Computer-Based Analysis of Qualitative Data: CAITA (Computer-Assisted Interpretive Textual Analysis)

Conceptual Overview and Discussion

Quantitative analysis of qualitative data uses computer technology to quantify qualitative data and to perform content analysis. Computer-based content analysis of qualitative data involves breaking textual data down into segments, then coding or classifying each segment. Researchers then use computer technology to count key words or codes and to compose operational indicators of variables. The quantitative measures are used to test or evaluate hypotheses. This reflects a positivistic paradigm where quantitative variables are created and relations among variables are used in a deductive manner to test or confirm theory.

The second form of computer-based analysis of qualitative data uses qualitative analysis involving nonmathematical processes of interpretation to understand the meanings or patterns in qualitative data. The central task of qualitative analysis is to understand the meaning of the text (i.e., qualitative [Page 185]data). The process is often inductive and not well structured. Computer technology cannot “do” qualitative analysis of data for researchers because the process is not algorithmic or mechanical and cannot be computerized. Computers do routine, clerical tasks and assist in data storage, searching, and display of patterns in data. But the researcher must interpret data.

Computer technology can assist in qualitative analysis of qualitative data by facilitating basic tasks of qualitative research, including making notes, editing notes and data, coding data, storing data, searching and retrieving particular segments of data, linking data, creating memos about data, and linking these memos to data segments. Software can generate data displays that place selected or retrieved data in condensed or organized formats, and it can help map data graphically. Software can also help researchers draw conclusions, build theory, and write reports. This assistance is done by simplification of routine tasks of data management and creation of data displays to help reveal or confirm patterns in data.

Computer-assisted interpretive textual analysis (CAITA) is a specific form of computer-based qualitative analysis that seeks to understand the meaning qualitative data hold for social actors and to develop “second-order” theory that builds on or subsumes members' “first-order” theories. The focus is thus on induction or theory building from data. Second-order theory development involves recovering themes and meanings from data, providing thick descriptions of how concepts operate in data, and grounding theory in data.

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