Ph.D. dissertations are generally in-depth data analyses. Most of the research work required for Ph.D. dissertations is data-based, and students usually produce a dissertation with accurate facts and figures. When it comes to analyzing interview data, it is generally seen as a good method of collecting qualitative data. Interviews reveal the actual thought process of individuals about any dissertation’s problem statement. Plus, interviews help add real-life experience, depth, and correctness to a Ph.D. paper.
How To Analyze Interview Data For A PhD Dissertation?
Whether the data obtained from interviews is a hundred percent correct or not, it is not easy to get numerical or quantitative data from interviews. It is because numeric data needs to be incorrect numbers. Interviews are more helpful when it comes to qualitative data. This article will provide effective methods to analyze interview data using different methods. After we conduct an interview and collect data, the next step will be to analyze interview data.
When Should We Start To Analyze Interview Data?
Normally, students do not have a lot of time to write Ph.D. dissertations. Besides, it is a hectic task where we must decide on the dissertation’s topic, conduct interviews, and other sessions to collect data, and finally, writing a dissertation is a separate task. So start analyzing your data with the first interview. This will save you from reading lengthy notes. Moreover, it will be easier to analyze data when you have conducted a fresh interview.
Methods To Analyze Interview Data
Let’s read the most popular ways to analyze interview data for a Ph.D. dissertation. There are two ways that researchers use to analyze their interview data.
- Inductive approach– The inductive method is specific to the general approach to evaluating data.
- Deductive approach – General to specific approach of data evaluation.
You can also hire a Ph.D. dissertation writing service for the above two approaches.
An inductive approach is an unstructured approach to data analysis in which we start with some defined observations and behaviors. Based on the defined observations, we find patterns in the behaviors of interviewed people.
How To Analyze Interview Data For A PhD Dissertation?
Whether the data obtained from interviews is a hundred percent correct or not, it is not easy to get numerical or quantitative data from interviews.
The inductive approach to data evaluation is further divided into two more approaches.
- Thematic content analysis
- Narrative analysis
Thematic Content Analysis
Thematic content analysis is used to find common behaviors and patterns. In this approach, a researcher finds emerging patterns in human behavior based on familiar patterns. Thematic content analysis is an easy yet rigorous method of collecting data.
Thematic content analysis is considered as the most common method of analyzing data for research or dissertations.
Narrative analysis is an approach in which a researcher thoroughly analyzes the individual’s stories and finds the patterns and aspects he can add to his research.
Analyzing each story based on initial observations helps find patterns in human thoughts, which helps the researchers collect stories and quotes relevant to the topic for their research paper. This approach is used to discover cultural values, historical impacts, and lifestyle values of individuals of different regions.
A deductive approach is an approach in which data is analyzed using a general to a specific approach. A deductive approach to analyzing interview data has a predefined method and rules to find the required data. In this approach, we are not normally concerned about the individual’s feelings and reactions.
In this approach, a researcher tests his theories and finds the final results. Predefined themes are tested, and results are derived based on these predefined themes. This method is not much difficult because a researcher tests its data on some previously defined tests. That’s why this method takes less time and effort. However, results generated during the deductive technique lack in-depth analysis. Deductive reasoning is an approach to test an existing theory.
A qualitative approach to data analysis does not always help find the best results. There are always chances to lose important data as data transform from its original form (source form) to your research paper. Here we can use the transcription technique, which helps maintain data integrity, and original data does not lose its worth. You can say that by using transcription, researchers can save data loss in qualitative analysis.
Recording Interviews And Transcription:
Transcribing an interview is defined as writing the oral video interview on a piece of paper. As technology advances daily, humans have changed their traditional working methods to technology-based methods. Now people do not take notes from any interview. Because taking notes from interviews has a high risk of data loss, researchers can suffer a lot, and their dissertations may not be authentic. Plus, while taking notes in an interview, the interviewer gets disturbed. No matter whether an individual is an adept writer, there are always chances of data loss while taking notes.
Recording an interview instead is the best way to get a sweat escape from the insane data loss of taking notes. So it is recommended to record videos of interviews and later analyze these videos. Videos give various edges over taking notes. You can stop videos on demand to take notes. Transcription can be done manually, but you should do it using tools or software. Different software adds more authenticity and helps to understand faint voices.
Analysis And Determining Patterns From Transcription:
After transcribing the interviews, the next step is to analyze them. You cannot do a good analysis of an interview unless you follow a step-by-step procedure. So here are some easy and important steps for the analysis of transcriptions:
- Read out the entire transcription in one go and annotate your data. Plus, assign codes to data.
- After assigning codes to the data, divide these codes into categories and subcategories.
- Finally, when you divide your codes into categories and subcategories, it will become easy to find patterns from them.
To analyze interview data, it is important to learn the techniques scientists use to analyze data. Inductive and deductive methods are two important methods to analyze interview data that are generally used in Ph.D. dissertations. You can use any of the methods depending upon your research requirements. The interview is the most important step in concluding effective results. So for collecting important data, interviews should be recorded. After an individual record the interview, the next step is to transcript it to avoid any data loss. The final step is to annotate data and give codes to annotations to find patterns from data.