# estimating percent effort

Many statisticians in American academic institutions have "soft money" appointments funded by a mutable portfolio of research grants. A fundamental aspect of holding such a position is proposing and negotiating "percent effort". We propose a few methods for percent effort estimation for use by soft money statisticians and scientists funding such statisticians.

### Thanks

Karl Broman and Chuck McCulloch provided comments on this draft three years ago. We had considered publishing this in a formal venue, but shelved the idea. They have not approved this version; so I take all responsibility for the content.

## Why are we writing about this?

Misunderstandings and disagreement regarding percent effort likely underly much interpersonal conflict involving soft money statisticians. Percent effort is a proxy for money, without being called so, and that makes many uncomfortable. Negotiations regarding percent effort conducted indelicately can degenerate into unpleasantness harming both the statistician and the funding collaborator. On the other hand, to negotiate effectively, and to have a productive working relationship, the statistician needs to build trust with the collaborator. A key ingredient is being transparent regarding what percent effort means, and how to come up with a number that appears fair to both parties.

We propose a few principles for percent effort estimation so the statistician can justify the number he/she proposes to a collborator. Note that these are principles, and not formulas. So, what may appear reasonable to one, may not appear so to another, and therefore one has to negotiate. We propose that one come up with a number that satisfy the following three principles that are framed as three questions.

## Three principles

### 1. How many projects like this can I handle?

Divide 100% by that number.

For example, if you can handle ten similar projects then that means the estimate should be 10%. This means that most collaborative projects should be at least 5% effort, preferably at least 10% effort. Very senior personnel may sometimes accept less effort, say 2%.

### 2. How many hours per year (month or week) would the project consume?

Divide that by the FTE equivalent.

The Biostatistics consulting unit uses a FTE equivalent of 1600 hours per year. This assumes you work 50 weeks a year, 40 hours a week (2000 hours), and about 20% of your time is spent on activities essential to your position, but not attributable to any specific project. This would include attending seminars, water cooler talk (not too much), writing letters of recommendation, attending conferences, and reviewing grants and papers.

The abovementioned 1600 hours FTE equivalent may or may not apply to you, but is a good place to start. Please use your judgment to estimate what is appropriate for your position.

So, if you think you might spend about 3 hours a week on a project, that would come out to about 10% effort.

### 3. What is the understanding with your collaborator?

Higher percent effort should mean faster response times and dedicated time slots.

By agreeing to a certain percent effort the statistician and collaborator enter into a contract. This is not always clearly spelled out, unfortunately, and can lead to misunderstandings. We are not suggesting that both parties treat this too formally, but it may be worth discussing mutual expectations. These expectations will also shape the percent effort.

For example, one might expect faster response times for a project covering 20% effort compared to 5% effort. A project covering 30% effort may ask the statistician to hold in-person office hours once a week.

## Case studies

We hope the above three principles appear reasonable to the reader. Now, we will apply them to a few imaginary situations involving Alice (a statistician) and Bob (a non-statistical collaborator).

### 1. Minimal effort

Bob would like Alice to provide statistical advice on a study of smoking and asthma. An experienced analyst in Bob's lab will perform the analysis under Alice's direction.

This may be a candidate for 5% effort for a mid-level or senior statistician. A junior statistician may consider 10% effort.

Principle 1. A moderately experienced faculty member would probably be able to handle 10-20 such projects assuming they don't all have use specialized techniques, and most involve judicious use of existing methods.

Principle 2. A 5% percent effort means that Alice can set aside about an hour and half weekly, or about 20 hours per quarter.

Principle 3. Alice would be somewhat available to Bob to answer questions, or to give feedback on an abstract or manuscript. It would not be unreasonable for Alice to take two weeks to respond to a query.

### 2. Advice and occasional analysis

Now, if this study involved analysis of longitudinal data of smoking patterns using mixed effects models that Bob's analyst is not fluent with, then one may expect Alice to request 10% effort. The understanding might be that Alice would perform the more complex analyses herself using R or Stata.

Principle 1. If Alice is fluent in analysis of longitudinal data, she may reasonably handle ten such projects (which require her to pick up the analysis).

Principle 2. Alice would devote about 40 hours per quarter (five days), or a little under two days a month. Although predicting how long data analysis would take is difficult, this may be reasonable. Many data analyses go through multiple rounds of revision, and so the amount of time devoted may be 10 or 20 times that of the final analysis.

Principle 3. Alice would be moderately available -- one might expect her to respond faster than in the previous case, or be available for a quick phone call in a week.

### 3. Specialized analysis

If the study has genomewide genetic data, and intends to perform a genomewide association analysis then Alice may request Bob to consider a greater intensity of involvement -- say 10-20% depending on complexity, and expectations from Alice.

Alice might agree to 15% effort with the understanding that she will follow the statistical literature closely, and if needed implement customized solutions to complete the statistical analysis. It seems reasonable that she could handle between 5-10 such projects (Principle 1), be available about 3 days a month for the project (Principle 2), and be expected to respond to queries, say within a week.

### 4. Leading a statistical core

If Alice is leading the statistical core of a center led by Bob with three related projects, then a starting point of discussion might be about 30% effort. Can one lead five such cores and remain sane (Principle 1)? Alice would have to lead a small team of programmers and be available to answer questions within a day or two (Principle 3). Alice may have office hours at Bob's center once a week. This could take between one or two days a week.

### 5. Leading projects

If Alice is leading her own projects, either as an independent principal investigator, or as part of Bob's lab, between 20-50% effort would be considered reasonable. Less than 20% effort may be seen as insufficient commitment -- to lead a research project one should be prepared to spend at least one day a week on average. Junior investigators would lean towards the higher end of the scale while senior investigators would likely be at the lower end.

## Concluding remarks

Statisticians on soft money appointments are supported by multiple projects from different funding sources. We believe that the three principles we laid out would help transparently negotiate percent effort. By agreeing to some ground rules, statisticians can reduce potential conflict, and build trusting long-term relationships with collaborators.

There is considerable flexibility in how the principles may be interpreted. For example, the FTE equivalent estimate is dependent on the individual statistician's circumstances. Both parties may use the process of negotiating percent effort to also negotiate mutual expectations.

We also recognize that estimating percent effort before working on a project amounts to extrapolation with attendant risks. If anything, many of us have a tendency to over-estimate what we can accomplish in a given amount of time. From this perspective it is helpful to be conservative in the estimates. It is often helpful to start out for a specified period (say six months or a year) and then adjust effort depending on realized complexity.

Finally, we note that while we have considered statisticians in soft money environments, the essential principles are applicable to non-statisticians, and academics in "hard money" environments with appropriate modifications. Hard money appointees are not usually required to adhere strictly to how they are paid, but as universities cut back on guaranteed funding for faculty, that might change.