I've been running economic experiments with undergraduate students at the University of Cape Town.  I've learnt a number of things about doing the experiments since I began.  Much of this stuff was not in the first 'bible' of experimental economics that I read a couple of years back - Daniel Friedman and Shyam Sunder's Experimental Methods: A Primer for Economists.  Nor have I seen it in many articles or other stuff publised about experimental economics, though I may have just forgotten the stuff that I've read. So here are some problems and insights that occurred to me.

1. Recruit more people: Recruit a lot of participants and expect that between 10-30% of those who signed up won't come, you'll reach the higher bound of missing participants more readily if it's raining badly and the participants would rather not leave their warm rooms. So recruit more participants than you think you'll need, that way if there are people who don't arrive, you'll be ok. Yes, you could make mistakes, but rather end up with slightly too many people than too few on a regular basis.  I over-recruited always, still I got burned with slightly too few (3 participants fewer than I'd have liked) on one day when it was raining heavily. 

2. Plan for Free Entry: Don't stick too strongly to your list of registered participants.  Plan for people who have not registered to show up.  I did this in my very first experiment, but I ended up turning two people away who I would have liked to have participate because I didn't prepare sufficiently for enough random arrivals.  Often they can slot into the roles of people who didn't arrive, so they counter the forger/weather/double-booked attrition.  To do this you obviously need to make the venue and time of the experiment public knowledge, which I had attempted to do through leaflets and UCT's online system (I don't know if this should be called foresight or paranoia).   I know that allowing day-of-experiment arrivals may affect your sample because people may have communicated the information through social networks, but when you're playing with university students it's unlikely to have a large effect and I expect it won't have any effect at all given the other constraints you deal with when doing economic and social experiments such as these.

3. Confront Disappointment Carefully: Some students don't like being randomly allocated to roles and don't understand that when everyone has the same probability of being a 'Participant A', 'Participant B', 'Participant C', etc that randomness makes the mechanism 'fair', even if the amounts of money at the end of the day if they had perceived them ex ante might be thought of as 'unfair' ex post.  I had a couple of students (literally two) who emailed me after the experiment because they were annoyed they got less money than other people did. One of them said it wasn't an experiment and that I'd used him.  I reminded him that many other disciplines don't give experimental participants the opportunity to make any money, or if they interview you they may not give any rewards, and when they survey you do may not give any kind of gift.  It seems as though the expectation of money influenced these two students and they got annoyed with me for the fact that others walked away with more than them. Oh well... I had to deal with this, 'in the name of science' (I reassure myself).  If you were wondering, the range of money that participants could walk away with was ZAR0 to ZAR120, roughly USD0 to USD16 in exchange rate terms, but substantially more, about $30 in PPP terms (by this site's estimates).  That said, other students enjoyed participating in the experiment and found it exciting and interesting. I reassured the disappointed students that the outcomes were fair. I made a point of not apolosing though as that would have meant I thought something was wrong - I simply pointed out that the system was fair, they just happened to get a partner with different preferences or maybe they had poor luck. I don't think we should apologise for this, though I could be wrong. 

4. Deputise: Recruiting good research assistants (RAs) was a crucial step for me.  I had recruited a couple of RAs for the first experiment, but not enough to deal with the havoc that can occur when people arrive late and want to play, when trying to ensure all forms are in, trying to stuff envelopes with money at a rapid pace, etc.  Two of my colleagues at UCT ended up helping me during the first experiment, which I appreciated greatly.  So I'd suggest that you hire at least one more RA than you initially think you need. Maybe you're gifted and can tell exactly how much of a problem X will be or Y might be. I wasn't that gifted, so I should have hired at least one more RA.  My first experiment went well, but it could have gone even better or as well as the second and third experiments did when I realised I should dedicate some (of my own) money to getting RAs.  You'll be happy you did it after the fact.  RAs make your life easier, though they do add their own complexities at times.  Also learning to deputise is itself a good skill to have and to develop. 

5. Plan/Play with other people: You may think you have everything covered.  You're probably wrong.  Bounce your ideas, your instructions, your forms, your questionnaires off of as many people as possible, both inside and outside of economics (or whatever your discipline happens to be).  I don't mean "check with your supervisor" obviously you've done that already.  I mean that I tested the instructions, forms, etc on my wife, my parents and my friends before I checked stuff with other economists.  It helped. As did checking the stuff with economists and my useful RAs.  Other people see things you don't.  Use these other people. Crowdsource.  Get other people involved, your ideas probably aren't so great that someone with a bit of insight could see a problem you didn't. 

Anyway, these are the five things I learnt while planning for and running experiments.  I'm sure more stuff will arise as I capture data, do the statistical work and write about the results.  The ideas above are logistical, non-technical suggestions, but I haven't really seen a blog post or article that deals with this kind of stuff.  Maybe I need to spend more time trawling the web, maybe I need to think harder about the problem before going in (I think I dealt with it well and I am proud of the quality of my data), but in the future I plan to ask more people for more help, to deputise carefully and to try to find more and new ways to recruit more people.