Assessing the impact of learning environments: How to use student ratings of classroom or school characteristics in multilevel modeling

By O. Lüdtke, Alexander Robitzsch, Ulrich Trautwein, M. Kunter. 2009

496 citations recorded by the source corpus.

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Citations in the corpus

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): “Moreover, our results indicate that simultaneous analyses at the student and the class level represent an interesting avenue for future research (Lüdtke et al., 2009).”
Coercive and Supportive Teacher Behaviour: Within- and across-Lesson Associations with the Classroom Social Climate. (2011, unknown): “Responses were given on a 3-point Likert-type scale running from 1 (not at all) to 3 (often), and items were 2 For details on the calculation of ICC1 and ICC2 see Lüdtke et al. (2009) or Snijders and Bosker (1999). combined into composite scores per measurement occasion and classroom.”
Students emotions during homework in mathematics: Testing a theoretical model of antecedents and ac (2011, methodology): “Centering student level predictor variables Individual and aggregated ratings of the learning environment (e.g., quality of task selection or homework challenge) were used simultaneously as predictors in the multilevel models (Lüdtke et al., 2009).”
Interpersonal adaptation in teacher-student interaction (2017, methodology): “…0.89, for Communion 0.95, with r ¼ 0.13 as the correlation between Agency and Communion; consensus between students of a class (ICC(k ¼ class size), Lüdtke, Robitzsch, Trautwein, & Kunter, 2009) varied in the 35 classrooms for Agency from 0.95 to 0.98 ( M ¼ 0.97, SD ¼ 0.01), for Communion from…”
Doubly Latent Multilevel Analyses of Classroom Climate: An Illustration (2014, result): “As noted by previous researchers (Lüdtke et al., 2011; Lüdtke et al., 2009; Marsh et al., 2012; Marsh et al., 2009), the recent development of doubly latent multilevel models (ML-SEM) allow for the use of multiple indicators to control measurement error at the individual-student and classroom…”
Effects of Inquiry-Based Science Instruction on Science Achievement and Interest in Science: Evidence from Qatar (2012, methodology): “Although students’ perceptions and ratings are the most appropriate sources of data for assessing the learning environment (Lüdtke et al., 2006, 2009), the reliability of students as data sources has repeatedly been questioned because of the idiosyncratic nature of students’ perceptions of their…”
Student ratings of teaching quality in primary school: Dimensions and prediction of student outcomes (2014, methodology): “5 Data Analyses Multilevel analyses have been successfully applied to student ratings of classroom environments (Lüdtke et al., 2009).”