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Within families: family-wide and child-specific influences on children’s socio-emotional development Jennifer Jenkins, Jon Rasbash, Tom O’Connor. Behavioral genetic findings of siblings being so different from one another once genetic effects were controlled .
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Within families: family-wide and child-specific influences on children’s socio-emotional development Jennifer Jenkins, Jon Rasbash, Tom O’Connor
Behavioral genetic findings of siblings being so different from one another once genetic effects were controlled
What is the effect of the family environment? Focus on the similar and different experiences of siblings in families
Themes • Do family-wide or child-specific aspects of the environment predict change in child behavior? • How similar are children’s experiences in families? Does this vary as a function of stresses in the environment? • What are children’s own contributions to the stressful environments that they experience?
Datasets • NLSCY, ABSS, NEAD • All involve the inclusion of multiple children per family: between 2-4 depending on dataset • Some involve multi-informant data • Some results involve examining change in the response variable: longitudinal design • All use multilevel modeling for the analysis Jenkins, Rasbash, O’Connor (2003) DP Jenkins, Simpson, Dunn, Rasbash, O’Connor (2005) CD Jenkins, Dunn, O’Connor, Rasbash, Behnke, JFP. In press Rasbash, Jenkins, O’Connor, In preparation
Majority of environmental studies of family influences family and child-specific processes are confounded Outcome Variable Family A Family Family B Level Child Level 1 2 Between Family Comparisons
Environmental studies using sibling design: unconfounds family and child
Do family-wide or child-specific aspects of the environment predict change in child behavior? Illustrate with results from sibling study
Are there within family differences on sibling dyad negativity? ICC = .52
Shared effects: Av maternal negativity predicts an increase in sibling negativity over 2 years Family average is a stronger predictor of sibling negativity than dyad-specific negativity
Mean of sibling negativity as a function of gender of sibling dyad * * Girl dyads differ significantly from mixed dyads
Thus there are some systematic reasons that some dyads get on better than others And shared experiences are important
Shared family environments? Families differ from one another on how much parental conflict children experience Family Yellow Family Pink Family Blue Exposure to parental conflict
Shared family environments? Children within families differ from one another on how much parental conflict they experience Family Pink Family Blue Family Yellow Exposure to parental conflict
Sibling similarity on experiences surrounding parental conflict ICC
To what extent do siblings live in shared environments? Does this vary as a function of environmental stress?Modeling differential experience in families
Differential parental positivity as a function of SES, marital problems and family size. family size = 2, no marital problems 5 family size = 2, marital problems family size > 2, marital problems family size > 2, no marital problems 4 differential positive parenting 3 2 1 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 household ses
Differential parental hostility as a function of single parenthood and marital dissatisfaction. 7.2 differential negativity 6.2 5.2 4.2 Intact/no marital problem Marital problem Single parent
Limitations of this method Measurement problems. Although in some of the studies the IV and DV are based on different informants, the family clustering information is based on single informant. Degree of family clustering that we see may be related to same person reporting on measures for different siblings
Another method for examining similar and differential experiences in families as well as consistency of behavior when interacting with different members of the family
Social relations model • Every person in a family interacts with every other person • Rate each person’s expression of negativity and positivity towards every other family member • Data are observational
Actor: c1 c2 m f Within family structure We start with 12 relationship scores in each family. These can be classified : partner actor dyad and family Family 1… Dyad d1 d2 d3 d4 d5 d6 Relationship: c1c2c1mc1f c2c1c2m c2f mc1mc2mf fc1fc2fm Partner: c1 c2 m f
Interpretation of variance components Family:the extent to which families differ from one another Actor: the extent to which individuals act similarly across relationships. Partner:the extent to which individuals elicit the same behaviors from others. Dyad:The extent to which emotion expression is specific to the dyad and based on reciprocity.
Consistency of individual emotional expression across dyads: a function of the dyad or the individual?
% of variance explained by actor, partner and dyad for positivity and negativity: People are very consistent in their positivity across relationships irrespective of what their interactional partner is doing. Not so of negativity. The partner effect is surprisingly small
Negativity is less internal to the person and more sparked off by behavior of interactional partner than positivityHigh reciprocity for negativity
Do characteristics of individuals affect the dyads in which they interact or do they exert their influence across the whole family? Example depression
An individual’s depression score may explain family level variance or only variance in dyads in which the individual is a member.
Example of data coding to examine effects on families or dyads
Family (spillover) and dyad effects of depression on NEGATIVITY
Family (spillover) and dyad effects of depression on POSITIVITY
Advantages of multilevel methods for family data • Differentiating between family-wide and child-specific processes in families • Highly stressful environments increase the variation in the within family environment • Social relations model allows us to look at consistency of individual’s behavior in families, how different families are from one another and why
Disadvantages of multilevel approach to families For psychologists the statistics get very complicated Cross discipline collaboration is essential!!!