Is there any difference in SPSS to specify a variable as ordinal or scale? I know they are different in meaning, but it seems SPSS treat them the same in calculation.
In practice, SPSS does not REQUIRE you to make the definition. I've seen many data sets where no one bothered to define in SPSS if the variable was ordinal or scale. However, as noted, that means that improper calculations can sometimes be made. It is up to the researcher to understand the difference and choose tests appropriately. Defining the level of measurement does have an advantage sometimes in that SPSS will not allow some variables to be used incorrectly - particularly in the case of nominal variables.
in the SPSS if you want measured Central tendency (mean, Median and Mode ), than you must have some short of knowledge about nominal, Ordinal and scale. the Choice of mean, median and mode is restricted by the level of measurement of a variable you defined. if the level of the measurement for a variable is nominal, you can calculate only mode, if the level of measurement of a variable is ordinal then you can calculate mode or median. if the level of measurement of a variable is interval/ratio, you can calculate mode and median,
ordinal scale:
A rank-ordered scale of measurement in which equal differences between numbers do not represent equal differences between the things measured.
nominal scale:
scale of measurement in whch numbers are used simply as names and not as quantites.
there are total four types of scales, namely Nominal, Ordinal, Interval and Ratio. Depending on the type of the scales, respective treatment can be given to those variables. for example nominal variable can only be counted hence no mean or standard deviation can be calculated on the same. ordinal variable can be counted and median can be calculated. Interval and ratio ( combined as scale in SPSS) are quantitative variables. most quantitative analysis can be performed on these. The basic difference between qualitative and quantitative variables is the FIXED Distance. qualitative variables do not have fixed distance. quantitative variables have fixed distance.
Nominal - Qualitative variable with out order (only categorisation possible) example : gender, departments, etc.
Ordinal - Qualitative variable with order (categorisation and order) example Rank, credit rating as High risk, medium risk, Low risk etc.
Interval - Quantitative variable without fixed origin.( has fixed distance but no fixed origin) example - temperature
Ratio - Quantitative variable with fixed origin. all quantitative variables are in this category , salary, expenditure, etc.
scale is simply a measurement of a phenomenon or characteristic, Working out a figure and saying that it is male or female amounts to scaling ie.classification popularly termed as nominal scale. It could be multinomial. and it is not necessary that we have only 0 and 1 scale. The nominal scale could be 1,2, 3 or more categories in which you place objects or persons. It depends on the classification one is interested in. Even continuous data can be translated into categories. For example, income data may be grouped into four or five categories. For a statistiscal analysis,, you may require to know number of people or respondents into particulal category.If one is interested in rank order, it can be ranked from lowest category to highest category. For example, a trait can be rated as highest, down to a minimum and not necessarily to a zero level. let me say that the SPSS will do what you want. Do try to understand the statistics
see last paragraph of the pdf file below. I feel it is more clear and makes sense. the scale data type includes interval and ratio. e.g., when you want to calculate the average of several variables.
http://www.sfu.ca/~palys/SPSS%20Lesson%201.pdf
if one understand what is concept ofnominal perfectly there may not be confusion with other. one can not add or subtract among fruits, cars, bangles,doors....etc. even the numbers representing number of things can be added. but the addition or subtraction of those numbers is meaningless when total can be asked.. but frequency.. how many of each are there is worth to know and give meaning. so evaluate or justify your self. when defining nominal. add those sum and and frequency that is count and ask yourself what meaning will come
It is better to practice with these type of variable before we try to understand .No need to confuse,just see some of the example.
There are four measurement scales (or types of data): nominal, ordinal, interval and ratio. These are simply ways to categorize different types of variables.
Nominal
Nominal scales are used for labeling variables, without any quantitative value. “Nominal” scales could simply be called “labels.” A good way to remember all of this is that “nominal” sounds a lot like “name” and nominal scales are kind of like “names” or labels. Examples are gender and hair color.
A sub-type of nominal scale with only two categories (e.g. male/female) is called “dichotomous.” If you are a student, you can use that to impress your teacher.
Ordinal
With ordinal scales, it is the order of the values is what’s important and significant, but the differences between each one is not really known. For example, is the difference between “OK” and “Unhappy” the same as the difference between “Very Happy” and “Happy?” We can’t say.
Ordinal scales are typically measures of non-numeric concepts like satisfaction, happiness, discomfort, etc.
“Ordinal” is easy to remember because is sounds like “order” and that’s the key to remember with “ordinal scales”–it is the order that matters, but that’s all you really get from these.
The best way to determine central tendency on a set of ordinal data is to use the mode or median; the mean cannot be defined from an ordinal set.
Interval
Interval scales are numeric scales in which we know not only the order, but also the exact differences between the values. The classic example of an interval scale is Celsius temperature because the difference between each value is the same. For example, the difference between 60 and 50 degrees is a measurable 10 degrees, as is the difference between 80 and 70 degrees. Time is another good example of an interval scale in which the increments are known, consistent, and measurable.
Interval scales are nice because the realm of statistical analysis on these data sets opens up. For example, central tendency can be measured by mode, median, or mean; standard deviation can also be calculated.
Like the others, you can remember the key points of an “interval scale” pretty easily. “Interval” itself means “space in between,” which is the important thing to remember–interval scales not only tell us about order, but also about the value between each item. However, the problem with interval scales is that they don’t have a “true zero.”
Ratio
Ratio scales are the ultimate nirvana when it comes to measurement scales because they tell us about the order, they tell us the exact value between units, AND they also have an absolute zero–which allows for a wide range of both descriptive and inferential statistics to be applied. Good examples of ratio variables include height and weight.
In summary, nominal variables are used to “name,” or label a series of values. Ordinal scales provide good information about the order of choices, such as in a customer satisfaction survey. Interval scales give us the order of values + the ability to quantify the difference between each one. Finally, Ratio scales give us the ultimate–order, interval values, plus the ability to calculate ratios since a “true zero” can be defined.
In ordinal level of measurement the order matters but the differences don't matter but in SPSS scale means measurement at the level of interval/ratio. Please read 4 levels of measurement.
the questions were answered by respondent with yes or no. this questions are nominal or ordinal?
the questions were answered by respondent with yes or no. this questions are nominal or ordinal?
Ans. Nominal
Question: One variable I have asks for year of study. The options are 1st, 2nd, 3rd, and Other. Does having the "Other" option make this variable nominal? Or is it still ordinal?
Question: For each of ten test questions, respondents either gain 0 points (false answer) or 1 point (correct answer). Added up, respondents can then score between 0 and 10 points on the test (overall score).
Question 1: I assume that the overall score can be treated as metric information, perhaps even as a ratio scale, because it would be viable to say that a person who earned 8 points scored twice as good as someone who only got 4 questions right. Correct?
Questions 2: Must the single scores (per item) be considered nominal information in this case? Or could they be treated as metric information (because there is an absolute difference between the scores)?
I'm asking also, because we are interested in conducting a factor analysis for a new test that we have developed. It is unclear to us, whether it is permissible to conduct a factor analysis with this kind of data (technically speaking, it works very well, with expected results - the problem is methodological).
I use ordinal scale as metric only when there is the same unit/space between each level. In case of diferentent levels, I recode them to obtain the same as the scale with less points. I use to folow the most of part of the literature.
Purist writers think that we must treat them as discrete variables. But we know that on a large scale the discrete tend to become continuous (ie age).
Hello
There are two types of variables: Discrete and continuous Discrete variables are the following scales: Nominal and ordinal And continuous variables have the following scale: Distance and Relative
Good luck
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