Data Analysis

    How will the researcher analyze the meaning of the data? In the context of research, analysis refers to the meticulous study of research variables to learn about their constituent parts and how they are related to each other within the given research problem. Research data, on the other hand, are factual information used to analyze research variables and produce relevant research results or findings.

    For qualitative analysis, data must be prepared, organized, and transcribed to written narratives, especially that large amount of information needs to be analyzed. The decision must also be made whether to process these vast amounts of data manually or using a computer program.

    Various software programs are available to help qualitative researchers analyze the vast amount of information. The commonly used data analysis software are NVivo, ATLAS.ti, Dedoose, webQDA, Ethnograph, f4analyse, Hyper RESEARCH, QDA Miner, Qiqqa, MAXQDA, Qualrus, and XSight. Open-source programs like Transana and Coding Analysis Toolkit are also available in the Internet. These software programs are mostly menu-driven and user-friendly to make coding, organization, and retrieval of information easier and faster. They also make transcriptions and provide different strategies for annotating, separating, categorizing, and creating custom reports with models or charts. Moreover, these programs can look for trends and relationships, and form and test theories. It must be noted, however, that these programs cannot think by themselves rather can only manage data efficiently. The researchers’ intervention is needed to decide about the grouping of the codes and discerning of themes.

    There are three common ways to analyze qualitative data. These are thematic, narrative, and content analyses.

    In a thematic analysis of qualitative data, the researcher looks across all the data to identify some recurring issues. Main themes that summarize all the views collected can be derived from these issues. The main stages of thematic analysis are as follows:

    • Read and annotate transcripts. In this stage, the researcher can have a feel for the data because primary observations are provided. However, an overview of the data cannot be achieved in this early stage.
    • Identify themes. In this stage, the researcher can look at the data in details to identify themes. In each transcript, the researcher may note at the outset what the interviewee is trying to impart in his/her responses. It is suggested that these themes must be enumerated and notes be made as abstract as possible. There are several themes researchers can identify this stage:
      • Ordinary themes are those that researchers expect to find (e.g., students’ exposure to bullying situations in school).
      • Unforeseen themes are those that researchers do not expect to come out in the investigation (e.g., school policies on bullying that are unimplemented).
      • Hard-to-classify themes are those that researchers find difficult to classify because they overlap with one another or several themes (e.g., students assemble in the playground).
      • Major and minor themes are those that researchers represent as major and secondary ideas in the database (e.g., major idea—desire to quit smoking; secondary idea—body’s reaction to smoking).
    • Develop a coding scheme. Initial themes can be collected to develop a coding scheme. This includes the enumerated themes and the codes applicable to the data. Each broad code have a number of subcodes. It is recommended to use a coding scheme as soon as initial data have been gathered.
    • Coding the data. The next step is applying these codes to the whole data set. This can be done on either the margins of the transcripts or the statements in line. In an ideal setting, the whole set of data should be coded to ensure honest and exhaustive analysis. There are six basic steps in coding data:
      • Get an idea of the entire data set. After reading the transcriptions carefully, write down some ideas as they arise in the margins of the transcription.
      • Select one interesting document. Choose the shortest and perhaps the most interesting transcript, reread it and ask the question, “What is the respondent talking about?” Discern the underlying meaning and jot it down in the margins of the transcript.
      • Start the document coding process. Divide the transcript into segments, put brackets in each of the segments, and give specific code to each phrase or word that exactly explains or describes the meaning of the text segment.
      • List all code words. After coding the whole text, look for redundant codes by grouping similar ones. Through this process, the list of codes can be reduced into a more manageable number. It is recommended to limit the number of codes from the start of the process, so that it is easier to manage the reduction of code number.
      • Review the list against the data. Apply this preliminary organizing process to the same transcripts and find out if new codes appear.
      • Categorize the codes for emerging themes or descriptions of the subject or setting. Themes or categories are the same codes combined together to identify major ideas in the data set. Identify five to seven code categories representing the most discussed responses of the subjects. These few themes will enable the researcher to write an in-depth information about a few themes, rather than a broad description about many themes.

    In narrative analysis, the researcher looks narratively within each case, so that the story of a research subject or the description of the setting is not lost. The narratives of the subjects reveal about themselves and their environment. The researcher may examine in details some cases to see how the themes show relationships in a particular case. This process uses documents and observations that focus on how stories are made rather than on the outcome of the narrative.

    Content analysis enables the researcher to focus on human behavior indirectly through discourse analysis. The written content of documents like reference books, newspapers, magazines, songs, advertisements, and photographs can be analyzed using content analysis. An individual’s or group’s attitudes, beliefs, ideas, and values are often exposed in their communication patterns or correspondences.

    Content analysis is often used in conjunction with other methods like ethnography and historical research. It can be utilized in any context in which the researcher wants to have a way of systematizing and quantifying data. Content analysis is very valuable in the analysis of data collected from observations and interviews.

    Lofland and colleagues (2006) presented six ways of looking for patterns in a particular research topic. The following should make sense out of the data gathered:

    1. Frequencies refer to how often a situation occurred.
      • Example: How often does bullying occur among selected public schools under study?
    2. Magnitudes provide the level of the situation.
      • Example: What are the levels of bullying? How severe are they in the research locale?
    3. Structures give information whether types and relationships exist in the given situation.
      • Example: What are the different types of bullying? Are they related in any particular manner?
    4. Processes denote if there are order and variation in the given research interest.
      • Example: Is there any order among the elements of structure? Do bullies begin with verbal, move to social, then to physical and cyberbullying? Does the order of the elements differ?
    5. Causes refer to how common and how often the causes are.
      • Example: What are the causes of bullying? Is it common in public schools or private schools? Does it occur more often during break time or after class?
    6. Consequences mean the effect, if there is, in both short-term and long-term periods and the changes that the situation caused.
      • Example: How does bullying affect the students in both short-term and long-term periods? What changes did it cause to the bullied student?
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