Most studies of children’s development and parents’ wellbeing have not dealt effectively with the complexity of multiple disadvantage. Traditional approaches have typically used a limited set of outcomes and predictors. Even studies utilizing multiple risk factors have often treated these as confounders, adjusting for their influence, while concentrating on a primary association of interest. Such strategies do not illuminate the real world essence of disadvantage, i.e. that adversities co-occur more than expected by chance and that multiple disadvantage is common. The main aim of the present paper is to address this neglected topic and develop summary measures of adversity using the 2004–2005 data from Wave 1 of the Longitudinal Study of Australian Children. Information was obtained from families of 5,107 babies (0–1 years) and families of 4,983 children (4–5 years). The prevalence of multiple disadvantage among families with young children and the degree to which summary adversity measures are associated with each other and with family and child outcomes is then estimated. Using factor analysis, 12 lower-order constructs and two higher-order components of adversity were developed, labelled (1) material and (2) psychosocial adversity. Findings show that the two component scores were more strongly associated with outcomes than were the more specific construct scores and that psychosocial adversity was somewhat more relevant to family wellbeing and child development than material adversity.
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We combine the mother and father measures to create a measure that is applicable to the main income earner. For single earner sole-parent families and single earner two-parent families, the main income earner is simply the sole income earner in the family—whether it is the mother or father. For dual-earner families, the parent that works more hours is defined as the main income earner. In the event where both mother and father work the same number of hours, main income earner is determined by the parent that has a permanent or ongoing job. In the remaining number of cases where both parents work the same number of hours and both have a permanent or ongoing job, the father is taken as the main income earner.
A complication in creating a summary score using fathers’ information is that it is only applicable to two-parent families. Our approach here was to create a summary score for sole-parent families separately creating a score ranging from 0 to 2. We then created a summary score for two-parent families where both the mother and father had answered the relevant question, creating a score ranging from 0 to 3. We created a third summary score for two-parent families where the fathers did not answer the relevant question, using only the mothers’ information, thus creating a score ranging from 0 to 2. This was done to minimise the loss of cases as a result of missing data, especially on the fathers’ information. We then converted all three scales into percentiles to approximate the placement on the shorter scales (0–2) in relation to the longer scale (0–3). All three scales were then combined into one summary score that took account of the different sub-populations as well as minimising loss due to fathers’ missing information.
As the employment conditions construct in the material disadvantage component and the time pressure (work) and parent–parent relationship constructs in the psychosocial disadvantage component were only applicable to sub-populations of the LSAC population, a total score that included those constructs would exclude sole-parent and jobless families. To include jobless families in the material disadvantage component, two scales were therefore created—the first included all six constructs and the second excluded the employment conditions construct so that jobless families were assigned scores. Similarly, in order to include sole-parent and jobless families in the psychosocial component, three scales were created—the first including all six constructs, the second excluding the parent–parent relationship construct so that sole-parent families were assigned scores, and the third excluding parent–parent relationships and time pressure (work) constructs so that sole-parent and jobless families were assigned scores.
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This paper uses unit record data from Growing Up in Australia, the Longitudinal Study of Australian Children. The study is conducted in partnership between the Department of Families, Housing, Community Services and Indigenous Affairs (FaHCSIA), the Australian Institute of Family Studies (AIFS) and the Australian Bureau of Statistics (ABS). The findings and views reported in this paper are those of the authors and should not be attributed to FaHCSIA, AIFS, ABS or the Department of the Prime Minister and Cabinet and cannot be taken in any way as expressions of Government policy. The main work reported was funded by FaHCSIA under the provisions of its Social Policy Research program. Bryan Rodgers and Peter Butterworth are supported by National Health & Medical Research Fellowships No. 471429 and No. 525410 respectively. The centre for Gambling Research is funded by the ACT Gambling and Racing Commission, which is the statutory body responsible for regulating gambling and racing activities in the ACT. The ANU co-funds the Centre.
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Gubhaju, B., Rodgers, B., Strazdins, L. et al. Developing Prospective Measures of Adversity Among Australian Families with Young Children. Soc Indic Res 113, 1171–1191 (2013). https://doi.org/10.1007/s11205-012-0134-5