Partially Nested Designs in Social Work Research
Principles and Practices
Datos Bibliográficos
| ID | 6448718 |
|---|---|
| Autores | Kyle Cox (0000-0002-7173-4701, University of North Carolina at Charlotte, autor de correspondencia), Ben Kelecy (University of Cincinnati), Jada Deiderich (University of North Carolina at Charlotte) |
| Año | 2023 |
| Volumen | 35 |
| Número | 3 |
| Páginas | 332-348 |
| Fecha de publicación | 2023-10-30 |
| Peer Reviewed | Sí |
| Open Access | Sí |
| Tipo | ARTICLE |
| Revista | Research on Social Work Practice (JOURNAL) |
| Identificadores de la revista | ISSN: 1049-7315 • E-ISSN: 1552-7581 |
| Editorial | SAGE Publishing (PUBLISHER • US) |
| DOI | 10.1177/10497315231208700 |
| OpenAlex | W4388021781 |
| Idioma | EN |
| Referencias citadas | 50 |
Purpose: Group-administered and shared facilitator treatments can induce nested data in a treatment arm that is not present in the control arm. Failure to accommodate these partially nested data structures produces study design inefficiencies, biased parameter estimates, and inaccurate inferences. This work introduces partially nested data structures. Method: We began by describing the features of partially nested data then discuss best practices and guidelines for study planning and analysis through examples commonly found in social work research. Results: The totality of this work provides social work researchers with the knowledge and tools to accommodate partially nested data in study planning and analysis including integration of comprehensive effects (i.e., mediation and moderation). Discussion: Improved understanding of partially nested data structures help researchers avoid the detrimental effects associated with disregarding them. Broadly, these methodological advances increase the capacity and quality of research in the field of social work
Data mining · Data science · Facilitator · Field (mathematics · Machine learning · Management science · Mediation · Moderation · Nested set model · Quality (philosophy · Relational database · Research design · Sociology · Statistics · Work (physics · Child and Adolescent Psychosocial and Emotional Development · Computer Science · Engineering · Mathematics · Psychology · Resilience and Mental Health · Social Psychology · Social Work Education and Practice · Social Work
Introduction to Statistical Mediation Analysis
People are variables too
Statistical analysis and optimal design for cluster randomized trials.
Mediators and Mechanisms of Change in Psychotherapy Research
Fitting Linear Mixed-Effects Models Using lme4
Using n -Level Structural Equation Models for Causal Modeling in Fully Nested, Partially Nested, and Cross-Classified Randomized Controlled Trials
Required Sample Size to Detect the Mediated Effect
Evaluating Group-Based Interventions When Control Participants Are Ungrouped
Structural Equation Modeling Approaches for Analyzing Partially Nested Data
Social Participation Interventions for Adults with Mental Health Problems
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The Effectiveness of Guided Imagery in Treating Compassion Fatigue and Anxiety of Mental Health Workers
A Comparison of Pregnancy-Only versus Mixed-Gender Group Therapy among Pregnant Women with Opioid Use Disorder
Outcome Literature Review of Integrative Body–Mind–Spirit Practices for Mental Health Conditions
A Randomized Controlled Trial of Yoga with Incarcerated Females
Social work research and the quest for effective practice
Grand Challenges for Social Work
Mindfulness Research in Social Work
Power Analysis in Social Work Intervention Research
Investigating multilevel mediation with fully or partially nested data
Statistical Power for Detecting Moderation in Partially Nested Designs
A Meta-Analysis of Motivational Interviewing
A Bibliography of Randomized Controlled Experiments in Social Work (1949–2013)
Integrating Mediators and Moderators in Research Design
Influences of School Latino Composition and Linguistic Acculturation on a Prevention Program for Youths
| Velocidad de citación | historical |
|---|---|
| Altamente citado | No |