Abstract

Traditionally, there have been two general approaches to analyzing data from a research study. One approach is quantitative in which participants receive scores of a quantitative nature. When those scores meet the criteria of an interval scale, the data are analyzed with parametric statistics. When the scores are not deemed to reflect at least an interval scale, the scores are analyzed with non-parametric techniques. Alternatively, participants engage in an interview rather than obtain scores on a scale or through a performance task. In the case of individual interviews, the transcripts are then analyzed with a qualitative technique [1]. One such approach, named Interpretive Phenomenological Analysis (IPA), was developed by Smith JA [2,3]. In Smith’s IPA, themes are extracted and applied back to the interviewed participants. Thus, Smith has primarily utilized an idiographic application of IPA. The current authors have developed an approach to IPA that makes it highly amenable to a nomothetic application. The Parekh-Berger Hierarchical IPA (HIPA) Model incorporates interrater reliability and extends the Specific Themes extracted from the interviews to Higher Order Themes, and a format has been developed to extend the Higher Order Themes to Superordinate Themes-where appropriate. In addition, a concept of Universal Specific Themes has also been developed as those Themes help extend IPA to a nomothetic application. This report provides a review of IPA and then details the Parekh-Berger HIPA Model. Specific examples of some results from the Parekh-Berger Model are provided as well a step by step approach to utilizing the Model as well as reporting the results of the HIPA Model .