Hello,
I am attempting to use a fixed-effects model to examine attitudes in a
longitudinal setting.
I have a panel dataset and I would like to explain the attitudes of
individuals (independent variable) at time t. The attitudes are
measured on a score from 0-10 and I am using a fixed-effects model
(using xtreg, fe and treating the attitude variable as an interval
level variable) as I think this is most appropriate model for me to
study within-person change over time.
I have included a set of time-varying predictors such as marital
status (coded as 0 =not married, 1 = married). I was hoping to get
some advice on if I am coding and interpreting the effect of the
time-varying predictors correctly.
Example.
Subject #1 is included for 5 waves of the data, and has married by
the second time period.
Id Wave Attitude Mar_status
1 1 6 0
1 2 8 1
1 3 9 1
1 4 8 1
1 5 9 1
Would this be the correct way to set up the time-varying mar_status variable?
If the coefficient for mar_status (marital status) in the
fixed-effects model is positive, could this be interpreted as meaning
that a change from not being married to getting married has a positive
effect on the attitudes.
I am very new to using longitudinal models and would appreciate
knowing if I am going in the right direction or not.
Thanks very much in advance
Anna
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