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I am currently assessing some results I have from a model I applied on a corpus of text data I have mined.

My problem is that my professor have told me to use a certain method, and I do not really know how to attack this problem the most sensible way.

The main idea is to assess if there is a relationship between months and/or years on my response variable. My data has the following nature:

DV: Binary (Event or non event)

IV1: Month where event occurred/not occurred

IV2: Year where event occured/not occurred

I have a total of 431.000 observations

As it is now I have transformed my data so that I have count data for each time period instead of a binary DV. I also did a logit transformation on defined as ln(DV/(1-IV3)), in order to sort out the effect of activity.

DV(Event): Events in a time period given IV1 and IV2

IV1(Year): Year 1995 to 2012

IV2(Month): Jan to Dec within IV1

IV3(Activity): Events + Non-events in time period given IV1 and IV2 (Neutralized)

I have a total of 176 observations

Right now my model is defined as: DV = b1*IV1 + b2*IV2 + errorbut I am struggling alot with the intuition of whether this make sense. I have been looking into poisson models and zero inflated models, but until now the most sensible I think is to do the logit transformation in order to neutralize activity and the look at year and month as factors in a normal linear regression, but then again I am thinking that I might as well use a logistic regression.

Does any of you know how to handle such a case?

I am using R, SPSS and rapidminer.

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When dealing with DVs that are not location-family, it's generally better to avoid having an additive error. If you're ignoring the potential for serial dependence (it is time series after all), you probably want a straight GLM, and you may with that want to consider whether a linear link function is really the most appropriate.

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