On the "category axis", the scale may be nominal, ordinal, interval or ratio scale. On the frequency axis the scale must be numerical.On the "category axis", the scale may be nominal, ordinal, interval or ratio scale. On the frequency axis the scale must be numerical.On the "category axis", the scale may be nominal, ordinal, interval or ratio scale. On the frequency axis the scale must be numerical.On the "category axis", the scale may be nominal, ordinal, interval or ratio scale. On the frequency axis the scale must be numerical.
The independent variable in ANOVA must be categorical (either nominal or ordinal). The dependent variable must be scale (either interval or ratio). However, it is possible to recode scale variables to categorical and vice versa in order to perform ANOVA. While this is a common practice in many social sciences, it is controversial. I have also seen studies where ordinal data is treated as scale in ANOVA. Personally, I do not endorse either practice as they are tailoring the data to fit the test instead of the proper method of selecting a test that fits the data.
One possible opposite of a numerical scale is a "nominal" scale. In the study of statistics, we use four "scales of measurement": nominal; ordinal; interval; ratio. The "nominal," scale, which simply names categories, is, in a sense, non-numerical. On "nominal" scales, people or objects with the same attribute are assigned the same scale-value. Examples of categories on nominal scales are ethnicity, gender, marital status, styles of housing, models of cars. For example, a nominal scale of "marital status" might be numbered as follows: 1. Single, never married. 2. Single, previously married. 3. Married. Although we may count the number of people (or items) in each category, the numerals assigned to the "nominal" scale have no 'numeric' meaning in the way that we usually think about numbers. On that view, a nominal scale may be said to be non-numerical and, therefore, the opposite of a numerical scale. Actually, integers form a subset of numbers, not the other way around.
It depends on how the variable is used. At its simplest, it would be a nominal or categorical value but, if used as part of a time series, it would be an ordinal variable.
The whole point of a nominal variable is that is has no numerical value associated with it. With a binary measure you can allocated the values 1 and 0 or +1 and -1 for observations where the attribute is present or absent. If there are more than 2 values that the nominal variable can take then you can allocate any numbers that you want but in all cases the numbers do not have a value: they are simply symbols which can help for sorting and for binary comparisons.
Nominal
The advantage of using a nominal scale is that it can help with classification. The disadvantage of using a nominal scale is that it is the most primitive system.
is environmental advertising nominal and ordinal scale
yes
No. It is a discrete quantitative variable.
Nominal Scale < Ordinal< Interval < Ratio
On the "category axis", the scale may be nominal, ordinal, interval or ratio scale. On the frequency axis the scale must be numerical.On the "category axis", the scale may be nominal, ordinal, interval or ratio scale. On the frequency axis the scale must be numerical.On the "category axis", the scale may be nominal, ordinal, interval or ratio scale. On the frequency axis the scale must be numerical.On the "category axis", the scale may be nominal, ordinal, interval or ratio scale. On the frequency axis the scale must be numerical.
It is a nominal scale.
A nominal variable is a variable measured in current dollars (the value of the dollar for the specific period discussed), and a real variable is a variable measured in constant dollars (the value of the dollar for the base period). That is, a real variable adjusts for the effects of inflation.
This question could be answered in a variety of ways. In statistics for the biological sciences we use scales of measurement for variable types. In this case there are 4 types of variables: nominal (aka categorical), ordinal, interval (aka scale), and ratio.
The independent variable in ANOVA must be categorical (either nominal or ordinal). The dependent variable must be scale (either interval or ratio). However, it is possible to recode scale variables to categorical and vice versa in order to perform ANOVA. While this is a common practice in many social sciences, it is controversial. I have also seen studies where ordinal data is treated as scale in ANOVA. Personally, I do not endorse either practice as they are tailoring the data to fit the test instead of the proper method of selecting a test that fits the data.
Mean