boys\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM (SD) Girls\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM (SD) Total\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eT-test\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eN\u003csub\u003etotal\u003c/sub\u003e (N\u003csub\u003eboys\u003c/sub\u003e, N\u003csub\u003egirls\u003c/sub\u003e)\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMARS-E\u003csub\u003eT1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.78 (7.47)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.30 (8.49)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.01 (8.07)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003et = 2.98**\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e360 (180, 178)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMARS-E\u003csub\u003eT2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.11 (6.33)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.91 (8.05)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.50 (7.36)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003et = 3.74**\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e376 (190, 184)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMARS-E\u003csub\u003eT3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.63 (5.95)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.99 (8.7)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.75 (7.73)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003et = 5.63**\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e375 (190, 183)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTax\u003csub\u003eT1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.05 (3.37)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.35 (3.68)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.70 (3.57)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003et = 3.49**\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e360 (178, 180)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterest\u003csub\u003eT1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.01 (3.88)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.10 (3.88)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.06 (3.88)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003et = 0.22\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e357 (178, 177)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf\u003csub\u003eT1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.96 (2.68)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.91 (2.73)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.43 (2.76)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003et = 3.69**\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e360 (178, 180)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArith\u003csub\u003eT1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.24 (1.10)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.22 (0.85)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00 (1.00)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003et = 4.31**\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e367 (179, 186)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e**p \u0026lt; 0.001. Tax = test anxiety, Interest = math interest, Self = math self-concept, \u003c/p\u003e \u003cp\u003eArith = standardized arithmetic fluency.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003ch2\u003e2.4 MARS-E Dimensionality and Invariance Testing\u003c/h2\u003e\u003cp\u003eTwo MA-models were tested on the data: the original 2-factor model, used in Henschel and Roick’s study [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], and a unidimensional model. The models were evaluated based on model fit indices and factor correlations. Model fit indices for all models were satisfactory by standard guidelines, recommended by Hu and Bentler [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. CFI and TLI were close to 0.95, SRMR-values close to 0.08, and RMSEA-values close to 0.06. Since the data for the Swedish MARS-E was categorical, the models were estimated with the Weighted Least Squares estimator with adjusted means and variances (WLSMV).\u003c/p\u003e\u003cp\u003eA study of potential gender differences in MA was carried out. Ensuring an unbiased comparison between genders, necessitates that the scale used is invariant across groups. Accordingly, the MA invariant model was subjected to a series of constraints to rigorously test for invariance. These constraints were measured as (1) configural, (2) metric, (3) scalar and (4) strict invariance. The configural invariance is the least restrictive model, maintaining same model specification for each group. In the case of the Swedish MARS-E the number of factors was determined by the dimensionality investigation, with other model parameters freely estimated. The second model, metric invariance, assumes configural invariance is met, with factor loadings additionally constrained to equality across groups. The third model, scalar invariance, requires metric invariance in addition to equal intercepts across groups [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Given that the data for the Swedish MARS-E consisted of categorical ordered variables, scalar invariance tested the equality of thresholds across groups instead of intercepts. Lastly, strict invariance is achieved if the model remains stable even when residuals are constrained to equality in addition of scalar invariance [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eA common cutoff value for assessing change in model fit during measurement invariance testing is a change in the CFI of less than or equal to 0.01 and a change in the RMSEA of less than or equal to 0.015 [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Accordingly, each model was evaluated both independently and juxtaposed against the preceding less restrictive model, to determine invariance.\u003c/p\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Measurement Model\u003c/h2\u003e \u003cp\u003eTwo separate factor models were specified and tested on the data. A 2-factor model, which aimed to separate the cognitive and the affective factor from each other, and a 1-factor model, conceptualising the construct as unidimensional. The 2-factor model resulted in extremely high factor correlations (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.95), questioning the model\u0026rsquo;s divergent validity as a distinct 2-factor construct. Such a high correlation suggests that the cognitive and affective components of MA might not be as separate as previously thought. Despite previous research finding that a 2-factor model fit their German sample [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], the Swedish MARS-E appeared more sensible as a unidimensional construct. The 1-factor model was both conceptually and statistically viable in the Swedish context as model fit indices were within the acceptable range (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) and all factor loadings were considered strong. Appendix B contains information for all factor loadings at each timepoint. The accumulated evidence led to the decision of proceeding with the 1-factor model.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCFA: math anxiety dimensionality and construct associations - T1.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModels\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eχ\u0026sup2; (DF)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCFI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTLI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRMSEA [90% C.I.]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSRMR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1-factor model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e262* (104)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.964\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.065 [0.055; 0.075]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2-factor model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e259* (103)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.065 [0.055; 0.075]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCriterion model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e899* (454)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.960\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.051 [0.047, 0.056]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e*\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. All models were estimated with the WLSMV-estimator. Criterion model comprised: math anxiety, test anxiety, math self-concept, math interest, math performance, and gender.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Reliability\u003c/h2\u003e \u003cp\u003eThe internal consistency of the 16 MA items was robust, as indicated by Cronbach\u0026rsquo;s alpha (α\u003csub\u003eT1-T3\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.91\u0026ndash;0.92). This level of reliability aligns with α-estimates reported by Henschel and Roick [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], who recorded alpha levels of α\u0026thinsp;=\u0026thinsp;0.94 and α\u0026thinsp;=\u0026thinsp;0.89 for the worry and affective components, respectively. Additionally, test-retest factor correlations obtained from the longitudinal strict invariance measurement model revealed strong associations between measurements at different time points: \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.76 between T1 and T2, and \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.681 between T1 and T3 (see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), underscoring the test\u0026rsquo;s reliability over time.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLatent factor correlations and reliability estimates for math anxiety.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eα\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.732\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eNote. All factor correlations significant at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e \u003cp\u003eCronbach\u0026rsquo;s α estimates (\u003cem\u003ek\u003c/em\u003e\u0026thinsp;=\u0026thinsp;16).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Measurement Invariance and differences\u003c/h2\u003e \u003cp\u003eMeasurement invariance across gender was assessed by comparing model fit between a less constrained model and an increasingly stricter constrained model, using data from T1, T2 and T3 measurements. Nested models for all three occasions were examined and changes in model fit were recorded. Notably, all gender invariance measurement models for each measurement occasion demonstrated acceptable model fit. The strictest model in the series was the strict model which displayed acceptable model fit (Strict\u003csub\u003eT1\u003c/sub\u003e χ\u0026sup2; = 387, DF\u0026thinsp;=\u0026thinsp;266, CFI\u0026thinsp;=\u0026thinsp;0.976, RMSEA\u0026thinsp;=\u0026thinsp;0.05; Strict\u003csub\u003eT2\u003c/sub\u003e χ\u0026sup2; = 457, DF\u0026thinsp;=\u0026thinsp;262, CFI\u0026thinsp;=\u0026thinsp;0.962, RMSEA\u0026thinsp;=\u0026thinsp;0.063; Strict\u003csub\u003eT3\u003c/sub\u003e χ\u0026sup2; = 476, DF\u0026thinsp;=\u0026thinsp;262, CFI\u0026thinsp;=\u0026thinsp;0.957, RMSEA\u0026thinsp;=\u0026thinsp;0.077). Furthermore, the model fit change, specifically regarding the CFI and the RMSEA, in each increasingly stricter model was minimal \u0026ndash; less than 0.01 and 0.015, respectively (see Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRegarding the longitudinal invariance measurement over three occasions, each model resulted in acceptable model fit. The results indicate that the strict model fitted the data marginally better than the scalar model, leaving the scalar model as the worst fitting but still within an acceptable range (Scalar\u003csub\u003etime\u003c/sub\u003e χ\u0026sup2; = 1561, DF\u0026thinsp;=\u0026thinsp;1119, CFI\u0026thinsp;=\u0026thinsp;0.968, RMSEA\u0026thinsp;=\u0026thinsp;0.031). The change in model fit was marginal when transitioning from less to more constrained models, as change in CFI and RMSEA was less than 0.01 and 0.015 for each transition, respectively.\u003c/p\u003e \u003cp\u003eA state of strict invariance implies that any differences between the group\u0026rsquo;s variances, covariances, and means are due to variations in the latent common factors [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. These measurement invariance results indicate the robustness of the measurement invariance assessment and allow for meaningful and fair gender and time comparisons.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInvariance test for gender T1 - T3.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModels\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eχ\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCFI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRMSEA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eΔ χ\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eΔ DF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eΔCFI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eΔRMSEA\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eGender Invariance T1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConfigural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e335.809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetric\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e352.884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.974\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScalar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e362.389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.978\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStrict\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e386.929\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eGender Invariance T2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConfigural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e421.794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.958\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetric\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e449.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27.313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScalar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e455.817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStrict\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e457.269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.962\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eGender Invariance T3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConfigural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e430.988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetric\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e459.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.951\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScalar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e469.448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStrict\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e475.939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.957\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eTime Invariance T1-T3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConfigural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1445.950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetric\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1487.731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e41.781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScalar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1561.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e73.327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStrict\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1555.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.971\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-5.897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eNote: Stricter \u0026ndash; liberal for χ\u0026sup2;, DF, RMSEA. Liberal \u0026ndash; stricter for CFI. Comparison is between the current and preceding model.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Factor correlations\u003c/h2\u003e \u003cp\u003eThe criterion model (measurement model) included MA, test anxiety, math self-concept, math interest, math performance, and gender, and displayed acceptable model fit (see \u0026lsquo;criterion model\u0026rsquo; in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Factor correlations are presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFactor correlations.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTAX\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSELF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eINT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eARITH\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTAX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.707**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.580**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.273**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.333**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.148**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.614**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eARITH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.343**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.110*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.484**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.209**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.176**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.204**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.225**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.204**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e**\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, *\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. MA\u0026thinsp;=\u0026thinsp;math anxiety, TAX\u0026thinsp;=\u0026thinsp;test anxiety, Self\u0026thinsp;=\u0026thinsp;math self-concept, Interest\u0026thinsp;=\u0026thinsp;math interest, Arith\u0026thinsp;=\u0026thinsp;arithmetic fluency.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMA and test anxiety correlated highly, however the two variables behaved differently in their relations to the other variables. MA correlated noticeably higher with the other math domain-specific variables than test anxiety. To further investigate the separateness of the two constructs a general factor model was estimated, consisting of all MA and test anxiety items. This model was significantly worse than a two-construct model (χ\u0026sup2; = 115, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and resulted in an overall bad model fit (CFA/TLI\u0026thinsp;\u0026lt;\u0026thinsp;0.9, RMSEA\u0026thinsp;\u0026gt;\u0026thinsp;0.1, SRMR\u0026thinsp;\u0026gt;\u0026thinsp;0.8).\u003c/p\u003e \u003cp\u003eGender correlated with all factors except with the math interest factor. Since gender was dummy coded 1 for boys and 0 for girls, the small negative correlation that gender exhibited with both MA and test anxiety, implies that boys were associated with lower levels of anxiety compared to girls. The small positive correlations indicate higher levels of self-concept in math and slightly higher scores of arithmetic fluency in favour of boys. There were no gender differences in math interest (see Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). These gender effects were also observed when employing t-tests on sum score variables (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThis paper provides valuable insights into how the Swedish MARS-E functions over time and gender, and how it relates to other attitude variables and math performance. The instrument was tested on a young Swedish population over 3 semesters, from grade 4 to 5, and displayed invariance over time as well as across gender. As reliability and measurement invariance was established, meaningful and fair comparisons over time and gender can be tested and reported with the Swedish MARS-E.\u003c/p\u003e \u003cp\u003eThe initial version of the MARS-E was developed by Henschel and Roick [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] and comprised 36 items that assessed two different factors of the MA construct: a cognitive and an affective factor. This instrument was translated into Swedish, condensed to 16 items (8 from the cognitive and 8 from the affective factor) and underwent three pilot tests in two different schools prior to the longitudinal project. The Swedish MARS-E showed good model fit as a 2-factor model, resembling the original model well. However, the unidimensional model proved equally effective (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In the interest of parsimony, in addition to the high factor correlations produced by the 2-factor model, the 1-factor model was adopted, which in turn allows a simpler interpretation of the results. Factor loadings for all three CFA\u0026rsquo;s are presented in Appendix B. Factor loadings for each CFA are considered strong (λ\u003csub\u003eT1\u0026minus;T3\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.614\u0026ndash;0.873), implying that each item is highly related to the construct. The inclusion of all latent variables in one model utilizing CFA (\u003cem\u003ecriterion model\u003c/em\u003e in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) resulted in adequate model fit, allowing for further analyses of the relationships with the MA-construct, providing evidence of criterion validity.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Construct associations\u003c/h2\u003e \u003cp\u003eMA displayed significant correlations with all measured criterion variables, with especially large relations to test anxiety and self-concept in math. Large correlations between these variables have been documented in previous studies [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The association between math and test anxiety has been a topic of prior discussion, with doubts raised about its divergent validity, given previous reported effect sizes ranging from \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.65 to 0.75 [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The present correlation of 0.71 is substantial, indicating a significant degree of overlap between these two constructs. However, 1) the correlation between the two factors were not perfect, and 2) the constructs had different relationships with the other factors, indicating discriminant validity. For example, the correlation with math performance and MA was \u003cem\u003er\u003c/em\u003e = -0.34 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) but only \u003cem\u003er\u003c/em\u003e = -0.11 and marginally significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.039) between MA and test anxiety. The differences were also evident when relating the two constructs with math self-concept (\u003csup\u003eΔ\u003c/sup\u003e\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.308) and math interest (\u003csup\u003eΔ\u003c/sup\u003e\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.185), where the stronger correlation was found with MA. Finally, 3) a comprehensive model employing items from both the MA and the test anxiety scale was estimated. However, this model exhibited poor fit indices, indicating a lack of evidence that the items measured a unified construct.\u003c/p\u003e \u003cp\u003eThe current results demonstrates that gender differences do exist in the lower grades. As mentioned in the introduction chapter, previous reports have shown mixed results regarding potential gender differences in the lower grades [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Looking further down the age span, studies have reported more consistent findings that indicate an absence of gender differences in MA [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. The results from this study suggest weak, but clear gender effects on MA, where girls are significantly more prone to feeling MA and perform worse on the arithmetic fluency test.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Limitations\u003c/h2\u003e \u003cp\u003eThis study makes significant contributions to the measurement and understanding of MA among Swedish primary school students. However, several limitations are to be considered. The translation and adaption of the MARS-E from German to Swedish and its condensation from 36 to 16 items may have implications for the instrument\u0026rsquo;s content validity and comparability to the German MARS-E. The scale used for measuring MA is a self-report questionnaire lacking defence against certain biases, such as social desirability or non-serious responses. The sample in the current study is limited to Swedish speaking primary school students, which could be a limiting factor in generalizability for other linguistic or educational contexts. Further research is required to validate these findings across linguistic and educational settings.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Conclusion\u003c/h2\u003e \u003cp\u003eThe Swedish MARS-E can be considered a valid and reliable tool for measuring math anxiety in a school-aged population. The MA construct was closely related to both test anxiety and self-concept in math. Importantly, MA also exhibited a negative association to arithmetic fluency and gender. The current study does not dissect the underlying causes for these differences, and future research has yet to address this question.\u003c/p\u003e \u003cp\u003eThe longitudinal stability of the Swedish MARS-E, as suggested by the invariance findings, provides opportunities for longitudinal studies, such as exploring the trajectory of MA over a more extended period. This could in turn reveal how early intervention strategies can alter the course of the MA development. The potential for early detection and intervention, maybe even before grade 4, could contribute to mitigating the long-term consequences of MA.\u003c/p\u003e \u003cp\u003eThe Swe MARS-E instrument not only provides researchers with a powerful tool to delve into the intricacies of math anxiety. It also simplifies the interpretation of the MA construct with its unidimensional nature, thus empowering educators, or other professions to assess MA in a very straightforward fashion. Investigating the cross-cultural application of the Swedish MARS-E would further develop our understanding of MA across different educational and cultural contexts.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cdiv class=\"Heading\"\u003e5 Statements and declaration\u003c/div\u003e \u003cp\u003e \u003cb\u003eSwedish Ethical Review Authority\u003c/b\u003e \u003c/p\u003e \u003cp\u003e All protocols associated with the current study were approved by the Swedish Ethical Review Authority. Reference number: 2020\u0026ndash;05982.\u003c/p\u003e \u003cp\u003e\u003cb\u003e Ethical guidelines/ Accordance\u003c/b\u003e\u003c/p\u003e \u003cp\u003e The present study was conducted in accordance with the Declaration of Helsinki and was fully approved by the Swedish Ethical Review Authority. Reference number: 2020\u0026ndash;05982.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSwedish Ethical Review Authority\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll protocols associated with the current study were approved by the Swedish Ethical Review Authority. Reference number: 2020-05982.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical guidelines/ Accordance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present study was conducted in accordance with the Declaration of Helsinki and was fully\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eapproved by the Swedish Ethical Review Authority. Reference number: 2020-05982.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eInformed Consent\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cbr\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from the participants legal guardians.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFunding was received from the Swedish Research Council, VR, Grant (2019\u0026ndash;03928).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConflict of interest:\u003c/em\u003e\u003c/strong\u003e The authors declare that they have no conflict of interest.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eBJ, HE and JK contributed to the study\u0026rsquo;s design. JF collected data, performed the statistical analysis and wrote the first draft of the manuscript. All authors critically revised the manuscript for important intellectual content. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data analysed for the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAshcraft, M. H. and Ridley, K. S., Math anxiety and its cognitive consequences: A tutorial review, in Handbook of mathematical cognition, J. I. D. Campbell Ed. Psychology Press, 2005, pp. 315\u0026ndash;327.\u003c/li\u003e\n\u003cli\u003eMononen, R., Niemivirta, M., Korhonen, J., Lindskog, M., and Tapola, A., Developmental relations between mathematics anxiety, symbolic numerical magnitude processing and arithmetic skills from first to second grade, Cognition and Emotion\u003cem\u003e. \u003c/em\u003e2022; vol. 36, no. 3, pp. 452-472, doi: 10.1080/02699931.2021.2015296.\u003c/li\u003e\n\u003cli\u003eAshcraft, M. H., Math anxiety: personal, educational, and cognitive consequences, Curr. Dir. Psychol. Sci.\u003cem\u003e \u003c/em\u003e2002; vol. 11, pp. 181\u0026ndash;185, doi: 10.1111/1467-8721.00196.\u003c/li\u003e\n\u003cli\u003eBeilock, S. L. and Maloney, E. A., Math anxiety: A factor in math achievement not to be ignored, Policy Insights from the Behavioral and Brain Sciences\u003cem\u003e. \u003c/em\u003e2015; vol. 2, no. 1, pp. 4-12, doi: 10.1177/2372732215601438.\u003c/li\u003e\n\u003cli\u003eHembree, R., The nature, effects, and relief of mathematics anxiety, J. Res. Math. Educ.\u003cem\u003e \u003c/em\u003e1990; vol. 21, pp. 33\u0026ndash;46, doi: 10.2307/749455.\u003c/li\u003e\n\u003cli\u003eMa, X., A meta-analysis of the relationship between anxiety toward mathematics and achievement in mathematics, J. Res. Math. Educ.\u003cem\u003e \u003c/em\u003e1999; vol. 30, pp. 520\u0026ndash;540, doi: 10.2307/749772.\u003c/li\u003e\n\u003cli\u003eNamkung, J. M., Peng, P., and Lin, X., The relation between mathematics anxiety and mathematics performance among school-aged students: a meta-analysis, Rev. Educ. Res.\u003cem\u003e \u003c/em\u003e2019; vol. 89, pp. 459\u0026ndash;496, doi: 10.3102/0034654319843494.\u003c/li\u003e\n\u003cli\u003eRamirez, G., Shaw, S. T., and Maloney, E. A., Math anxiety: Past research, promising interventions, and a new interpretation framework, Educational psychologist\u003cem\u003e. \u003c/em\u003e2018; vol. 53, no. 3, pp. 145-164, doi: 10.1080/00461520.2018.1447384.\u003c/li\u003e\n\u003cli\u003eDowker, A., Sarkar, A., and Looi, C. Y., Mathematics anxiety: What have we learned in 60 years?, Frontiers in psychology\u003cem\u003e. \u003c/em\u003e2016; vol. 7, p. 508, doi: 10.3389/fpsyg.2016.00508.\u003c/li\u003e\n\u003cli\u003eAshcraft, M. H. and Moore, A. M., Mathematics anxiety and the affective drop in performance, J. Psychoeduc. Assess.\u003cem\u003e \u003c/em\u003e2009; vol. 27, pp. 197\u0026ndash;205, doi: 10.1177/0734282908330580.\u003c/li\u003e\n\u003cli\u003eBarroso, C., Ganley, C. M., McGraw, A. L., Geer, E. A., Hart, S. A., and Daucourt, M. C., A meta-analysis of the relation between math anxiety and math achievement, Psychological Bulletin\u003cem\u003e. \u003c/em\u003e2021; vol. 147, no. 2, p. 134, doi: 10.1037/bul0000307.\u003c/li\u003e\n\u003cli\u003eCarey, E., Hill, F., Devine, A., and Sz\u0026uuml;cs, D., The chicken or the egg? The direction of the relationship between mathematics anxiety and mathematics performance, Front. Psychol.\u003cem\u003e \u003c/em\u003e2016; vol. 6, p. 1987, doi: 10.3389/fpsyg.2015.01987.\u003c/li\u003e\n\u003cli\u003eEysenck, M. W., Derakshan, N., Santos, R., and Calvo, M. G., Anxiety and cognitive performance: attentional control theory, Emotion\u003cem\u003e. \u003c/em\u003e2007; vol. 7, p. 336, doi: 10.1037/1528-3542.7.2.336.\u003c/li\u003e\n\u003cli\u003eFinell, J., Sammallahti, E., Korhonen, J., Ekl\u0026ouml;f, H., and Jonsson, B., Working Memory and its mediating role on the relationship of math anxiety and math performance: A meta-analysis, Frontiers in Psychology\u003cem\u003e. \u003c/em\u003e2022; vol. 12, p. 798090, doi: 10.3389/fpsyg.2021.798090.\u003c/li\u003e\n\u003cli\u003eCaviola, S., Toffalini, E., Giofr\u0026egrave;, D., Ruiz, J. M., Sz?cs, D., and Mammarella, I. C., Math performance and academic anxiety forms, from sociodemographic to cognitive aspects: A meta-analysis on 906,311 participants, Educational Psychology Review\u003cem\u003e. \u003c/em\u003e2022; pp. 1-37, doi: 10.1007/s10648-021-09618-5.\u003c/li\u003e\n\u003cli\u003eMa, X. and Xu, J., The causal ordering of mathematics anxiety and mathematics achievement: a longitudinal panel analysis, Journal of adolescence\u003cem\u003e. \u003c/em\u003e2004; vol. 27, pp. 165\u0026ndash;179, doi: 10.1016/j.adolescence.2003.11.003.\u003c/li\u003e\n\u003cli\u003eHenschel, S. and Roick, T., Relationships of mathematics performance, control and value beliefs with cognitive and affective math anxiety, Learning and Individual Differences\u003cem\u003e. \u003c/em\u003e2017; vol. 55, pp. 97-107, doi: 10.1016/j.lindif.2017.03.009.\u003c/li\u003e\n\u003cli\u003eZhang, J., Zhao, N., and Kong, Q. P., The relationship between math anxiety and math performance: A meta-analytic investigation, Frontiers in psychology\u003cem\u003e. \u003c/em\u003e2019; vol. 10, p. 1613, doi: 10.3389/fpsyg.2019.01613.\u003c/li\u003e\n\u003cli\u003eRobson, D. A., Johnstone, S. J., Putwain, D. W., and Howard, S., Test anxiety in primary school children: A 20-year systematic review and meta-analysis, Journal of School Psychology\u003cem\u003e. \u003c/em\u003e2023; vol. 98, pp. 39-60, doi: 10.1016/j.jsp.2023.02.003.\u003c/li\u003e\n\u003cli\u003eKazelskis, R., Reeves, C., Kersh, M. E., Bailey, G., Cole, K., and Larmon, M., Mathematics anxiety and test anxiety: separate constructs?, J. Exp. Educ.\u003cem\u003e \u003c/em\u003e2000; vol. 68, pp. 137\u0026ndash;146, doi: 10.1080/00220970009598499.\u003c/li\u003e\n\u003cli\u003eAhmed, W., Minnaert, A., Kuyper, H., and Van der Werf, G., Reciprocal relationships between math self-concept and math anxiety, Learning and individual differences\u003cem\u003e. \u003c/em\u003e2012; vol. 22, no. 3, pp. 385-389, doi: 10.1016/j.lindif.2011.12.004.\u003c/li\u003e\n\u003cli\u003eJusticia-Galiano, M. J., Mart\u0026iacute;n-Puga, M. E., Linares, R., and Pelegrina, S., Math anxiety and math performance in children: The mediating roles of working memory and math self-concept, British Journal of Educational Psychology\u003cem\u003e. \u003c/em\u003e2017; vol. 87, no. 4, pp. 573-589, doi: 10.1111/bjep.12165.\u003c/li\u003e\n\u003cli\u003eAltemus, M., Sarvaiya, N., and Epperson, C. N., Sex differences in anxiety and depression clinical perspectives, Frontiers in neuroendocrinology\u003cem\u003e. \u003c/em\u003e2014; vol. 35, no. 3, pp. 320-330, doi: 10.1016/j.yfrne.2014.05.004.\u003c/li\u003e\n\u003cli\u003eHill, F., Mammarella, I. C., Devine, A., Caviola, S., Passolunghi, M. C., and Sz?cs, D., Maths anxiety in primary and secondary school students: Gender differences, developmental changes and anxiety specificity, Learning and individual differences\u003cem\u003e. \u003c/em\u003e2016; vol. 48, pp. 45-53, doi: 10.1016/j.lindif.2016.02.006.\u003c/li\u003e\n\u003cli\u003eGierl, M. J. and Bisanz, J., Anxieties and attitudes related to mathematics in grades 3 and 6, The Journal of experimental education\u003cem\u003e. \u003c/em\u003e1995; vol. 63, no. 2, pp. 139-158, doi: 10.1080/00220973.1995.9943818.\u003c/li\u003e\n\u003cli\u003eDasgupta, N. and Stout, J. G., Girls and women in science, technology, engineering, and mathematics: STEMing the tide and broadening participation in STEM careers, Policy Insights from the Behavioral and Brain Sciences\u003cem\u003e. \u003c/em\u003e2014; vol. 1, no. 1, pp. 21-29, doi: 10.1177/23727322145494.\u003c/li\u003e\n\u003cli\u003eRichardson, F. C. and Suinn, R. M., The mathematics anxiety rating scale: psychometric data, Journal of counseling Psychology\u003cem\u003e. \u003c/em\u003e1972; vol. 19, no. 6, p. 551, doi: 10.1037/h0033456.\u003c/li\u003e\n\u003cli\u003ePlake, B. S. and Parker, C. S., The development and validation of a revised version of the Mathematics Anxiety Rating Scale, Educational and psychological measurement\u003cem\u003e. \u003c/em\u003e1982; vol. 42, no. 2, pp. 551-557, doi: 10.1177/0013164482042002.\u003c/li\u003e\n\u003cli\u003eSuinn, R. M., Taylor, S., and Edwards, R. W., Suinn mathematics anxiety rating scale for elementary school students (MARS-E): Psychometric and normative data, Educational and Psychological Measurement\u003cem\u003e. \u003c/em\u003e1988; vol. 48, no. 4, pp. 979-986.\u003c/li\u003e\n\u003cli\u003eValentine, J. C., DuBois, D. L., and Cooper, H., The relation between self-beliefs and academic achievement: A meta-analytic review, Educational psychologist\u003cem\u003e. \u003c/em\u003e2004; vol. 39, no. 2, pp. 111-133, doi: 10.1207/s15326985ep3902_3.\u003c/li\u003e\n\u003cli\u003eShavelson, R. J., Hubner, J. J., and Stanton, G. C., Self-concept: Validation of construct interpretations, Review of educational research\u003cem\u003e. \u003c/em\u003e1976; vol. 46, no. 3, pp. 407-441.\u003c/li\u003e\n\u003cli\u003eMarsh, H. W., Relich, J. D., and Smith, I. D., Self-concept: The construct validity of interpretations based upon the SDQ, Journal of Personality and social psychology\u003cem\u003e. \u003c/em\u003e1983; vol. 45, no. 1, p. 173, doi: 10.1037/0022-3514.45.1.173.\u003c/li\u003e\n\u003cli\u003eMarsh, H. W., Self description questionnaire-I, Cultural Diversity and Ethnic Minority Psychology\u003cem\u003e. \u003c/em\u003e1990, doi: 10.1037/t01843-000.\u003c/li\u003e\n\u003cli\u003eR\u0026auml;s\u0026auml;nen, P., Aunio, P., Laine, A., Hakkarainen, A., V\u0026auml;is\u0026auml;nen, E., and Korhonen, J., Effects of gender on basic numerical and arithmetic skills: Pilot data from third to ninth grade for a large-Scale online dyscalculia screener, Frontiers in education\u003cem\u003e. \u003c/em\u003e2021; vol. 6, p. 683672, doi: 10.3389/feduc.2021.683672.\u003c/li\u003e\n\u003cli\u003eTeam, R. C., R: A Language and Environment for Statistical Computing. [Online]. Available: https://www.R-project.org/\u003c/li\u003e\n\u003cli\u003eRevelle, W., psych: Procedures for psychological, psychometric, and personality research. [Online]. Available: https://CRAN.R-project.org/package=psych\u003c/li\u003e\n\u003cli\u003eTierney, N. and Cook, D., Expanding Tidy Data Principles to Facilitate Missing Data Exploration, Visualization and Assessment of Imputations, Journal of Statistical Software\u003cem\u003e. \u003c/em\u003e2023; vol. 105, no. 7, pp. 1\u0026ndash;31, doi: 10.18637/jss.v105.i07.\u003c/li\u003e\n\u003cli\u003eKline, R. B., Principles and practice of structural equation modeling, 4th ed. New York: Guilford publications; 2016.\u003c/li\u003e\n\u003cli\u003eLittle, R. J., A test of missing completely at random for multivariate data with missing values, Journal of the American statistical Association\u003cem\u003e. \u003c/em\u003e1988; vol. 83, no. 404, pp. 1198-1202. [Online]. Available: https://www.jstor.org/stable/pdf/2290157.\u003c/li\u003e\n\u003cli\u003eHeymans, M. W. and Eekhout, I., Applied missing data analysis with SPSS and (R)studio, 2019. [Online]. Available: https://bookdown.org/mwheymans/bookmi/.\u003c/li\u003e\n\u003cli\u003eHu, L. T. and Bentler, P. M., Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives, Structural equation modeling: a multidisciplinary journal\u003cem\u003e. \u003c/em\u003e1999; vol. 6, no. 1, pp. 1-55, doi: 10.1080/10705519909540118.\u003c/li\u003e\n\u003cli\u003eLiu, Y., Millsap, R. E., West, S. G., Tein, J. Y., Tanaka, R., and Grimm, K. J., Testing measurement invariance in longitudinal data with ordered-categorical measures, Psychological methods\u003cem\u003e. \u003c/em\u003e2017; vol. 22, no. 3, p. 486, doi: 10.1037/met0000075.\u003c/li\u003e\n\u003cli\u003eChen, F. F., Sensitivity of goodness of fit indexes to lack of measurement invariance, Structural equation modeling: a multidisciplinary journal\u003cem\u003e. \u003c/em\u003e2007; vol. 14, no. 3, pp. 464-504, doi: 10.1080/10705510701301834.\u003c/li\u003e\n\u003cli\u003eHarari, R. R., Vukovic, R. K., and Bailey, S. P., Mathematics anxiety in young children: An exploratory study, The Journal of experimental education\u003cem\u003e. \u003c/em\u003e2013; vol. 81, no. 4, pp. 538-555, doi: 10.1080/00220973.2012.727888.\u003c/li\u003e\n\u003cli\u003edel-R\u0026iacute;o, M. F., Susperreguy, M. I., Morales, M. F., Peake, C., and Angulo, M., Kindergarten children\u0026rsquo;s math anxiety and its relationship with mathematical performance (Ansiedad matem\u0026aacute;tica en ni\u0026ntilde;os y ni\u0026ntilde;as de k\u0026iacute;nder y su relaci\u0026oacute;n con el rendimiento matem\u0026aacute;tico), Studies in Psychology\u003cem\u003e. \u003c/em\u003e2023; vol. 44, no. 2-3, pp. 542-561, doi: 10.1080/02109395.2023.2254158.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"diedu","sideBox":"Learn more about [Discover Education](https://www.springer.com/journal/44217)","snPcode":"44217","submissionUrl":"https://submission.nature.com/new-submission/44217/3","title":"Discover Education","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4360120/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4360120/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe current study assessed reliability and validity evidence of the shortened Swedish Mathematics Anxiety Rating Scale \u0026ndash; Elementary (MARS-E), using data from three time points. After initial pilot tests, a total of 429 students participated in the study, completing the MARS-E twice during grade 4 and once during grade 5. Confirmatory factor analyses supported a one-factor structure at each timepoint. The scale displayed both longitudinal and gender measurement invariance across timepoints, ensuring both stability and fairness across gender and time. Factor correlations with criterion variables were examined, revealing a strong correlation between math anxiety (MA) and test anxiety (TA) (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.707). However, the two constructs had distinct relationships to other criterion variables, such as math performance (\u003cem\u003er\u003c/em\u003e\u003csub\u003e\u003cem\u003ema\u003c/em\u003e\u003c/sub\u003e = -0.343, \u003cem\u003er\u003c/em\u003e\u003csub\u003e\u003cem\u003eta\u003c/em\u003e\u003c/sub\u003e = -0.110) and self-concept in math (\u003cem\u003er\u003c/em\u003e\u003csub\u003e\u003cem\u003ema\u003c/em\u003e\u003c/sub\u003e = -0.580, \u003cem\u003er\u003c/em\u003e\u003csub\u003e\u003cem\u003eta\u003c/em\u003e\u003c/sub\u003e = -0.273). Gender correlations provided evidence of girls being more strongly associated with higher math anxiety scores compared to boys.\u003c/p\u003e \u003cp\u003eThe current paper provides evidence of the Swedish MARS-E as a valid, easily interpreted, unidimensional instrument for measuring math anxiety in Swedish primary school students. Additionally, the study highlights the gender disparities concerning math anxiety in a longitudinal study conducted in primary schools.\u003c/p\u003e","manuscriptTitle":"Reliability and validity evidence of the Swedish shortened mathematics anxiety rating scale elementary (MARS-E)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-27 16:50:18","doi":"10.21203/rs.3.rs-4360120/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-08-23T09:52:13+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-20T22:48:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"282156166092289499166408812870068694663","date":"2024-08-14T00:44:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"318140701929225220139488297470103721079","date":"2024-08-13T00:45:19+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-29T17:54:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"52732282516480552499099176610508780371","date":"2024-05-24T15:14:40+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-16T12:59:30+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-16T12:53:18+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-16T12:52:46+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Education","date":"2024-05-02T15:53:24+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"diedu","sideBox":"Learn more about [Discover Education](https://www.springer.com/journal/44217)","snPcode":"44217","submissionUrl":"https://submission.nature.com/new-submission/44217/3","title":"Discover Education","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1f5ee0b1-28f0-46b4-aea0-0e45b8ce6583","owner":[],"postedDate":"May 27th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-11-11T07:24:00+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-27 16:50:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4360120","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4360120","identity":"rs-4360120","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}