Dear netters,

Sorry for cross-posting this question. I am sure R-Help is not a

research methods discussion list, but we have many statisticians in

the list and I would like to hear from them. Any function/package in R

would be able to deal with the problem from this researcher?

---------- Forwarded message ----------

From: Heidi Bertels

Date: Tue, Jun 5, 2012 at 4:31 PM

Subject: How to best analyze dataset with zero-inflated loglinear

dependent variable?

To: RMNET <

[hidden email]>

Dear colleagues,

I have what I think is an interesting dataset, but I have never

analyzed anything alike. As background: over a 12 week period,

employees developed a business plan and attempted to obtain funding

for their project in teams of 3–5 corporate entrepreneurs. Data of

team members was obtained and aggregated for a total of 39 teams (we

have the same data for the executive champions of the teams). The

funding amount obtained is the dependent variable for the study. As

independent variables, there are a series of criteria (e.g., venture

team understood the business aspects) and obstacles (e.g., competition

for resources within the company) that every team member rated in

terms of how essential it was to the funding decision (or how

significant an obstacle it was). Each employee at the end of the

twelve week period was asked to evaluate these on a 5-point scale.

The dependent variable is "problematic" because of its distribution.

It is zero-inflated (many projects did not receive funding), and for

those projects that did receive funding, the distribution is

loglinear. I believe this is called a zero-inflated loglinear

continuous dependent variable. I can't use regular regression analysis

because the assumption of linearity is violated. Does anyone have any

ideas?

I have already done independent t-tests, but would like to go much

further. I could split up the sample in two ways. First "funded/not

funded" (logistic regression) and then funded only with funding amount

(assuming there might also be differences between projects that

received high versus low funding), but then the N gets even smaller...

Thank in advance,

Heidi Bertels

University of Pittsburgh

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