Is the data nominal tallied, or ordinal (ranked)?
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Transcript of Is the data nominal tallied, or ordinal (ranked)?
Is the data nominal tallied or ordinal (ranked)?
As you now know, a non-parametric-relationship type test will be used for at least one of the following reasons:
As you now know, a non-parametric-relationship type test will be used for at least one of the following reasons:1. The distribution is skewed
As you now know, a non-parametric-relationship type test will be used for at least one of the following reasons:1. The distribution is skewedOR
As you now know, a non-parametric-relationship type test will be used for at least one of the following reasons:1. The distribution is skewed2. The data is ordinal (ranked)
As you now know, a non-parametric-relationship type test will be used for at least one of the following reasons:1. The distribution is skewed2. The data is ordinal (ranked)OR
As you now know, a non-parametric-relationship type test will be used for at least one of the following reasons:1. The distribution is skewed2. The data is ordinal (ranked)3. The data is nominal tallied
The purpose of this presentation is to help you determine if your problem has:
The purpose of this presentation is to help you determine if your problem has:
At least one variable that is Ordinal (Rank Ordered)
The purpose of this presentation is to help you determine if your problem has:
or
At least one variable that is Ordinal (Rank Ordered)
The purpose of this presentation is to help you determine if your problem has:
Both variables that are NOMINAL TALLIED
At least one variable that is Ordinal (Rank Ordered)
Let’s begin with:
Let’s begin with:
At least one Ordinal (Rank
Ordered) Variable
There are two ways to express ordinal data:
As rank-ordered data:
As rank-ordered data:
1st, 2nd, 3rd, 4th, 5th . . .
Or as percentiles:
Or as percentiles:
1%, 10%, 50% or 99%
Or as percentiles:
1%, 10%, 50% or 99%
A percentile means the percent of observations, scores or
persons below a point (e.g., 10%le means 10% of all observations, scores or
persons fall below this point)
Here is an example of a question of independence with one rank-ordered variable:
Is the Nielsen rating rankings for TV shows independent of shows that lean conservatively or liberally?
Is the Nielsen rating rankings for TV shows independent of shows that lean conservatively or liberally?
SHOW LEANS1 = Conservative2 = Liberal
NIELSEN RATINGS
RANKINGS2 4th
2 2nd
1 1st
1 3rd
Is the Nielsen rating rankings for TV shows independent of shows that lean conservatively or liberally?
SHOW LEANS1 = Conservative2 = Liberal
NIELSEN RATINGS
RANKINGS2 4th
2 2nd
1 1st
1 3rd
This is ordinal or rank
ordered data.
So we would select:
Both variables that are NOMINAL TALLIED
At least one variables that is Ordinal (Rank Ordered)
Nominal Tallied data is simply the amount of certain levels within a category.
For example:
Gender is a category with two levels:
Gender is a category with two levels:- Male
Gender is a category with two levels:- Male - Female
Gender is a category with two levels:- Male - Female
“Tallied” simply means the number in each of the levels of the category.
For example:
For example:- Male - 46- Female - 54
For example:- Male - 46- Female - 54
These are called the
tallies.
Now let’s add age as a variable.
Age can be a category with as many levels as desired:
Age can be a category with as many levels as desired:1 – Infant/toddler (0-2 years)2 – Children (3-12 years)3 – Teenagers (13-19 years)4 – Young Adults (20-39 years)5 – Middle Age (40 – 64 years)6 – Seniors (65 and older)
Age can be a category with as many levels as desired:1 – Infant/toddler (0-2 years) - 1002 – Children (3-12 years) - 803 – Teenagers (13-19 years) - 2004 – Young Adults (20-39 years) - 2885 – Middle Age (40 – 64 years) - 2016 – Seniors (65 and older) - 86
Age can be a category with as many levels as desired:1 – Infant/toddler (0-2 years) - 1002 – Children (3-12 years) - 803 – Teenagers (13-19 years) - 2004 – Young Adults (20-39 years) - 2885 – Middle Age (40 – 64 years) - 2016 – Seniors (65 and older) - 86
These are called nominal
TALLIES.
Let’s imagine that you want to know if age is independent of gender.
Let’s imagine that you want to know if age is independent of gender. Your expectation is that the number of persons that are at a particular gender level and age level are the same.
So, if you selected a sample of 600, you would expect that the “nominal tallied” data set would look like this:
So, if you selected a sample of 600, you would expect that the “nominal tallied” data set would look like this:
0-2 3-12 12-19 20-39 40-64 65+MaleFemale
So, if you selected a sample of 600, you would expect that the “nominal tallied” data set would look like this:
0-2 3-12 12-19 20-39 40-64 65+Male 50 50 50 50 50 50
Female 50 50 50 50 50 50
But let’s say you collect a sample of 600 that is actually arrayed as follows:
But let’s say you collect a sample of 600 that is actually arrayed as follows:
0-2 3-12 12-19 20-39 40-64 65+
Male 100 25 80 100 80 60
Female 25 25 20 25 20 40
But let’s say you collect a sample of 600 that is actually arrayed as follows:
0-2 3-12 12-19 20-39 40-64 65+Male 100 25 80 100 80 60
Female 25 25 20 25 20 40
Because the data is not arrayed as expected then gender and age may not be independent of one another.
But let’s say you collect a sample of 600 that is actually arrayed as follows:
0-2 3-12 12-19 20-39 40-64 65+Male 100 25 80 100 80 60
Female 25 25 20 25 20 40
Independence can be tested for statistical significance.
So we would select:
At least one variables that is
Ordinal (Rank Ordered)
Both variables that are NOMINAL TALLIED
What type of variables does your problem have?
What type of variables does your problem have?
Both variables that are NOMINAL TALLIED
At least one variable that is ORDINAL (Rank Ordered)