Types of Data: Nominal, Ordinal, Interval/Ratio – Statistics Help
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Types of Data: Nominal, Ordinal, Interval/Ratio – Statistics Help

Types of data: Nominal Ordinal Interval/Ratio Data is central to statistical
analysis When we wish to find out more about a phenomenon or process we collect data. Usually we collect several measures on each person or thing of interest. Each thing we collect data about is called an observation. If we are interested in how people respond, then each observation will be a person. OR an observation could be a business or a product, or a period in time, such as a week. Variables record the measurements we are interested in. Age, sex and chocolate preference can all be stored as variables. For each observation we record a score or value for each of the variables. When we store this data in a spreadsheet or database, each row corresponds to a single observation and each column is a variable. Level of measurement The level of measurement used for a variable determines which summary statistics, graphs and analysis are possible and sensible. The Nominal level is the most basic level of measurement. Nominal is also known as categorical or qualitative. Examples of nominal variables are sex, preferred type of chocolate and colour. These are descriptions or labels with no sense of order. Nominal values can be stored as a word or text or given a numerical code. However, the numbers do not imply order. To summarise nominal data we use a frequency or percentage. You can not calculate a mean or average value for nominal data. The next level of measurement is ordinal. Examples of ordinal variables are rank, satisfaction, and fanciness! Ordinal variables have a meaningful order, but the intervals between the values in the scale may not be equal. For example the gap between first and
second runners in a race may be small, whereas there is a bigger gap between second and third. Similarly there may be a big difference between satisfied and unsatisfied, but a smaller difference between unsatisfied and very unsatisfied. Like Nominal data, ordinal data can be given as frequencies. Some people state that you should never
calculate a mean or average for ordinal data. However it is quite common practice, particularly in research regarding people’s behaviour to find mean values for ordinal data. You should be careful if you do this to think about what it means and if it is justifiable. The most precise level of measurement is interval/ratio. This label includes things that can be
measured rather than classified or ordered, such as number of customers weight, age and size. Interval ratio data is also known as
scale, quantitative or parametric. Interval/Ratio data can be discrete, with whole numbers or continuous, with fractional numbers. Interval/Ratio data is very mathematically versatile. The most common summary measures are the mean, the median and the standard deviation. The way data should be represented in a graph
or chart depends on the level of measurement. Nominal data can be displayed as a pie chart, column or bar chart or stacked column or bar chart. In most cases the best choice for a single set of nominal data is a column chart. Ordinal data must not be represented as a pie chart, but is best shown as a column or bar chart. Interval/ratio data is best represented as a bar chart or a histogram. For these the data is grouped. Box plots illustrate the summary statistics
for a variable in a neat way. Data which occurs over time is best displayed as a line chart. Here is an example using different types of data. Helen sells choconutties. Helen is interested in developing a new product to add to her line of choconutties. She develops a questionnaire and asks
a random sample of 50 of her customers to fill it out. She asks them their age and sex, how much they spend on groceries each week, how many chocolate bars they buy in a week, and which they like best out of dark, milk and white chocolate. She asks them how satisfied they are with choconutties: very satisfied, satisfied, not satisfied, very unsatisfied. And she asks them how likely they are
to buy a whole box of 10 packets of choconutties. Helen enters the data in a spreadsheet. Each row has responses from one customer. Each column contains the measurements
or scores for one variable. The type of chocolate preferred is nominal data. This can be shown in a pie chart or bar chart. We can summarise by saying that 46% of customers prefer Dark chocolate, 40% prefer milk chocolate, and 14% prefer white chocolate. The measures of satisfaction and likelihood are ordinal level data. These should not be shown in a pie chart. The values should be put in a logical order in a column chart. We could say that 32% are very satisfied with choconutties and 72% of people are satisfied or very satisfied. and 72% of people are satisfied or very satisfied. The average satisfaction score comes to 2.06, which could be interpreted as satisfied. However it is debatable whether it is sensible to calculate a mean satisfaction score. Age, amount spent on groceries and number of chocolate bars are all interval/ratio data. These can be displayed on bar charts or histograms. We can say that for the customers in the sample, the mean age is 38 years,
the mean amount spent on groceries is $192, and the mean number of chocolate bars bought per week is 3.3. These are all meaningful summary statistics. The type of analysis that is sensible
for a given dataset depends on the level of measurement. You can find out more about this in the video, “Choosing the test”.

About Ralph Robinson

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100 thoughts on “Types of Data: Nominal, Ordinal, Interval/Ratio – Statistics Help

  1. such a boring accent ! Aussie ! please do a cosmetic surgery on your accent ! any way its useful , thanks alot

  2. Do you teach statistics for people studying finance. I get a lot of the concepts but the way formulas are presented, make it seem complicated.
    Enjoyed and learnt from this clip, good use of cartoons makes it fun, lol. As if stats could be fun.

  3. Very informative lecture, thank you. how do you respond if you are given a table of information and you are asked how would you record the responses

  4. OMG, this is boring as hell! I start a statistics summer course in about a week from today. I'm in trouble. :-/

  5. Hi I've watched the video and I kind of get an idea. You mention that ordinal are in a specific order. Like for example, customers give a ratings of how useful a mobile app is from 1 star to 5 star. My question is can it be classified as ordinal data if the gaps are equal? I'm kind of new to statistics so your answer is greatly appreciated. Thanks in advance!

  6. found this very useful as a beginner in stats however I wish to ask one thing. Most of my readings present interval scale and ratio scale as 2 separate levels of measurement however you refer to them as one…. may I ask why?

  7. Thank you very much. Those information helped me a lot. Could you please tell me how data from one level of measurement can be converted into another?

  8. Do not calculate arithmetic means for ordinal data even if "everybody" else is doing it. Smoking crack is still bad for your health even if it's legalize or promoted.

    However, you can calculate the mode and median for ordinal data. You can even calculate the mode for nominal data.

  9. Thank you very much. Everything you uploaded is very helpful for every one . May you be successful more and more.

  10. This is the best stat illustration. I would like to share my socio economic stat for advice. please share with me your email address.

  11. "However, it's debatable whether it's sensible to calculate a mean satisfaction score" it is? Why? you should have clarified that a bit… i tried to google it but i couldn't really find anything that said so…

  12. really I thanks you.This is a nice presentation before this video I am confused with level of measurement now i got key information.But i need to ask some question if you are willing

  13. Thank you very much. I got many information and knowledge from your video , I hope always stand with us. smart knowledge!

  14. Thank you for making this video! It was really easy to comprehend, and I think it will well-prepare me for next year's AP Statistics!

  15. Great video and very clearly summarises the main ideas. I've been looking at other videos in the past week, but this was the best by far. Keep it up!

  16. Hi,
    I was wondering if anybody on here can help me?
    My data is non parametric, I have applied the Kruskal Wallis test and there are significant differences. I need to know where the differences lie. My question is, is there a way to apply a Bonferroni correction to Mann Whitney U test in PSPP? I have written the syntax like this:
    /MANN-WHITNEY = TotalCu BY byPlot (1, 5)
    /POSTHOC=BONFERRONI, ALPHA ([0.0020833])

    but I get this error as the output:
    .17-23: error: Syntax error at 'POSTHOC': expecting end of command
    /POSTHOC=BONFERRONI, ALPHA ([0.0020833])

  17. i do not agree with the sex being interval/ratio. I would say its nominal qualitative data or more specific, its binary data. It is DESCRIBING not giving a QUANTITY.
    Also, arent interval and ratio data seen as two seperate data?

  18. Thank you so much this is so much clearer and more concise than my lecturers explain things and I pay ยฃ9000 a year for that

  19. There seems to be 4-5 different words for everything in statistics. Is that to make it look like a real profession?

  20. This is so much easier to understand the first time through vs the complete waste of professors I am currently dealing with… tenure= forgot that the job is called teaching

  21. i have a question: is number of customer truly an interval/ratio? cause i feel its nominal cause u can either be a customer or not a customer and it a variable that can only be counted just like number of males(sex) in a class. or maybe am just getting things mixed up

  22. I swear to god. School is against Humanity. So called professors be teaching some easy shit for hours to not only waste our times, but also their times.

  23. 1:32 what would the purpose of giving them a numerical code be if not to give them an order?
    I'm guessing you mean it is not used to give a chronological order, like when they were sampled. so yeah, what could the usage of numbering them be? could it to be to put them on some kind of scale or spectrum?

  24. Thank you Dr. Nic!!! Your video has and will help me tremendously in my statistic class. Before finding your video I wasn't sure that I would be able to pass this class successfully. Now I am confident that I can and will ace this class!

  25. i think this channel is great for biostatistics,,,,hence i have recommended ur videos to medical students in my medical lecture review for medical students…as i think its simple and had great quality !!https://www.youtube.com/watch?v=OoFknuzmcsw&t=12s

  26. Very useful video, thankyou. Surely with nominal data, you could used the mode as a measure of average? obviously a mean and median will not work. eg. "Blue was the colour that most appeared" would be the mode.

  27. This all seems so foreign to me. I'm still confused about what true zero is. I hear it referenced for ratio and interval, but I can't find out what it actually means. For some reason, I'm having a really hard time being able to grasp this.

  28. Hello! My name is Ana-Mihaela Lupan. I am a student and i am working in a research lab. I have a problem with analyzing percentage data. I have experiments where I measure using a flow cytometer the number of cells positive for a certain marker. the result is (and has to be) a percentage (% + cells from total number of cells). I have to use percentage because the number of cells introduced in recording is different from sample to sample due to various reasons and the most accurate measure is the percent for further comparisons between groups. I have a total of 2 replicate experiments (biological replicates) for each group. For each group I have to find the central tendency and dispersion (from the 2 experiments). I used mean and SD (because these are the most used in literature). The problem popped-out when i managed to label the majority of cells. I had for one group: EXP 1 = 54.25% positive cells, EXP 2 = 97.38% positive cells => Mean (1,2) = 75.82%, SD = 30.5%. The problem is that Mean+SD > 100%, which mean that i could have a percentage of 105% positive cells, which is impossible. I am sure I make something wrong, but I can't figured out what. I am a beginner, but I think the problem is that i shouldn't use mean and SD for characterization of percentage data. Please help me with this issue. Thanks!

  29. Thank you for this video! I got here from your blog where you say that Likert scale can be (sometimes) treated like something continuous-like to compute the mean. It does feel right. I am working now on a project where I have participants rating utterances (1-9) for comprehensibility and accentedness. Now this does feel like assigning a score and I think this is also a case where it is OK to compute the mean (or normalize with z-scores) but I would love to hear your opinion about it.

  30. I'm still so confused by this stuff… I feel it's so simple but for some reason I haven't been able to grasp the differences – especially ratio/interval

  31. Helen looks so crazy and angry when she asks for survey participation! ๐Ÿ™‚ great video really helped to learn the info!

  32. Hey qualification like
    Below secondary
    Above secondary
    Masters ….. these will be in nominal scale ???

  33. I have a question ,why ratio data can not be represented as a pie chart? Can we treat all the ratio data as a whole,and show their as frequency?

  34. Great video. I guess the distinction between interval and ratio types of data needs a separate video using your unique style and excellent teaching abilities.

  35. You say which graph and such is correct for what type of data, but what I would like to know is why those graphs are correct for this and that?

  36. Anyone what to hazard a guess what test to use here?
    Do Americans prefer Americanos and Italians prefer Cappuccinos? (They were given a straight choice between the two)

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