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People with highly variable jobs such as emergency room nurses may appreciate "have a routine day", while mothers of young children may wish to "have a quiet day".
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These variables explained > 1/3 of the variance of the dependent variable job satisfaction.
Occupational activity and socio-economic status are based on the same variable (job title).
Separate linear regression analyses were conducted with one independent variable (a job factor, organisational factor or respondent characteristic) and one dependent variable (job satisfaction or occupational commitment).
A variable job strain was calculated by dividing job demands plus 1 by job control plus 1 to avoid the problem of dividing by zero.
We used logistic regression to measure the key predictors of job satisfaction because the dependent variable (job satisfaction) was a binary variable, which made linear regression unsuitable.
Significant information for explaining the dependent variable, job satisfaction, was given by the independent variables, professional experience, workload, and social capital.
Risk factors included in the models were age (continuous variable), job classification (Reference farmer) and national region (Reference Ile de France).
Following, for example, for the variable "job satisfaction" we used six questions out of the QUALICOPC questionnaire, and did not employ any further validated questionnaires.
We defined the exposure as a binary variable: job strain (high demands and low control) versus no job strain (all other combinations) according to the job strain model (25).
Separate logistic regression analyses were performed with one independent variable (job satisfaction, occupational commitment, one of the job factors, one of the organisational factors or one of the respondent characteristics).
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