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Mediators of Positive Youth Development Intervention Change: Promoting Change in Positive and Problem Outcomes?

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Abstract

Advances in applied developmental science have contributed to the large literature on positive youth development (PYD) interventions. This study reports an investigation of a PYD program using an outcome-mediation evaluation model that drew on the treatment intervention science literature. The Changing Lives Program (CLP) is a community supported gender and ethnic inclusive PYD intervention framework. Using an empowerment approach, the CLP was implemented in a practice setting as a selective/indicated positive youth development program for multi-ethnic, multi-problem at risk youth in urban alternative high schools. The outcome-mediation evaluation model provided evidence in support of the direct outcome effects of the intervention. The model also proved effective in identifying at a micro process level a number of plausible causal mechanisms for use in the development of more conceptually complete models of the causal mechanisms that underlie intervention change. Implications for future development of theory informed empirically supported intervention models of PYD programs are discussed.

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Notes

  1. Following recommendations of Bollen and Long (1993), a variety of global fit indices were used, including indices of absolute fit, indices of relative fit and indices of fit with a penalty function for lack of parsimony. First, the chi-square and its probability value (p-value) were examined. The higher the p-value is, the closer the fit between the hypothesized model and model fit (Byrne 2001), with a target p-value of greater than .05. The comparative fit index (CFI) was used as an index of fit based on the comparison of the hypothesized model with the independence model. A CFI value of greater than .95 was used to indicate model fit. The root mean square error of approximation (RMSEA) accounts for the error of approximation in the population. An RMSEA of less than .08, and a p-value for the test of closeness of fit for the RMSEA of greater than .50 were used to indicate model fit. The standardized root mean square residual (SRMR) compares the observed covariance matrix against that constructed through the model. An SRMR of less than .05 was used to indicate model fit.

  2. Following the logic of Rausch et al. (2003), the scores of the baseline measures (PE1, IDR1, INFO1, INTERN1, EXTERN1) were used for the analysis of covariance of a quasi-experimental outcome design with two waves of assessment (pretest, posttest) to evaluate whether participation in the CLP was associated with change in IDR, INFO, PE, INTERN, and EXTERN relative to the comparison control condition. Specifically, CLP was defined as a two-valued dummy variable (scored 1 or 0) for the two intervention conditions (CLP vs. control). By design, difference in this variable (intervention vs. control) was hypothesized to be related to differential outcome (change in IDR, INFO, PE, INTERN, EXTERN) at posttest (IDR2, INFO2, PE2, INTERN2, EXTERN2) controlling for pretest (IDR1, INFO1, PE1, INTERN1, EXTERN1). The hypothesized differences were evaluated using covariate-adjusted change in which the baseline (pretest) measure of the outcome and the outcome at the posttest are strategically used as covariates to define different features of change (Rausch et al. 2003).

  3. Two exogenous interpersonal contextual factor covariates, Gender (G) and Ethnicity (E), were included in the analysis of outcome as measured at pretest, as were all possible interaction terms (i.e., CLP*G, CLP*E, G*E, CLP*G*E), with CLP designated as the focal independent variable. Non-significant interactions were dropped from the final model, leaving two significant interaction terms (CLP*E and CLP*G). To reduce clutter, Fig. 3 excludes the paths associated with the constitutive terms E and G, as these path coefficients are contingent upon the higher order CLP*E and CLP*G interaction terms (Jaccard and Turrisi 2003). Fig. 3 includes paths associated with the focal independent variable.

  4. Baseline scores for predictor variables were used as covariates in order to account for possible lagged effects on outcome variables at post-test. That is, the effect of change in a predictor variable on change in an outcome variable was modeled by using baseline scores for both the predictor variable and the outcome variable as covariates for the outcome variable at post-test. Due to likely reciprocity of effects, the error term for IDR at posttest (IDR2) was allowed to correlate with PE at pretest (PE1).

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Authors and Affiliations

  1. Department of Psychology, Florida International University, Miami, FL, USA

    Kyle Eichas, Rachel A. Ritchie, Aida Varela, Arlene Garcia, Roberto Rinaldi, Rebecca Wang, Wendy K. Silverman, James Jaccard & William M. Kurtines

  2. State of Florida Department of Corrections, Miami, FL, USA

    Richard E. Albrecht

  3. Department of Social Sciences, Miami Dade College, Miami, FL, USA

    Arlen J. Garcia

  4. Department of Educational and Psychological Studies, Florida International University, Miami, FL, USA

    Marilyn J. Montgomery

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Eichas, K., Albrecht, R.E., Garcia, A.J. et al. Mediators of Positive Youth Development Intervention Change: Promoting Change in Positive and Problem Outcomes?. Child Youth Care Forum 39, 211–237 (2010). https://doi.org/10.1007/s10566-010-9103-9

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