Abstract
The study of preferences of leisure-time activities provides important information on the characteristics and inclinations of specific demographics. Modeling these data offers a useful insight in the identification of service demand and thus helps to define effective social strategies. Individual preferences concerning leisure-time activities of Campania region high school students are here analyzed, as expressions of a subjective perception of time and as a result of external constraints on choices. The main approach concerns: first, analyze individual changes in the allocation of time among different leisure activities and second, discern the role that external factors play in determining adolescents’ preferences. The duality of the issue is addressed considering the allocation of time as a budget-time problem where individual leisure time activities constitute relative contributions to the total amount of time, adding to the model the specific characteristics of the respondents. More specifically, a methodology that combines regression and multivariate analysis (External Information model) is reformulated in a compositional framework. The usefulness of the compositional approach is to preserve the adolescents’ statements of preferences leading to a correct outcome when the External Information model is performed. Results provided evidences that gender is an important factor of influence on adolescents’ choices as well as the compound of parents’ level of education and the total amount of free-time spent in a day by each individual considered.
Similar content being viewed by others
References
Aguiar, M., & Hurst, E. (2009). A summary of trends in american time allocation: 1965–2005. Social Indicators Research, 93(1), 57–64.
Aitchison, J. (1983). Principal component analysis of compositional data. Biometrika, 70(1), 57–65.
Aitchison, J. (1986). The statistical analysis of compositional data. London: Chapman & Hall.
Aitchison, J. (1999). Logratios and natural laws in compositional data analysis. Mathematical Geology, 31(5), 563–580.
Aitchison, J., & Greenacre, M. (2002). Biplots of compositional data. Journal of the Royal Statistical Society: Series C (Applied Statistics), 51(4), 375–392.
Aitchison, J., & Kay, J. W. (2004). Possible solutions of some essential zero problems in compositional data analysis. In S. Thió-Henestrosa & J. A. Martín-Fernández (Eds.), Compositional data analysis workshop. Spain: Girona.
Aitchison, J., Barceló-Vidal, C., Martín-Fernández, J., & Pawlowsky-Glahn, V. (2000). Logratio analysis and compositional distance. Mathematical Geology, 32(3), 271–275.
Bartko, W. T., & Eccles, J. S. (2003). Adolescent participation in structured and unstructured activities: A person-oriented analysis. Journal of Youth and Adolescence, 32(4), 233–241.
Brajša-Žganec, A., Merkaš, M., & Šverko, I. (2011). Quality of life and leisure activities: How do leisure activities contribute to subjective well-being? Social Indicators Research, 102(1), 81–91.
Bren, M., Tolosana-Delgado, R., & van den Boogaart, K. G. (2003). News from “compositions”, the R package. http://dugi-doc.udg.edu/bitstream/handle/.
Busser, J. A., Hyams, A. L., Carruthers, C. P., et al. (1996). Differences in adolescent activity participation by gender, grade and ethnicity. Journal of Park and Recreation Administration, 14(4), 1–20.
Coleman, D., Iso-Ahola, S., et al. (1993). Leisure and health: The role of social support and self-determination. Journal of Leisure Research, 25(2), 111–128.
De Roiste, A., & Dinneen, J. (2005). Young people’s views about opportunities, barriers and supports to recreation and leisure. Dublin: National Children’s Office.
Di Palma, M. A., Filzmoser, P., Gallo, M., & Hron, K. (2017). A robust Parafac model for compositional data. Journal of Applied Statistics, 1–23. https://doi.org/10.1080/02664763.2017.1381669.
Egozcue, J. J., Barceló-Vidal, C., Martín-Fernández, J. A., Jarauta-Bragulat, E., Díaz-Barrero, J. L., & Mateu-Figueras, G. (2011). Elements of simplicial linear algebra and geometry. In V. Pawlowsky-Glahn & A. Buccianti (Eds.), Compositional data analysis: Theory and applications (pp. 141–157). Chichester: Wiley.
Egozcue, J. J., Pawlowsky-Glahn, V., Mateu-Figueras, G., & Barcelo-Vidal, C. (2003). Isometric logratio transformations for compositional data analysis. Mathematical Geology, 35(3), 279–300.
Engle, M. A., Gallo, M., Schroeder, K. T., Geboy, N. J., & Zupancic, J. W. (2014). Three-way compositional analysis of water quality monitoring data. Environmental and Ecological Statistics, 21(3), 565–581.
Filzmoser, P., Hron, K., & Reimann, C. (2009). Univariate statistical analysis of environmental (compositional) data: Problems and possibilities. Science of the Total Environment, 407(23), 6100–6108.
Fisher, K., & Robinson, J. (2009). Average weekly time spent in 30 basic activities across 17 countries. Social Indicators Research, 93(1), 249–254.
Fry, J. M., Fry, T. R., & McLaren, K. R. (2000). Compositional data analysis and zeros in micro data. Applied Economics, 32(8), 953–959.
Gallo, M. (2015). Tucker3 model for compositional data. Communications in Statistics-Theory and Methods, 44(21), 4441–4453.
Gallo, M., & Buccianti, A. (2013). Weighted principal component analysis for compositional data: Application example for the water chemistry of the Arno river (Tuscany, central Italy). Environmetrics, 24(4), 269–277.
Gallo, M., & D’Ambra, L. (2008). Nonlinear constrained principal component analysis in the quality control framework. In C. Preisach, H. Burkhardt, L. Schmidt-Thieme, & R. Decker (Eds.), Data analysis, machine learning and applications. Studies in classification, data analysis, and knowledge organization. Heidelberg, Berlin: Springer.
Hendry, L. B. (1983). Growing up and going out: Adolescents and leisure. Aberdeen: Aberdeen University Press.
Hendry, L. B., Glendinning, A., & Shucksmith, J. (1996). Adolescent focal theories: Age-trends in developmental transitions. Journal of Adolescence, 19(4), 307–320.
Hron, K., Templ, M., & Filzmoser, P. (2010). Imputation of missing values for compositional data using classical and robust methods. Computational Statistics & Data Analysis, 54(12), 3095–3107.
Huebner, A. J., & Mancini, J. A. (2003). Shaping structured out-of-school time use among youth: The effects of self, family, and friend systems. Journal of Youth and Adolescence, 32(6), 453–463.
Hui-fen, Z., Zhen-shan, L., Dong-qian, X., & Yang, L. (2012). Time use patterns between maintenance, subsistence and leisure activities: A case study in china. Social Indicators Research, 105(1), 121–136.
Kraus, R. (1971). Recreation and leisure in modern society (pp. 493). New York: Appleton Century Crofts.
Larson, R. W., & Verma, S. (1999). How children and adolescents spend time across the world: Work, play, and developmental opportunities. Psychological Bulletin, 125(6), 701.
Lloyd, K. M., & Auld, C. J. (2002). The role of leisure in determining quality of life: Issues of content and measurement. Social Indicators Research, 57(1), 43–71.
Martin-Fernandez, J. A., Barceló-Vidal, C., & Pawlowsky-Glahn, V. (2000). Zero replacement in compositional data sets. In Data analysis, classification, and related methods (pp. 155–160). Berlin, Heidelberg: Springer.
Martín-Fernández, J. A., Barceló-Vidal, C., & Pawlowsky-Glahn, V. (2003). Dealing with zeros and missing values in compositional data sets using nonparametric imputation. Mathematical Geology, 35(3), 253–278.
Martín-Fernández, J. A., Pawlowsky-Glahn, V., Egozcue, J. J., & Tolosona-Delgado, R. (2018). Advances in principal balances for compositional data. Mathematical Geosciences, 50(3), 273–298.
Mateu-Figueras, G., Pawlowsky-Glahn, V., & Egozcue, J. J. (2011). The principle of working on coordinates. In V. Pawlowsky-Glahn & A. Buccianti (Eds.), Compositional data analysis: Theory and applications (pp. 29–42). Springer.
Muller, I., Hron, K., Fiserova, E., Smahaj, J., Cakirpaloglu, P., & Vancakova, J. (2016). Interpretation of compositional regression with application to time budget analysis. arXiv preprint arXiv:160907887.
Passmore, A., & French, D. (2000). A model of leisure and mental health in australian adolescents. Behaviour Change, 17(3), 208–220.
Pawlowsky-Glahn, V., Egozcue, J. J., & Tolosana-Delgado, R. (2015). Modeling and analysis of compositional data. Chichester West Sussex: John Wiley & Sons.
R Development Core Team. (2008). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org, ISBN 3-900051-07-0.
Ridge, T. (2002). Childhood poverty and social exclusion: From a child’s perspective. Bristol: Policy Press.
Schmidt, J. A., & Padilla, B. (2003). Self-esteem and family challenge: An investigation of their effects on achievement. Journal of Youth and Adolescence, 32(1), 37–46.
Shaw, S. M., Caldwell, L. L., & Kleiber, D. A. (1996). Boredom, stress and social control in the daily activities of adolescents. Journal of Leisure Research, 28(4), 274–292.
Shinew, K. J., Floyd, M. F., McGuire, F. A., & Noe, F. P. (1996). Class polarization and leisure activity preferences of african americans: Intragroup comparisons. Journal of Leisure Research, 28(4), 219–232.
Shinew, K. J., Floyd, M. F., & Parry, D. (2004). Understanding the relationship between race and leisure activities and constraints: Exploring an alternative framework. Leisure Sciences, 26(2), 181–199.
Simonacci, V., & Gallo, M. (2017). Statistical tools for student evaluation of academic educational quality. Quality & Quantity, 51(2), 565–579.
Takane, Y., & Shibayama, T. (1991). Principal component analysis with external information on both subjects and variables. Psychometrika, 56(1), 97–120.
Templ, M., Filzmoser, P., & Reimann, C. (2008). Cluster analysis applied to regional geochemical data: Problems and possibilities. Applied Geochemistry, 23(8), 2198–2213.
Tinsley, H. E., & Eldredge, B. D. (1995). Psychological benefits of leisure participation: A taxonomy of leisure activities based on their need-gratifying properties. Journal of Counseling Psychology, 42(2), 123.
van den Boogaart, K. G., & Tolosana-Delgado, R. (2008). Compositions: A unified R package to analyze compositional data. Computers & Geosciences, 34(4), 320–338.
Veal, A. J., Darcy, S., & Lynch, R. (2015). Australian leisure. London: Pearson Higher Education AU.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Di Palma, M.A., Gallo, M. External Information Model in a Compositional Perspective: Evaluation of Campania Adolescents’ Preferences in the Allocation of Leisure-Time. Soc Indic Res 146, 117–133 (2019). https://doi.org/10.1007/s11205-018-1898-z
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11205-018-1898-z