The Use of Multilevel Modeling for the Investigation of Family Process

Jenkins, Jennifer M.; Cheung, Candice Chi-Hang; Frampton, Kristen L.; Rasbash, Jon; Boyle, Michael H.; Georgiades, Katholiki

doi:10.3233/dev-2009-3204

Cited by 24 publications

(30 citation statements)

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“…Predictor variables can be modeled as having potentially different effects on between-family processes from those that they have on within-family processes (Jenkins et al, 2009). The between-family effect is measured by including the family mean of maternal negativity and positivity.…”

Section: Methodsmentioning

confidence: 99%

“…The simplest multilevel model fitted to family data is the two-level hierarchical model where individuals (at level 1 of the data hierarchy) are nested within families (level 2). These models partition the variation in individuals' outcomes into two variance components: a between-family variance and a within-family variance (Jenkins et al, 2009). In the current model, we have relationship scores nested within families, corresponding to this simple two-level structure.…”

Section: Cross-classified Multilevel Modelmentioning

confidence: 99%

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The role of maternal factors in sibling relationship quality: a multilevel study of multiple dyads per family

Jenkins

Rasbash

Leckie

et al. 2011

Child Psychology Psychiatry

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Section: Methodsmentioning

confidence: 99%

Section: Cross-classified Multilevel Modelmentioning

confidence: 99%

The role of maternal factors in sibling relationship quality: a multilevel study of multiple dyads per family

Jenkins

Rasbash

Leckie

et al. 2011

Child Psychology Psychiatry

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“…[16][17][18] Despite decades of theory and practice, a comparatively small body of empirical literature directly validates these systemic organizing principles, 19,20 especially in the context of CR and developmental health. There are three notable advantages to employing family-wide science in the study of CR and developmental health, as summarized by Jenkins et al 21 First, and most obviously, family-wide science can disentangle processes that are specific to particular children, versus those that apply to entire families. In behavioral genetics terms, it is possible to separate nonshared and shared family processes, respectively.…”

Section: Advantages Of Considering the Family As A Hierarchical Entitymentioning

confidence: 99%

Cumulative risk and developmental health: an argument for the importance of a family‐wide science

Browne

Plamondon

Prime

et al. 2015

WIRES Cognitive Science

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A substantial body of research links social disadvantage and developmental health via a cascade running from poverty, to cumulative psychosocial risk, to disrupted family dynamics, to child biological regulatory systems and neurocognitive processing, and finally to morbidity across the lifespan. Most research in this area employs single-dyad or between-family methodology. While informative, there are limitations to this approach. Specifically, it is impossible to determine how risk alters psychosocial environments that are similar for all persons within a household, versus processes that are unique to particular children. This is important in light of literature citing the primacy of child-specific environments in driving developmental health. Methodologically speaking, there are both benefits and challenges to family-wide approaches that differentiate between- and within-family environments. This review describes literature linking cumulative risk and developmental health via family process, while articulating the importance of family-wide approaches. Areas of shortcoming and recommendations for a family-wide science are provided.

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“…Even fewer study all siblings in the family, assess the variance distribution between relationship, individual, and family levels, or investigate sibling relationship positivity, as this study does. This paper goes beyond describing the merits of multilevel modeling in family research, which have been detailed elsewhere (Boyle & Willms, 2001;Jenkins et al, 2009), and instead describes in detail the benefits of full-informant and multilevel analyses in sibling relationships. It also identifies the risks of studying only single sibling dyads when more exist in the family, and provides an example, using two scales of sibling relationship quality, of how full sibling group data and multilevel modeling can add clarity to scale meaning and interpretation.…”

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confidence: 99%

Variance Distribution in Sibling Relationships: Advantages of Multilevel Modeling Using Full Sibling Groups

Marciniak¹

2015

Family Process

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The majority of research on sibling relationships has investigated only one or two siblings in a family, but there are many theoretical and methodological limitations to this single dyadic perspective. This study uses multiple siblings (541 adults) in 184 families, where 96 of these families had all siblings complete the study, to demonstrate the value in including full sibling groups when conducting research on sibling relationships. Two scales, positivity and willingness to sacrifice, are evaluated with a multilevel model to account for the nested nature of family relationships. The distribution of variance across three levels: relationship, individual, and family are computed, and results indicate that the relationship level explains the most variance in positivity, whereas the individual level explains the majority of variance in willingness to sacrifice. These distributions are affected by gender composition and family size. The results of this study highlight an important and often overlooked element of family research: The meaning of a scale changes based on its distribution of variance at these three levels. Researchers are encouraged to be cognizant of the variance distribution of their scales when studying sibling relationships and to incorporate more full sibling groups into their research methods and study design.

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The Use of Multilevel Modeling for the Investigation of Family Process

Cited by 24 publications

References 0 publications

The role of maternal factors in sibling relationship quality: a multilevel study of multiple dyads per family

The role of maternal factors in sibling relationship quality: a multilevel study of multiple dyads per family

Cumulative risk and developmental health: an argument for the importance of a family‐wide science

Variance Distribution in Sibling Relationships: Advantages of Multilevel Modeling Using Full Sibling Groups

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