What are the 4 types of measuring?

You can see there are four different types of measurement scales (nominal, ordinal, interval and ratio). Each of the four scales, respectively, typically provides more information about the variables being measured than those preceding it.

uth.tmc.edu - 1.2 Variables and Measures

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Now, you might ask, why do I need to know about types of variables or measures? You need to know, in order to evaluate the appropriateness of the statistical techniques used, and consequently whether the conclusions derived from them are valid. In other words, you can't tell whether the results in a particular medical research study are credible unless you know what types of variables or measures have been used in obtaining the data.

Variables and Measures Practice

Exercise 1: You need to know the types of variable to: No Response

Know biostatistical vocabulary

Evaluate medical research studies

Compute statistics

None of the above

Lesson 1: Summary Measures of Data 1.2 - 2

Biostatistics for the Clinician

One isn't necessarily better than another category. But, it is true you typically have more information with some than with others, and you're more used to working with some than with others. With interval and ratio variables for example, you can do averages and things like that. You know there are numbers. You can add them up, divide and things like that. Its a little trickier sometimes with nominal and ordinal variables. But in human experiments there's no way you can get around it. You often work with nominal or ordinal variables.

Figure 1.1: Types of Variables

Lesson 1: Summary Measures of Data 1.2 - 3

Biostatistics for the Clinician

Four Types of Variables

Variables and Measures Practice

Exercise 2: How many different levels of measure for variables exist?

No Response

1

2

3

4 Nominal Variables

Variables and Measures Practice

Exercise 3: Can the distances between the categories of a nominal variable be measured?

No Response

Yes

No

Ordinal Variables

So, ordinal scales both name and order. Some other examples of ordinal scales are rankings (e.g., football top 20 teams, pop music top 40 songs), order of finish in a race (first, second, third, etc.), cancer stage (stage I, stage II, stage III), and hypertension categories (mild, moderate, severe).

Variables and Measures Practice

Exercise 4: Nominal variables name only. Ordinal variables:

No Response

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Name only

Order only

Both name and order

Lesson 1: Summary Measures of Data 1.2 - 4

Biostatistics for the Clinician

Interval Variables

Interval variables have the property that differences in the numbers represent real differences in the variable. Another way to say this is that equal equal differences in the numbers on the scale represent equal differences in the underlying variables being measured. For example, look at the difference between 36 degrees and 37 degrees compared to the difference between 40 degrees and 41 degrees on either Fahrenheit or Celsius temperatures? Is the difference the same? Because the differences in the numbers are the same, when you have an interval variable you know temperature intervals are the same. So, with interval variables you now know not only whether one value is higher than another, but that the distances between the intervals on the scales are the same. Again, you have a higher level of information. Interval scales not only name and order, but also have the property that equal intervals in the numbers measured represent real equal differences in the variables. Examples of interval scales include the Fahrenheit and Celsius temperatures previously mentioned, SAT, GRE, MAT, and IQ scores. In general, many of the standardized tests of the psychological, sociological and educational displines use interval scales. Interval measures all share the property that the value of zero is arbitrary. On the Celsius scale, for example, 0 is the freezing point of water. On the Fahrenheit scale, 0 is 32 degrees below the freezing point of water.

Variables and Measures Practice

Exercise 5: Interval variables:

No Response

Name, order & have equal intervals

Name and order only

Order only

Name only

Lesson 1: Summary Measures of Data 1.2 - 5

Biostatistics for the Clinician

Ratio Variables

They are called ratio variables because ratios are meaningful with this type of variable. It makes sense to say 100 feet is twice as long as 50 feet, because length measured in feet is a ratio scale. Likewise it makes sense to say a Kelvin temperature of 100 is twice as hot as a Kelvin temperture of 50 because it represents twice as much thermal energy (unlike Fahrenheit temperatures of 100 and 50). With ratio variables, the only difference from interval variables is that you have a true zero so that you can actually talk about ratios. That is a person's lung capacity can be twice somebody else's lung capacity. In order to make those kinds of statements you have to have be able to compute meaningful ratios and you can only do that if you have a true zero. But really for the purposes of any statistical tests it makes no difference whether you have interval or ratio variables.

Variables and Measures Practice

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Exercise 6: Ratio variables have:

No Response

A real 0

Equal intervals

Order

Name

All except "No Response" above

Lesson 1: Summary Measures of Data 1.2 - 6

Biostatistics for the Clinician

Now one kind of variable isn't necessarily better than another. You are a little more used to working with quantitative variables. For example, you can do averages and things like that with quantitative variables, you know there are numbers, you can add them up and divide and things like that. With qualitative variables it's not so clear cut. Its a little trickier some times. But when you are working with humans there's no way you can get around it.

Variables and Measures Practice

Exercise 7: Interval or ratio variables should not be regrouped into nominal or ordinal measures.

No Response

True

False

Variables and Measures Practice

Exercise 8: Nominal and ordinal variables require:

No Response

Parametric methods

Nonparametric methods

Lesson 1: Summary Measures of Data 1.2 - 7

Biostatistics for the Clinician

Variables and Measures Practice

Exercise 9: Variables you manipulate are:

No Response

Independent variables

Dependent variables

Lesson 1: Summary Measures of Data 1.2 - 8

Biostatistics for the Clinician

uth.tmc.edu - 1.2 Variables and Measures

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