UCT Language Attitudes Update #3: The Analysis

Great White Shark Cage Diving in Gansbaai

I believe this photo is my most appropriate one yet, since it pretty much sums up what I saw when I first looked at my data – a large, Great White Shark, coming slowly towards me, although in the case of my data, I had no cage protecting me (I did with the sharks).

For those of you who have never done data analysis, let me give you some advice: If you are a Mathematics Major, but only study theoretical math, do not let people tell you that your “math background” will help you with data analysis. Yes, it theoretically should, and it does make things a little easier, such as when you’re able to understand half of the theory behind principal components analysis rather than none of it, and you can prove to yourself that the numbers are doing what they should. However, you still need to understand how to organize your data, how to move and extract large chunks of it without copying and pasting, and how to put it in beautiful charts and graphs so that someone can actually make something of it. These are all things I’ve learned a little about in the past few weeks.

Execel and Matlab = my new best friends. W&M has a license such that all students can download and use Matlab on their personal computers, so that’s what I did. I am lucky enough to be engaged to a beautiful woman who also happens to use Excel and Matlab a lot, so I got a tutorial from her on Excel macros and how to read in my data and extract what I want for analysis. If you’re curious, the “find,” “strcmp,” and “strfind” functions are also new acquaintances of mine.

While in South Africa and for an entire week after, I organized my data into Excel spreadsheets, and with some help got it all into a format that was Matlab-friendly. Then came the analysis – aka “type things into Matlab and hope it does what you want – which was long a grueling. You see, even with computer aid, there was still a lot of copying and pasting for me to do due to my crippling lack of computer know-how that would probably prevent it (I hope, or else a lot of scholars have really worn-out ‘C’ and ‘V’ keys).

The biggest issue I ran into with my data was how to deal with incompleteness. Some of the trials I ran had to be cut short, and thus a lot of my participants did not finish the matched-guise study 🙁 What I ended-up having to do was eliminate their matched-guise studies, but luckily I made sure they all did the supplemental questionnaire, so for that analysis I still had 81 pieces of data! 🙂

For my final Monroe Project, I just did a lot with averages. I chose not to do any Factor Analysis just yet, since I need to study that a lot more before attempting it. For the sake of doing this initial analysis of the data, I  narrowed the 18 scales per recording (270 total) rated by each participant to six per recording (90 total). The six I chose were: poor-rich, unintelligent-intelligent, friendly-unfriendly, unlikeable-likeable, passive-active, and weak-strong. I chose these specifically by first being sure to choose two each from Zahn and Hopper’s (1985) three major factors (superiority, social attractiveness, and dynamism), while still maintaining what I felt was semantic distance within those factors. I also was sure to not include ones such as unclear-clear and untalented-talented, which seemed to confuse some participants (they asked about them during the study).

Narrowing the data as such truly made the analysis using Matlab loads easier. For the project I did some simple analyses using things such as gender, languages spoken, family languages, languages on the recordings, etc. Some results I found were expected, such as English speakers being rated higher on the superiority scales and Xhosa on the social attractiveness scales. Also, some confirmed results I know I’ll find in the qualitative data, such as Afrikaans experiencing a negative extrinsic attitude overall.

I only analyzed the quantitative data for my Monroe Project, since I plan to use the other, qualitative data for my Honors Thesis in Linguistics! So exciting and daunting at the same time, but no matter what this was an awesome experience that will definitely stay with me forever! =)

Thanks for reading,

Mike

Comments

  1. mrdziuban says:

    This seems like a really cool idea, Mike. I wouldn’t normally have thought about how different language abilities can affect someone’s attitude about various characteristics or aspects of their life. Good luck taking it even further with your thesis!