THE POST-INTERVIEW PROCESS
Interviews give researchers comprehensive data about their question or topic. Since the data in a research interview is not accountable to statistical analysis or quantifiable, it is called a qualitative research method. Unlike experiments which collect a small amount of quantitive data, the interview will gather a wide range of data from multiple subjects. The interview is just one option for collecting qualitative data. Even though it is a less formal quantitative method of data collection, it still needs to be designed systematically. Once all this data is collected there is a post interview processes that requires the researcher to analyze, validate and compare the data plus reinterview if needed.
Analyzing Data
The data that is collected during an interview is not going to be just the words given by the respondents but also the interviewer's questions, assumptions, and biases. An interview is a coupled creation with the interviewer and respondent. Analyzing the data will give a concrete form to its meaning. This process is also referred to as interpreting the data. The words of the respondents is the raw data, much like test numbers. Raw data is not where the meaning lay but instead is what will be interpreted. There are two central strategies for the analysis of data collected in an interview. First, gain an in depth familiarity with the data. Second, put the data into analytical categories to allow its meaning to be assessed.
In the first strategy, the researcher is going to study the data to become familiar with it. How they go about doing this is going to depend on the method in which it was collected. They may listen to recordings of the interviews over and over, read and reread notes of the interviews and/or the interview transcripts. How ever it is done the purpose is to begin to see the patterns and categories that the data contains.
Once the researcher is familiar with the data they will be able to clearly see that each respondent is talking about category A, category B and so on. Deep thought about these categories will cause the researcher to then understand and interpret what the respondent is really saying. The categories that emerge from the data are grounded in and reflect it. The researcher does not place the data in preconceived categories using their own judgement but lets the data emerge into the categories on its own.
With the second strategy, the researcher is going to set up the categories before the interview process even starts. This is taking in to account that behind every question is a hypothesis that is trying to be answered. There is a reason that the questions are being asked. This strategy is going to work best when the researcher knows specifically what data they are trying to collect. If it is more of an exploratory interview the categories may not be clear and strategy one is going to work best.
Validating the Analyzed Data
The researcher needs to remember that the weak points of interview, namely that it produces unquantifiable data, can not be gotten around. To validate the data that they have categorized and analyzed is the processes of going through the “checks and balances” of the date to see if it holds up under sustained scrutiny. The beast ways to do this are going to be through a process called triangulation and by reinterviewing if necessary.
Triangulation
The process of triangulation involves the the comparison of two sets of data. Such as when more then one interview is conducted with the same respondent, where some questions remain the same thorough out each interview. The answers given by the respondent at each interview can be compared against each other. If the respondent responds to the common interview questions with just variations of wording that have the same meaning, then the answer is consistent and therefore validated. Developmental validity will occur when the questions have a clear direction they develop in. When there are several respondents all being asked the same questions, the similarities in their answers is also argument for the validity of the data.
Reinterviewing
There are three strategies that reinterviewing is going to entail. First, after the data has been summarized and interpreted, take the data back to the respondent to see if the researchers interpretation of the respondents answers is correct. If the respondent agrees then it will show that the data interpretation is more than opinion and is valid. If the respondent does not agree then the data needs to be reassessed until the researcher has a clear understanding of what the respondents true answers to the questions are.
Second, the researcher will seek out evidence that is in contradiction or negative. If a group of people who are interviewed all agree on certain points of the questions, seek out those who do not agree. For example if a groups of parents are asked if they all agree with a new school policy and they do, the researcher would then seek out those parents opposed to the policy. These points of view contradicting each other will add strength to the data interpretation.
Third, consultations are going to be held with experts in the area being studied who can be neutral and critical of the data. The researcher must first ask themselves if this person can take the data, analysis, interpretations, and summary to form a line from data to interpretation. Then if the person can complete this line between the data and its conclusion, is the conclusion possible? The researcher needs not question if the interpretations are correct but are they possible.
The researchers needs to ask if the interpretations make sense and can the be supported by the evidence presented? Then last did the person they are consulting come to the same conclusions based on this evidence? If they did then the data is validated, if they did not then the data needs to be looked at again and reinterviewing done if needed.
