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Family structure and child outcomes: an illusive relationship Don Kerr King’s University College University of Western Ontario 2004 Canadian Population Society Meetings Winnipeg, Manitoba. To what extent is "family structure” important in predicting child outcomes? 2 issues:
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Family structure and child outcomes: an illusive relationship Don Kerr King’s University College University of Western Ontario 2004 Canadian Population Society Meetings Winnipeg, Manitoba
To what extent is"family structure” important in predictingchild outcomes? • 2 issues: • Why should “family structure matter”? • What empirical evidence is currently available on this issue with the NLSCY?
Behavioral Scales for Children 4-11, NLSCY (first cycle) by Family Type
Why should “family structure matter”? • In both step and lone parent families child loses out from the lack of co-residence with one biological parent • -> less parental supervision • -> lower transfer of social & human capital (Amato & Booth, 1997) • There is no consensus on this issue!!!
In working with the first wave of the NLSCY • Multivariate analyses: -> family type is found to be a useful predictor of childhood difficulties • -> association persists with controls for low income, age, educ, etc. • What does this cross sectional association represent??? > the impact of being raised in a lone parent/step family OR > the impact of “antecedent” factors i.e. what lead to the formation of the lone parent/step family in the first place? (conflict, abuse?)
In Longitudinal analysis: • -> most common approach: • “autoregressive or residual change analysis” • -> An alternative is now possible (4 cycles of the NLSCY) • Latent Growth Curve Models (LGM)
Autoregressive or Residual change approach 1994 2000 Child outcome 2000 Child outcome 1994 Family structure, income, etc. • Many critiques of this approach • Rogosa et al. 1982; Rogosa and Willett (1985)
As an alternative: • Latent Growth Curve Models (LGM) • Duncan et al (1999) • -> does not model variance at a specific point in time • -> attempts to model individual trajectories on dependent variable over time
Example: with consistent measures of hyperactivity: over 4 cycles (1994, 1996, 1998, 2000) Score on hyperactivity scale
Latent Growth Models Intercept Slope Scale on externalizing problems 1994 1996 1998 2000 E2 E3 E4 E1
Latent Growth Models Mean -.565* Var .466 Mean 4.9 Var 6.7 Intercept Slope Scale on externalizing problems 1994 1996 1998 2000 E2 E3 E4 E1
Latent Growth Models Lone parent 1994-2000 -.05ns .176* Mean -.565 Var .466 Mean 4.9 Var 6.7 Intercept Slope Scale on externalizing problems 1994 1996 1998 2000 E2 E3 E4 E1
Latent Growth Models Lone parent 1994-2000 Low income 1994-2000 -.05ns -.033ns .176* .099* Mean -.565 Var .466 Mean 4.9 Var 6.7 Intercept Slope Scale on externalizing problems 1994 1996 1998 2000 E2 E3 E4 E1
GFI=.970 CFI=.971 χ2 =355.6 df = 13 Latent Growth Models Lone parent 1994-2000 Step Family 1994-2000 Low income 1994-2000 -.05ns .132* -.033ns .107* .176* .099* Mean -.565 Var .466 Mean 4.9 Var 6.7 Intercept Slope Scale on externalizing problems 1994 1996 1998 2000 E2 E3 E4 E1
Conclusion: • Results are preliminary • Results are very mixed as to the importance of family structure • Future research -> additional controls/behavioral scales • LGM looks particularly promising in the analysis of change