Skip to main content

Advertisement

SpringerLink
Log in

A Complexity Approach to Psychological Well-Being in Adolescence: Major Strengths and Methodological Issues

  • Published:
Social Indicators Research Aims and scope Submit manuscript

Abstract

Psychological well-being in adolescence is an increasing field of study. Deepening in its knowledge during this period of life can be of a lot of help to the designing of more adjusted prevention programs aimed to avoid or reduce the problems adolescents might be experiencing. Complexity theories can be a productive alternative to the important limitations explanations about psychological well-being in adolescence have nowadays. Answers to a questionnaire have been obtained from a sample of 968 Catalan adolescents from 12 to 16 years old including 29 psychological well-being indicators measuring 8 dimensions related to satisfaction with specific life domains, self-esteem, perceived social support, perception of control and values.A structural equation modelling approach to complexity that focuses on the non-linearity property has been followed. Given the large number of dimensions, the model has been estimated in two steps. First, a confirmatory factor analysis model has been fitted to the 29 indicators and appropriate factor scores have been saved. Then all possible products and squared terms of the factor scores have been computed and have been used as predictors of the dependent variable using an ordered logit model.The results show that a non-linear model including interaction effects among the 8 dimensions, age and gender, has a higher explanatory power to predict satisfaction with life as a whole, compared to a linear model estimated from those same indicators.This work must be understood as a first step, basically a methodological one, to the future elaboration of new models of psychological well-being in adolescence to be based on the principles defended by complexity theories.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Agresti A. (1990). Categorical Data Analysis. Wiley, New York

    Google Scholar 

  • Aldrich J.H. (1984). Linear Probability, Logit and Probit Models. Sage, Newbury Park

    Google Scholar 

  • Anderson T.W. and Rubin H. (1956). Statistical inference in factor analysis. In: Neyman, J. (eds) Proceedings of the Third Berkeley Symposium on Mathematical Statistics and Probability, pp 111–150. (University of California Press, Berkeley)

    Google Scholar 

  • Batista-Foguet J.M., Coenders G. and Alonso J. (2004a). Análisis factorial confirmatorio. Su utilidad en la validación de cuestionarios relacionados con la salud [Confirmatory factor analysis. Its utility for validating health-related questionnaires]. Medicina Clínica 122: 21–27

    Article  Google Scholar 

  • Batista-Foguet J.M., Coenders G. and Saris W.E. (2004b). A parsimonious approach to interaction effects in structural equation models: an application to consumer behavior. Working Papers of ESADE 183: 1–28

    Google Scholar 

  • Batista-Foguet J.M., Coenders G., Saris W.E. and Bisbe J. (2004c). Simultaneous estimation of indirect and interaction effects using structural equation models,. Metodološki Zvezki, Advances in Methodology and Statistics 1: 163–184

    Google Scholar 

  • Ben-Zur H. (2003). Happy adolescents: The link between subjective well-being, internal resources and parental factors. Journal of Youth and Adolescence 32(2): 67–79

    Article  Google Scholar 

  • Billiet, J.B. and M.J. McClendon: 1998, ‘On the identification of acquiescence in balanced set items using structural models’, Advances in Methodology, Data Analysis, and Statistics. (Metodološki Zvezki 14 FDV, Ljubljana), 129–150

  • Bollen K.A. (1989). Structural Equations with Latent Variables. Wiley, New York

    Google Scholar 

  • Boomsma A. and Hoogland J. (2001). The robustness of LISREL modelling revisited. In: Cudeck, R., du Toit, S. and Sörbom, D. (eds) Structural Equation Modelling: Present and Future, pp 139–168. (Scientific Software International, Lincolnwood, IL)

    Google Scholar 

  • Bostic T.J. and Ptacek J.T. (2001). Personality factors and the short-term variability in subjective well-being. Journal of Happiness Studies 2: 355–373

    Article  Google Scholar 

  • Byrne, D.: 2002, ‘Complexity theory and social research’, Social Research Update 18 (available at: http://www.soc.surrey.ac.uk/sru

  • Casas F., Rosich M. and Alsinet C. (2000). ‘El bienestar psicológico de los preadolescentes’ [The psychological well-being of preadolescents]. Anuario de Psicología 31(2): 73–86

    Google Scholar 

  • Casas, F., C. Figuer, M. González and G. Coenders: 2004, ‘A Satisfaction with life domains and salient values for future: Data from children and their parents in five different countries’, in W. Glatzer, S. von Below and M. Stoffregen (eds.), Challenges for Quality of Life in the Contemporary World. Advances in Quality-of-life Studies, Theory and Research (Social Indicators Research Series, 24), 123–141

  • Casas F., González M., Figuer C. and Coenders G. (2004). Subjective well-being, values and goal achievement: The case of planned versus by chance searches on the Internet. Social Indicators Research 66: 123–141

    Article  Google Scholar 

  • Chin W.W. (1998). The partial least squares approach for structural equation modelling. In: Marcoulides, G.A. (eds) Modern Methods for Business Research, pp 237–246. (Lawrence Erlbaum, Mahwah, NJ)

    Google Scholar 

  • Chin W.W. and Newsted P.R. (1999). Structural equation modelling analysis with small samples using partial least squares. In: Hoyle, R. (eds) Statistical Strategies for Small Sample Research, pp 307–341. (Sage Publications, Thousand Oaks)

    Google Scholar 

  • Coenders, G., J. Bisbe, W.E. Saris and J.M. Batista-Foguet: 2003, ‘Moderating effects of management control systems and innovation on performance. Simple methods for correcting the effects of measurement error for interaction effects in small samples’, Working Papers of the Department of Economics, University of Girona, 7, 1–21 (available at: http://www.udg.edu/fcee/economia/english/document.htm)

  • Coenders G., Casas F., Figuer C. and González M. (2005). Relationships between parents’ and children’s salient values for future and children’s overall life satisfaction. A comparison across countries. Social Indicators Research 73: 141–177

    Article  Google Scholar 

  • Cohen J. and Cohen P. (1983). Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences. Lawrence Erlbaum, Hillsdale

    Google Scholar 

  • Conner M., Sparks P., Povey R., James R., Shepherd R. and Armitage C.J. (2002). Moderator effects of attitudinal ambivalence on attitude–behaviour relationships. European Journal of Social Psychology 32: 705–718

    Article  Google Scholar 

  • Cortina J.M., Chen G. and Dunlap W.P. (2002). Testing interaction effects in LISREL: Examination and illustration of the available procedures. Organizational Research Methods 4: 324–360

    Article  Google Scholar 

  • Cummins R.A. (2000). Personal income and subjective well-being: a review. Journal of Happiness Studies 1: 133–158

    Article  Google Scholar 

  • Cummins, R.A., E. Gullone and A. Lau: 2002, ‘A model of subjective well-being homeostasis: the role of personality’, in E. Gullone and R. Cummins, The Universality of Subjective well-being Indicators. A Multi-disciplinary and Multi-national Perspective (Social Indicators Research Series, 16), 7–46

  • Davis-Blake A., Broschak J.P and George E. (2003). Happy together? How using non standard workers affects exit, voice, and loyalty among standard employees. Academy of Management Journal 46: 475–485

    Article  Google Scholar 

  • Diener E. (1994). ‘El bienestar subjetivo'’ [Subjective well-being]. Intervención Psicosocial 3(8): 67–113

    Google Scholar 

  • Diener E. and Lucas R.E. (1992). Personality and subjective well-being. In: Kahneman, D., Diener, E. and Schwartz, N. (eds) well-being: the Foundations of Hedonic Psychology, pp 213–243. (Rusell Sage Foundation, New York)

    Google Scholar 

  • Dijkstra T. (1983). Some comments on maximum likelihood and partial least squares methods. Journal of Econometrics 22: 67–90

    Article  Google Scholar 

  • du Toit M. and du Toit S (2001). Interactive LISREL, User’s Guide. Scientific Software International, Chicago, IL

    Google Scholar 

  • Eid M. and Diener E. (2004). Global judgments of subjective well-being: situational variability and long-term stability. Social Indicators Research 65: 245–277

    Article  Google Scholar 

  • Bravo A. and Fernández Valle J. (2000). Estructura y dimensiones de apoyo en la red social de los adolescentes [Structure and dimensions of social support in the social network of adolescents]. Anuario de Psicología 31(2): 87–105

    Google Scholar 

  • Fornell C. and Cha J. (1994). Partial least squares. In: Bagozzi, R.P. (eds) Advanced Methods in Marketing Research, pp 52–78. (Blackwell, Cambridge)

    Google Scholar 

  • Furnham A., Badmin N. and Sneade I. (2002). Body image dissatisfaction: gender differences in eating attitudes, self-esteem, and reasons for exercise. The Journal of Psychology 136(6): 581–596

    Article  Google Scholar 

  • Garaigordobil M., Cruz S. and Pérez J.I. (2003). Análisis correlacional y predictivo del autoconcepto con otros factores conductuales, cognitivos y emocionales de la personalidad durante la adolescencia’ [Correlational and predictive analysis of self-concept with other behavioural, cognitive and emotional factors of personality during adolescence]. Estudios de Psicología 24(1): 113–134

    Article  Google Scholar 

  • González, M.: 2005, El benestar psicològic a l’adolescència: la perspectiva de les ciències de la complexitat [Psychological well-being in adolescence: the perspective of complexity sciences] (available at: http://www.tdx.cesca.es/TDX-0114105–121406/)

  • Hartmann F.G. and Moers F. (1999). Testing contingency hypothesis in budgetary research: an evaluation of the use of moderated regression analysis. Accounting, Organizations and Society 24: 291–315

    Article  Google Scholar 

  • Helsen M., Vollebergh W and Meeus W. (2000). Social support from parents and friends and emotional problems in adolescence. Journal of Youth and Adolescence 29(3): 319–335

    Article  Google Scholar 

  • Huebner E.S. (2004). Research on assessment of life satisfaction of children and adolescents. Social Indicators Research 66: 3–33

    Article  Google Scholar 

  • Huebner E.S., Ash C. and Laughlin J.E. (2001). Life experiences, locus of control and school satisfaction in adolescence. Social Indicators Research 55: 167–183

    Article  Google Scholar 

  • Hulland J. (1999). Use of Partial Least Squares (PLS) in strategic management research: a review of four recent studies. Strategic Management Journal 20: 195–224

    Article  Google Scholar 

  • Irwin J.R. and McClelland G. (2001). Misleading heuristics and moderated multiple regression models. Journal of Marketing Research 38: 100–109

    Article  Google Scholar 

  • Jaccard J., Turrisi R. and Wan C.K. (1990). Interaction Effects in Multiple Regression. Sage Publications, Newbury Park

    Google Scholar 

  • Jöreskog K.G. (1998). Interactions and non-linear modeling: issues and approaches. In: Schumacker, R.E. and Marcoulides, G.A. (eds) Interactions and non-linear effects in structural equation models, pp 239–250. (Lawrence Erlbaum, Mahwah, NJ)

    Google Scholar 

  • Jöreskog, K.G.: 2000, Latent Variable Scores and their Uses (Scientific Software International, Lincolnwood, IL), available at: http://www.ssicentral.com/lisrel/corner.htm)

  • Jöreskog K.G. and Wold H. (1982). The ML and PLS techniques for modelling with latent variables: historical and comparative aspects. In: Wold, H. and Jöreskog, K.G. (eds) Systems Under Indirect Observation: Causality, Structure, Prediction, Part 1, pp 263–270. (North-Holland, Amsterdam)

    Google Scholar 

  • Jöreskog K.G. and Sörbom D. (1993). LISREL8: Structural Equation Modelling with the SIMPLIS Command Language. Scientific Software International, Chicago

    Google Scholar 

  • Jorge E. (2002). La investigación social y el dato complejo. Una primera aproximación [Social Research and the Complex Datum. A First Approach]. Publications of the University of Alacant, Alacant

    Google Scholar 

  • Kasser T. and Ahuvia A. (2002). Materialistic values and well-being in business students. European Journal of Social Psychology 32: 137–146

    Article  Google Scholar 

  • Kenny D.A. and Judd C.M. (1984). Estimating the non-linear and interactive effects of latent variables. Psychological Bulletin 96: 201–210

    Article  Google Scholar 

  • Klein A. and Moosbrugger H. (2000). Maximum likelihood estimation of latent interaction effects with the LMS method. Psychometrika 65: 457–474

    Article  Google Scholar 

  • Li H., Harmer P., Duncan T.E., Duncan S.C., Acock A. and Boles S. (1998). Approaches to testing interactions effects using structural equation modelling methodology. Multivariate Behavioral Research 33: 1–39

    Article  Google Scholar 

  • Marsh H.W., Wen Z. and Hau K.T. (2004). Structural equation models of latent interactions: evaluation of alternative estimation strategies and indicator construction. Psychological Methods 9: 275–300

    Article  Google Scholar 

  • Mathews K.M., White M.C. and Long R.G. (1999). Why study the complexity sciences in the social sciences?. Human Relations 52(4): 439–462

    Google Scholar 

  • McDonald R.P. (1996). Path analysis with composite variables. Multivariate Behavioral Research 31: 239–270

    Article  Google Scholar 

  • Menard, S.: 1995, ‘Applied Logistic Regression Analysis’, Quantitative applications in the Social Sciences, 132, Sage University Papers

  • Moulder B.C. and Algina J. (2002). Comparison of methods for estimating and testing latent variable interactions. Structural Equation Modelling 9: 1–19

    Article  Google Scholar 

  • Munné F. (2004). El retorno de la complejidad y la nueva imagen del ser humano: hacia una psicología compleja [The return of complexity and the new image of the human-being: towards a complex psychology]. Revista Interamericana de Psicología 38(1): 15–22

    Google Scholar 

  • Munné F. (1995). Las teorías de la complejidad y sus implicaciones en las ciencias del comportamiento [Complex theories and their implications for behavioural sciences]. Revista Interamericana de Psicología 29(1): 1–12

    Google Scholar 

  • Munné F. (1993). La teoria del caos y la psicología social. Un nuevo enfoque epistemológico para el comportamiento social [Chaos theory and social psychology. A new epistemological approach for social behaviour]. In: Fernández Cisneros, I. and Martínez García, F. (eds) Epistemología y Procesos Psicosociales Básicos, pp 37–48. (Eudema, Seville)

    Google Scholar 

  • Musitu G. and Cava M.J. (2002). ‘El rol del apoyo social en el ajuste de los adolescentes’ [The role of social support in the adjustment of the adolescents]. Intervención Psicosocial 11(3): 1–14

    Google Scholar 

  • Newsom J.T., Prigerson H.G., Schulz R. and Reynolds C.F. (2003). Investigating moderator hypotheses in aging research: Statistical, methodological, and conceptual difficulties with comparing separate regressions. International Journal of Aging & Human Development 57: 119–150

    Article  Google Scholar 

  • O’Loughlin C. and Coenders G. (2004). Estimation of the European customer satisfaction index: maximum likelihood versus partial least squares. Application to postal services. Total Quality Management and Business Excellence 15: 1231–1255

    Article  Google Scholar 

  • Pampel, F.C.: 2000, ‘Logistic regression. A primer’, Quantitative applications in the Social Sciences, 106, Sage University Papers

  • Park Y.S. and Kim U. (1998). Locus of control, attributional style and academic achievement: Comparative analysis of Korean, Korean-Chinese, and Chinese students. Asian Journal of Social Psychology 1: 191–208

    Article  Google Scholar 

  • Pearlin L.I. and Schooler C. (1978). The structure of coping. Journal of Health and Social Behaviour 19: 2–21

    Article  Google Scholar 

  • Polce-Lynch M., Myers B.J., Kliewer W. and Kilmartin C. (2001). Adolescent self-esteem and gender: Exploring relations to sexual harassment, body image, media influence, and emotional expression. Journal of Youth and Adolescence 30(2): 225–244

    Article  Google Scholar 

  • Pollard E. and Lee P.D. (2003). Child well-being: a systematic review of the literature. Social Indicators Research 61: 59–78

    Article  Google Scholar 

  • Raykov T. and Marcoulides G.A. (2000). A First Course in Structural Equation Modelling. Lawrence Erlbaum, Mahwah, NJ

    Google Scholar 

  • Rosenberg M. (1965). Society and the Adolescent Self-image. Princeton University Press, Princeton

    Google Scholar 

  • Sagiv L. and Schwartz S.H. (2000). Value priorities and subjective well-being: Direct relations and congruity effects. European Journal of Social Psychology 30: 177–198

    Article  Google Scholar 

  • Schumacker R.E. (2002). Latent variable interaction modeling. Structural Equation Modelling 9: 40–54

    Article  Google Scholar 

  • Schumacker R.E. and Marcoulides G.A. (1998). Interaction and non-linear Effects in Structural Equations. Lawrence Erlbaum, Mahwah, NJ

    Google Scholar 

  • Spector P.E. (1992). Summated Rating Scale Construction. An Introduction. Sage Publications, Newbury Park

    Google Scholar 

  • Vaux A., Phillips J., Holly L., Thomson B., Williams D. and Stewart D (1986). The Social Support Appraisals (SSA) Scale: Studies of Reliability and Validity. American Journal of Community Psychology 14(2): 195–219

    Article  Google Scholar 

  • Veenhoven R. (1994). ‘El estudio de la satisfacción con la vida’ [The study of life satisfaction]. Intervención Psicosocial 3(9): 87–116

    Google Scholar 

  • Wold H. (1975). Path models with latent variables: The NIPALS approach. In: Blalock, H.M. (eds) Quantitative Sociology. International Perspectives on Mathematical and Statistical Modelling, pp 307–357. (Academic Press, New York)

    Google Scholar 

  • Wold H. (1982). Soft modelling: The basic design and some extensions. In: Jöreskog, K.G. and Wold, H. (eds) Systems under Indirect Observation. Causality, Structure, Prediction, pp 1–54. North-Holland, Amsterdam

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Research Institute on Quality of Life Studies, Faculty of Economics, University of Girona, Campus Montilivi, Spain

    Mònica González, Ferran Casas & Germà Coenders

Authors
  1. Mònica González
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ferran Casas
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Germà Coenders
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mònica González.

Rights and permissions

Reprints and permissions

About this article

Cite this article

González, M., Casas, F. & Coenders, G. A Complexity Approach to Psychological Well-Being in Adolescence: Major Strengths and Methodological Issues. Soc Indic Res 80, 267–295 (2007). https://doi.org/10.1007/s11205-005-5073-y

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11205-005-5073-y

Key words

Search

Navigation