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      The Structure of Academic Self-Concept When Facing Novel Learning Content: Multidimensionality, Hierarchy, and Change


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          Academic self-concepts of ability are key factors in promoting education and learning throughout students’ school career. Yet we know little about their structure and structural change when students leave high school to face novel academic tasks. The present study investigated the structure and structural change of first-year students’ study-related self-concepts of ability. Data stems from a longitudinal study with two measurement points covering the initial study phase (t1: N = 341; age: M = 21.6; SD = 3.56; 57.5% female). Self-concepts were assessed regarding the participants’ study program and four of its subordinate subjects. Confirmatory factor analyses and structural equation models were used to compare structural models and to investigate structural stability and directional effects. Results support the assumption of multidimensionality (i.e., distinct self-concepts for different subjects) and hierarchy with a generic field-of-study-specific self-concept at the apex. Specifying generic field-of-study-specific self-concept as a method factor (i.e., indicated by both subject-specific and field-of-study-specific items) was most consistent with theoretical assumptions. The structural model was invariant over the first months at university. Generic field-of-study-specific self-concept and subject-specific self-concepts largely developed independently from one another. The results emphasize the recently suggested conceptualization of generic self-concept as a method factor to reflect self-concept hierarchy. Self-concepts were structurally stable over time. Several significant horizontal effects (i.e., stability within subjects) suggest that students align their self-concepts closely to the curriculum they encounter in educational contexts and, therefore, may benefit from ample feedback on their performance to develop appropriate subject-specific self-concepts.

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          Evaluating Goodness-of-Fit Indexes for Testing Measurement Invariance

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            Sensitivity of Goodness of Fit Indexes to Lack of Measurement Invariance

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              Missing data: our view of the state of the art.

              Statistical procedures for missing data have vastly improved, yet misconception and unsound practice still abound. The authors frame the missing-data problem, review methods, offer advice, and raise issues that remain unresolved. They clear up common misunderstandings regarding the missing at random (MAR) concept. They summarize the evidence against older procedures and, with few exceptions, discourage their use. They present, in both technical and practical language, 2 general approaches that come highly recommended: maximum likelihood (ML) and Bayesian multiple imputation (MI). Newer developments are discussed, including some for dealing with missing data that are not MAR. Although not yet in the mainstream, these procedures may eventually extend the ML and MI methods that currently represent the state of the art.

                Author and article information

                Eur J Psychol
                Eur J Psychol
                Europe's Journal of Psychology
                September 2019
                27 September 2019
                : 15
                : 3
                : 491-508
                [a ]Department of Psychology, Educational Psychology, Bielefeld University, Bielefeld, Germany
                [2] University of Wroclaw, Poland
                Author notes
                [* ]Department of Psychology, Educational Psychology, Bielefeld University, PO 100131, 33501 Bielefeld, Germany. julia.gorges@ 123456uni-bielefeld.de

                This is an open-access article distributed under the terms of the Creative Commons Attribution (CC BY) 4.0 License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                : 23 July 2018
                : 18 January 2019
                Research Reports

                academic self-concept,self-concept structure,higher education,motivation,confirmatory factor analysis


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