Most surveys are cross-sectional, which means they examine a sample of the population at one point in time. Analysis performed on cross-sectional data
are only able to give a ‘snapshot’ of information about the surveyed
population at that time (Rose 2000a). Longitudinal surveys, on the other hand, take a number of repeated measures of the same group of individuals over a period of time; this leaves longitudinal analysis with the unique benefit of being able to examine transitions between states (Rose 2000a). The ability to investigate transitions between states is the key strength of longitudinal analysis and the reason why it is used in this thesis. This research aims to identify the predictors and impacts of different caring behaviours (dual care in particular). Movement in and out of the various caring responsibilities is a
‘transition between states’, the observation of which allows us to begin
answering the questions of what characteristics make people more likely to take on different caring responsibilities and what the impacts of changes to caring responsibilities are. These answers are only obtainable through
136 longitudinal analysis, which is why it features so strongly throughout this thesis.
The key strength of longitudinal analysis has been outlined above. Along with its enhanced investigative power, longitudinal analysis also brings with it some unique limitations. The two key limitations are attrition and the issue of order of events between time points. Both of these limitations are outlined in the discussion of the HILDA data set. To review those concerns: attrition occurs
when respondents ‘drop out’ of a survey, or do not participate in every wave.
Attrition can pose a serious issue for the validity of analysis because the attrition may be non-random (ie. some characteristics make people more like to leave the survey), resulting in a biased sample (Winkels and Withers 2000). The issue of order of events between time points is also described as the issue of time-varying variables (Singer and Willett 2003, Allison 2010). This issue occurs when events happen in between data collection points and the
researcher is unable to ascertain the true order in which they occurred. Both limitations were discussed more fully in the HILDA section of this chapter, where it was argued that these limitations are not serious enough to discount the key benefit of longitudinal analysis.
Having made the decision to utilise longitudinal analysis, I then decided on two specific longitudinal analytical techniques; event-history analysis and multilevel modelling. These techniques are now introduced, although the in- depth explanation of how they work is reserved for discussion in the
corresponding results chapters.
Event-history analysis
Event-history analysis, also known as survival analysis, is used for answering questions about whether or when events occur (Singer and Willett 2003). It is appropriate for this thesis because the first key research question asks what the predictors of dual care are. In other words, this research seeks to discover
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whether or not Australians become dual carers, given their other characteristics.
As well asking whether Australians become dual carers (based on other characteristics) this research also predicts two other caring responsibilities (informal care and child care) and investigates how their predictors differ from the predictors of dual care. In order to understand how dual care differs from other caring responsibilities, event-history analysis is first run with dual caring as the event of interest. Essentially, the event of becoming a dual carer is the dependent variable and other demographic and socio-economic
characteristics form the independent variables, predicting the likelihood of dual caring occurring. Following the dual care model, further models are run with informal care and child care (separately) as the dependent variables. This allows an exploration of how different independent variables can predict different types of care. For example, gender may be a strong predictor of child care and dual care, but only a weak predictor of informal care.
The details of the methodology of event-history analysis are presented alongside their results in Chapter Six – The Predictors of Dual Care.
Multilevel modelling
Many researchers utilise multilevel model analysis to explore the differences
within groups and differences between groups (Rabe-Hesketh and Skrondal
2012). When considered in a longitudinal setting, multilevel modelling offers a unique way to understand change in a data set. It allows us to examine within- individual change and inter-individual differences in change (Singer and Willett 2003). In longitudinal analysis, individual respondents form the higher- level groups and the repeated measurements over time for each respondent form the lower level of the multilevel model.
Multilevel modelling is used in this thesis to explore change within four caring groups; dual carers, informal carers, child carers and non-carers (the reference group). It is also used to explore change between those four groups. By
138 analysing the change within and between dual carers, informal carers, child carers and the non-caring population across a range of variables, this analysis reveals the impacts of dual caring and shows how they are different to the impacts of informal caring, child caring and having no caring responsibilities. In this way, multilevel modelling facilitates answering of the second key research question, which asks; what are the impacts of dual caring, and how are they different from the impacts of providing informal care, child care or not having any caring responsibilities?
For each variable on which the impacts of caring were being investigated, a number of models were run. Multilevel models allow for changes in elevation and slope, changes in elevation but not slope, and changes in slope but not elevation. A model which allows for a change in both elevation and slope allows the impact of caring status to have an immediate effect, as well as allowing that effect to intensify or diminish over time. Models allowing for changes in elevation only allow the impact of caring to change immediately and those allowing for changes in slope only do not allow for an initial impact, but do allow for an impact which changes over time. In other words,
multilevel models allow us to investigate how events or characteristics of individuals impact on those individuals, both when they happen and over time. The details of this methodological approach are presented in Chapter Seven – The Impacts of Dual Care.
Conclusion
The primary purposes of this data chapter were; to outline the specific data sets analysed in the research, to demonstrate the ways in which key variables have been defined, constructed or modified for analysis, and to introduce the specific analytical techniques used.
HILDA and Census data were introduced and the rationale for using each data set was provided. The key strengths of HILDA include being nationally
139 representative; meaning the analysis is generalizable to the wider Australian population, and being longitudinal; which allows for analysis addressing causation (or at a minimum order of events) rather than simply correlation. The primary strengths of the Australian Census data are that it is a complete survey of the Australian population, and that the questions regarding
provision of care are very straightforward and unlikely to exclude any kinds of carers.
The limitations of each data set were also outlined. HILDA data faces
limitations in the form of attrition, the measurement of the provision of care and identifying the order of events occurring between time points. Census data is limited in the lack of longitudinal data access, lack of individual-level data access and a lack of information regarding physical and mental health and well-being. Considered together, the outlined strengths and weaknesses of each data set shape decisions about the ways in which each source is used. In addition to providing information about the data used in this thesis, the data chapter also introduced the specific analytical approaches and techniques employed. This included an examination of quantitative and longitudinal approaches at a more general level, and then a more specific exploration of event-history analysis and multilevel modelling. It was demonstrated that event-history analysis allows researchers to answer questions about how
certain variables can predict event occurrence. Event-history analysis therefore facilitates answering of the research question regarding what characteristics predict each type of caring responsibility (informal care, child care or dual care). Multilevel modelling was shown to be useful in measuring the impacts of characteristics or events over time. Therefore, multilevel modelling is used to investigate the ways in which different caring responsibilities impact a wide variety of other areas.
140 The presentation of results obtained through use of the aforementioned data sets and analytical techniques begins in the following chapter, Chapter Five –
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