Classroom Climate and Contextual Effects: Conceptual and Methodological Issues in the Evaluation of Group-Level Effects

By Herbert W. Marsh, O. Lüdtke, Benjamin Nagengast, Ulrich Trautwein, Alexandre J. S. Morin, Adel S. Abduljabbar, Olaf Köller. 2012

596 citations recorded by the source corpus.

Subfields: Education, Social Psychology

View Paper: https://www.semanticscholar.org/paper/ecc2918ffd1ab0f13c230661c768cd8007cdd68f

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In Social Psychology

Citations in the corpus

Classroom climate and children’s academic and psychological wellbeing: A systematic review and meta-analysis (2020, methodology): “, cross-sectional versus longitudinal) used to assess classroom characteristics (Marsh et al., 2012; Wang & Degol, 2016).”
Multilevel and Single-Level Models for Measured and Latent Variables When Data Are Clustered (2016, background): “The decision hinges on the research questions to be addressed, the scope of the data, and the measurement structure of any constructs hypothesized at the cluster level (Kozolowski & Klein, 2000; Marsh et al., 2012).”
How All Students Can Belong and Achieve: Effects of the Cultural Diversity Climate Amongst Students of Immigrant and Nonimmigrant Background in Germany (2019, methodology): “In this study, multilevel analyses allowed us to simultaneously investigate effects of shared perceptions of the classroom climate as well as effects of individual deviations from these shared perceptions (Marsh et al., 2012).”
The reciprocal relations between self-concept, motivation and achievement: juxtaposing academic self-concept and achievement goal orientations for mathematics success (2014, background): “Future research should consider measuring the class and/or school motivational climate to test this contention (e.g. Marsh et al., 2012; Papaioannou, Marsh, & Theodorakis, 2004).”
Girls' and boys' perceived mathematics teacher beliefs, classroom learning environments and mathematical career intentions (2015, background): “Classroom climate (Marsh et al., 2012) factors of classroom-aggregate perceived teacher beliefs and learning environments were additionally expected to predict students’ motivations and mathematical career intentions.”
A Bayesian Approach to Multilevel Structural Equation Modeling With Continuous and Dichotomous Outcomes (2015, methodology): “…have combined features of MLM and SEM into a flexible framework that extends conventional latent variable modeling to accommodate hierarchical data, called multilevel SEM (Gottfredson, Panter, Daye, Allen, & Wightman, 2009; Johnson, Burlingame, Olsen, Davies, & Gleave, 2005; Marsh et al., 2012).”
Antecedents of Norwegian beginning teachers’ turnover intentions (2015, background): “Such climate variables provide considerable challenges for analysis because they should really be analyzed from a multilevel perspective as aggregated school-level variables (Marsh et al., 2012).”
Social Support and Classroom Management Are Related to Secondary Students’ General School Adjustment: A Multilevel Structural Equation Model Using Student and Teacher Ratings (2018, methodology): “Coefficients were standardized by the total standard deviation (i.e., continuous predictors: B SD[x]/SD[y]; dichotomous predictors: B/SD[y]; see also Marsh et al., 2012). p .05. p .01. p .001.”
Student ratings of teaching quality in primary school: Dimensions and prediction of student outcomes (2014, methodology): “From a methodological point of view, these findings further emphasise the need to distinguish between different levels (within and between classes) when student surveys are analysed (Marsh et al., 2012).”
Perceived mastery climate, felt trust, and knowledge sharing (2018, background): “Accordingly, mastery climate and felt trust may operate at two levels of analysis (Lau et al., 2014; Marsh et al., 2012; Salamon & Robinson, 2008).”