Justine McNamara

Children in Housing Disadvantage in Australia: Development of a Summary Small Area Index

The importance of suitable, affordable housing in promoting the well-being of children is widely acknowledged. However, despite growing interest in geographic differences in child well-being, little is known about the spatial distribution of characteristics associated with housing disadvantage for Australian children. This paper seeks to develop knowledge in this area by creating a headline indicator of child housing disadvantage. It uses spatially disaggregated Census data from the Australian Bureau of Statistics (ABS) Census of Population and Housing 2006 capturing overcrowding, public housing tenure and dwelling type, and creates synthetic estimates of housing stress using spatial microsimulation techniques. These variables are then combined into a single index of housing disadvantage. Next, the spatial microsimulation techniques and index creation methodology are described, and the results of the validation process, including sensitivity analysis of alternative index creation approaches, are presented.

The Challenges of Combining Two Databases in Small-Area Estimation: An Example Using Spatial Microsimulation of Child Poverty

Spatial microsimulation techniques have become an increasingly popular way of fulfilling the need for generating small-area data estimates. However, the technique also poses numerous methodological challenges, including the utilisation of two different databases simultaneously to produce estimates of population characteristics at the local level. An important but neglected question is whether different distributions of key variables within these two databases may affect the validity of the spatial estimation results. This study uses the significant policy issue of small-area estimates of child poverty rates in Australia to examine this question. The different income distributions for families with children in the two databases and the consequent effect on child-poverty estimates are assessed, while the apparent validity of these synthetic small-area poverty rates is gauged.

Career development in higher education through group mentoring: A case study of desirable attributes and perceptions of a current programme

Although group mentoring is not widely utilised in higher education settings, it has been viewed as successfully helping the research careers of academics (Walkington, Vanderheide, & Hughes, 2008). This paper explores the results of a qualitative and quantitative study about perceptions of one university-based group mentoring programme. The results enable an understanding of challenges that might exist in attracting potential participants to a group mentoring programme. Results indicate that organisations implementing a group mentoring programme need to make sure goals and outcomes of the programme are properly communicated, and that these also need to align with academics’ priorities for career development.

Spatial Microsimulation Using a Generalised Regression Model

This chapter outlines a method of spatial microsimulation that uses a reweighting algorithm implemented with the SAS programming language. The reweighting algorithm derives population weights by benchmarking the unit record level survey data to the reliable spatially disaggregated census data. These weights can then be applied to the sample to derive final populations for the small area, just like survey weights provided by national statistical agencies allow aggregation to national totals.