Database for the modeling of job satisfaction of basic education teachers in Peru (ENDO-2018)

Published: 30 January 2023| Version 2 | DOI: 10.17632/b7wbthz6hs.2


This data set is the result of the research entitled: Modeling job satisfaction of basic education teachers through machine learning techniques. It was obtained from the characteristic selection process using the filter methods: Chi-Square categorical variables and ANOVA F-Test for numerical variables, this filtering was carried out in order to obtain the most important variables in the prediction of job satisfaction. of basic education teachers in Peru. The raw database was obtained from the National Survey of Teachers (url: developed by the Ministry of Education of Peru. during the year 2018. This filtered database can be used to predict the job satisfaction of basic education teachers. Description of the columns. Columns P401_12_No and P401_12_Yes, represent whether the teacher suffered depression problems, P501_A (total gross income for the previous month (scaled value)), P509 (Perception of living conditions), P818_1 (Satisfaction with his life), P818_6 (Satisfaction with their self-esteem), P818_8 (Satisfaction with their family relationships), P819_1 (Satisfaction with their pedagogical activity), P819_4 (Satisfaction with the relationship with their colleagues), P819_5 (Satisfaction with the relationship with the Director), P819_8 ( Satisfaction with their salary), JobSatisfaction (Teacher job satisfaction)


Steps to reproduce

The data set was collected through the National Survey of Teachers (ENDO), this database is public and is stored in the data repository of the Ministry of Education of Peru (MINEDU). ENDO en collects information related to teachers in Peru, in order to know their sociodemographic and socioeconomic characteristics, their training and professional career, their perceptions of working conditions that affect their well-being, and regarding the policies and programs promoted by MINEDU that impact its work, as well as its values and expectations for the future. The technical sheet ( explains the details and protocols for collecting information.


Universidad Nacional Amazonica de Madre de Dios


Artificial Intelligence, Machine Learning, Feature Selection, Employee Job Satisfaction