The picture of my desk above illustrates the main issue that we had for the blog during the summer of 2017.
When we all met for our summer meeting, the main problem we had was that we either couldn’t access our Google Drive to get our information or couldn’t connect to the wifi. So to get around not being able to connect to the wifi, Laura suggested that she could get the data from her laptop since Prof. Quizon couldn’t access the drive. However, another problem came up. The laptops we use for this blog is either our personal computers or the laptops the school provides. To log into the laptop the school provides, you need to log into your student email and to do that, you need to have wifi access. But for some odd reason, Laura’s laptop could not recognize the campus wifi.
After finally being able to connect to the wifi and getting all the data we needed, we all discussed issues that came up at that point.
One of the main issues, besides connecting to the internet and getting our data, was how to code some of our data into the excel because all our data was qualitative data. What we decided to do and how we did in detail it is on a different post but it all came down to figuring out how to categorize something into something else.
The second issue, which is something more personal to me than what it is for the others, is how being an alumni affects the productivity of the blog and internship. One of the main issues is just getting onto the blog because we all use our student emails to log in. Not being a student anymore complicates things. The quick fix was to switch to my personal email and then relinquish admin rights after I hand over to the next group.
The final issue touches the first issue but in more detail. It had to do with how to categorize something that doesn’t have a category. For example, how would you categorize learning a language from a hymn or song? Would you say the person can speak and recognize it but not understand it? This issue was brought up by Stephen when he realized that some students who took the survey said they can sing and recognize a language but not actually read or understand it.
The easiest and fastest way we decided to address this problem is just to make a special category for these cases since it only affected about five or six entires. After going through all our issues and trying to figure out a way around them, we all had pizza and left to enjoy the July weather.
When deciding what pages to include in our Menu, I had to really think about what pages are on regular websites. I decided that Our Mission Statement should be a our homepage so that when you arrive at our site, you know about our project and our goals. I revised the mission statement several times and finally decided upon the finished product you see now.
My second thought was having a page explaining what exactly we mean by Language Maps and Language Clouds. Dr. Quizon thankfully authored this page with working links.
As a team, we decided to rename the blog page to “The Project”. This was a unanimous decision. We wanted to take people step by step through our process.
Our “Contact Us” page is for anyone who has questions, comments, or wants to use our research which is covered by Creative Commons. The “Contribute” page will be an open forum for anyone who would like to add their languages to our research. We are working now with a WordPress expert who is going to build our questionnaire which will input directly into an Microsoft Excel spread sheet, already coded.
We encourage you to check back soon and contribute your own languages!
In gathering our data, we have recorded many different languages. Here is how you say hello in some of them!
English ~ Hello/Hi
Italian ~ Ciao
German ~ Hallo
French ~ Bonjour
Tagalog ~ Kamusta
Spanish ~ Hola
Russian ~ Здравствуй (Zdravstvuy)
Welch ~ Helo
Dutch ~ Hoi
Japanese ~ こんにちは (Kon’nichiwa)
Korean ~안녕 (annyeong)
Polish ~ cześć
Gaelic ~ Haigh
Portuguese ~ Oi
Chinese (Cantonese/Mandarin) ~ 你好 Nǐ hǎo
In order to present our data by the participant, the ethical thing was to avoid revealing the actual name of the individuals who gave us our data. In this, we used Microsoft Excel to generate and assign random numbers, rather than simply numbering every subject individually. These numbers would then act as the I.D.’s for each participant. On a separate spreadsheet, we put participants first and last names in columns ‘A’ and ‘B’ respectively (here I have put in ten fake names* to show you an example). For our data, we had a list of all the participants’ names in alphabetical order by last name.
Next, I used the RAND, or random function. By putting the =RAND() function into column ‘C’ from cells C1 to C10, we were given a random decimal number. Then, I had tried to use the =RANDBETWEEN function in column ‘D’, inputting =RANDBETWEEN(1,10). Although this gave us a random whole number between 1 and 10, there were repeats of the same number. So now one of the biggest problems was finding a way to have excel create random intergers that did NOT repeat.
Finally, with a little help from the library and the internet, I used the following formula to generate NON-REPEATING whole numbers in column ‘D’;
The result was what we were looking for, anonymity for our participants. With this success, we copied and pasted the numbers next to the names in the list of participants in our data set.**
*None of these names are meant to have any relation to any person(s) alive or deceased.
**When I input the function into column ‘D’, the random values in column ‘C’ changed automatically, but remained random. you need to keep this formula in this column in order for the function in ‘D’ to work.
There may be other ways of achieving the same outcome, but this formula worked best in excel.
Initially, we were going to use Google’s spreadsheet because we could all edit it in one place, but we encountered a few problems. Some of the data in the Microsoft Excel spreadsheet when opened in the Google spreadsheet would overlap into other columns, making it hard to read. Additionally, there would be the occasion where data that was present in the Excel sheet was missing in Google’s spreadsheet. As another point, we all had the same version (2013) of Microsoft Excel pre-downloaded on our laptops which made Microsoft Excel compatibility easy. It was unanimously decided that we use Microsoft Excel to input data. However, we also decided to use Google Drive to save and share our data on a cloud. Google Drive also updated us via email anytime one of us contributed to our shared folder.
We created three folders in google docs to organize our saved spreadsheets and other files. These three were ‘1st DH Raw Data’, ‘2nd DH Raw Data’, and ‘DH Meeting Docs’. The third folder held our meeting minutes, or what our discussions were when we met and what goals we discussed to have done before we next met. Both the first and second raw data folders had sub folders of ‘checked’ and ‘unchecked’, where the previously naming convention came in handy. Additionally, both raw data set folders had their respective index card scanned copies were saved there. In doing this, we kept all files organized well and were able to share files efficiently. Although we all saved the most recent files to our desktops and to a shared USB drive for backup, Google Drive assured that our updated and previous files were in one place that we could all access from any computer.
In the Fall 2015 Linguistic Anthropology class taught by Dr. Quizon, students were asked to share information about any and all languages that they knew. She gave out note cards and instructed the class to write down one language per card. Underneath the name of the language, they were asked to write down anything they wished to say about this language. They used descriptors of their own design making these cards rich with open-ended qualitative data. On the reverse of each card, they were asked to write their names.
With support from Seton Hall’s Digital Humanities Fellowship initiative, Dr. Quizon and three student interns who completed the course in the previous semester took a closer look at this data and explored ways to visualize the information. Were there intriguing or interactive ways to plot linguistic information? Could the data be mapped? Were there patterns to be discovered when expressed in visual form?
The class of 35 students was surveyed twice: once in the beginning of the semester, and again towards the end of the semester. The Language Maps, Language Clouds research team took these two sets of note cards, devised ways to capture, organize and analyze the information using linguistic concepts, explored ways to visualize the results of our queries, and aimed to share our findings online. Our goal is to share both processes and results as we seek to deepen our understanding of the data an interesting, interactive setting.
Even though we all participated in every aspect of the project, we each had an area of expertise. Ellie learned how to use and troubleshoot Viewshare and later, with Dr. Quizon, explored Tableau. She worked with Anastasia who was in charge of Excel and added knowledge of its features as needed for the project. I was in charge of learning how to build a blog on WordPress.