scaled v. ordinal v. nominal data(3)
TRANSCRIPT
This presentation will assist you in determining if the data associated with the problem you are working on
This presentation will assist you in determining if the data associated with the problem you are working on
Participant Score
A 10
B 11
C 12
D 12
E 12
F 13
G 14
This presentation will assist you in determining if the data associated with the problem you are working on
Participant Score
A 10
B 11
C 12
D 12
E 12
F 13
G 14
This presentation will assist you in determining if the data associated with the problem you are working on is:
This presentation will assist you in determining if the data associated with the problem you are working on is:
Scaled
This presentation will assist you in determining if the data associated with the problem you are working on is:
Scaled
Ordinal
This presentation will assist you in determining if the data associated with the problem you are working on is:
Scaled
Ordinal
Nominal Proportional
Before we begin, it is important to note that with questions of difference, where you are comparing groups, the data you should classify as scaled, ordinal, or nominal proportional are data that represent RESULTS (weight gain, driving speed, IQ, etc.),
In this case, you are NOT classifying what are called CATEGORICAL variables like gender, treatment/control group, type of athlete, school type, ethnicity, political or religious affiliation, etc.
What is scaled data?
Note – scaled data has two subcategories (1) interval data (no zero point but equal
intervals) and (2) ratio data (a zero point and equal
intervals)
What is scaled data?
For the purposes of this presentation we will not discuss these further but just focus on both as scaled data.
Attribute #1 – scaled data assume a quantity.
Meaning that 2 is more than 3 and 4 is more than 3 and 20 is less than 30, etc.
For example: 40 degrees is more than 30 degrees. 110 degrees isless than 120 degrees.
Attribute #1 – scaled data assume a quantity.
Meaning that 2 is more than 3 and 4 is more than 3 and 20 is less than 30, etc.
For example: 40 degrees is more than 30 degrees. 110 degrees isless than 120 degrees.
Attribute #1 – scaled data assume a quantity.
Meaning that 3 is more than 2 and 4 is more than 3 and 20 is less than 30, etc.
For example: 40 degrees is more than 30 degrees. 110 degrees isless than 120 degrees.
Attribute #1 – scaled data assume a quantity.
Meaning that 3 is more than 2 and 4 is more than 3 and 20 is less than 30, etc.
For example: 40 degrees is more than 30 degrees. 110 degrees isless than 120 degrees.
Attribute #1 – scaled data assume a quantity.
Meaning that 3 is more than 2 and 4 is more than
3 and 20 is less than 30, etc.
For example: 40 degrees is more than 30 degrees. 110 degrees isless than 120 degrees.
Attribute #1 – scaled data assume a quantity.
Meaning that 3 is more than 2 and 4 is more than 3 and 20 is less than 30, etc.
For example: 40 degrees is more than 30 degrees. 110 degrees isless than 120 degrees.
100 degrees is more than 40 degrees
Attribute #1 – scaled data assume a quantity.
Meaning that 3 is more than 2 and 4 is more than 3 and 20 is less than 30, etc.
For example: 40 degrees is more than 30 degrees. 110 degrees isless than 120 degrees.
60 degrees is less than 80 degrees
Attribute #1 – scaled data assume a quantity.
Meaning that 3 is more than 2 and 4 is more than 3 and 20 is less than 30, etc.
For example: 40 degrees is more than 30 degrees. 110 degrees isless than 120 degrees.
60 degrees is less than 80 degrees
If the data represents varying amounts then this is the first requirement for data to be
considered - scaled.
Attribute #2 – scaled data has equal intervals or each unit has the same value.
Meaning the distance between 1 and 2 is the same as
the distance between 14 and 15 or 1,123 and
1,124.
Attribute #2 – scaled data has equal intervals or each unit has the same value.
Meaning the distance between 1 and 2 is the same as
the distance between 14 and 15 or 1,123 and
1,124. They all have a unit value of 1 between them.
40o - 41o
100o - 101o
70o – 71o
Each set of readings are the same distance
apart: 1o
The point here is that each unit value is the same across the
entire scale of numbers
40o - 41o
100o - 101o
70o – 71o
Each set of readings are the same distance
apart: 1o
Note, this is not the case with ordinal numbers where 1st place in a marathon might be 2:03 hours, 2nd place 2:05 and 3rd place 2:43.
They are not equally spaced!
Height
Attribute #1: We are dealing with amounts
Persons HeightCarly 5’ 3”Celeste 5’ 6”Donald 6’ 3”Dunbar 6’ 1”Ernesta 5’ 4”
HeightPersons HeightCarly 5’ 3”Celeste 5’ 6”Donald 6’ 3”Dunbar 6’ 1”Ernesta 5’ 4”
Attribute #2: There are equal intervals across the scale. One inch is the same value regardless of where you are on the scale.
Intelligence Quotient (IQ)Persons Height IQCarly 5’ 3” 120Celeste 5’ 6” 100Donald 6’ 3” 95Dunbar 6’ 1” 121Ernesta 5’ 4” 103
Intelligence Quotient (IQ)Persons Height IQCarly 5’ 3” 120Celeste 5’ 6” 100Donald 6’ 3” 95Dunbar 6’ 1” 121Ernesta 5’ 4” 103
Attribute #1: We are dealing with amounts
Intelligence Quotient (IQ)Persons Height IQCarly 5’ 3” 120Celeste 5’ 6” 100Donald 6’ 3” 95Dunbar 6’ 1” 121Ernesta 5’ 4” 103
Attribute #2: Supposedly there are equal intervals across this scale. A little harder to prove but most researchers go with it.
Pole Vaulting PlacementPersons Height IQ PVPCarly 5’ 3” 120 3rd
Celeste 5’ 6” 100 5th
Donald 6’ 3” 95 1st
Dunbar 6’ 1” 121 4th
Ernesta 5’ 4” 103 2nd
Pole Vaulting PlacementPersons Height IQ PVPCarly 5’ 3” 120 3rd
Celeste 5’ 6” 100 5th
Donald 6’ 3” 95 1st
Dunbar 6’ 1” 121 4th
Ernesta 5’ 4” 103 2nd
Attribute #1: We are dealing with amounts
Pole Vaulting PlacementPersons Height IQ PVPCarly 5’ 3” 120 3rd
Celeste 5’ 6” 100 5th
Donald 6’ 3” 95 1st
Dunbar 6’ 1” 121 4th
Ernesta 5’ 4” 103 2nd
Attribute #2: We are NOT dealing with equal intervals. 1st place (16’0”) and 2nd place (15’8”) are not the same distance from one another as 2nd Place and 3rd place (12’2”).
Ordinal scales use numbers to represent relative amounts of an attribute.
1st Place16’ 3”
2nd Place16’ 1”
Ordinal scales use numbers to represent relative amounts of an attribute.
1st Place16’ 3”
2nd Place16’ 1”
3rd Place15’ 2”
Ordinal scales use numbers to represent relative amounts of an attribute.
3rd Place15’ 2”
2nd Place16’ 1”
1st Place16’ 3”
Relative Amounts of Bar Height
Corporal2
Sargent3
Lieutenant4
Major5
Colonel6
General7
Private1
Example of relative amounts of authority
Corporal2
Sargent3
Lieutenant4
Major5
Colonel6
General7
Private1
Notice how we are dealing with amounts of authority
Example of relative amounts of authority
Corporal2
Sargent3
Lieutenant4
Major5
Colonel6
General7
Private1
But,
Example of relative amounts of authority
Corporal2
Sargent3
Lieutenant4
Major5
Colonel6
General7
Private1
But, they may not be equally spaced.
Example of relative amounts of authority
Corporal2
Sargent3
Lieutenant4
Major5
Colonel6
General7
Private1
But, they may not be equally spaced.
Example of relative amounts of authority
Corporal2
Sargent3
Lieutenant4
Major5
Colonel6
General7
Private1
But, they may not be equally spaced.
Example of relative amounts of authority
You can tell if you have an ordinal data set when the data is described as ranks.
Persons Pole Vault Placement
Carly 3rd
Celeste 5th
Donald 1st
Dunbar 4th
Ernesta 2nd
Nominal data is different from scaled or ordinal, because they do not deal with amounts nor equal intervals.
1 = Canadian
2 = American
Being Canadian is not numerically or quantitatively more than being
American
1 = Canadian
2 = American
The numbers 1 and 2 do not represent amounts. They are just a way to distinguish the two groups numerically.
The word “Nom” in nominal means “name”. Essentially we are using data to name, identify, distinguish, classify or categorize.
Here is how the nominal data would look like in a data set:
Persons GenderCarlyCelesteDonaldDunbarErnesta
Here is how the nominal data would look like in a data set:
Persons GenderCarlyCelesteDonaldDunbarErnesta
1 = Male2 = Female
Here is how the nominal data would look like in a data set:
Persons GenderCarly 2Celeste 2Donald 1Dunbar 1Ernesta 2
1 = Male2 = Female
Persons Gender PreferenceCarly 2Celeste 2Donald 1Dunbar 1Ernesta 2
1 = Like ice-cream2 = Don’t like ice-cream
Persons Gender PreferenceCarly 2 1Celeste 2 1Donald 1 1Dunbar 1 2Ernesta 2 2
1 = Like ice-cream2 = Don’t like ice-cream
Persons Gender PreferenceCarly 2 1Celeste 2 1Donald 1 1Dunbar 1 2Ernesta 2 2
Religion
1 - Buddhist2 - Catholic3 - Jew4 - Mormon5 - Muslim6 - Protestant
Persons Gender PreferenceCarly 2 1Celeste 2 1Donald 1 1Dunbar 1 2Ernesta 2 2
Religion42561
1 - Buddhist2 - Catholic3 - Jew4 - Mormon5 - Muslim6 - Protestant
Nominal proportional data is simply the proportion of individuals who are in one category as opposed to another.
A claim is made that four out of five veterans (or 80%) are supportive of the current conflict. After you sample five veterans you find that three out of five (or 60%) are supportive. In terms of statistical significance does this result support or invalidate this claim?
Veterans SupportiveA 2B 2C 1D 1E 1
1 = supportive2 = not supportive
If the question is stated in terms of percentages (e.g., 60% of veterans were supportive), then that percentage is nominal proportional data
If your data is nominal proportional as shown in these examples, select
Scaled
Ordinal
Nominal Proportional