Development of sex differences in math achievement, self-concept, and interest from grade 5 to 7

Sewasew, Daniel; Schroeders, Ulrich; Schiefer, Irene M.; Weirich, Sebastian; Artelt, Cordula

doi:10.1016/j.cedpsych.2018.05.003

Cited by 32 publications

(30 citation statements)

References 77 publications

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“…These three models have typically been investigated using statistical models from the family of cross-lagged panel models (CLPMs; e.g., Usami et al, 2019) claiming to investigate "causal relations between academic achievement and academic self-concept" (Marsh & Craven, 2006, p. 151). Thus, specific patterns of results of the cross-lagged parameters were often interpreted causally and as evidence in favor of one of the three models outlined above (e.g., Helmke & van Aken, 1995;Marsh et al, 2005;Marsh & Martin, 2011;Pinxten et al, 2010;Sewasew et al, 2018). However, the assumptions under which statistical relations in CLPMs can be interpreted causally have been seldom made explicit.…”

Section: Educational Impact and Implications Statementmentioning

confidence: 99%

“…Based on prior research on reciprocal relations between self-concept and achievement, particularly three types of structural models have been discussed (e.g., Huang, 2011;Marsh et al, 2005;Preckel et al, 2017;Seaton et al, 2015;Sewasew et al, 2018;Wu et al, 2021), depicted in Figure 1: a model with prior scores predicting post scores (a); a model with additional lag-2 effects (e.g., with additional paths from the first to the third measurement occasion), also referred to as full-forward model (b); and a model with lag-2 effects and additional confounders (c).…”

Section: Causal Model Perspectivementioning

confidence: 99%

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How causal is a reciprocal effect? Contrasting traditional and new methods to investigate the reciprocal effects model of self-concept and achievement

Hübner¹,

Wagner²,

Zitzmann³

et al. 2022

Preprint

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The relationship between students’ subject-specific academic self-concept and their academic achievement is one of the most widely researched topics in educational psychology. A large body of this research has considered cross-lagged panel models (CLPMs), oftentimes synonymously referred to as reciprocal effects models (REMs), as a gold standard to investigate the causal relations between the two variables and has reported evidence for a reciprocal relationship between self-concept and achievement. However, more recent methodological research questioned the plausibility of assumptions that need to be satisfied in order to interpret results from traditional CLPMs causally. In this substantive-methodological synergy, we aimed at contrasting traditional and more recently developed methods to investigate reciprocal effects of students’ academic self-concept and achievement. Specifically, we compared results from CLPMs, from full forward CLPMs (FF-CLPMs), and from random intercept CLPMs (RI-CLPMs) with two weighting approaches developed to study causal effects of continuous treatment variables. To estimate these different models, we used rich longitudinal data of N = 3,757 students from lower secondary schools in Germany. Results from CLPMs, FF-CLPMs, and weighting methods support the reciprocal effects model, particularly when considering math self-concept and grades. In contrast, results from the RI-CLPMs were less consistent. Implications from our study for the interpretation of effects from the different models and methods as well as for school motivation theory are discussed.

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Section: Educational Impact and Implications Statementmentioning

confidence: 99%

Section: Causal Model Perspectivementioning

confidence: 99%

How causal is a reciprocal effect? Contrasting traditional and new methods to investigate the reciprocal effects model of self-concept and achievement

Hübner¹,

Wagner²,

Zitzmann³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…To our knowledge, there are only a few simulation studies that investigate the performance of Haberman linking [97,102,103]. In contrast to the IA method, HL has only been scarcely applied [104][105][106][107][108][109][110][111]. The linking of multiple groups using other linking functions has been treated in [102,[112][113][114].…”

Section: Estimation and Applicationsmentioning

confidence: 99%

Lp Loss Functions in Invariance Alignment and Haberman Linking with Few or Many Groups

Robitzsch

2020

Stats

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The comparison of group means in latent variable models plays a vital role in empirical research in the social sciences. The present article discusses an extension of invariance alignment and Haberman linking by choosing the robust power loss function ρ(x)=|x|p(p>0). This power loss function with power values p smaller than one is particularly suited for item responses that are generated under partial invariance. For a general class of linking functions, asymptotic normality of estimates is shown. Moreover, the theory of M-estimation is applied for obtaining linking errors (i.e., inference with respect to a population of items) for this class of linking functions. In a simulation study, it is shown that invariance alignment and Haberman linking have comparable performance, and in some conditions, the newly proposed robust Haberman linking outperforms invariance alignment. In three examples, the influence of the choice of a particular linking function on the estimation of group means is demonstrated. It is concluded that the choice of the loss function in linking is related to structural assumptions about the pattern of noninvariance in item parameters.

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“…The linking of multiple groups using other linking functions has been treated in [84,[87][88][89]. In contrast to the IA method, HL has only been scarcely applied [90][91][92][93][94][95][96][97][98].…”

Section: Haberman Linkingmentioning

confidence: 99%

$L_p$ Loss Functions in Invariance Alignment and Haberman Linking

Robitzsch¹

2020

Preprint

View full text Add to dashboard Cite

The comparison of group means in latent variable models plays a vital role in empirical research in the social sciences. The present article discusses extensions of invariance alignment and Haberman linking concerning the choice of linking functions for comparisons of many groups. Robust linking functions are proposed for invariance alignment and robust Haberman linking that are particularly suited to item response data under partial invariance. In a simulation study, it is shown that both linking approaches have comparable performance, and in some conditions, the newly proposed robust Haberman linking outperforms invariance alignment.

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Development of sex differences in math achievement, self-concept, and interest from grade 5 to 7

Cited by 32 publications

References 77 publications

How causal is a reciprocal effect? Contrasting traditional and new methods to investigate the reciprocal effects model of self-concept and achievement

How causal is a reciprocal effect? Contrasting traditional and new methods to investigate the reciprocal effects model of self-concept and achievement

Lp Loss Functions in Invariance Alignment and Haberman Linking with Few or Many Groups

$L_p$ Loss Functions in Invariance Alignment and Haberman Linking

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