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Re: st: analysing experimental panel data

From   "Justina Fischer" <>
Subject   Re: st: analysing experimental panel data
Date   Thu, 18 Oct 2012 08:56:19 +0200

Hi Matthew

too me your set-up looks like (experiment - panel)

experimental rounds = time
subject = ID
quota = group variable, time invariant 
treatment = event happening to some IDs at a specific round

then you can compare individual behavior for same time point (rounds), but also different treatments; in some cases this might boil down to a round-specific cross-sectional analysis


-------- Original-Nachricht --------
> Datum: Thu, 18 Oct 2012 02:08:52 +0000
> Von: Matthew Sunderland <>
> An: "" <>
> Betreff: st: analysing experimental panel data

> Hi All
> I am seeking advice on how best to analyse data arising from an
> experiment. We surveyed 2,000 people asking them to hypothetically purchase and
> consume alcohol for an imaginary Saturday night. 
> We collected data for three imaginary nights - First we presented
> participants with a set of alcohol prices reflecting current prices (baseline). We
> presented participants with two mores set of prices in a randomized order
> reflecting  price increase resulting from i) the establishment of a minimum
> price and ii) an increase in the rate of tax. Participants comprise six
> quotas, differentiated by gender and recent cannabis and ecstasy use. 
> Alcohol consumption is measured by the number of standard drinks, calculated by
> us from participant reports of how many items of alcohol they would consume
> eg glasses of wine, stubbies of beer etc. About 30% of the participants
> did not drink at baseline. 
> We'd like to know: Do the two reforms have different impacts? Do people in
> different quotas respond differently to the reforms?  Do people with
> different levels of base-line drinking  respond differently to the reforms? 
> One option we've thought of is for us to run two sets of fixed effects
> analysis (washes unobserved heterogeneity relating to alcohol consumption and
> quota membership)- using panel data for drinking at baseline and one of the
> reforms. Another option is for us to simply control for baseline
> consumption. We're thinking of running the analysis in two steps - a logit for
> whether or not someone drinks and an OLS regression for drinkers  -  log of
> standard drinks consumed, controlling for the predicted values coming from
> the logit. 
> Thanks,
> Dr Matthew Sunderland
> Drug Policy Modelling Program,  National Drug and Alcohol Research Centre
> The University of New South Wales
> Sydney NSW AUSTRALIA 2052
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