State whether the data from the following statements is nominal, ordinal, interval or ratio
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variables are recorded. In scientific research, a variable is anything that can take on different values across your data set (e.g., height or test scores). There are 4 levels of measurement: Depending on the level of measurement of the variable, what you can do to analyze your data may be limited. There is a hierarchy in the complexity and precision of the level of measurement, from low (nominal) to high (ratio). Going from lowest to highest, the 4 levels of measurement are cumulative. This means that they each take on the properties of lower levels and add new properties.
Why are levels of measurement important?The level at which you measure a variable determines how you can analyze your data. The different levels limit which descriptive statistics you can use to get an overall summary of your data, and which type of inferential statistics you can perform on your data to support or refute your hypothesis. In many cases, your variables can be measured at different levels, so you have to choose the level of measurement you will use before data collection begins. Example of a variable at 2 levels of measurementYou can measure the variable of income at an ordinal or ratio level.
At a ratio level, you can see that the difference between A and B’s incomes is far greater than the difference between B and C’s incomes. At an ordinal level, however, you only know the income bracket for each participant, not their exact income. Since you cannot say exactly how much each income differs from the others in your data set, you can only order the income levels and group the participants. Receive feedback on language, structure and formattingProfessional editors proofread and edit your paper by focusing on:
See an example Which descriptive statistics can I apply on my data?Descriptive statistics help you get an idea of the “middle” and “spread” of your data through measures of central tendency and variability. When measuring the central tendency or variability of your data set, your level of measurement decides which methods you can use based on the mathematical operations that are appropriate for each level. The methods you can apply are cumulative; at higher levels, you can apply all mathematical operations and measures used at lower levels.
Quiz: Nominal, ordinal, interval, or ratio?Frequently asked questions about levels of measurementHow do I decide which level of measurement to use? Some variables have fixed levels. For example, gender and ethnicity are always nominal level data because they cannot be ranked. However, for other variables, you can choose the level of measurement. For example, income is a variable that can be recorded on an ordinal or a ratio scale:
If you have a choice, the ratio level is always preferable because you can analyze data in more ways. The higher the level of measurement, the more precise your data is. Sources in this articleWe strongly encourage students to use sources in their work. You can cite our article (APA Style) or take a deep dive into the articles below. This Scribbr article
Is this article helpful?You have already voted. Thanks :-) Your vote is saved :-) Processing your vote... How do you know if data is nominal ordinal interval or ratio?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 are 5 examples of ordinal data?Examples of ordinal variables include: socio economic status (“low income”,”middle income”,”high income”), education level (“high school”,”BS”,”MS”,”PhD”), income level (“less than 50K”, “50K-100K”, “over 100K”), satisfaction rating (“extremely dislike”, “dislike”, “neutral”, “like”, “extremely like”).
What are examples of nominal ordinal interval and ratio?Age. *. Weight.. Height.. Sales Figures.. Ruler measurements.. Income earned in a week.. Years of education.. Number of children.. What are the example of nominal?Nominal data are used to label variables without any quantitative value. Common examples include male/female (albeit somewhat outdated), hair color, nationalities, names of people, and so on. In plain English: basically, they're labels (and nominal comes from "name" to help you remember).
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