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Article: Multilevel structural equation modeling for social work researchers: An introduction and application to healthy youth development

TitleMultilevel structural equation modeling for social work researchers: An introduction and application to healthy youth development
Authors
KeywordsImplementation
Mediation and moderation
Multilevel structural equation modeling (MSEM)
Prevention
Social and emotional learning (SEL)
Issue Date2018
Citation
Journal of the Society for Social Work and Research, 2018, v. 9, n. 4, p. 689-719 How to Cite?
AbstractObjective: To achieve the grand challenge goal of unleashing the power of prevention, we must determine how and under what conditions an intervention leads to desired outcomes. These questions remain largely unknown partly due to analytical challenges involving testing mediation and moderation hypotheses with multiple dependent variables in nested data. This paper introduces multilevel structural equation modeling (MSEM) and demonstrates multilevel mediation and moderation analysis to understand the mechanisms by and contexts in which preventive interventions work. Method: Using illustrative research questions, we review the conceptual backgrounds of multilevel modeling and structural equation modeling and explain how MSEM combines these methods. We then analyze longitudinal data from a quasi-experimental study of a social and emotional learning program to examine how classroom teachers’ baseline social–emotional competence (SEC) relates to students’ year-end SEC, focusing on the mediation of instruction and the moderation of implementation leadership. Results: Teachers’ SEC was directly related to students’ year-end SEC (95% CI [.04,.95]) and indirectly related through the number of lessons delivered (95% CI [.01,.35]), controlling for students’ baseline SEC and grade level. When teachers reported more implementation leadership at baseline, however, teachers’ own SEC contributed less to the number of lessons they delivered (95% CI for interaction effect [−2.50, −.27]). MSEM techniques enabled examination of how teachers’ SEC relates to their implementation behaviors and, in turn, to students’ social and emotional development, and how these relationships are modified by implementation contexts. Conclusions: Identifying mechanisms and contexts in which students benefit from classroom-level interventions can help refine interventions and/or target implementation supports for taking preventive interventions to scale.
Persistent Identifierhttp://hdl.handle.net/10722/316507
ISSN
2023 Impact Factor: 1.6
2023 SCImago Journal Rankings: 0.528
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorLee, Juyeon-
dc.contributor.authorShapiro, Valerie B.-
dc.contributor.authorKim, B. K.Elizabeth-
dc.contributor.authorYoo, Joan P.-
dc.date.accessioned2022-09-14T11:40:38Z-
dc.date.available2022-09-14T11:40:38Z-
dc.date.issued2018-
dc.identifier.citationJournal of the Society for Social Work and Research, 2018, v. 9, n. 4, p. 689-719-
dc.identifier.issn2334-2315-
dc.identifier.urihttp://hdl.handle.net/10722/316507-
dc.description.abstractObjective: To achieve the grand challenge goal of unleashing the power of prevention, we must determine how and under what conditions an intervention leads to desired outcomes. These questions remain largely unknown partly due to analytical challenges involving testing mediation and moderation hypotheses with multiple dependent variables in nested data. This paper introduces multilevel structural equation modeling (MSEM) and demonstrates multilevel mediation and moderation analysis to understand the mechanisms by and contexts in which preventive interventions work. Method: Using illustrative research questions, we review the conceptual backgrounds of multilevel modeling and structural equation modeling and explain how MSEM combines these methods. We then analyze longitudinal data from a quasi-experimental study of a social and emotional learning program to examine how classroom teachers’ baseline social–emotional competence (SEC) relates to students’ year-end SEC, focusing on the mediation of instruction and the moderation of implementation leadership. Results: Teachers’ SEC was directly related to students’ year-end SEC (95% CI [.04,.95]) and indirectly related through the number of lessons delivered (95% CI [.01,.35]), controlling for students’ baseline SEC and grade level. When teachers reported more implementation leadership at baseline, however, teachers’ own SEC contributed less to the number of lessons they delivered (95% CI for interaction effect [−2.50, −.27]). MSEM techniques enabled examination of how teachers’ SEC relates to their implementation behaviors and, in turn, to students’ social and emotional development, and how these relationships are modified by implementation contexts. Conclusions: Identifying mechanisms and contexts in which students benefit from classroom-level interventions can help refine interventions and/or target implementation supports for taking preventive interventions to scale.-
dc.languageeng-
dc.relation.ispartofJournal of the Society for Social Work and Research-
dc.subjectImplementation-
dc.subjectMediation and moderation-
dc.subjectMultilevel structural equation modeling (MSEM)-
dc.subjectPrevention-
dc.subjectSocial and emotional learning (SEL)-
dc.titleMultilevel structural equation modeling for social work researchers: An introduction and application to healthy youth development-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1086/701526-
dc.identifier.scopuseid_2-s2.0-85057780095-
dc.identifier.volume9-
dc.identifier.issue4-
dc.identifier.spage689-
dc.identifier.epage719-
dc.identifier.eissn1948-822X-
dc.identifier.isiWOS:000453873200010-

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