Do you need statistics to understand your data?


Statistics come in many shapes and forms, whether it’s descriptive, such as the mean that provides us with a simple understanding of the data, inferential which are more complex, or as a visual representation such as a graph.  All of these types of statistics are used within psychology to enhance our understanding of raw data in a research study, by collectively transforming it into more manageable data.

It is a common misconception that when qualitative data is collected statistics are not used to analyse it due to the data being non-numerical.  This is not always the case.  Often, qualitative data is collected via observations and used to write a report based on the findings, thus leaving it in a non-numerical form, such as case studies.  A case study that had huge impact on the field of Psychology is Freud’s analysis of Anna O.  However, on many occasions qualitative data is collected through nominal research, therefore is categorical, and is then transformed into quantitative data in order to analyse it easier.  This could be done by creating a statistical table using the frequencies or by creating a pie chart etc, as demonstrated by Mendenhall, Beaver & Beaver, page 12.

Often, experimental error and variability within data can obscure meaningful differences.  By just looking at the data without performing any statistical procedure it can be very difficult to identify a pattern or consistent differences between treatment conditions.  For example, those who attended the Personality & Individual Differences lecture this week will recognise the 2 graphs shown below for Attention Blinks.  It is very hard to identify a pattern on the graph which shows all the individual results, but after a statistical procedure to show the mean values the pattern is much clearer.  Sometimes large differences can be identified without using statistics, but these mathematical procedures can help determine the extent of the difference more accurately.


However, although the use of statistics can help to understand data and identify patterns we may not naturally notice ourselves, it can also lead to confusion.  Sometimes after using a statistical technique your results may look too good to be true, and they often are.  Frequently, statistically significant treatment effects are not as meaningful in the real world as they initially appear.  For example, studies have shown that if the average human consumes 20-30g of wine a day, it could reduce their risk of heart disease by 40% (Renaud & Logeril, 1992).  This statistic looks amazing and encourages us to follow the suggestion and drink the recommended amount of wine per day.  However, if in reality your susceptibility to getting heart disease is only 5% then this reduction is not really that significant practically, and the other health risks you may intensify by increasing your alcohol consumption may not be equal in comparison.

The aim of a researcher is not only to find evidence to support a hypothesis, but also to understand why this happens.  Although statistics can be used to find cause and effect relationships it does not suggest anything about why these relationships exist.  Statistics help to describe what the data has found, however it provides no insight as to what this means in reality, what this revelation can lead to, or why your data is like this.  Therefore they can be helpful in many circumstances to analyse data and lead to the acknowledgment of findings that may not have been discovered otherwise, however they do not provide a complete picture of the findings and are not necessary in the understanding of data.


7 responses »

  1. Hey I really liked your blog. It was very indebted and had a lot of excellent examples. You even showed both sides of the argument and that is not always easy to do. I agree with your out look that we do need Stats in order to understand our data but it really isn’t always a requirement. A little more on the other side of the argument would have been more informative! i.e the qualitative due to being non-numerical. If you add more to this point your blog would really stand out. Your analysis of it was brilliant and you gave lots of examples but as I said I agree with you on the topic but more about the other side would have made your argument stand out a lot more as there is more information to add to that point! Over all well done! 🙂

  2. I really like the analytical nature of your writing. You mention that often qualitative data is transformed into quantitative data in order to be statistically analysed. Do you think that this is a beneficial methodology to examine your data? Behaviour is complex so can it really be simplified to quantitative data without something that may be a key element to our behaviour being lost? Nelson and Quintana (2010)1 state that collecting and keeping qualitative data as a qualitative data allows the researcher to gain a key insight into elements of the behaviour that may be lost if the data was to be quantified and statistically analysed.


  3. I think you deserve a medal for your thoroughness of this blog on its subject matter, extremely dense with info and well structured for easy understanding. You made your point and fully rounded out the argument well. What I would suggest is that you possibly could retrieve more details on important points such as the translation of qualitative to quantitative and the pros and cons of such a method for example how Anna O’s behaviour could’ve been translated effectively for statistical analysis and what would be lost in such a translation. Perhaps making the point that as inferential statistics are based on a theoretical model of distribution etc how it stands in validity with qualitative in terms of benefits of knowing. Though really there is such a thing as asking too much and for all I know you cut those to save time and space, so well done!

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  5. Thanks to everyone for the comments!
    To Cam and psud22psych, I think that how beneficial stats are to analyse qualitative data is very dependent on the aims of the study and the way the data is collected. If the data is something categorical then I think using stats could be useful, or when the frequency of certain behaviours in a situation is of importance. However if it is just generally observing the complexities of behaviour, in particular less common behaviour and how this varies across situations then I think it is the detail in the report than is more important than the figures. It is hard to perceive how the characteristics of behaviour could possibly be quantified, but finding a way whilst still keeping a lot of the detail of qualitative data could assist in a study. For example, a study by DeWhurst & Marley combined both quantitative methods of detecting sounds and changed in posture, and also the qualitative method of using a 2-way mirror to analyse the posture and look for problems with the measurement of quantitative data. From observing the behaviour aswell as collecting quantitative data they were able to see that some birds cheeped louder than others so this needed amplifying more to detect the frequency easier. This meant that they could use the two types of data together to try and get a better understanding of what was actually happening. I think with a lot of qualitative data to observe behaviour, statistics could possibly be used as an aid to understanding it, but that it is the intricate detail and the background detail that is much more important.

    Link to study:

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  7. Having read this I believed it was extremely enlightening.
    I appreciate you spending some time and effort to put this
    article together. I once again find myself spending
    a lot of time both reading and posting comments. But so what, it was still worth it!

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