Rachel M. MacNair, Ph.D.
has served over 325 clients since the year 2000.
She has far more experience on dissertation work than most faculty serving on
dissertation committees do.
Telephone: (816)753-2057 E-mail: firstname.lastname@example.org
For students working on their dissertations or other research projects which require statistics, I offer the following
• Running or checking on the statistical tests you are using.
• Advising on additional statistical procedures that would be fruitful for your study, and running those as well.
• Explaining to you carefully what the statistics mean until you understand well enough to write the results and explain
what you did to your committee or professor.
• Reading over your dissertation, thesis, or paper to catch any errors or to suggest improvements. You can avoid
embarrassment when presenting it to others, and get feedback from someone who is not giving a grade before offering
it to someone who is.
• Practice oral defense sessions by telephone.
I specialize in a quick turn-around. Results are generally back
within two weeks.
Click here for my curriculum vitae.
• Editing of writing for APA style, grammar and organization
• Literature searches.
$75.00 per hour (rounded to the next quarter-hour)
for a running tab, to be invoiced at either around the 5-hour point or when there's a break in the need for services
$198.00 for a prepaid three hour block, a 12% discount. Refunds will be given for unused portion. Three hours is
the mode average for what my clients need.
$250.00 for a prepaid five-hour block, a 33% discount, for those who think they will need more time. Refunds will
be given for unused portion. Five hours is the median for what my clients need.
$112.50 per hour Rush Job Rate (or count time-and-a-half on prepaid block)
• A tight deadline of one week or less (this can be waived if I would have gotten to it that quickly anyway);
• Direct telephone calls to me when no previous appointment was made (not counting initial inquiries);
• Telephone appointments on Sunday afternoon.
(I am not available at all on Saturdays or Sunday mornings.)
$50.00 per hour for tutoring for classes
For telephone appointments, the clock starts at the time of the appointment, and the minimum of a quarter-hour is
charged for a missed appointment.
Late fee: $20.00 for each full 30 days after invoice.
For speedy payments:
To use a credit card, go to www.paypal.com, select the "Send Money" tab, and follow directions.
The e-mail address for paying by credit card, or paying from another PayPal account, or sending an e-check is:
Make check payable to Rachel MacNair and send to:
Rachel M. MacNair, Ph.D.
811 Cleaver II Blvd.
Kansas City, MO 64110-1683
Do not send by certified mail.
Regular mail has proven more reliable.
Other helpful resources available for graduate students:
Secrets and Tips for Dissertation Completion
by William G. Wargo
available in both paperback and e-book
Let me explain . . .
Why does sample size makes a difference? Suppose you toss a coin ten times, and it comes out 7 heads, 3 tails. Then
suppose you toss it 100 times, and it comes up 52 heads, 48 tails. Then you toss it 1,000 times, and it comes up 502
heads, 498 tails. The first time, it was 70% heads, the second 52% heads, and the third time the expected 50% when
you round it. Yet in all cases, it was only two off of the 50-50! Being only two off shows up much more at the smaller
number. If you had 70% heads after throwing it 100 or 1,000 times, you'd figure the coin must be loaded. You have
enough cases to say that it's not coming out due to mere chance with an unloaded coin. But you can't say that with just
ten throws -- after all, it's only two off.
If you take the average of these numbers:
4, 6, 6, 4, 5, 5, 3, 7, 5, 5, 6, 4, 6, 4
you will see that the mean is 5.
(I made it simple, with means being 5 in each pair).
But if you have instead these scores:
1, 9, 9, 1, 7, 3, 2, 8, 7, 3, 8, 2
you can see that you still have a mean of 5. But the numbers in the first set were pretty close to 5, whereas the
numbers in the second set are all over the place. The mean average is the same, but the standard deviation for that
second group is going to be much higher than for the first group. The standard deviation is the measure that tells you
about how much variability there is this way, since there are times when the difference in high and low might mean
something to what you're trying to study.
Correlations, Causation, and Careful Measurement
One of the basic points of statistical reasoning is that if you find two things are correlated with each other, you still don't
know what's causing what -- correlation is not causation. People get into trouble all the time by mixing themselves up on
Remember first the difference between a positive and a negative correlation. When it's positive, the numbers tend to go
up and down together -- say, the outdoor temperature and the consumption of iced drinks. When the correlation is
negative, then when one goes up, the other tends to go down, and vice-versa -- the outdoor temperature and the
consumption of hot cocoa.
Now, let's say we know that there's a positive correlation between poverty and crime. The first thing we have to figure
out is how we measure poverty and crime.
Poverty would generally be measured by income level. We'd actually be measuring income, and seeing that the lower
income is associated with higher crime.
This has problems. You could have someone who inherited a big house with
plenty of garden space, close enough to places to walk with no car needed. By
contrast, someone with the same income who must rent an apartment, scrounge
up food and suffer for lack of a car is considerably more poor.
Nevertheless, it would be very difficult to take account of this when making the measurement. Most measurements have
these kinds of problems. When measuring large numbers of people, this is probably the best you can do. In social
science, you'll never be perfect.
So what about measuring crime? Do you take arrests, convictions, or police reports from an area? Each one will
have a different impact on your final measure. If poor people get more false arrests than affluent people, then
measuring arrests will get you a higher correlation than the reality of crime. Yet how do you know?
Let's say we'll measure crime by convictions. Wanting to avoid mere traffic violations, we'll make it convictions for
murder, assault, shoplifting, burglary, and armed robbery.
Now, we find a positive correlation between this measure of poverty and this measure of crime. Did the poverty cause
the crime? Did A cause B? There's some sense to that, in that people who are more financially stretched might be more
likely to take the risks that go with, say, shoplifting. People who can afford the price are more likely to pay it rather than
risk arrest, and people so rich that the price is pocket change are even more likely to go ahead and pay for it. This isn't
always the case. Famous movie stars have been caught shoplifting. But of course this is only a correlation we're talking
about, and we wouldn't expect it to be 1 to 1. Why it might tend to be true is not puzzling.
Yet the causation may also be the other way around. Maybe crime causes poverty -- B causes A. Areas that are over-
ridden with crime are less likely to attract stores and businesses that would employ people. Another point is that
stealing a pair of shoes from someone who only has one will mean more poverty than stealing a pair from someone who
has ten pairs. Thus, crime is causing poverty.
Another possibility is that something else is causing both. This could then make them end up being correlated. In this
case, a lack of education, say, may cause both poverty and crime.
Which one is it? Actually, there's no reason to pick just one. They could all be true at the same time. Poverty and crime
cause each other in a feedback loop, and other factors cause them both as well. Reality is complicated. We have
complicated statistics to take all these into account at once, which is why we often go well beyond simple correlations in
studying complicated realities.
But there's an important point here concerning the measure of crime -- a bias. While murder and assault go across
class lines, the remaining three (shoplifting, burglary, and armed robbery) were crimes that are simply more likely to be
done by poor people.
Suppose we replace shoplifting with tax evasion, another way of getting something
without paying for it. We replace burglary with embezzlement. Then we replace
armed robbery with selling vicious and illegal weapons to brutal dictators, or being an
owner of factories who ignores safety regulations -- both things that do far more damage
and injury than individual instances of armed robbery. Suddenly, we would find that the
correlation is reversed. There would be a negative correlation between poverty and crime,
or a positive correlation between affluence and crime. That's because we selected the
kinds of crime that one has to be affluent to even be able to do.
This is important, because while many people think that proposing a link between poverty and crime will move us to
have another reason to get rid of poverty, other people will use the information to have a prejudice against poor
people. Prejudice is bad enough by itself, but all the worse when science is used to back it up, and the science is poorly
This will also be true, of course, in any kind of study. Whatever the conclusions, it is important to pay attention to how
you measured what you said you were measuring. You must watch your reasoning about the direction of causation.
Think of alternative ways of explaining your results. Look at how different interpretations could apply.
It's also important to list the limitations of your study well. This is not a sign of weakness. To the contrary, every
study has limitations. The best scholar is the one that can articulate what they are.
“There are only two kinds of data. The first kind is Terrible Data: data that are
ambiguous, potentially misleading, incomplete, and imprecise. The second
kind is No Data. Unfortunately, there is no third kind, anywhere in the world.”
Funder, D. C. (1997). The personality puzzle. New York: W.W. Norton, pp. 32-33
“Statistics is a fascinating study, and the statistician, after mastering a new
method for refining data, very naturally is eager to see it used. In view of the
crudity which of necessity characterizes most of the instruments used to
collect data, statistical refinement to the fourth decimal place may be like
putting a razor edge on a hoe, or calculating to the exact second the ending of
the Mesozoic era of geological time. It is also possible for psychological
sense to be lost in a welter of statistical manipulation. There is no area of
psychology where common sense is more needful."
Clark, W. H. (1958). The Psychology of Religion. New York: MacMillan, p. 44
"All the statistics in the world won't help you if you asked the wrong question in
the first place."
-- John Tukey