Kinh Nghiệm Hướng dẫn In which level of data measurement are the data categorized and ordered only? 2022
Họ tên bố (mẹ) đang tìm kiếm từ khóa In which level of data measurement are the data categorized and ordered only? được Cập Nhật vào lúc : 2022-12-19 02:40:21 . Với phương châm chia sẻ Bí kíp Hướng dẫn trong nội dung bài viết một cách Chi Tiết Mới Nhất. Nếu sau khi tham khảo Post vẫn ko hiểu thì hoàn toàn có thể lại Comment ở cuối bài để Ad lý giải và hướng dẫn lại nha.If you’re new to the world of quantitative data analysis and statistics, you’ve most likely run into the four horsemen of levels of measurement: nominal, ordinal, interval and ratio. And if you’ve landed here, you’re probably a little confused or uncertain about them.
Nội dung chính Show- Overview: Levels of measurementLevels of Measurement 101Need a helping hand?What is nominal data?What is ordinal data?What is interval data?What is ratio data?Why does it matter?Let’s recap.Psst… there’s more (for không lấy phí)What level of measurement is suitable for putting data into categories with order?Is the level of measurement where data are classified into categories and the order of those categories is not important?Which one of the following levels of measurement has an ordered categories?In which data variables can be categorized be ranked or ordered?
Don’t stress – in this post, we’ll explain nominal, ordinal, interval and ratio levels of measurement in simple terms, with loads of practical examples.
Overview: Levels of measurement
Here’s what we’ll be covering in this post. Click to skip directly to that section.
- What are levels of measurement in statistics?Nominal dataOrdinal dataInterval dataRatio dataWhy does this matter?Recap & visual summary
Levels of Measurement 101
When you’re collecting survey data (or, really any kind of quantitative data) for your research project, you’re going to land up with two types of data – categorical and/or numerical. These reflect different levels of measurement.
Categorical data is data that reflect characteristics or categories (no big surprise there!). For example, categorical data could include variables such as gender, hair colour, ethnicity, coffee preference, etc. In other words, categorical data is essentially a way of assigning numbers to qualitative data (e.g. 1 for male, 2 for female, and so on).
Numerical data, on the other hand, reflects data that are inherently numbers-based and quantitative in nature. For example, age, height, weight. In other words, these are things that are naturally measured as numbers (i.e. they’re quantitative), as opposed to categorical data (which involves assigning numbers to qualitative characteristics or groups).
Within each of these two main categories, there are two levels of measurement:
Categorical data – nominal and ordinalNumerical data – interval and ratioLet’s take look each of these, along with some practical examples.
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What is nominal data?
As we’ve discussed, nominal data is a categorical data type, so it describes qualitative characteristics or groups, with no order or rank between categories. Examples of nominal data include:
- Gender, ethnicity, eye colour, blood typeBrand of refrigerator/motor vehicle/television ownedPolitical candidate preference, shampoo preference, favourite meal
In all of these examples, the data options are categorical, and there’s no ranking or natural order. In other words, they all have the same value – one is not ranked above another. So, you can view nominal data as the most basic level of measurement, reflecting categories with no rank or order involved.
What is ordinal data?
Ordinal data kicks things up a notch. It’s the same as nominal data in that it’s looking categories, but unlike nominal data, there is also a meaningful order or rank between the options. Here are some examples of ordinal data:
- Income level (e.g. low income, middle income, high income)Level of agreement (e.g. strongly disagree, disagree, neutral, agree, strongly agree)Political orientation (e.g. far left, left, centre, right, far right)
As you can see in these examples, all the options are still categories, but there is an ordering or ranking difference between the options. You can’t numerically measure the differences between the options (because they are categories, after all), but you can order and/or logically rank them. So, you can view ordinal as a slightly more sophisticated level of measurement than nominal.
What is interval data?
As we discussed earlier, interval data are a numerical data type. In other words, it’s a level of measurement that involves data that’s naturally quantitative (is usually measured in numbers). Specifically, interval data has an order (like ordinal data), plus the spaces between measurement points are equal (unlike ordinal data).
Sounds a bit fluffy and conceptual? Let’s take a look some examples of interval data:
- Credit scores (300 – 850)GMAT scores (200 – 800)IQ scoresThe temperature in Fahrenheit
Importantly, in all of these examples of interval data, the data points are numerical, but the zero point is arbitrary. For example, a temperature of zero degrees Fahrenheit doesn’t mean that there is no temperature (or no heat all) – it just means the temperature is 10 degrees less than 10. Similarly, you cannot achieve a zero credit score or GMAT score.
In other words, interval data is a level of measurement that’s numerical (and you can measure the distance between points), but that doesn’t have a meaningful zero point – the zero is arbitrary.
Long story short – interval-type data offers a more sophisticated level of measurement than nominal and ordinal data, but it’s still not perfect. Enter, ratio data…
What is ratio data?
Ratio-type data is the most sophisticated level of measurement. Like interval data, it is ordered/ranked and the numerical distance between points is consistent (and can be measured). But what makes it the king of measurement is that the zero point reflects an absolute zero (unlike interval data’s arbitrary zero point). In other words, a measurement of zero means that there is nothing of that variable.
Here are some examples of ratio data:
- Weight, height, or lengthThe temperature in Kelvin (since zero Kelvin means zero heat)Length of time/duration (e.g. seconds, minutes, hours)
In all of these examples, you can see that the zero point is absolute. For example, zero seconds quite literally means zero duration. Similarly, zero weight means weightless. It’s not some arbitrary number. This is what makes ratio-type data the most sophisticated level of measurement.
With ratio data, not only can you meaningfully measure distances between data points (i.e. add and subtract) – you can also meaningfully multiply and divide. For example, 20 minutes is indeed twice as much time as 10 minutes. You couldn’t do that with credit scores (i.e. interval data), as there’s no such thing as a zero credit score. This is why ratio data is king in the land of measurement levels.
Why does it matter?
At this point, you’re probably thinking, “Well that’s some lovely nit-picking nerdery there, Derek – but why does it matter?”. That’s a good question. And there’s a good answer.
The reason it’s important to understand the levels of measurement in your data – nominal, ordinal, interval and ratio – is because they directly impact which statistical techniques you can use in your analysis. Each statistical test only works with certain types of data. Some techniques work with categorical data (i.e. nominal or ordinal data), while others work with numerical data (i.e. interval or ratio data) – and some work with a mix. While statistical software like SPSS or R might “let” you run the test with the wrong type of data, your results will be flawed best, and meaningless worst.
The takeaway – make sure you understand the differences between the various levels of measurement before you decide on your statistical analysis techniques. Even better, think about what type of data you want to collect the survey design stage (and design your survey accordingly) so that you can run the most sophisticated statistical analyses once you’ve got your data.
Let’s recap.
In this post, we looked the four levels of measurement – nominal, ordinal, interval and ratio. Here’s a visual summary of each.
Remember, the level of measurement directly impacts which statistical techniques you can use in your analysis, so make sure you always classify your data before you apply any given technique.
Psst… there’s more (for không lấy phí)
This post is part of our research writing mini-course, which covers everything you need to get started with your dissertation, thesis or research project.
What level of measurement is suitable for putting data into categories with order?
The ordinal level of measurement indicates an ordering of the measurements.Is the level of measurement where data are classified into categories and the order of those categories is not important?
Nominal. A nominal scale describes a variable with categories that do not have a natural order or ranking. You can code nominal variables with numbers if you want, but the order is arbitrary and any calculations, such as computing a mean, median, or standard deviation, would be meaningless.Which one of the following levels of measurement has an ordered categories?
The ordinal level of measurement groups variables into categories, just like the nominal scale, but also conveys the order of the variables.In which data variables can be categorized be ranked or ordered?
Ordinal data is a type of categorical data with an order. The variables in ordinal data are listed in an ordered manner. Tải thêm tài liệu liên quan đến nội dung bài viết In which level of data measurement are the data categorized and ordered only?