Presentation on theme: "SADC Course in Statistics Appreciating General Forms of Longitudinal Data (Session 06)"— Presentation transcript:
SADC Course in Statistics Appreciating General Forms of Longitudinal Data (Session 06)
To put your footer here go to View > Header and Footer 2 Learning Objectives By the end of this session, you will be able to explain ways in which time series data poses different requirements to those of longitudinal research discuss basic features of longitudinal approaches and how these compare to the features of cross-sectional (as well as time series) studies move on to further unaided study of reports and other literature about longitudinal work
To put your footer here go to View > Header and Footer 3 Demands of time series methods In many studies data collection through time is important. Key information can be gleaned from linking data files from different times, but this may not yield time series data. Time series methods assume we have a long series of regularly repeated measurements of the same quantitative variable, according to precisely the same definitions and measurement protocol.
To put your footer here go to View > Header and Footer 4 Longitudinal studies The word longitudinal is used to relate to studies with observations over an extended period of time. They need not be precisely the same observations every time, e.g. school-childrens educational achievement may be assessed several times in their school lifetimes – each time by a different test, as the children progress. Formal (external) testing may be after varied lengths of time say 4, 3, 2 & 2 years.
To put your footer here go to View > Header and Footer 5 Example: cohort studies - 1 Epidemiological terminology; used more widely. Classic medical examples (i)look at, and arrange to follow up, an initial disease free group (usually large number) (ii)track, and measure, their exposure to risk factors (e.g. diet, environment, smoking) & (iii) at conclusion of study (often years later) assess disease incidence, outcome (iv) relate disease status statistics to risk factors.
To put your footer here go to View > Header and Footer 6 Example: cohort studies - 2 Main measures are Before & After. Interest is in a long-term change, rather than month- by-month. There are thousands being observed in the cohort, rather than one rain gauge in a meteorological time series. Nature of interim measurements may change as science progresses e.g. new risk factors identified.
To put your footer here go to View > Header and Footer 7 Longitudinal vs. cross-sectional In many cases a research-oriented study may either be conceived of as being on a one-off basis or on repeated observation (longitudinal = Longl below) basis. A key benefit of Longl is follow-up i.e. revisit same subjects to measure change. Before = B and after = A measures on same subjects show real difference through time. If B and A are on separate subjects, the difference may be due to subjects, not due to time.
To put your footer here go to View > Header and Footer 8 Tracking - 1 Follow-up or matching requires putting serious resources into tracking systems, so subjects dont disappear. If they do, B/A comparison is void, and (i) cost of B data collection is wasted, (ii) features of original sample design are damaged. Tracking may use name, address, GPS measures, family and friend and neighbour contacts, club facilities to promote loyalty, school registrations to follow familys children.
To put your footer here go to View > Header and Footer 9 Tracking - 2 Keeping control of tracking information is itself a data management issue! Tracking rather static rural populations is much easier than with urban poor e.g. slum dwellers. Generally rural pop.n move less and social networks usually easier for outsider interviewers to interrogate. Definitional problems accompany movers even if traced. Say farm worker becomes a migrant mine worker: complex effects on physical and mental health.
To put your footer here go to View > Header and Footer 10 Institutional settings - 1 Time series data usually emerge from large institutions with stable systems able, and motivated, to ensure precisely the same definitions and measurement protocol. Thus definition and focus usually inflexible. Time series datasets generally have – or are treated as – ONE key measure of interest + some subsidiary, explanatory, variables. Definition of the one measure must be agreed by many people, commonly used and understood.
To put your footer here go to View > Header and Footer 11 Institutional settings - 2 Longl studies often represent research new or imperfectly-understood theme, need to look at wider range of variables, study as a whole done as a one-off, not a repeated monitoring exercise Difficulties arise because (i) compared to cross-sectional studies, longl work is costly e.g. tracking costs; (ii) needs quite long- term commitment from researchers.
To put your footer here go to View > Header and Footer 12 Is longitudinal research necessary? Consider study of poverty, and livelihoods of poor. How people became poor is a process taking place through time. Measuring once cannot truly capture that process. Precarious livelihoods are often a feature of poverty: earning opportunities are unstable seasonal, short-lived, or few – thus one-off observation often misinterprets longer-term situation with regard to trend, seasonality, occurrence of shocks (family or crop health, climate etc) – so answer, as in this example, is Yes, longitudinal research is necessary!
To put your footer here go to View > Header and Footer 13 Study design: cause & effect - 1 Often a strong focus on cause and effect (look back at final session of Demography & Epidemiology module). Simple example is study of an intervention with classic design involving sets of matched pairs with and without the intervention:- Before After Intervention NO Intervention
To put your footer here go to View > Header and Footer 14 Study design: cause & effect - 2 If the befores are matched but a difference shows up in the afters – consistently over many matched pairs, there is strong support that the consistent intervention (e.g. micro-finance initiative brought in at village level) was the cause. If design lacks the matched non-intervention cases, the causal argument is weaker. Note this form of impact assessment must be planned at inception, NOT afterwards!
To put your footer here go to View > Header and Footer 15 Longl design: sample structure - 1 Need to track, revisit and build relationship with sampled respondents is critical. Cost dictates they are grouped like sample clusters. Often also want to see effects in context of locality where households or businesses are located so community data collection part of study. Standard statistical theory of cluster sampling (in module H6) is not related to longitudinal problems. Clusters are not supposed to be interesting in their own right and are randomly selected.
To put your footer here go to View > Header and Footer 16 Longl design: sample structure - 2 Main analytic tool is comparison of results for same community/household/business over time. Less stress on representativeness of the communities than in cluster sampling, & random selection of clusters is implausible:- need to generate interesting interim findings, relevant to clusters (communities) and to overall policy, so as to maintain funding. thus choose series of cluster settings carefully as sentinel sites (see Basic Module BX), not at random but so that each sentinel site shows something important as study progresses.
To put your footer here go to View > Header and Footer 17 Longl design – sample structure - 3 Within each sentinel site, normally choose a probability-based sample e.g. simple random sample of qualifying households (e.g. qualifying in having joined micro-finance group). Tracking decision, based on detailed study objectives, is whether to track same individuals or individuals in same positions in the sentinel site sample e.g. EITHER follow time progress of households who were at time zero in the micro-finance group OR compare 2-yr-olds there in 2010 with 2-yr-olds there in 2006, because focused on health status of 2-yr-olds.
To put your footer here go to View > Header and Footer 18 Literature This is a very large area of research. A few books are:- De Vaus, D. (2001) Research Design in Social Research. Sage, London. Hakim, C. (2000, 2 nd ed.) Research Design: successful designs for social and economic research. Routledge, London. Menard, S. (1991) Longitudinal Research. Sage University Papers 76. Rose, D. (editor) (2000) Researching Social and Economic Change: the uses of household panel studies. Routledge, London. Ruspini, E. (2002) Introduction to Longitudinal Research. Routledge, London.
To put your footer here go to View > Header and Footer 19