Different levels of analysis require different methods of
analysis.
Simple cross-sectional (one wave) analysis is straightforward
but does not benefit from the longitudinal nature of the data.
Analysis of consecutive pairs of waves is straightforward.
For instance,
cross-tabulate state at and for categorical
variables (loglinear models of transitions)
logistic regression on yes-no change of state (can be
extended to duration modelling, i.e., ``event history analysis'')
linear regression models of the form
and
More sophisticated models (fixed-effects, random-effects):
The overall error is partitioned into an individual-specific
component which is time invariant, and ,
which varies from period to period.
Depending on the partitioning we get, inter alia,
fixed effects and random effects models.
These are conveniently fitted in Stata: will discuss further in
Week 2 (see xt set of commands).
Conceptually related to these models are multi-level models
which also take account of grouping of observations (individuals
within households, or wave-observations within individuals).
What they have in common is allowing separate random error
processes at observation (i.e., ) level and group () level.
These models are repeated-measurement in their orientation:
longitudinality enters as repeated (and perhaps ordered)
observations.
A major alternative perspective is to model durations (hazard
rate models, event history models)
Exploratory methods such as sequence analysis are also
useful.
All methods have shortcomings: time is hard to deal with
Methods which relate to (even pooling such
transitions for waves) focus only on annual transition
patterns
Most hazard modelling approaches focus only on single spells
(even when all spells are pooled)
Holistic methods such as sequence analysis give a better
general descriptive overview, but have no analytical power