Hi!
I am investigating whether people who experience a reduction in cycling time to a certain place will also show an increase in cycling, between Time 1 and Time 2.
I have the following dataset:
DaysCycledit is the number of days cycled previous week at Time 1 and Time 2 for each participant;
Cycling_minutes_savedit is the reduction (in absolute values) from Time 1 to Time 2 in minutes in cycling time from residence to a specific location. This will have 0 for everybody at Time 1, and a value corresponding to the number of minutes saved at Time 2.
Time is coded 0 for Time 1 and 1 for Time 2.
Which of the following models makes the most sense to fit, and how should I do this in Stata?
1) Mixed model:
2) Linear regression:
3) None of these, and if so, how to specify a "correct" one? Yours,
Kjell Vegard
I am investigating whether people who experience a reduction in cycling time to a certain place will also show an increase in cycling, between Time 1 and Time 2.
I have the following dataset:
DaysCycledit is the number of days cycled previous week at Time 1 and Time 2 for each participant;
Cycling_minutes_savedit is the reduction (in absolute values) from Time 1 to Time 2 in minutes in cycling time from residence to a specific location. This will have 0 for everybody at Time 1, and a value corresponding to the number of minutes saved at Time 2.
Time is coded 0 for Time 1 and 1 for Time 2.
Which of the following models makes the most sense to fit, and how should I do this in Stata?
1) Mixed model:
Code:
DaysCycledit = Cycling_minutes_savedit + Time + (1 | subject_ID)
Code:
DaysCycledit=2 - DaysCycledit=1 =Cycling_minutes_savedit + DaysCycledit=1
Kjell Vegard
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