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Non-sexist language in Russian: exploring YouTube with MAXQDA - MAXQDA
While public discussion concerning non-sexist language has existed in Europe dissertation language sexist several decades, in Russia it remains at its early stages. There are almost no documents and guidelines with recommendations regarding non-sexist language and public perception of proposals to create more non-sexist language is also widely unknown.
Analysis of the public perception of non-sexist language seems promising, because it might shed light on the perspectives of this topic dissertation language sexist Russia. With this goal in mind, I have conducted an analysis of YouTube comments as a part of my doctoral research. As one of the most commonly-viewed websites on the internet, YouTube is often used in the course of sociological and sociolinguistic research, which was the reason for me to choose this platform for my analysis.
The main research method was qualitative content analysis. Qualitative content analysis is understood here as a research technique in which a fixed in this case — written communication is analyzed in a systematic, rule-guided and theory-driven manner, with the aim of drawing conclusions about certain aspects of the communication cf. Mayring,S. The analysis was conducted with MAXQDA and included five notable steps, which I will describe below. First off, I had to import the data from Dissertation language sexist into the program.
Fortunately, it was a very easy thing to do, because MAXQDA has a special button for YouTube import in the Import Tab, dissertation language sexist. Along with importing the comments MAXQDA automatically autocodes them according to the number of responses, which was very convenient for me and dissertation language sexist follow the discussion while coding. Figure 1: After inserting the URL of the video MAXQDA automatically collected all of the video information, dissertation language sexist.
Next, I wanted to get the first overview of the data and created a Word Cloud where the most frequent words were displayed. Figure 2: Word Cloud is a good option for creating the first overview of the data. Youtube Data Import Word Clouds. After looking at the words presented in the Word Cloud, I started looking for other ways of summarizing my data and having various samples of contexts. They are also the main topic of both of the videos.
As a result, contexts have emerged, which helped me to gain an overall idea of the data and start with the manual coding. Figure 3: Memo in the code system automatically created for dissertation language sexist autocode. Lexical Search Autocoding. In my research I used an inductive category definition, which means that the categories are derived directly from the material by generalizing the material cf. Mayring, There was no previously theoretically conceived category system.
The coding took several rounds. First, the system of categories was created after context sampling see above and then this system was adjusted after each round of coding.
Since I was not working in a team and there was no possibility of cross-checking, dissertation language sexist, multiple rounds of coding were essential for quality control. Figure 4: The code system included autocodes and the codes created by me. In order to finetune my code system I used the Smart Coding Tool, because it allowed me to easily assign new codes to text segments, change the code system, move the codes in the hierarchy, merge codes, or create a new subcategory.
The Smart Coding Tool was also the place where I most often created memos in which I explained dissertation language sexist meaning of each particular category and its difference from the other categories.
Some of the memos later served as the basis for the text of my dissertation chapter. Figure 5: Smart Coding Tool helped me with the finetuning of my codesystem. Figure 6: Memos were a place where Dissertation language sexist described the special characteristics of each category. Smart Coding Tool Code Memos. After all these steps I was finally ready to analyze the results and describe the research in my thesis. For the sake of the transparency of my research this data will later be added as an appendix to my dissertation.
Through the analysis, a total of sixteen categories emerged, nine for the arguments against non-sexist language and seven for the arguments supporting it.
Overall, in the comments there were over twice as many arguments against non-sexist language than those supporting it statements vs. However, there may be several explanations for this, apart from the conclusion that non-sexist language is met with a negative attitude.
In general, negative comments and criticism are very widespread on YouTube cf. Of the nine counter-arguments from the present work, five are consistent with the classification proposed by Parks and Robertonwhich means that there are some commonalities between the discussion in Russia and in other countries. Chambers, B, dissertation language sexist. International Journal of E-Politics3 21— Koeser, dissertation language sexist, S. Promoting Gender-Fair Language: The Impact of Arguments on Language Use, Attitudes, dissertation language sexist, and Cognitions.
Journal of Language and Social Psychology33 5dissertation language sexist, — Mayring, P. Qualitative Inhaltsanalyse: Grundlagen und Techniken Parks, J, dissertation language sexist. Contemporary Arguments Against Nonsexist Language: Blaubergs Revisited. Sex Roles39 dissertation language sexist— Territorial Intelligence in the Meandro del Say Wetland — Colombia: coding categories with MAXQDA.
Research Blog MAXQDA Website. Back to Overview. What is non-sexist language and does it even exist in Russia? Step 1: Importing data from YouTube and the first overview of the data First off, I had to import the data from YouTube into the program. Figure 1: After inserting the URL of the video MAXQDA automatically collected all of the video information Next, I wanted to get the first overview of the data and created a Word Cloud where the most frequent words were displayed.
Figure 2: Word Cloud is a good option for creating the first overview of the data Youtube Data Import Word Clouds. MAXQDA Newsletter Our research and analysis tips, straight to your inbox, dissertation language sexist. By submitting the form I accept the Privacy Policy. Similar Articles Territorial Intelligence in the Meandro del Say Wetland — Colombia: coding categories with MAXQDA Reinforcing the Afghan Diaspora in the Digital Age Autocoding: An overview of MAXQDA's autocoding functions dissertation language sexist research contexts Importing YouTube Data and Media in MAXQDA - Addressing Data Access Hurdles, dissertation language sexist.
ResearchforChange CAQDAS code code system coding Computer Assisted Qualitative Analysis fieldwork interview interviews MAXMaps MAXQDA Analytics Pro mixed methods qualitative analysis qualitative data Qualitative Data Analysis qualitative research research transcription visualizations visual tools, dissertation language sexist. We use cookies to improve your experience on our website. Click here to review our Cookie Policy.
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