Evaluating Data Analysis Software

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Qualitative data is characterized by the deep, rich aspects that enable researchers to enter the realm of the participants in a study. Qualitative research projects are characterized by considerable coordination challenges and tight deadlines. Business clients of market research providers and academic research colleagues anticipate that the value qualitative researchers bring to inquiry is the ability to analyze and interpret, providing insights or contributions to themes. But often these processes are given short shrift with regard to time allotments in the overall inquiry process.

Challenges of Qualitative Data Analysis (QDA)

Data analysis software is a strong tool for textual analysis, and the benefits fall primarily into three categories: (1) Efficient systematic analysis, (2) effective retrieval and identification of data, and (3) capacity. Data analysis software is a grounded in machine learning -- algorithms and mathematical approaches to textual analysis that are interpretation neutral. That is, either patterns exist in the data or they do not -- there is no human sentiment or expectation involved, so from this perspective, computer-based data analysis is markedly robust. Importantly, computers are not creative or generative -- functioning primarily on a convergent basis according to the objectives of the programming. Data analysis software is very good at sorting and arragning data, a first step in qualitative data analysis. However, researchers must express both divergent and convergent thought. Qualitative researchers need to provide the thinking aspect of data analysis.

Qualitative data analysis software programs are designed to extract patterns and meaning through a data mining process or conduct a code-retrieval exploration of text. Software adds value to research processes by facilitating the interpretive thinking that must follow data collection, and reducing the amount of time that it takes to prepare data for interpretation. The goal is to link the raw data output to emerging themes Data analysis software is particularly useful for this process, in that, it allows the researcher to return to the data, retrieve information and illustrative quotations, and test the emerging themes against different data combinations. In this way, both confidence and accuracy are enhanced. Through the systematic analysis, a data audit is simplified and accountability is ensured.

Researchers who choose not to use data analysis software for qualitative data analysis will rely on the traditional methods of deriving meaning from data, such as a constant comparative process which facilitates the identification of themes that emerge from the data. Triangulation of data from different sources plays an important part in traditional approaches to qualitative data analysis.

Data Analysis Software Packages