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Fig. 3 Latent class analysis of the Czech teacher sample.
Fig. 2 Latent class analysis of the German teacher sample.
Third, the duration (e.g., less than 2 h vs. one semester) and teacher sample sizes (e.g., 14 vs. 89) varied greatly across studies.
An analysis of the German teacher sample, for instance, showed no bias with regard to teachers' gender and their school subjects (Eickelmann et al. 2014a).
We removed these teachers from our sample, ending up with 339 teachers in our active teacher sample who participated in random assignment.
Our results indicate that our teacher sample began our intervention with average scores similar to other teachers from the Midwestern United States.
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Each investigation was coded on several variables describing the child, parent, and teacher samples, as well as reported outcome results.
Since the descriptive analyses and ANOVAs were based on teacher data, the mathematics teacher sampling weight (MATWGT) was applied to generate accurate estimates in each country.
Studies on variables associated with burnout among teacher samples are scarce, particularly in France.
Lastly, individual resources and personality factors have been studied in teacher samples to explain why individuals in the same work environment and having the same educational and experience backgrounds often respond differently in terms of burnout.
Furthermore, differences in the probabilities of being sampled as a teacher were accounted for by using teachers' sampling weights (Mplus option 'WEIGHT = TEAsparouhovparouhov 2005).
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