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”.

#### Zubair Ahmad · March 8, 2016 at 3:58 pm

i found it much more easy and helpful ,

#### may ju · March 11, 2016 at 4:10 am

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

Cute graphics!

#### MURALI N · May 13, 2016 at 4:38 am

Very useful video. Gives the gist of data types in a simple and effective manner..

#### shruti jhanwar · May 23, 2016 at 5:33 am

Could you please tell me the difference between scaling and coding of data?

#### Ameen Ali · May 28, 2016 at 9:00 am

I just wanted to thank you for all the videos. They're all very informative and easy to grasp.

#### John Smith · May 31, 2016 at 8:53 am

"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…

#### endale bekele · June 11, 2016 at 2:22 pm

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

#### Laura Carrillo · June 24, 2016 at 8:29 am

thank you so much <3

Thank you.

#### Zainab Dookhy · July 11, 2016 at 7:36 pm

Thank you for posting omg

#### teshome gadisa · July 14, 2016 at 5:20 am

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

#### Ani · August 29, 2016 at 12:10 am

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!

good knowledge

#### Sohaib Sandhu · October 7, 2016 at 8:56 am

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!

THANK YOU!

#### Kamal Karkonasasi · October 18, 2016 at 3:15 pm

Thank a lot. It was great explanation for types of data

#### Jennifer L A Shaw · October 28, 2016 at 5:22 am

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:
NPAR TESTS
/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])
Thanks!
J

#### Chelsea · November 20, 2016 at 5:14 am

Thank you so much! You are superb in explaining things in the simplest way! Keep it up! :))

#### SAAKET DALAL · December 1, 2016 at 8:49 pm

Can I Get In english pls

#### Samantha Gonah · December 16, 2016 at 11:25 am

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?

#### elliehavana · January 11, 2017 at 6:12 am

Thank you so much this is so much clearer and more concise than my lecturers explain things and I pay £9000 a year for that

#### Shee Bee · March 26, 2017 at 7:56 pm

Well explained and easy to understand. Thank you!

#### Yrgen2 · April 10, 2017 at 3:44 am

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

#### shamee noori · April 27, 2017 at 12:19 am

Excellent video thank you so much, the pictures and illustrations are really helpful

#### Ula Kalicinska · May 8, 2017 at 7:57 pm

This really helped me with my psychology revision,thank you xx

#### Miss Trixie · June 7, 2017 at 3:18 pm

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

#### surya ramadhana · June 7, 2017 at 10:35 pm

thanks for making this video. My understanding increase since watching your video. Keep it up

#### mohamad Romdoni · June 8, 2017 at 8:31 am

Thanks for sharing. This is very good job even for me who want to learn statistic from zero.

#### FUTURE S.E · July 5, 2017 at 12:02 pm

Wow, well explained, so helpful. Thank you.

#### Ramakrishna Adivishnu · July 5, 2017 at 5:30 pm

very clearly understood. thankyou for the good job done.

#### osarhiemen iyare · July 9, 2017 at 1:15 am

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

#### Dr Zubairul Islam · August 1, 2017 at 11:12 am

oh my god, love you.

#### Vitamin Kk · September 19, 2017 at 5:15 pm

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.

sorry

#### Taraxx · November 29, 2017 at 5:03 pm

your accent and voice makes me aggressive

#### Arslan Charyyev · January 3, 2018 at 2:15 pm

4:10 Is that Mello from death note ….

#### bea haxby · January 20, 2018 at 4:14 pm

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?

#### Linda Maynard · January 20, 2018 at 10:00 pm

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!

#### Vinie Kouamou · February 24, 2018 at 9:16 am

Great video. thanks

#### Glamorous world · February 27, 2018 at 11:30 am

My Professor played this video today to mke us understand,,,
Thank u nice work

Thanks

#### Mutwiri Martin · March 9, 2018 at 12:01 pm

am humbled by this video
actually it has shown
the simpler methods of data presentation

#### Jaidin Spence · March 13, 2018 at 12:54 am

Statistics on the same page as you

#### dr.sadaf alam · March 13, 2018 at 9:49 am

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

#### Lizzy · March 18, 2018 at 10:38 pm

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.

#### arundhati Garud · March 23, 2018 at 2:29 pm

It was a very helpful video.. Thank you

Thanks 🙂

#### Michael Deneys · May 11, 2018 at 6:31 am

too many overlays and popups

#### JTKD2718 · May 20, 2018 at 5:23 am

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.

#### Lupan Ana - Mihaela · May 24, 2018 at 10:31 pm

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!

#### Janani Ram · May 27, 2018 at 6:36 am

Aren't these types of measurement scales, while the data types are qualitative and quantitative?

#### yeeen123 · July 8, 2018 at 7:37 am

Why cannot put ordinal data in a pie chart?

#### Francisse Elegino · July 15, 2018 at 8:22 am

Thanks! I passed the board because of this video.

#### Maria Berger · July 21, 2018 at 9:31 am

That video has a really good explanation! Thanks for making it 🙂

#### Marzena Karpinska · August 19, 2018 at 3:21 am

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.

#### Victor Zivanai · September 28, 2018 at 5:48 am

interval and ratio level are 2 different things

#### Avi Shalom · October 8, 2018 at 9:13 pm

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

#### Afif Khaja · November 1, 2018 at 6:29 pm

Succinct and well-demonstrated. Thank you

#### Crazy for concepts · November 11, 2018 at 8:35 pm

I want my clg fees back!

#### AmirGTR · December 19, 2018 at 7:22 am

whoever made these animations is my fucking hero

Thanks.

#### Yuv Sabharwal · February 7, 2019 at 7:31 pm

Brilliantly explained , thank you so much !!!!

#### Emyr Griffiths · February 11, 2019 at 7:32 pm

Great video just what I needed thanks

#### Waqar Saeed · February 22, 2019 at 1:23 pm

4:17 oh no no that's not Helen that's Cruella! It's a trap 😛

#### Kristen Ness · March 5, 2019 at 9:22 pm

Helen looks so crazy and angry when she asks for survey participation! 🙂 great video really helped to learn the info!

like the accent
KIWI????'

#### Nabiela Yaccop · April 10, 2019 at 2:03 am

are you using SPSS or Microsoft Excel?

👌

#### wajeeha ahmed · April 16, 2019 at 2:01 pm

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

#### jinjin he · April 23, 2019 at 7:17 am

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?

#### sirj j · May 3, 2019 at 7:23 am

Seems great. But this could be a 1 hour video that breaks down each topic in more detail.

#### Karla Mendiburu · May 6, 2019 at 9:45 am

Best and easiest to understand video about data analysis!

#### Amr Ebied · May 20, 2019 at 4:20 am

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.

#### IseeYasIdodge · May 20, 2019 at 9:10 am

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?

#### utubemania · May 20, 2019 at 7:10 pm

Why ordinal data can't be shown in pie chart? Please explain.

#### K. Jevon .D · June 1, 2019 at 10:49 pm

Really good explanation

#### Adrian Vonier · June 9, 2019 at 8:08 pm

Did anyone notice Lt. Cmdr. Data at 0.06? 😀

#### paschal murphy · June 26, 2019 at 10:12 pm

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)

Good

#### Courtney Blank · August 5, 2019 at 6:59 am

Loving the half a second Star Trek reference in the beginning haha 😀

#### Josh Alicea · September 4, 2019 at 5:26 pm

Helen looks shifty

Very clear!

#### Brad H · September 15, 2019 at 4:45 am

Helen is a very aggressive surveyor.

#### Mahnoor Khalid · September 16, 2019 at 6:10 am

How come be gender be an interval ratio data data?

#### María Fernanda Restrepo Suescún · September 17, 2019 at 11:21 pm

Interval and ratio are different measurements. They should not be placed as the same.

#### Мышь Полевая · October 12, 2019 at 12:04 pm

This video is awesome! Thank you! P.S. Привет магистрантам ИТМО 🙂

#### preet kaur · October 22, 2019 at 10:55 am

.-. why do i go to college, again?

Thanks!

#### Samia Sakah · November 2, 2019 at 10:07 pm

thank you soo much 💜

#### Sage Films · November 12, 2019 at 3:02 pm

This was great! Thank you!

*dAta

#### Enos Machapa · November 20, 2019 at 11:23 pm

The explanation made me easily understand the topic. Found it very helpful.

#### Habte Chapa · December 15, 2019 at 3:26 pm

The understanding I have got about SPSS is realy interesting

#### Patrícia Teixeira Maggi · December 17, 2019 at 4:38 pm

Great explanation. Thank you.

#### Chrono Hax · January 24, 2020 at 2:02 am

Thank you very much teacher 😀