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By Peter Flom, April 23, 2010 1:35 pm

There are many reasons to write a grant, and many places to apply for one – from small grants for a few thousand dollars, to multi-year grants for many millions of dollars.  If your grant involves any sort of data analysis or data collection, even something very simple, it can be worth your while to consult with a statistician.  It is better to consult early in the process.  Although consulting costs money in the short term, it can save you a lot of time and money in the long term, and can improve your chances of getting a grant.

Some ways a statistician can help a grant writer -

1) Finding instruments – not all statisticians can do this, but many (including myself) can.  There are a huge array of psychological instruments out there.

2) Making data collection appropriate – when people come to me with data, it’s often collected in ways that make it hard to analyze.  Then I spend hours manipulating the data into the proper format.  If they had come to me before starting, it would have taken me a lot less time to show them a better way.

3) Power analysis.  Many federal agencies such as the National Institute of Health actually require power analysis.  Even if you aren’t required to do one, it can be very helpful to do so – to see how many subjects you will need to detect various effects.

4) Analysis plan.  If you come to the statistician (such as me) early, then he or she or I can help you answer the questions you want to ask, rather than the questions that the statistical techniques you are familiar with can answer.  There is a wide range of statistical techniques out there, and it’s better to let the substantive questions drive the analysis then the other way round.  A good carpenter has a big set of tools; but if you are not a carpenter, you may only have a few.

5) Doing the actual analysis – Once you get your grant, and start collecting data, you’ll want to analyze it. A good statistician can do it accurately and quickly, and show you the results in ways you understand

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By Peter Flom, April 14, 2010 1:29 pm

Modus tollens in logic
In logic, there is an argument style called modus tollens:

If  H0 then R
Not R
therefore
Not H0

This is a valid argument.

Modus tollens misapplied to p-values
Some people mis-apply this to p-values, saying:

If H0 then probably not R
Not R
therefore
Probably not H0

This is not valid.
Continue reading 'p-values and modus tollens'»

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By Peter Flom, April 14, 2010 1:04 pm

The answer you get depends on the question you ask

In many substantive fields, students take one, two, or perhaps three statistical courses during graduate school.  These typically cover things such as descriptive statistics, ANOVA, regression, and perhaps a couple variants of regression such as logistic regression.  These are good tools for many purposes, but it’s a very limited toolbox.  This limits the number of questions you can ask.  Perhaps the really interesting substantive question is one that you can’t answer with those methods.  But if you ask a statistician or data analyst, you may find that the right method to answer your question does exist.

You can’t see something you’re not looking for

The more specific your question, the better you will be able to answer it; but if it’s too specific, you may miss something else.  Researchers need to learn to adapt the focus of their investigations.

If you’re not surprised, you haven’t learned anything (well, not much, anyway)

Isaac Asimov once said “The most exciting phrase to hear in science, the one that heralds new discoveries, is not ‘Eureka!’ (I found it!) but ‘That’s funny …’”. That is, surprising.  It’s fine to confirm what you already suspected, but the real advances are made when you find things you did not expect.

and

Any analysis worth doing can be done in more than one way

This gets back to the toolbox – Which method should I use? but, even within a method, there are often options.  Should I transform variables?  Which covariates should I include?  How complex should my model be? What effect sizes should I report?

Often, these and other related questions do not have simple answers, but rather a range of reasonable choices.

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By Peter Flom, April 10, 2010 4:19 pm

Why we need survival analysis
When the dependent variable is continuous, we would ordinarily first think of linear regression. It’s a very good methods when you want to look at the relationship between a continuous dependent variable and one or more independent variables.

But, like nearly all statistical techniques, they make assumptions. And one of the assumptions that is so clear as to usually go unstated is that we know the value of the dependent variable; usually, this is not a problem. If we want to model, say, what people weigh, we can weigh them. But in one common type of analysis, we don’t always know the dependent variable – that’s when the dependent variable is time to an event. In that case, we need survival analysis.

Continue reading 'What is survival analysis?'»

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By Peter Flom, March 17, 2010 1:38 pm

The nominal ordinal interval ratio scheme
Stevens (Stevens 1946) divided types of variables into four categories, and these have become entrenched in the literature. The levels are nominal, ordinal, interval and ratio. To fully understand these, you have to use the same methods that Stevens used, which involve permissible transformations. However, it will be clearer to first describe each level more casually.
Nominal responses
Nominal comes from the Latin for ‘name’ and nominal variables are those that are simply names – they have no order. Examples are hair color or religion.

Continue reading 'Nominal, ordinal, interval, ratio: Stevens’ typology and some problems with it'»