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This study presents the first evaluation of Dat-e Adolescence, a dating violence prevention program aimed at adolescents in Spain. A cluster randomized control trial was used involving two groups (a control group and experimental group) and two waves (pre-test and post-test six months apart). 1,764 students from across seven state high schools in Andalucía (southern Spain) participated in the study (856 in the control group and 908 in the experimental group); 52.3% were boys (n = 918), with ages ranging from 11 to 19 years (average age = 14.73; SD = 1.34). Efficacy evaluation was analyzed using Latent Change Score Models and showed that the program did not impact on physical, psychological or online aggression and victimization, nor did it modify couple quality. It was, however, effective at modifying myths about romantic love, improving self-esteem, and improving anger regulation, as a trend. These initial results are promising and represent one of the first prevention programs evaluated in this country. Future follow-up will allow us to verify whether these results remain stable in the medium term.
Pubmed ID: 30321224
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Statistical modeling program that provides a wide choice of models, estimators, and algorithms in a program that has graphical displays of data and analysis results. Mplus allows the analysis of both cross-sectional and longitudinal data, single-level and multilevel data, data that come from different populations with either observed or unobserved heterogeneity, and data that contain missing values. Analyses can be carried out for observed variables that are continuous, censored, binary, ordered categorical (ordinal), unordered categorical (nominal), counts, or combinations of these variable types. In addition, Mplus has extensive capabilities for Monte Carlo simulation studies, where data can be generated and analyzed according to any of the models included in the program. The Mplus modeling framework draws on the unifying theme of latent variables. The generality of the Mplus modeling framework comes from the unique use of both continuous and categorical latent variables. Continuous latent variables are used to represent factors corresponding to unobserved constructs, random effects corresponding to individual differences in development, random effects corresponding to variation in coefficients across groups in hierarchical data, frailties corresponding to unobserved heterogeneity in survival time, liabilities corresponding to genetic susceptibility to disease, and latent response variable values corresponding to missing data. Categorical latent variables are used to represent latent classes corresponding to homogeneous groups of individuals, latent trajectory classes corresponding to types of development in unobserved populations, mixture components corresponding to finite mixtures of unobserved populations, and latent response variable categories corresponding to missing data.
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