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The Strengths and Difficulties Questionnaire (SDQ) is a frequently used screening instrument for behavioral problems in children and adolescents. There is an ongoing controversy—not only in educational research—regarding the factor structure of the SDQ. Research results speak for a 3-factor as well as a 5-factor structure. The narrowband scales (5-factor structure) can be combined into broadband scales (3-factor structure). The question remains: Which factors (narrowband vs. broadband) are better predictors? With the prediction of child and adolescent outcomes (academic grades, well-being, and self-belief), we evaluated whether the broadband scales of internalizing and externalizing behavior (3-factor structure) or narrowband scales of behavior (5-factor structure) are better suited for predictive purposes in a cross-sectional study setting. The sample includes students in grades 5 to 9 (N = 4642) from the representative German Health Interview and Examination Survey for Children and Adolescents (KiGGS study). The results of model comparisons (broadband scale vs. narrowband scales) did not support the superiority of the broadband scales with regard to the prediction of child and adolescent outcomes. There is no benefit from subsuming narrowband scales (5-factor structure) into broadband scales (3-factor structure). The application of narrowband scales, providing a more differentiated picture of students’ academic and social situation, was more appropriate for predictive purposes. For the purpose of identifying students at risk of struggling in educational contexts, using the set of narrowband dimensions of behavior seems to be more suitable.
The Strengths and Difficulties Questionnaire (SDQ) is a frequently used screening instrument for behavioral problems in children and adolescents. There is an ongoing controversy—not only in educational research—regarding the factor structure of the SDQ. Research results speak for a 3-factor as well as a 5-factor structure. The narrowband scales (5-factor structure) can be combined into broadband scales (3-factor structure). The question remains: Which factors (narrowband vs. broadband) are better predictors? With the prediction of child and adolescent outcomes (academic grades, well-being, and self-belief), we evaluated whether the broadband scales of internalizing and externalizing behavior (3-factor structure) or narrowband scales of behavior (5-factor structure) are better suited for predictive purposes in a cross-sectional study setting. The sample includes students in grades 5 to 9 (N = 4642) from the representative German Health Interview and Examination Survey for Children and Adolescents (KiGGS study). The results of model comparisons (broadband scale vs. narrowband scales) did not support the superiority of the broadband scales with regard to the prediction of child and adolescent outcomes. There is no benefit from subsuming narrowband scales (5-factor structure) into broadband scales (3-factor structure). The application of narrowband scales, providing a more differentiated picture of students’ academic and social situation, was more appropriate for predictive purposes. For the purpose of identifying students at risk of struggling in educational contexts, using the set of narrowband dimensions of behavior seems to be more suitable.
In intervention research, single-case experimental designs are an important way to gain insights into the causes of individual changes that yield high internal validity. They are commonly applied to examine the effectiveness of classroom-based interventions to reduce problem behavior in schools. At the same time, there is no consensus on good design characteristics of single-case experimental designs when dealing with behavioral problems in schools. Moreover, specific challenges arise concerning appropriate approaches to analyzing behavioral data. Our study addresses the interplay between the test power of piecewise regression analysis and important design specifications of single-case research designs. Here, we focus on the influence of the following specifications of single-case research designs: number of measurement times, the initial frequency of the behavior, intervention effect, and data trend. We conducted a Monte-Carlo study. First, simulated datasets were created with specific design conditions based on reviews of published single-case intervention studies. Following, data were analyzed using piecewise Poisson-regression models, and the influence of specific design specifications on the test power was investigated. Our results indicate that piecewise regressions have a high potential of adequately identifying the effects of interventions for single-case studies. At the same time, test power is strongly related to the specific design specifications of the single-case study: Few measurement times, especially in phase A, and low initial frequencies of the behavior make it impossible to detect even large intervention effects. Research designs with a high number of measurement times show robust power. The insights gained are highly relevant for researchers in the field, as decisions during the early stage of conceptualizing and planning single-case experimental design studies may impact the chance to identify an existing intervention effect during the research process correctly.
In intervention research, single-case experimental designs are an important way to gain insights into the causes of individual changes that yield high internal validity. They are commonly applied to examine the effectiveness of classroom-based interventions to reduce problem behavior in schools. At the same time, there is no consensus on good design characteristics of single-case experimental designs when dealing with behavioral problems in schools. Moreover, specific challenges arise concerning appropriate approaches to analyzing behavioral data. Our study addresses the interplay between the test power of piecewise regression analysis and important design specifications of single-case research designs. Here, we focus on the influence of the following specifications of single-case research designs: number of measurement times, the initial frequency of the behavior, intervention effect, and data trend. We conducted a Monte-Carlo study. First, simulated datasets were created with specific design conditions based on reviews of published single-case intervention studies. Following, data were analyzed using piecewise Poisson-regression models, and the influence of specific design specifications on the test power was investigated. Our results indicate that piecewise regressions have a high potential of adequately identifying the effects of interventions for single-case studies. At the same time, test power is strongly related to the specific design specifications of the single-case study: Few measurement times, especially in phase A, and low initial frequencies of the behavior make it impossible to detect even large intervention effects. Research designs with a high number of measurement times show robust power. The insights gained are highly relevant for researchers in the field, as decisions during the early stage of conceptualizing and planning single-case experimental design studies may impact the chance to identify an existing intervention effect during the research process correctly.