Data on working environment for teachers and STR in Japanese schools were obtained from TALIS 2018 dataset . TALIS is an international, large-scale survey that asks teachers and school leaders about working conditions and learning environments at their schools. In order to obtain nationally representative sample of teachers for each ISCED (International Standard Classification of Education) level in each participating country and economy, a stratified two-stage probability sampling design was used . As a result of this sampling, in Japan, 197 primary schools and 196 lower-secondary schools were sampled, and teachers working in the sampled schools participated in the survey. The number of teachers participated in the survey was 3308 for primary schools and 3555 for lower secondary schools. By excluding the teachers who were missing key variables used in the statistical analyses (see below), the study sample included 2761 and 3006 teachers for primary and lower-secondary school, respectively.
STR was measured at the school level and obtained from the TALIS dataset file (variable named as “stratio” in the dataset). The ratio was derived by dividing the total number of students enrolled by the number of employed teachers in a given school .
Total working hours of teachers were obtained from the answers to the following question in teacher questionnaire: “During your most recent complete calendar week, approximately how many 60-minute hours did you spend in total on tasks related to your job at this school?”
Hours spent on individual tasks were obtained from the answers to the following question: “Approximately how many 60-minute hours did you spend on the following tasks during your most recent complete calendar week, in your job at this school?” Tasks are categorized into the following 10 types: a) Individual planning or preparation of lessons either at school or out of school; b) Team work and dialogue with colleagues within this school; c) Marking/correcting of student work; d) Counselling students (including student supervision, mentoring, virtual counselling, career guidance and behaviour guidance); e) Participation in school management; f) General administrative work (including communication, paperwork and other clerical duties); g) Professional development activities; h) Communication and co-operation with parents or guardians; i) Engaging in extracurricular activities (e.g. sports and cultural activities after school); j) Other work tasks .
Workload stress and related variables
For the variables of workload stress, workplace well-being and stress, and job satisfaction, we utilized the relevant scale scores that were pre-derived and stored in the dataset (variable named as “t3wload”, “t3wels”, and “t3jsenv” in the dataset, respectively). According to the TALIS 2018 Technical Report , these variables were derived using latent modelling within the framework of confirmatory factor analysis based on the responses to the questions presented in Table 1. Note that for the variable of workplace well-being and stress, larger values indicate poorer workplace well-being and higher workplace stress.
Our data about teachers were hierarchically nested within schools. Therefore, the appropriate analysis method for these data is multilevel-analysis. Because our interest in this study was in the average effects of STR and not in the heterogeneity of its effects across schools, we adopted a random intercept model as our method of analysis . For the sampling weights, only level two weights (final school weights) were used . Stata version 16.1 was used for estimation. The dependent variables were work hours, workload stress, workplace well-being, and job satisfaction. The key explanatory variable was STR. The other control variables were dummy variables for gender (Male: 0, Female: 1), employment status (Fixed-term: 0, Permanent: 1), years of teaching experience, and a dummy variable for school type (Public: 0, Private: 1).