acrl podcast 4

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Know Your Users : 

Know Your Users Podcast 4 Data Collection and Analysis

Data Collection : 

Data Collection A general model for any interview-based method:

Pre-Interview Preparation : 

Pre-Interview Preparation Schedule in advance for a specific location. Private enough to ensure confidentiality. Quiet enough to allow for good quality recordings. A “neutral” location Some students are uncomfortable meeting in a faculty member or librarian’s office. Remember to get contact information . The location should have all the equipment required for the interview. Arrive early to set up and test any equipment.

Starting the Interview : 

Starting the Interview Introduce yourself and briefly explain the research. Present and explain the consent form. How the data will be used and retained. How the participant’s confidentiality will be protected. Give the participant time to read and sign the form. Answer any questions the participant has. If you are offering incentives give them to the participant.

The First Few Minutes : 

The First Few Minutes Open with a few “ice breaker” questions. Not directly related to the research. Build rapport with the participant. Get the participant used to talking to the researcher and being recorded. The first few minutes of an interview can be intimidating. Participants will vary in how fast they get used to discussing themselves. Be sure to double check if your recording equipment is working.

During the Interview : 

During the Interview Prepare an interview guide in advance. Use this guide as a reference rather than a script, and allow the interview to flow as naturally as possible from topic to topic. Be mindful of the timing of the interview. Avoid making interviews that are too long. Forty-five minutes to one hour. Thank the participant for their time. Remind them of your contact information.

Transcription : 

Transcription Most interviews need to be transcribed for effective analysis. Budget enough time/money. Hire a professional or experienced transcriptionist. This will pay dividends in speed and accuracy even if his/her hourly base rate is higher. Utilizing student assistants can be also be effective. Transcription is often more difficult than expected.

Coding : 

Coding After fieldnotes, coding is usually the first phase of ethnographic analysis. Coding is the process of generating short phrases, or codes, and assigning them to sections of interview text in order to summarize and/or interpret the meaning of the text. This process begins with open coding, to uncover patterns that arise in the data, and moves to closed coding, to elaborate on these initial patterns and solidify understanding of the relationships between themes.

Open Coding : 

Open Coding Review the texts/transcript. Code all sections that seem important or relevant. Codes are not limited in scope, and the researchers are free to add any codes.

Open Coding : 

Open Coding Open coding is used to discover themes and patterns within the data. Be careful not to limit their codes to predetermined themes, or a preconceived hierarchy. If it seems important or interesting, code for it. The idea is to open up your understanding of what is being said in the data and to allow you to be attentive to the meanings that participants ascribe to their experience.

Open Coding : 

Open Coding Generating open codes: Create codes that describe parts of a process. What participants are doing and how. What meanings they assign to the process. What consequences result from their acting (or not acting) What seems important to you. Select a particular process that is of interest. e.g. How students get help when doing research, highlight all of the relevant sections of text, and code only those sections of text. (See toolkit p. 23). See Emerson, Fretz and Shaw (1995).

Open Coding : 

Open Coding Produces a great number of codes. Redundant and unnecessary codes. Organize the codes into a thematic hierarchy: Group codes related to similar topics. Combine redundant codes . Eliminate codes that are not relevant to your research topics, Create new codes to address any gaps you discover. Cross-reference codes that fall into multiple categories. Much of this organizational work can be automated using coding software.

Memoing : 

Memoing Throughout the coding process, you should write memos about the data Explore what different concepts mean. Range in length from a few sentences to a few paragraphs. Can elaborate on the meaning of a code in various transcripts Relate different codes to each other. Memos help the researcher explore more fully what he/she is learning during the research process.

Closed Coding : 

Closed Coding Once you have a final list of codes, select the themes that are most important. Themes that have recurred most frequently in the data, Themes that seem most important to participants or groups. Apply the codes that fall under these themes to all your transcripts Use only codes from a predefined list Creates a standardized group of codes Allows all the data under analysis can be queried in a uniform fashion. If any codes are added at this point, they must be retroactively applied to all transcripts.

Summary : 

Summary Even with the help of specialized software, coding is a time consuming process. Not simply drudge work but a first and vital step in data analysis. Creates a framework of meta-data that guides later stages of analysis and reporting. Helps break down seemingly overwhelming amounts of information into more manageable pieces. It can be difficult to maintain a standard set of coding language between individuals. Consider delegating coding to a small number of people

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