Question

Hi. I'm looking for support in identifying the levels of measurement for each of the following variables.
Study_ID
ANEMIA
AGE
GENDER
RACE
CURRSMOKE
CURRALC
MEAN ARTERIAL PRESSURE.
WEIGHT
STRESS

Study ID	currsmoke	curralc	anemia	race	anemia_base	gender	age	weight	mean artieral pressure	stress
1854	0	0	0	1	0	M	49	70.93	101.1987145	NONE
1857	1	0	1	1	0	M	49	72.16715815	60.48758335	NONE
1860	1	0	0	1	0	F	76	55.49	79.49940676	LOW
1863	0	0	1	1	0	F	76	55.3346844	101.8303633	LOW
1866	0	1	0	0	0	M	51	73.3	67.84825575	HIGH
1869	0	0	0	0	0	M	51	68.74440505	106.5259832	LOW

Answer 1

# Nominal scale : When measuring using a nominal scale, one simply names or categorizes responses.
For example : Gender, favorite color, religion etc.
So nominal variable is not numerical , its just categorical variable , you can categorize that variable in different categories .

# Ordinal scale : Data at this level can be ordered, but no differences between the data can be taken that are meaningful.
For example : Class ranks : distinction, first class , second class , pass class , fail.   
For ordinal scale we can rank the categories

# Interval scale : The interval level of measurement deals with data that can be ordered, and in which differences between the data does make sense.They do not have a true zero point.
For example : The Fahrenheit and Celsius scales of temperatures,
You can talk about 30 degrees being 60 degrees less than 90 degrees,so differences do make sense. However 0 degrees (in both scales) cold as it may be does not represent the total absence of temperature.

# Ratio scale : Data at the ratio level possess all of the features of the interval level, in addition to a zero value. Due to the presence of a zero, it  now makes sense to compare the ratios of measurements.Phrases such as "four times" and "twice" are meaningful at the ratio level.
For example : A measurement such as 0 feet does make sense, as it represents no length. Furthermore 2 feet is twice as long as 1 foot.

Therefore levels of measurement for the given variables are :

Study_ID : Nominal ( because it is identity number of individual )

ANEMIA : Nominal ( because it is binary )

AGE : Ratio

GENDER : Nominal

RACE : Nominal

CURRSMOKE : Nominal

CURRALC : Nominal

MEAN ARTERIAL PRESSURE : Ratio

WEIGHT : Ratio

STRESS : Ordinal ( because categorize in order None , low , high )