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Academic self-concept in science: Multidimensionality, relations to achievement measures, and gender differences☆

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TLDR
In this article, the authors analyzed data from self-concept measures, grades and standardized achievement tests of 6036 German 10th graders across three science subjects (biology, chemistry, and physics) using structural equation modeling.
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This article is published in Learning and Individual Differences.The article was published on 2014-02-01. It has received 108 citations till now. The article focuses on the topics: Academic achievement & Achievement test.

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Students' self-concept and self-efficacy in the sciences: Differential relations to antecedents and educational outcomes.

TL;DR: For instance, this article found that science self-concept was better predicted by the average peer achievement (Big-Fish-Little-Pond Effect), whereas science selfefficacy was more strongly affected by inquiry-based learning opportunities.
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Assessing task values in five subjects during secondary school: Measurement structure and mean level differences across grade level, gender, and academic subject.

TL;DR: For instance, this paper evaluated an instrument for assessing multiple value dimensions across grade level and academic subjects and tested for differences between grade levels in these subjects, finding that students in higher grades showed lower means on positive value facets and higher means on cost facets.
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Extending expectancy-value theory predictions of achievement and aspirations in science: dimensional comparison processes and expectancy-by-value interactions

TL;DR: This paper integrated dimensional comparison theory and expectancy-value theory and tested predictions about how self-concept and value are related to achievement and coursework aspirations across four science domains (physics, chemistry, earth science, and biology).
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Evidence for a positive relation between interest and achievement: Examining between-person and within-person variation in five domains

TL;DR: In this paper, the authors examined the incremental effect of academic interest on achievement beyond general cognitive ability and students' background characteristics in five domains (math, German, biology, chemistry, and physics) and found a unique effect of interest over and above the other predictors across the five domains.
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Contrast and assimilation effects of dimensional comparisons in five subjects : An extension of the I/E Model

TL;DR: In this article, the authors extended the original I/E model with three science domains (biology, chemistry, and physics) using structural equation modeling, and analyzed the domain-specific self-concepts, grades, and test scores of a representative sample of 9th-grade students in Germany (N = 20,050) across 5 domains.
References
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Book

Statistical Power Analysis for the Behavioral Sciences

TL;DR: The concepts of power analysis are discussed in this paper, where Chi-square Tests for Goodness of Fit and Contingency Tables, t-Test for Means, and Sign Test are used.
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Cutoff criteria for fit indexes in covariance structure analysis : Conventional criteria versus new alternatives

TL;DR: In this article, the adequacy of the conventional cutoff criteria and several new alternatives for various fit indexes used to evaluate model fit in practice were examined, and the results suggest that, for the ML method, a cutoff value close to.95 for TLI, BL89, CFI, RNI, and G...
Book

Principles and Practice of Structural Equation Modeling

TL;DR: The book aims to provide the skills necessary to begin to use SEM in research and to interpret and critique the use of method by others.
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Evaluating Goodness-of-Fit Indexes for Testing Measurement Invariance

TL;DR: In this paper, the authors examined the change in the goodness-of-fit index (GFI) when cross-group constraints are imposed on a measurement model and found that the change was independent of both model complexity and sample size.
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Missing data: Our view of the state of the art.

TL;DR: 2 general approaches that come highly recommended: maximum likelihood (ML) and Bayesian multiple imputation (MI) are presented and may eventually extend the ML and MI methods that currently represent the state of the art.
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