PPAL 6200
Research Methods and Info
Systems
Class 3: Jan 17-18, 2012
Class Outline
• Some Key Terms and Thinking about
“measurement”
• Describing Data “Distributions”
• Break
• Describing Data with Statistics
• Break
• A Very Special Distribution: The Normal
Distribution
Some Key Concepts
unless noted source is Moore
– Data
• Numbers with a context (xxiv). The context including how
data is collected can alter results.
– Variable
• An empirical property that can take on two or more values
(Frankfort-Nachmias & Nachmias 1996:50) Don’t get
suckered in by small and rapid changes, look at the big
picture (xxvii)
– Case
• An individual, event or other thing for which we have data
– Measurement
• The assignment of numbers to objects, events or variables
according to rules (ibid: 156-157)
– Levels of Measurement
• Nominal, Ordinal, Interval, Ratio
– Validity
• Are you measuring what you thought you are measuring?
– Reliability
• Are you measuring it accurately?
– Spuriousness
• Is there something else involved? Beware the lurking variable
(xxvii)
– Statistics
• The science of learning from data (xxiv)
The Book Title Says It All…
• This is a class in the “basic practice of
statistics” with a little bit of practical advice
thrown in regarding management of
information systems
• Inside the front cover of the book is a
wonderful set of flow through figures that
show how one can go about statistical
thinking in a disciplined manner and three
four step plans to guide your work
Describing Data Distributions with
Graphs
• As the introductory sections of the book
noted, you really cannot go wrong to begin
your work by visualizing the individual
variables that comprise your data (and on
occasion plotting them against another
variable such as time).
• The distribution tells you what values a
variable takes and how often it does so
Ways we can Visualize and
Explore Data
• Exploratory analysis is not meant to allow us to reach
any deep conclusions it is meant to help us better
understand the data set and the relationships within it
• We want to look both for an overall pattern
(consistencies) and deviation from it (often called
outliers)
• Tables
– Tables are effective tools for visualizing data, provided that we
do not have too many variables, nor too many cases
• At a certain point we need to graphically depict our data
to make it understandable as a snapshot
Which Graph?
• The graphic depictions we employ are
dependent on:
– The type of data we have
• Level of Measurement
• Whether Stationary or Chronological
Some Common Graphs
• Pie Chart (good for showing percentages
when few categories of a nominal or
ordinal variable)
Percentage of Students Picking a Given Major
• Bar Charts are equally useful for nominal
and ordinal variables but have the benefit
of allowing more flexibility
Foreign Born Population of US States by Percentage
Histograms
• Histograms can be confusing as they look
like Bar Graphs sometimes. In fact you
can make them by carefully specifying a
Bar Graph. However they are really quite
different.
• They are meant for use with Interval and
Ratio data where there is a lot of variability
among cases because there are so many
possible values for the data
• Therefore we have to “group the data” to a
certain extent to allow us to represent it
• What a histogram shows is the percentage
of cases that have a score within the
groups represented by the bars
• You will notice that this graph looks a bit
different from the one in the book.
• This is because the scaling that my
software used is a bit different from that
used by the person who did the examples
in the book.
This brings up a good point
• Be careful how you manipulate data as
you will see in the next section of the talk.
these to graphs portray the same
information but one will give us a more
interesting result.
Describing a Distribution
• Once we get to developing histograms we can
start to evaluate the shape of our data in a
number of interesting ways (Shape, Centre,
Spread)
– What is the shape of the plot? Is it single peaked or
multi-peaked?
– Where is the peak? Is it at the centre or off-centre
(skewed)? When the tail of a distribution heads off to
one side unevenly we say it is skewed to that side
(this is confusing)
– What about outliers? Any unusually high or low
scores?
As you can see below: Regrouping our Data
makes one figure more symmetrical
A stemplot is not so elegant
• Granted it is not so elegant but it does
allow us to figure out what is happening
inside of those bars….
Thinking about these Graphs
• When we look at these graphs we have to
keep in mind the questions we have
started
– Shape
– Centre (other than time-series)
– Outliers
Remember…
• I have posted some tips on how to use
Excel to make graphs on the course
website and you can also find advice in
the technical manuals you will find there
as well.
Using Descriptive Statistics to
Explore your Data
• We are continuing our exploration of data.
• In the last chapter we graphically depicted
data
• Now we are going to look at how we can
describe data using “summary” statistics
• We will look at statistics that provide
measures of central tendency
• We will also look at statistics that provide
measures of dispersion
Sometimes Statistics are So
Simple…
• Sometimes statistics are so simple we
have to do something to make them look
fancier than they are. Enter “The Mean”.
• The mean simply means taking the
average of something.
• You all know how to do this. You add up
the group, then you divide it by the number
of items in the group.
But just to make sure you know I know what
I am doing I have a formula
1
X 
n
Xi
We may talk about these formulas
but…
• Don’t worry, we may talk about the
formulas that mathematically describe
statistics so you can get a better
understanding of how they work.
• I might also hand calculate a few to
demonstrate this
• But no one today hand calculates real data
• Neither should you that is why we have
software
The Median
• The Median is the mid point of a
distribution. Half the observations have
values less than the median, half have
values more
• The formula looks like this
• Note the formula gives the location of the
median (the observation which has a
value equal to the median) not its value
M  ( N  1) / 2
Here is where Stem & Leaf Graphs
can come in handy (N=20)
Mean and Median which one?
• In general the Mean is more susceptible to
distortion by
– abnormally large cases, in the language of the
book a distribution skewed to the right
– or abnormally small cases, in the language of
the book a distribution skewed to the left.
• For example, one Bill Gates among a
thousand people will seriously distort the
“Mean” income of this sample. However,
it will have little or no impact on the
“Median” Income
Level of Measure Matters Also
• You cannot take the mean of a categorical
variable (one measured at the nominal or ordinal
level).
• You can however calculate the median of a
variable measured at the ordinal level.
• This is a good point to stop and remind you
about the stupidity of machines.
• Unless the variables are tagged in the data set
as to level of measure, your computer really
won’t care and will happily chug along
calculating even meaningless statistics such as
the mean of your categorical variables.
One more
• The Mode is the measure of central
tendency for nominal data. It is simply the
category with the largest number of cases.
If all we knew was how well the
data clumped together…
• Even though the Median is less
susceptible to distortion by an abnormally
large or small case, it can still provide a
very weak description of your data if the
observations are widely dispersed.
• This is why we are often interested in the
Quartiles
Just like the Median only smaller
• Quartiles are just like the Median only on a
smaller scale. Instead of defining the mid
point of the distribution they define the
break-point between:
– The first quarter and the second quarter
– The break between the second quarter and
the third quarter (which is the Median by the
way)
– The break between the third quarter and the
fourth quarter
The Five-Number Summary
• Moore is very big on the use of the fivenumber summary to summarily describe
data.
• Minimum value
• Q1
• M
• Q3
• Maximum value
You can graphically depict this with
a box plot
• Fortunately all the computer programs we
are employing can easily generate both
the numerical summary and the
accompanying box plots
• SPSS can generate all this and more
using its “Frequencies” and “Explore”
commands. Excel does the job just as
nicely.
Here is an example of an SPSS Box plot for before
tax income for men and women in Ontario from the
Survey of Household Spending
• Notice on the previous slide how the
distance from the first quartile to the
median and then to the third quartile is not
necessarily symmetrical and then that the
whiskers on the box plot are also not
symmetrical. This is an indication of skew
• Unlike the example in the book my
whiskers indicate not max and min value
but percentiles,
Here is the five number summary
for Men and Women
Spotting outliers
• Obviously our box plots provide an
excellent way to spot outliers.
• A statistic that can also help is the
“interquartile range”. This is just the
range between quartile one and three.
• When an observation lies 11/2 times the
Interquartile range above quartile three or
below quartile 1, it is often considered to
be an outlier.
While I used ratio level data…
• While I used ratio level data for my
example of the five-number summary, it
should be noted that there is nothing here
(quartiles, Median, maximum, minimum
value) that would not work with data
measured at the interval or ordinal level
Range
• Along with quartiles (which works when
data is at least measured at the ordinal
level) we must also remember to look at
“Range” which is the only measure of
dispersion that works at the nominal level.
Standard Deviation
• The best way to describe Standard Deviation
(notation S) is that it is the square root of
Variance (notation S2)
• So why do you need variance? A bit of math if
you look at the formula in your book.
The Formula for S2
• Variance is the sum
of the squared
distances of each
observation from the
mean over N-1 (N-1
being the degree of
freedom).
S
2
2
1

(

x
)

x
i
n 1
2
S
The Formula for
involves a
squaring
• We have to square these distances as,
otherwise -- in a symmetrical distribution -- they
would cross cancel and there would be no
variance.
• The problem with variance is all that squaring
produces numbers that are very large and not
too intuitive to read on their own (though you will
see later that variance is an important tool and
even a building block for other things).
• Taking the square root produces a much
more usable number (S).
• Quite simply, when you know X
and S
• You can go up and down a list of numbers
and figure out which list is more
concentrated about its mean and which is
more diffuse
If you want a quick example
Frequency
Value
Frequency
Value
1
0
1
0
1
1
1
2
1
2
1
4
1
3
1
6
1
4
1
8
1
5
1
10
1
6
1
12
1
7
1
14
1
8
1
16
1
9
1
18
1
10
1
20
N= 11
∑ = 55
N= 11
∑ = 110
Mean = 5
S2=11
Mean = 10
S2= 44
S= 3.3
S= 6.6
But once again, keep in mind…
If the mean is susceptible to distortion from
extreme variables, S is doubly so due to
all those squarings
Source for Graphics: Moore 2009
Normal Distributions
• When Exploring Data
– Always start by plotting your individual
variables
– Look for overall patterns (shape, centre,
spread) and for deviations such as outliers
– Calculate appropriate summary statistics to
identify the centre and spread
Source for Graphics: Moore 2009
Density Curves and Normality
• Sometimes data takes on a recognizable
shape
• Density Curves are those that:
– Are always on or above the (x) axis
– Have exactly an area of 1 under the curve
• Which means any portion of the area can be
expressed as a percentage (eg. 0.68).
• Density curves come in all shapes and
sizes and can be centred or skewed.
Source for Graphics: Moore 2009
Source for Graphics: Moore 2009
Describing a density curve
• Our measures of central tendency and
dispersion work just as well on density
curves as on actual observations
• Although these are theoretical
constructions we can describe them like
real data
Source for Graphics: Moore 2009
A special set of curves
• Normal curves are a subset of density curves all
are
– Symmetrical and single peaked
– It is completed described by giving its mean μ and
standard deviation Ố
– The mean is at the centre of the distribution and is the
same as the median
– Changing μ without changing Ố moves the graph but
does not alter it
– The larger Ố is the more spread out the curve is.
The Normal Curve
and the 68-95-99.7 rule
Source for Graphics: Moore 2009
The abbreviation of a Normal
Distribution
• In the rest of the book the parameters of a
normal distribution are summarized by the
notation
N ( , )
Why is the normal distribution so
important?
• It is a good description of the distribution of
some important real world data
• It is a good approximation of many chance
outcomes
• Statistical tests with distributions based on
normality work just as well with many nonnormal but roughly symmetrical distributions.
• In many statistical inference procedures there is
an assumption of normality we test against. If
the results we see could be expected to occur,
then there is little reason to believe we have
found a meaningful result
This is handy
• One reason normality is handy is because it
provides us a way to standardize variables so
that we can in fact compare apples and oranges
(or at least variables measured on two different
scales).
• Suppose you are interested in how educating
girls (measured in percent enrolled in schooling)
and international trade (measured in dollars)
impact economic development
• How can you clearly state the impact of years of
schooling and dollars in the same equation?
• What you can do is convert each set of
scores so that each observation is
expressed as a measure of how far it falls
away (either positively or negatively) from
the mean for the variable in question.
• This is called a Z score.
And here is the Z formula
Z
x

• As a result the two variables will now be
on a common scale and you can compare
the impact of schooling for girls and
international trade on economic
development.
• Finally, as the example in the book shows
• If you believe your observations are normally
distributed and you know the Mean and
Standard Deviation, you can work out
proportions
• In the case they show in the book the question
was what proportion of first year university
students were likely to be eligible to play sports,
given the league requirement that they score
820 on the SAT before beginning their first year
of university.
• If we know the total area under a normal curve is
= 1 and we subtract the area to the left of 820
we will have an answer. To work out the area
you need to: guess, use a calculator or software
or the applet on the book website, or convert the
information to Z scores and use Table A.
Source for Graphics: Moore 2009
Guestimation
Distribution is normal; Mean = 1026; Std.D.= 209.
Therefore the value = 1st Std. below mean is 817 which is
pretty close to 820 (the score you need so as to be eligible)
Therefore we have 68% + the rest of the right side
= 68 + (100-68)/2 = 68 + 16 = 84%
Approximately 84% of students qualify
Source for Graphics: Moore 2009
Using software or the applet
• To do the applet we will go to the website
for the book
http://courses.bfwpub.com/bps5e.php
Using Excel as a software example
• To use Excel you
would go to the stats
plugin and select
Probability Calculations
Normal Distribution
Using the Z scores and tables
• Start by calculating
the Z score that would
correspond to a score
of 820
• Therefore we need to
find the area under
the normal curve
which is equal to
-0.99
Z 
x

820  1026
Z
 0.99
209
• To use the
table you first
find the row
that
corresponds to
the first digit
-0.9 then draw
your finger
across until you
find the column
for the second
in this case
“.09)
• Therefore the
answer is .1611
• So now that we have found the area under
the normal curve when expressed as Z
scores that corresponds to a score of 820
it is the same mathematical problem as
before
1 – 0.1611≈ 84%
• Therefore about 84% of students would
qualify to play sports
Have a fun week
•
•
Nikolai Bogdanov-Belsky Counting in their heads (1895)
Posted on line by Tamir Khason Khason.net