Determinants in modelling early school dropout
Recognizing the importance of continuity in education, it was deemed necessary to carry out a study whose main objective is to identify the factors that lead to the intention to drop out in order to formulate effective strategies to combat this phenomenon. Early school dropout has negative consequ...
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| Language: | English |
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Editura ASE Bucuresti
2024-12-01
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| Series: | Cactus |
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| Online Access: | https://cactus-journal-of-tourism.ase.ro/wp-content/uploads/2024/11/II.1-Cibu-et-al.pdf |
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| author | Bianca-Raluca Cibu Camelia Delcea Adrian Domenteanu |
| author_facet | Bianca-Raluca Cibu Camelia Delcea Adrian Domenteanu |
| author_sort | Bianca-Raluca Cibu |
| collection | DOAJ |
| description | Recognizing the importance of continuity in education, it was deemed necessary to carry out a study whose main
objective is to identify the factors that lead to the intention to drop out in order to formulate effective strategies
to combat this phenomenon. Early school dropout has negative consequences both for individual development
and for social and economic progress. Therefore, this paper aims to contribute to the understanding and
prevention of this phenomenon through a rigorous analysis of its determinants. In this context, eight relevant
dependent variables have been identified in the literature that are believed to play a significant role in the
intention to drop out of school. These variables include factors such as school absenteeism, alcohol or substance
abuse, attitude, awareness, family, family supervision, school environment and school rules. The analysis used in
the study examines these significant variables through structural equation modeling (SEM). SmartPLS software
was used to conduct this analysis, which allows the use of Partial Least Squares SEM (PLS-SEM) and
Bootstrapping modeling techniques. The data used for this research was collected using a well-structured
questionnaire consisting of 28 questions aimed at capturing students' perceptions and experiences of school and
the factors that might contribute to their intention to drop out of school. A total of 669 respondents completed the
questionnaire, providing a solid database for analysis.
|
| format | Article |
| id | doaj-art-bfea5d0701b144a0acea887e1f1049c5 |
| institution | Kabale University |
| issn | 2247-3297 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Editura ASE Bucuresti |
| record_format | Article |
| series | Cactus |
| spelling | doaj-art-bfea5d0701b144a0acea887e1f1049c52025-02-04T13:48:56ZengEditura ASE BucurestiCactus2247-32972024-12-016262110.24818/CTS/6/2024/2.02Determinants in modelling early school dropoutBianca-Raluca Cibu0https://orcid.org/0009-0003-8597-7514Camelia Delcea1https://orcid.org/0000-0003-3589-1969Adrian Domenteanu2https://orcid.org/0009-0001-9615-1174Bucharest University of Economic StudiesBucharest University of Economic StudiesBucharest University of Economic StudiesRecognizing the importance of continuity in education, it was deemed necessary to carry out a study whose main objective is to identify the factors that lead to the intention to drop out in order to formulate effective strategies to combat this phenomenon. Early school dropout has negative consequences both for individual development and for social and economic progress. Therefore, this paper aims to contribute to the understanding and prevention of this phenomenon through a rigorous analysis of its determinants. In this context, eight relevant dependent variables have been identified in the literature that are believed to play a significant role in the intention to drop out of school. These variables include factors such as school absenteeism, alcohol or substance abuse, attitude, awareness, family, family supervision, school environment and school rules. The analysis used in the study examines these significant variables through structural equation modeling (SEM). SmartPLS software was used to conduct this analysis, which allows the use of Partial Least Squares SEM (PLS-SEM) and Bootstrapping modeling techniques. The data used for this research was collected using a well-structured questionnaire consisting of 28 questions aimed at capturing students' perceptions and experiences of school and the factors that might contribute to their intention to drop out of school. A total of 669 respondents completed the questionnaire, providing a solid database for analysis. https://cactus-journal-of-tourism.ase.ro/wp-content/uploads/2024/11/II.1-Cibu-et-al.pdfpls-sembootstrappingromaniaeducationeu periphery |
| spellingShingle | Bianca-Raluca Cibu Camelia Delcea Adrian Domenteanu Determinants in modelling early school dropout Cactus pls-sem bootstrapping romania education eu periphery |
| title | Determinants in modelling early school dropout |
| title_full | Determinants in modelling early school dropout |
| title_fullStr | Determinants in modelling early school dropout |
| title_full_unstemmed | Determinants in modelling early school dropout |
| title_short | Determinants in modelling early school dropout |
| title_sort | determinants in modelling early school dropout |
| topic | pls-sem bootstrapping romania education eu periphery |
| url | https://cactus-journal-of-tourism.ase.ro/wp-content/uploads/2024/11/II.1-Cibu-et-al.pdf |
| work_keys_str_mv | AT biancaralucacibu determinantsinmodellingearlyschooldropout AT cameliadelcea determinantsinmodellingearlyschooldropout AT adriandomenteanu determinantsinmodellingearlyschooldropout |