See also Qualitative coding tutorial.
Segment 1: Possible data sources when collecting qualitative data
1. Observational Data
- Classroom Observations: Sitting in on lessons, taking detailed field notes on teacher practice, student interactions, lesson flow, material use, and classroom environment. You can have structured observation checklists or a more open field-note format.
- Video or Audio Recordings: Capturing classroom activities allows for repeated review and finer analysis of nonverbal behaviors, pauses, intonation, and other subtle interaction cues.
- Artifacts: Collecting student work, classroom posters, handouts, lesson plans, and other physical or digital materials used within the classroom context.
2. Interviews & Focus Groups
- Student Interviews: One-on-one or small group interviews to explore student perceptions of their learning, challenges, motivations, and experiences in the classroom.
- Teacher Interviews: Gain insights into the teacher’s beliefs, methodologies, classroom management strategies, and their reflections on the teaching process.
- Focus Groups: Facilitated discussions with groups of students or teachers, encouraging dynamic exchanges of experiences and perceptions.
3. Textual Data
- Student Work: Collect writing samples, homework, project drafts, and test responses to analyze language development, error patterns, and content understanding.
- Reflective Journals: Both students and the teacher can keep journals to document personal thoughts, reactions to lessons, and their evolving experiences.
- Lesson Plans & Curriculum Documents: Analyze the objectives, materials, and the overall design of the learning experiences.
- Policy Documents: Examine school-wide or district-level policies that influence the ELL classroom environment (i.e., language support policies, assessment guidelines).
4. Other Qualitative Sources
- Stimulated Recall: Have teachers or students review a lesson recording and comment on their thoughts and decision-making processes during specific moments.
- Think-Aloud Protocols: Ask students to verbalize their thought processes while completing tasks or solving problems.
- Photo Elicitation: Use photographs related to the classroom experience to prompt interviews or reflective discussions.
Key Considerations
- Research Question: The types of data you’ll choose depend heavily on what you aim to investigate.
- Triangulation: Combining multiple sources of data provides a richer, more nuanced understanding of the classroom experience.
- Ethics: Always ensure informed consent and protect the confidentiality of your participants, especially when working with students.
Segment 2: Qualitative Coding – Finding the story in your data
Dr. Mendez: Imagine you’ve conducted interviews, collected observations, or gathered open-ended survey responses. How do you find those important insights buried within all that text? The answer is qualitative coding. Think of coding as attaching descriptive labels to sections of your data that represent specific ideas.
Let’s talk about how this works for English language learners. Start building a simple vocabulary of common codes. Words like “attitude,” “experience,” “challenges,” or “strategies” are all excellent starting points for research in applied linguistics. Remember, don’t be afraid to translate between English and your native language to help create this code vocabulary – sometimes your perfect word might come to you more easily in another language!
Segment 2: Statistics – Turning Numbers into Insights
Dr. Mendez: OK, now let’s jump into descriptive statistics. While qualitative research gives you the ‘why’ of things, statistics lets you see the ‘what’.
Imagine you have data on language test scores, numbers on how frequently specific language features are used, or things like that. Descriptive statistics lets you summarize these numbers. Some important tools you’ll want to know about are:
- Measures of Central Tendency: These are tools like the mean, median, and mode, that help you get a sense of the “average” within your data.
- Measures of Spread: Things like range, variance, and standard deviation tell you if your scores are all clustered closely together, or if they are widely spread out.
Don’t panic over the terminology! There are great online resources and even statistics dictionaries designed specifically for English language learners. Start slow and you’ll build a solid understanding.
Segment 3: Tools and Tips
Dr. Mendez: There’s specialized software to help ease the work of qualitative coding – tools like NVivo or Atlas.ti. For statistics, you can start with the basics in Excel and move on to more powerful software like SPSS. Don’t worry if these programs seem intimidating initially! Focus on simple calculations first, and then gradually explore the software options as you become more comfortable.
Finally, remember you’re not alone! Seek out a research mentor or a study group. Having someone to explain concepts in a different way can make all the difference in your learning.
Outro
Dr. Mendez: I hope this episode took some of the fear out of coding and statistics. They’re powerful ways to tell a compelling story with your applied linguistics research data. Start small, use the resources available for English language learners, and don’t be afraid to ask for help when you need it. We’ll continue this journey of data analysis in future episodes!
Outro Music
Note: This streamlined structure allows for deeper explanations and practical tips focused on challenges faced by English language learners.
Segment 3: Coding examples
See also
Absolutely! Here’s a breakdown of descriptive, in vivo, emotional, and value coding examples, along with potential scenarios where you might find them in applied linguistics research:
1. Descriptive Coding
- Function: Summarizes the basic topic of a segment of data. Often the first pass when coding.
- Example: Imagine a student interview transcript includes the statement: “I take extra pronunciation classes on the weekends.” A descriptive code might be “additional language practice”.
- Applied Linguistics Scenario: Analyzing student interviews about strategies for language improvement.
2. In Vivo Coding
- Function: Captures the participant’s actual language, preserving their voice and specific word choices.
- Example: A teacher in a focus group says: “My students just clam up when it’s time to speak English.” The in vivo code would be “clam up.”
- Applied Linguistics Scenario: Examining teacher perceptions of student anxiety in language classrooms.
3. Emotional Coding
- Function: Identifies and labels emotions expressed by participants.
- Example: An interviewee states, “Switching languages all the time makes me feel so frustrated!” The emotion code could be “frustration.”
- Applied Linguistics Scenario: Researching the emotional impact of code-switching on multilingual learners.
4. Values Coding
- Function: Explores participants’ values, beliefs, and attitudes that are reflected in their statements.
- Example: A participant says, “Everyone should have the opportunity to learn other languages. It opens up your world.” The values code might be “multiculturalism” or “language as opportunity”.
- Applied Linguistics Scenario: Investigating community attitudes towards bilingual education programs.
Important Notes:
- Coding Isn’t One-Size-Fits-All: The best coding approach depends on your research question and data. You can use multiple types of coding in a single study.
- Context is Key: The same phrase might get a different code depending on the broader context of the data.
- Iterative Process: Coding evolves as you become more familiar with your data. You might start with descriptive codes and then refine them into more nuanced emotional or values codes.
