What level of measurement is a characterized by data that consist of names labels or categories?
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Not all data is created equally. It is helpful to classify data sets by different criteria. Some is quantitative, and some are qualitative. Some data sets are continuous and some are discrete. Show
Another way to separate data is to classify it into four levels of measurement: nominal, ordinal, interval and ratio. Different levels of measurement call for different statistical techniques. We will look at each of these levels of measurement. Nominal Level of MeasurementThe nominal level of measurement is the lowest of the four ways to characterize data. Nominal means "in name only" and that should help to remember what this level is all about. Nominal data deals with names, categories, or labels. Data at the nominal level is qualitative. Colors of eyes, yes or no responses to a survey, and favorite breakfast cereal all deal with the nominal level of measurement. Even some things with numbers associated with them, such as a number on the back of a football jersey, are nominal since it is used to "name" an individual player on the field. Data at this level can't be ordered in a meaningful way, and it makes no sense to calculate things such as means and standard deviations. Ordinal Level of MeasurementThe next level is called the ordinal level of measurement. Data at this level can be ordered, but no differences between the data can be taken that are meaningful. Here you should think of things like a list of the top ten cities to live. The data, here ten cities, are ranked from one to ten, but differences between the cities don't make much sense. There's no way from looking at just the rankings to know how much better life is in city number 1 than city number 2. Another example of this are letter grades. You can order things so that A is higher than a B, but without any other information, there is no way of knowing how much better an A is from a B. As with the nominal level, data at the ordinal level should not be used in calculations. Interval Level of MeasurementThe interval level of measurement deals with data that can be ordered, and in which differences between the data does make sense. Data at this level does not have a starting point. The Fahrenheit and Celsius scales of temperatures are both examples of data at the interval level of measurement. 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. Data at the interval level can be used in calculations. However, data at this level does lack one type of comparison. Even though 3 x 30 = 90, it is not correct to say that 90 degrees Celsius is three times as hot as 30 degrees Celsius. Ratio Level of MeasurementThe fourth and highest level of measurement is the ratio level. 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. Distances, in any system of measurement, give us data at the ratio level. A measurement such as 0 feet does make sense, as it represents no length. Furthermore, 2 feet is twice as long as 1 foot. So ratios can be formed between the data. At the ratio level of measurement, not only can sums and differences be calculated, but also ratios. One measurement can be divided by any nonzero measurement, and a meaningful number will result. Think Before You CalculateGiven a list of Social Security numbers, it's possible to do all sorts of calculations with them, but none of these calculations give anything meaningful. What's one Social Security number divided by another one? A complete waste of your time, since Social Security numbers are at the nominal level of measurement. When you are given some data, think before you calculate. The level of measurement you're working with will determine what it makes sense to do. The level of measurement refers to the relationship among the values that are assigned to the attributes for a variable. What does that mean? Begin with the idea of the variable, in this example “party affiliation.” That variable has a number of attributes. Let’s assume that in this particular election context the only relevant attributes are “republican”, “democrat”, and “independent”. For purposes of analyzing the results of this variable, we arbitrarily assign the values 1, 2 and 3 to the three attributes. The level of measurement describes the relationship among these three values. In this case, we simply are using the numbers as shorter placeholders for the lengthier text terms. We don’t assume that higher values mean “more” of something and lower numbers signify “less”. We don’t assume the value of 2 means that democrats are twice something that republicans are. We don’t assume that republicans are in first place or have the highest priority just because they have the value of 1. In this case, we only use the values as a shorter name for the attribute. Here, we would describe the level of measurement as “nominal”. Why is Level of Measurement Important?First, knowing the level of measurement helps you decide how to interpret the data from that variable. When you know that a measure is nominal (like the one just described), then you know that the numerical values are just short codes for the longer names. Second, knowing the level of measurement helps you decide what statistical analysis is appropriate on the values that were assigned. If a measure is nominal, then you know that you would never average the data values or do a t-test on the data. There are typically four levels of measurement that are defined:
In nominal measurement the numerical values just “name” the attribute uniquely. No ordering of the cases is implied. For example, jersey numbers in basketball are measures at the nominal level. A player with number 30 is not more of anything than a player with number 15, and is certainly not twice whatever number 15 is. In ordinal measurement the attributes can be rank-ordered. Here, distances between attributes do not have any meaning. For example, on a survey you might code Educational Attainment as 0=less than high school; 1=some high school.; 2=high school degree; 3=some college; 4=college degree; 5=post college. In this measure, higher numbers mean more education. But is distance from 0 to 1 same as 3 to 4? Of course not. The interval between values is not interpretable in an ordinal measure.  In interval measurement the distance between attributes does have meaning. For example, when we measure temperature (in Fahrenheit), the distance from 30-40 is same as distance from 70-80. The interval between values is interpretable. Because of this, it makes sense to compute an average of an interval variable, where it doesn’t make sense to do so for ordinal scales. But note that in interval measurement ratios don’t make any sense - 80 degrees is not twice as hot as 40 degrees (although the attribute value is twice as large). Finally, in ratio measurement there is always an absolute zero that is meaningful. This means that you can construct a meaningful fraction (or ratio) with a ratio variable. Weight is a ratio variable. In applied social research most “count” variables are ratio, for example, the number of clients in past six months. Why? Because you can have zero clients and because it is meaningful to say that “…we had twice as many clients in the past six months as we did in the previous six months.” It’s important to recognize that there is a hierarchy implied in the level of measurement idea. At lower levels of measurement, assumptions tend to be less restrictive and data analyses tend to be less sensitive. At each level up the hierarchy, the current level includes all of the qualities of the one below it and adds something new. In general, it is desirable to have a higher level of measurement (e.g., interval or ratio) rather than a lower one (nominal or ordinal). Is characterized by data that consist of names labels or categories only the data Cannot be arranged in an ordering scheme such as low to high?Sample - a sub-collection of elements drawn from a population. Nominal - data consists of names, labels or categories only. The data cannot be arranged in an ordering scheme (such as low to high). Examples are car companies, eye colors, or gender.
What level is data consisting of categories measured?The ordinal level of measurement groups variables into categories, just like the nominal scale, but also conveys the order of the variables. For example, rating how much pain you're in on a scale of 1-5, or categorizing your income as high, medium, or low.
Are names nominal or ordinal?A nominal variable is one of the 2 types of categorical variables and is the simplest among all the measurement variables. Some examples of nominal variables include gender, Name, phone, etc.
What are the 4 levels of measurement?There are 4 levels of measurement, which can be ranked from low to high:. Nominal: the data can only be categorized.. Ordinal: the data can be categorized and ranked.. Interval: the data can be categorized and ranked, and evenly spaced.. Ratio: the data can be categorized, ranked, evenly spaced and has a natural zero.. What is ordinal and nominal?Nominal data is classified without a natural order or rank, whereas ordinal data has a predetermined or natural order. On the other hand, numerical or quantitative data will always be a number that can be measured.
What is nominal level of measurement?Nominal level of measurement is the least precise and informative, because it only names the 'characteristic' or 'identity' we are interested. In other words, in nominal variables, the numerical values just "name" the attribute uniquely. In this case, numerical value is simply a label.
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