We take an introductory look at some methods of data analysis and communication. We explain the different analyses that spreadsheets, statistical software and qualitative data analysis software can help with, and we investigate some of the principles and practice of data visualisation.
Most data can be reduced to numbers or counts of items. Depending on what you're trying to achieve, you may only need to calculate descriptive statistics such as counts, averages, and percentages.
You're best off using an approach you've been taught, or that your supervisor or others in your department use.
Not sure which statistical test you need? Use our tool to help you identify an appropriate test for your data:
SPSS is used for statistical analysis. The add-on module, SPSS Amos, enables structural equation modelling.
R is a programming language and software environment for statistical computing and graphics. We've got a load of resources for R on our Coding Practical Guide:
Python is a coding language which can be used for statistical analyses. We've got a load of resources for Python on our Coding Practical Guide:
Stata is a general-purpose statistical software package. It's capabilities include data management, statistical analysis, graphics, simulations, and custom programming.
Want to brush up your skills with using statistical software? You can use our workshop materials and recordings at any time, or book to attend an upcoming session:
Data can be organised in a number of standardised and interoperable text-based formats. Whether you're importing existing data, or exporting it for use in another tool, it's worth understanding the common formats in use.
An archival standard for spreadsheet-formatted data is to use delimited text: each cell is separated by a special character (usually a comma or a tab character), and each row by a different character (usually a line-break character).
The most common delimited formats are CSV (comma-separated values) and TSV (tab-separated values):
Spreadsheet files can be saved into these formats for use elsewhere, but only the superficial text values are saved, not any formatting or underlying formulae.
Cells containing commas (or tabs) are encoded in quotation marks. If your data contains complicated combinations of commas (or tabs) and quotation marks, you may have problems saving as csv (or tsv), though you could potentially save with a different delimiter!
Not all data you find online is in a friendly format. You may occasionally come across tables of useful statistics on webpages or in PDFs. Sometimes you can copy and paste them into something like a spreadsheet without any problems, but not always.
If data on a webpage has been formatted as a table or a list, and copying and pasting isn't pulling information across as you'd like, you should be able to import the data into a spreadsheet using an import function. Even if the data has been formatted in a non-standard way, you may still be able to extract usable information using an import function like IMPORTXML in Google or WEBSERVICE in Excel, but you might have to dig a bit deeper into the HTML.
So long as the data in the PDF is encoded as text (rather than as an image), it can be extracted into a spreadsheet format. On University computers you can use ABBYY FineReader to convert a PDF to Excel format. If you're on your own machine you could use Google Drive to convert the PDF to a Google Doc, and then copy and paste. There are also free tools like Tabula, though, as ever, you should think critically when using software from the internet.
If your data is just an image (a photograph or photocopy of some data, with no machine-readable element), you'll need to employ some optical character recognition (OCR). If you're on campus, the scanning options on the printers/photocopiers includes OCR. Alternatively, you could use Google Drive to convert a PDF to a Google Doc. Either way, the results may not be structured in a very useful way, and you may have to do a lot of repair. It may be easier to simply enter the data yourself.
IT Services have a number of software packages for statistical analysis:
Qualitative data analysis is the analysis of things other than numbers — usually text information. It's mostly a case of just reading stuff, but it can also be advantageous to find ways of quantifying content.
NVivo is a qualitative data, text management and organisational tool which enables an analysis of very rich text-based and/or multimedia information, where deep levels of analysis on small or large volumes of data are required. It is often used for qualitative research and literature review.
NVivo can be used to analyse more than just text-based files: images, audio and video can also be marked up. If you have an audio file of an interview, for example, you can annotate it directly in NVivo, without the need for transcription:
If you do feel the need for a transcription, you're going to have to create it yourself (unless you can persuade somebody else to do it for you!). To save time, you could transcribe directly into NVivo and code as you go; or you could choose to code it up in some other way.
Auto-transcription is possible (though seldom very reliable). NVivo offers a paid transcription service, but there are free alternatives such as the ones we discuss on our Subtitling Skills Guide (if you're conducting an interview over Zoom, you can also enable its built-in auto-transcription). Be aware, though, that even with an automatic transcript, you're going to need to do a lot of work if what you're actually wanting is a perfectly punctuated verbatim text. It may well be nearly as quick to transcribe it manually.
If you create a transcript that you're wanting to analyse in NVivo, be sure to make use of Styles in your document. This will allow you to do some basic auto-coding (i.e. to identify the interviewer and interviewee). You can also sync a transcript to a video though this might require some preparation if you're working from a conventional subtitle file.
It's one thing finding some data, but you probably need to manipulate it in some way before you can interrogate it...
We might think of ‘data’ as values stored without context. Through processing that data we can seek to provide context and determine meaning. But even simple spreadsheet operations require us to have some understanding of what's in that dataset, and what constitutes ‘good’ data in the first place.
As an example, let’s ‘deconstruct’ some information:
“The appointment with Dr Watt is on Tuesday at 2:30pm at the Heslington Lane surgery.”
This information contains the following fields of data:
If you wanted to record appointments in a computer-based system you would need to use separate ‘fields’ for these — which in a spreadsheet might translate to separate columns.
When faced with an existing dataset, our first challenge might well be to reverse this process and rebuild our understanding of what information these fields convey. If you've got the data from a third-party source, look out for any explanatory notes that might help you with this.
Data processing systems struggle if you don’t stick to recognised data types, or if you add in values that don’t match others in the same context, For instance, in addition to text, spreadsheets observe the following special data types:
Mon or Fri
For software to be able to analyse a number or a date, it needs a number or a date that it can parse — that it can understand and calculate with. If a value doesn't match the necessary rules to qualify as 'parsable', it will be treated as text. This may have an affect on how you're able to interrogate that data. If you represent a number or date in a way that does not allow the program to determine its type correctly, you will not be able to sort and filter correctly, you will not be able to add up, find averages, find the interval between two dates, etc... You might be able to understand that 20 + c.10 = c.30, but a computer can't make that leap. You're going to have to clean your data.
The success of any data processing will depend in large part on the quality of the source data you're working with. Data is often messy: columns might contain a mix of text and numerical data; some rows may have missing data; or perhaps you're trying to mash together two separate spreadsheets and the column names don’t quite match, or people have used a label in slightly different ways.
This is when you need to clean your data (a process also known as data munging or data wrangling). You need your data to be in a useful shape for your needs: if you're analysing or visualising data, what information (and types of data) does that analysis or visualisation require?
It’s all about ensuring that your data is validated and quantifiable. For instance, if you have a column of 'fuzzy' dates (e.g. c.1810 or 1990-1997), you might want to create a new column of 'parsed' dates — dates that are machine-readable (e.g. 1810, 1990). This might mean that you're losing some information and nuance from your data, and you'll need to keep that in mind in your analysis. But you'll at least have quantifiable data that you can analyse effectively.
For small, straightforward datasets, you can do data cleaning in a spreadsheet: ensure that numbers and dates are formatted as their appropriate data type, and use filters to help you standardise any recurring text. Excel even has a Query Editor tool that makes a lot of this work even easier.
The larger a dataset, the harder it is to work with it in a spreadsheet. Free tools like OpenRefine offer a relatively friendly way to clean up large amounts of data, while programming languages like R and Python have functions and libraries that can help with the tidying process.
The way your data is laid out has an impact on how you can analyse it.
Data is conventionally displayed as a two-dimensional table (rows and columns). Generally this will be laid out as a relationship between a case (a 'tuple') in each row, and its corresponding attributes (each with their own data type) in columns. Take this example of list structured data from a student fundraiser:
|1||Student ID||Foreame||Surname||Year||College||Bean bath||10k run||Parachute jump||Tandem joust|
Sometimes a single 'flat file' table of rows and columns is not enough. For instance:
You need to work with information about people and the research projects they are involved in. There will be several fields of data about the people, but also several about the projects.
It would be impossible to design one table that is suitable to hold all the data about people and projects, so in this case we create separate tables – one for people and one for projects – and find ways to express the connections between them.
In this example, one person can be involved in many projects, and one project can involve many people. This is a clear indication that the data is relational, and any attempt to work with it using a simple table will entail compromises.
This approach marks out the fundamental difference between a spreadsheet and a relational database.
Even the fundraising example in the table above may be better thought of as multiple tables: one table could index the students alongside their forenames, surnames, year, and college; a second table could list all the bean bathers (by Student ID) and the corresponding amount raised; a third could list the 10k runners, etc.
Depending on the analysis you need to do, it may be necessary to restructure your data. One common approach is to reorganise your data into what we might call a 'pivotable' format.
In our student fundraiser example, we have multiple columns all sharing the same attribute: amount raised. We might therefore look to move all these values into a single column:
This table looks unusual when we're used to seeing one row per student. Now it's effectively one row per fundraising performance (we might even imagine a unique ID ascribed to each activity a student performs). But it means that all the fundraising amounts are now in the same column (G): we can get a total for that column very easily, and can even filter based on the activity, the student, or any other field. If we're using a spreadsheet, we can use this data in a pivot table, and if we're looking to make a visualisation, this is also the ideal format for a lot of visualisation tools.
Restructuring data is not always straightforward. But some of the data wrangling tools below may help you. We've also got some guidance on using spreadsheets to unpivot 'pivoted' data.
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