"I am a Humanities and Social Sciences students. Why do I need Maths?"
As a student in Humanities or Social Sciences, even if you are not doing calculations, you need to understand what various sets of data mean. For example,
you might be interpreting or representing numbers like population statistics, financial growth, immigration numbers, rates of birth, death, marriage, correlations and causations of events to name a few.
Even if you are engaging in qualitative research (‘how’ and ‘why’ type questions), quantitative (numerical) data is useful for context and to understand the bigger picture of events or case studies. Although numbers are not stated, quantitative calculations are often used as part of fundamental analysis. For instance, simply talking about an increase in migration patterns depends on basic maths to compare different periods, populations, or countries.
These statements arise from basic arithmetic that you probably learnt at school:
Addition, subtraction, multiplication, division, and a sense of scale using common measures to make comparisons.
Modelling, probability, and statistical thinking are the next level of Maths required in the Humanities and Social Sciences. These allow you to see patterns and make predictions.
Much of the data you will use as an undergraduate in the Humanities and Social Sciences will be provided for you, but there are also programs which will do the calculations for you, such as:
SPSS is a software package used for statistical analysis. SPSS allows you to conduct statistical analyses and summarise data using graphs and other visual representations. All Library computers, and computers in some training labs, have SPSS.
NVivo is a qualitative data analysis software package that is used to analyse text-based and/or multimedia information (e.g. interviews and focus groups). NVivo can be downloaded from the Student IT support site.
Microsoft Excel allows you to organise, format, and calculate data with formulas using a spreadsheet system. Excel is part of the Office 365 package.
Your lecturer may also suggest other software, but make sure you get some training in how to use it! So look out for any training that the university is offering.
As more and more information becomes available in digital format, there is more statistical analysis to do.
If you know how results are calculated, you can interpret them correctly.
Here we focus on the presentation of data rather than calculations themselves, but other sections of this module of Achieve@Uni outline the basic principles for you. You may also find it useful to look at Rachel Chrastil’s course ‘Quantitative Literacy and the Humanities'.
If working with numbers makes you feel uncomfortable or anxious then begin by looking the Maths Anxiety page in our Maths module. Once you understand basic principles of sorting or grouping data, it will feel less overwhelming and more manageable.
Numbers or words?
Statistical data is not always presented as tables or charts. It might be simpler to enter a number rather than creating a chart where the information is not obvious.
If you do use charts, graphs or tables make sure they add value to your work, and that they are clearly labeled, introduced and explained in the main text.
It is acceptable to use rounded numbers in your text, e.g. “approximately 1.7 million” rather than an exact figure, like “1,705,213”. However, corresponding information in a table should be exact.
Spell out a number if it begins a sentence. For example: “Ten per cent of the subjects indicated….” NOT “10% of subjects indicated…”
A graph or chart is an effective way of representing a set of data. In the Statistics module look at the section on when and how to use graphic information; different graphs convey different kinds of data.
Make sure the graph has its correct label/caption so that it can be interpreted by itself.
All charts, tables, maps or figures should always be referred to in your main text.
Use data with care
When using other people’s data, make sure it has been peer reviewed or comes from sources that can be trusted.
Remember to acknowledge your sources, so readers can examine the data and draw their own conclusions. See the Achieve@Uni module on Referencing.
When using a percentage, check the sample size. It might sound good to say, “A recent undergraduate survey revealed 66% of respondents preferred to read online”, but if only 3 people completed the survey the percentage is meaningless!
If the sample size is not indicated as part of the data you should probably reconsider if it is credible.
Even if you have a large sample, consider whether it is representative of the population as a whole, otherwise note its limitations in your discussion.
Cause and effect
Two or more events that happen to coincide are not necessarily related so be careful what you infer from statistical data.
There may be a third variable that accounts for the correlation or it might just be a coincidence. Even if there is a relation this does not explain why it is occurring or even the direction of the cause and effect.
For example, the consumption of doughnuts might strongly correlate with improvements in first year test scores. But this does not mean that eating doughnuts will improve your test results – and in this case, it is most likely a coincidence!
For more information on variables see the Achieve@Uni Statistics module.
In some Humanities and Social Science subjects, even there are no specific numbers, the thought processes of logic and probability are valuable.
This means that the critical thinking developed in your studies can be applied to statistical results to consider the value of the data, the methods of collection, the extrapolation of results or biases in the sample.
To explore these concepts further and work through some practice exercises for the Humanities and Social Sciences, the following resources provide a great way to begin!
Social Research Methods, Third Edition, guides students through all the steps of the research process, from formulating a question to writing up their report. Written by a team of active Australian research practitioners Social Research Methods, Third Edition, bridges the gap between theory and practice with substantive examples of each method in practice.