Adults’ Perceptions of Child Well-Being. Developing and Validating a Helpful Measuring Instrument

1. Adults’ Perceptions of Child Well-Being. Developing and Validating a Helpful Measuring Instrument Eva Expósito(1), Esther López(1), Enrique Navarro(2) & Bianca Thoilliez(2) • National OpenUniversity (Spain) • Complutense University of Madrid (Spain)

2. Introduction • Growing interest for research on child well- being. • Increasing interest from different institutions and organizations to develop indicators capable of measuring the specificity of child well-being. • In the Spanish context, we can highlight different activities carried out under the Program for the Child Friendly Cities and the recent publication of the report “Proposal of a System of Indicators of Child Well-Being in Spain”, driven by the Spanish Committee for UNICEF.

3. Objective To build and validate an instrument that allows us to know what are the determinants of child well-being that adults recognize as more important.

4. ResearchDesign • PHASE A:Construction of theinstrument. • PHASE B:Analyze the underlying structure of the data matrix. • PHASE C: Toevaluatethepsychometricproperties of theinstrument. Twoperspectives: Classical Test Theory and Item Response Theory.

5. Phase A: Construction of theinstrument (I) • Content matrix, based on the report “Child poverty in perspective: an overview of child wellbeing in rich countries” published by the Innocenti Research Center in 2007 (Unicef, 2007).

6. Phase A: Construction of theinstrument (II) • Future education professionals evaluate in a scale from 1 to 6, the degree of importance they confer to the various aspects related with child well-being proposed

7. Phase A: Construction of theinstrument (III) • SAMPLE: 805 students registered during the academic course 2010/2011 in different degrees related with the field of education (schoolteaching, pedagogy and social education) in publics and privates universities of the Region of Madrid (Spain).

8. Phase B: Structure of the data matrix • Factoringprocesswasdevelopedusing: • Componentsextractionmethod • Varimaxrotation • 19 factors => 69,67%. • A greater weight or saturation of the item, notes that is more important in the factor explanation. Those with less weight may be candidates for disposal.

9. Phase C: Psychometric properties of the instrument (I) • Classical Test Theory: Provides information on the precision of the test. That is, the instrument measures with little error • Reability (Cronbach’s Alfa) = 0.95 • Item # = 95

10. Phase C: Psychometric properties of the instrument (II) • Classical Test Theory: Correlationitem- total dimension

11. Fase C: Psychometric properties of the instrument (III) • Item Response Theory: Thekeyassumption of IRT modelsisthatthereis a functionalrelationbetweenthevalues in the variable thatmeasuretheitems and thesubjects’ probability of gettingright. • Subjects who score high in their perceptions of child well-being tend to give the highest ratings in a given item (>5). By contrast, subjects with lower scores on the construct "child well-being" tend to give lower ratings on the item.

12. Fase C: Psychometric properties of the instrument (IV) • Item Response Theory: 4 differentmodels (extensionstoRasch’s simple logisticmodel –suitablefor use whenitems are scoredpolytomously-) • PartialCreditModel (PC): Masters (1982) Allows the analysis of a collection of cognitive or attitudinal items that can have more than two levels of response. • ONE- DIMENSIONAL • MULTI-DIMENSIONAL • Rating ScaleModel (MEC): (Andrich, 1978) Allows the analysis of sets of rating items that have a common, multiple-category response format. The rating scale model is of particular value when examining the properties of the Likert-type items that are commonly used in attitude scales. • ONE- DIMENSIONAL • MULTI-DIMENSIONAL

13. Fase C: Psychometric properties of the instrument (V) • Item Response Theory - Model 4 model fits better than the other models do. - Differences between the desviance of the models are significant.

14. Fase C: Psychometric properties of the instrument (VI) • Item Response Theory: Example of itemsthatfitwell

15. Fase C: Psychometric properties of the instrument (VI) • Item Response Theory: Characteristic Curve of Item

16. Fase C: Psychometric properties of the instrument (VII) • Item Response CumulativeProbability Curves

17. Fase C: Psychometric properties of the instrument (VIII) • Item Response Theory: Itemsthatfitbad

18. Fase C: Psychometric properties of the instrument (IX) • Item Response Theory: Curvas Características de los ítems

19. Fase C: Psychometric properties of the instrument (X) • Item Response CumulativeProbability Curves

20. Conclusions • The reability of the instrument is good. • Partial credit model fits betther than Rating Scale do. • Multidimensional modelfitswell. So, theoreticalmatrixissupported. • In some items, the categories 0 and 1 haven’t any frequency. • Number of itemsishigh. So, It would be interesting to eliminate malfunctioning items. • Apply the instrument to other groups of adults.

21. References • Andersen, E. B. (1977): Sufficientstatistics and latenttraitmodels. Psychometrika, 46, 69-81. • Andrich, D. (1978): A rating formulationforordered response categories. Psychometrika, 43, 561-573. • Masters, G. N. (1982): A Raschmodelforpartialcreditscoring. Psychometrica, 47, 149-174. • Wu, M. L, Adams, R. J., Wilson, M. R., Haldane, S A. (2007): ACERConQuestVersion 2.0: generaliseditem response modelling software. HACER Press: Victoria

22. Thankyouforyourattention!!!