Perhaps my experience is atypical, but I often bill clients for what I'll call here "R&D" -- learning about unfamiliar methods or developing new ones to meet the client's needs. In fact, I consider this learning on the job a key perk of stats consulting.
My perspective on this is that if something I spend time on during our contract period benefits the client, then I'm comfortable billing the client for it. That includes R&D. If a client isn't comfortable with that for some reason, we can either negotiate a compromise or decide not to work together.
I completely agree, Michael, that any R&D a client pays for should be discussed and agreed upon in advance and handled transparently. Maybe my clients are unusual, but they rarely object to my spending billable time on R&D for their projects. In brief, here's how I typically handle this:
* Before I'm hired I tell the client that a substantial part of my billable work will involve R&D to find or develop appropriate methods. We discuss this, and it's specified in my contract, so the client has ample opportunity to negotiate (or hire someone else).
* I sometimes offer a markedly reduced fee for either the entire project or the R&D component, especially if the method is something I'm especially interested in learning or developing. I tell the client this, so s/he knows I'm sacrificing some income while spending time on activity that doesn't directly produce deliverables.
* I usually include "approval intervals," whereby after each fixed amount of billable time (e.g., 5 hrs) I update the client on my progress and obtain his or her approval before continuing. This gives the client a way to track my billable contributions and, if s/he's uncomfortable with the amount of R&D (or anything else), discuss alternative strategies with me.
This has worked well so far. For example, on a 120-hour project that involved investigating temporal precedence in bivariate panel data, I spent about 15 hours reading literature to find an appropriate method and well over 30 hours understanding details of a particular approach (J. J. McArdle and colleagues' bivariate dual change-score model) and implementing it in LISREL and Mplus. Some of my considerable R&D time involved consulting with experts in this approach and running small Monte Carlo simulations to check its performance with data like my client's. I learned a lot, and I think the client did, too -- she really wanted to understand the technique and its limitations. As far as I know she was pleased with my contributions; she and her colleagues published the study in a top clinical psychology journal.
Cheers,
Adam
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Adam Hafdahl
Owner & Principal Consultant
ARCH Statistical Consulting, LLC
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Original Message:
Sent: 04-30-2012 00:31
From: Michael Chernick
Subject: Rigor vs. simplicity (or, Consultants cause complications)
I thnik that before you do work for a client that is not clearly an agreed part of the assignment you explain why you want to do it and get the client's approval. That way there is no controversy about whether or not they should pay you for the work. In the case of data cleaning I think data cleaning should always be part of data base management prior to analysis. If you see errors in the data it should be corrected. It shouldn't matter whether or not you foresee a problem with the analysis as a consequence. there is always the danger of unforeseen problems with the current work or future work. The client benefits from data cleaning. Nevertheless it is the client's choice as to what he is willing to pay you to do and you can only charge for what is agreed upon. That also goes to your question about doing research for a client. That too would have to be agreed upon by both parties and I would think that if they would allow this they would limit the amount of time spent on the research. But I find it unlikely that a typical client would pay for research. I agree with Edith that this is something you would do on your own initiative for your benefit and benefit to future clients.
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Michael Chernick
Director of Biostatistical Services
Lankenau Institute for Medical Research
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Original Message:
Sent: 04-29-2012 23:11
From: Adam Hafdahl
Subject: Rigor vs. simplicity (or, Consultants cause complications)
Thanks for your thoughts, Michael. In my reply to Edith I addressed your comment about charging a client for developing (or finding/learning) methods, so I won't say more about that here.
Your examples about choosing between techniques seem relatively clearcut, so below I'll pose one that seems harder to resolve, based on some previous work I've done for a few different clients:
I've had clients bring me large, messy data sets that I've spent considerable billable time checking for errors and anomalies before running primary analyses. Although I've found several things to correct in these clients' data, it's not clear to me that these improvements to the data are worth the amount of time I spent on "cleaning," and I suspect that sometimes the client would rather not pay for these improvements (though only rarely has a client complained). This is an example where being more "rigorous" (i.e., more fastidious about data cleaning) can be costly for a client, the client might prefer a "simpler" approach (involving less cleaning), and it's often unclear in advance -- before doing the checking -- whether any improvements will be worth the time and expense.
What interests me about situations like this example is that it can be challenging to explain to some clients why an apparently costlier approach might be preferable. I can often empathize with these clients: If I hire a contractor to build something for me and s/he offers options ranging in quality and price, I'll often avoid the most expensive options because (to me) their benefits aren't worth the additional expense.
Cheers,
Adam
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Adam Hafdahl
Owner & Principal Consultant
ARCH Statistical Consulting, LLC
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