So this week I decided to do quite a simple blog, but on a topic that I’ve always struggled to get into my brain. Every experiment and every piece of research talks about variables in varying terminology yet is always something that confuses me each time I read about them. Obviously I understand the dependent and the independent variable. However, when reading through my Andy Field book (having a particularly nerdy moment, it has to be said that these are rare) I’ve come across different types of variables that I’ve never really heard of before.

A binary variable- there are only two categories, for example; yes or no.

It has to be said, that the following I have actually heard of, but I always get confused between which ones should be applied to which research. I’ve included these in my blog in the hope that others like me, who may get slightly more confused than the average population will be able to gain a greater understanding.

A nominal variable- there are more than two categories

Ordinal variable- these are the same as a nominal variable but they have a logical order

Interval variable- equal intervals on the variable represent equal differences in the property being measured

Ratio variable- the same as an interval variable, but the ratios of score on the scale must also make sense.

Variables can also be split into continuous and categorical; binary, nominal and ordinal are all categorical whereas interval and ratio are continuous.

Continuous means that the entities get a distinct score and categorical means that the entities are divided into distinct categories.

So, hopefully after reading this you’ll have gained more of an insight into the wonderful topic of variables.. or just revised something you may have already known. For those of you who may want to read more about variables then its all in Andy Fields ‘Discovering Statistics Using SPSS’ book, or have a look at this website http://www.socialresearchmethods.net/kb/variable.php which explains things quite well too 🙂

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Comments on:"Blog for 05/02/12" (7)giggles20said:I like how you have tackled the issue which many of us undergraduate students struggle to come to terms with. My problem is that I also struggle with knowing what variable comes under what category and your blog is helpful in breaking it down into simple terms, just like the great Andy Field does.I think it would have been a good idea to include some simple examples within your blog, ones which relate to each type of variable. For example in Andy Field’s book of discovering statistics he explains a nominal variable by using the example of being either a vegan, vegetarian or a omnivore. (Andy Field, discovering statistics, 3rd edition). Personally I feel this helps when attempting to learn the different categories with relation to the different variables.

Overall very good blog 🙂

counseyyysaid:I found this really useful to read, and I liked how it was almost like a glossary as well, with the key terms listed and defined. However, the topic of variables has not really been analysed or evaluated, which would of taken this post one step further. Overall it has been helpful and interesting to read 🙂

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psuc98said:Really useful descriptive blog and I think you explained everything clearly and it’s something a lot of people find confusing.

A criticism is that although you explained them well you didn’t really provide much discussion; you could have discussed the advantages and disadvantages of using one of them for example binary variables have 2 distinct categories but there would be a lot of things that don’t fit into distinct categories and where using this would be unhelpful. If you were measuring whether someone was male or female it would be useful; everyone falls into one of the categories. If you were measuring something like people’s opinions on their degree course you may have more trouble, to simply ask people ‘do you like it yes/ no’ does not allow any room for opinion, they may like all of it except having to comment on and read so many blogs in their statistics module.

Overall your post was really helpful and informative but unfortunately every blog has to include discussion so you could have given more examples are arguments for the different types of variables.

cerijaynesaid:After finishing reading this blog, I was relieved to see I was not the only one who found these variables confusing. When opening Erins exam in January and seeing one or two questions based on such, immediate panic set in, I should really know the difference between these. I can do anovas, cronbachs alpha and so on but I cannot tell the difference between binary, nominal, ordinal, interval and ratio variables. Then when opening your blog I realised I did not do much about it till now, I now know the differences, after reading this blog and my text book.

Despite your blog being clear and cohesive, I think it could be more detailed. If I were explaining such variables I would have maybe given examples of them, how they relate to one another, when is it ok to compute different calculations depending upon the type of variable. This would have not only helped the reader but you yourself. For instance ‘interval variable’, unlike categorical variables such as binary, nominal and ordinal, this is an continuous variable , continuous meaning that we have a score for each person and can take on any value on the measurement scale we are using. To say our data is interval we must be certain that equal intervals on the scale represent equal differences in the property being measured. An example maybe temperature in degrees Fahrenheit, for instance the difference between 29 and 30 degrees is the same magnitude as the difference between 78 and 79 degrees. Lastly interval data can be used to compute frequency distributions, median and percentiles, add or subtract, mean, standard deviations, standard error of the mean but it cannot be used for ration or coefficient of variation.

Still a relevant and great blog though but could have been expanded upon 🙂

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