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Variables can have different levels also known as a__________. For example, if we are looking at the completion of pre-op documentation, we may just be looking at whether the documentation was fully completed or not fully completed. In other words, yes fully completed or no not fully completed. This results in a variable (indicator) with two levels - yes or no; thus, it has two attributes. Now, suppose that the researcher wants to capture more information about the completion of documents. In this instance, the researcher would expand the levels. For example, they might now be 100% completed, 99-80% completed, 79-50% completed, below 50% completed. How many levels (i.e., attributes) does this variable now have? If you said f______, you are correct. Now, let us think about the attributes in more detail. In the former example (using completion of documents and its two levels - fully completed and not fully completed), the words are labels indicating 100% completed versus less than 100% completed. These attributes (the word labels) are qualitative and therefore, are said to be nominal representations of the data. We can take these qualitative attributes and assign them numerical values such as one (1) and two (2) (see table 1 below). Variable: Completion of pre-op documentation Attribute (level) - Fully completed Attribute (level) - Not fully completed Table 1. Completion of pre-op documentation - Two-level Variable Variable: Completion of pre-op documentation Value Label 1 Fully completed 2 Not fully completed Now consider the latter example (using completion of documents and its four levels). The percents (100, 99-80, 79-50, and below 50) are categorical ranges associated with the degree to which the pre-op documentation has been filled out. Foundationally, these labels are qualitative, although you see numbers. Thus, they are nominal Practice Worksheet_Fill-ins representations of the data. The difference here is that the order of the attributes (levels) is important. This would be seen when assigning a value to each attribute (see tables 2 and 3 below). Table 2. Completion of pre-op documentation - Four-level Ordinal Variable Variable: Completion of pre-op documentation Value Label 1 Less than 50% completed 2 79-50% completed 3 99-80% completed 4 100% completed Not: Table 3. Completion of pre-op documentation - Four-level unordered Variable Variable: Completion of pre-op documentation Value Label 1 75-80% completed 2 100% completed 3 Less than 50% completed 4 90-80% completed The values provide order, which is important to this four-level variable because it shows us that as the value for the attribute increases more of the documentation is filled out. However, the distance between each value (1 to 2, 2 to 3 and 3 to 4) is not equal when looking at the attributes. In other words, the ranges represented by the values are not equidistant and the values themselves do not have computational meaning (e.g., 2 is not two times greater than 1). The assignment of numerical values to qualitative labels allows investigators to run mathematical calculations. However, a variable's level of measurement determines the type of statistical analysis that can be done. This gets us into levels of measurement. However, before we move on, let us discuss basic rules about attributes. Attributes of variables must be m__________ e_________, which means that each attribute it uniquely distinct from the other. No attribute should overlap with another. For example, if we ask about someone's level of education and include the following two attributes: Could you see someone being confused and checking both or checking the wrong one. When creating our variables and how they are measured we must be sure to provide clear definitions and distinctions between the attributes. Also, attributes must be e_________. In other words, the attributes used to measure the variable must capture all possibilities. By way of example, consider a survey question on race/ethnicity. Can we possibly provide all options on a survey? The standard is to provide attributes for the most common groups and then add an attribute for 'other.' This would make the attributes exhaustive. Decisions on how variables are measured along with their mutually exclusive and exhaustive attributes are based on understanding the current research literature and how variables are handled by other investigators along with suggestions for improving their measures. This is why we did r_________ of the l__________ before we finalize our research question and methods.

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