I enrolled in the module FSS1, Quantitative methods in the Social Sciences, as part of my doctoral program. The module is a three day workshop taught by Fionnuala Ní Mhordha from the Geography Department at Maynooth University. I was unsure of what to expect from this module but it turned out to be an in-depth introduction to the statistical program, SPSS developed by IBM.
The module was structured into three days of workshops. This resulted in three intensive session on statistics and training in the uses of the program SPSS. The workshop environment meant that there was opportunity for engagement between the tutor and the class. When learning a new program, this type of process has benefits and limitations. From my perspective, the classes worked very well and the workshop model allowed me to experiment with the program and get feedback from the tutor if I made any mistakes.
As someone studying digital humanities and starting a doctorate in history with an emphasis upon military records, this module proved invaluable and provided me with the necessary program (SPSS) to help interrogate one of my core resources, the Irish Army Census, 1922. To give an example of the workflow I developed from this module, I have outlined how it is being currently utilised in my PhD research.
As part of my PhD research, I’m currently transcribing the Irish Army Census, 1922 ; a census taken of men serving in the National Army in November 1922. It has been partially transcribed so far, and I am currently transcribing the full census with all fields into a database. This is currently stored in a FileMaker Pro database. I used 15,045 transcribed records for the assignment for this module to answer a series of initial research queries. The workflow for this started with my current dataset which is within a FileMaker Pro database. From my FileMaker Pro database, I exported a .csv file containing the information transcribed, all text with the exception of an age field and date field. This was then coded using SPSS into a series of numerical values. Listed below is a frequency table of the Marital Status of 15,045 soldiers listed on the Irish Army Census.
Using the auto code feature in SPSS I was able to take my text based data and transform it into a set of numerical values, in this case ranging from 1 to 4.
1 = Married
2 = Single
3 = Widower
4 = Missing (This means that the census return has no entry)
Taking the data that is already numerical, in this case age data, SPSS can create a well designed histogram that illustrates the data in an effective manner. In this example below, a frequency table of age and a histogram of age show that the majority of men in the dataset examined (15,045) are aged 18 – 24. A cumulative percent of 67.8%.
This initial statistical frequency test will form the basis of further research on the age profile of the National Army. The initial results are provisional as this test was only done with 50% of the total servicemen listed on the Irish Army Census. However, it is a good example of some of the types of techniques that can be applied after participating in this FSS1 module.