In this step, you will utilize your understanding of the essential structure of the themes comprising the meaning units or parts of your natural language sample, in order to clarify and interpret it.

To clarify and interpret your raw data via its essential structure, it is useful to first bound your results by way of describing the setting in which they occur, both psychologically and physically. We refer to this setting as a horizon. Describing the horizon of your results both facilitates further insight into your natural language sample, and also provides a general scope of the populations or conditions to which your results may be applied or generalized. To describe the horizon of your results, you will examine your data for the absence of natural language expressions, and the re-examine the attitude you assumed in step 1.

To best clarify and interpret your results, it is useful to bound their scope by briefly considering those gists, statements, and themes that are not present in your data. In other words, it is beneficial to consider the universe of potential responses that one might have acquired in a given pool of natural language, but did not. Such absences define the horizon of your results, and therefore help to set their context into relief.

In doing so, sometimes one finds that an expected gist, statement, or theme is conspicuously absent. Other times, a more subtle pattern of absence may be present (e.g., a relative dearth of emotional terms, failure to describe an otherwise readily identifiable aspect of the experience in-question).

Review the attitude you assumed in step 1, and the theoretical, conceptual, and practical decisions that led to the production of your natural language sample. Identify the limitations created by the particular theory, concepts, instruments, populations, procedures, sample sizes, or questions involved in your project. Detail these limitations, and discuss their impact on your phenomenon as it is revealed through your results.

Once you have bounded your data by describing their horizon, you now contextualize variation their themes according to identifiable patterns found across—and within—states or classes of the geists and other variables acting upon them. Doing so provides an understanding of the settings to which your results might be generalized (or extended). Doing so also indicates the manner and proportionality to which the essential structure should be modified by adding specific confined or divergent gists and concepts, in order to generate messages tailored to specific audiences, places, and times (i.e., for use under specific states or classes of your variables, or other geists).

Describe the states or classes of geists or other variables that are associated with the expression of your essentialized themes in their unaltered form. Such states or classes define the conditions under which the revelatory statements comprising the essentialized theme may be generalized or applied with confidence. The greater the proportion of themes present in your data are found across states and classes of geists, the more unity on those themes are expressed in your data, and the more universal is the essential structure of your natural language data.

Describe the geists and states of other variables that appear to contain or confine whole themes or revelatory statements, or are associated with substantially variant proportionality in particular concepts or gists (i.e., are related to thematic divergence). Such information reveals the amount and kind of diversity in elaboration or perspective on the themes in your natural language pool, as well as the conditions related to it. If developing an array of messages tailored in frequency and content to specific states or classes of your variables, be sure to note the precise differences in proportion between these states or classes, so that they can be replicated.

After you have described the horizon of your results and contextualized their thematic variation, bracket to ensure that you have accounted for any remaining variation in the essential structure of your natural language pool, as well as any remaining influence from otherwise unaccounted for geists and other active variables. See previous steps for review of procedures related to bracketing for these aspects of your natural language data.

Next, bracket to account for the attitude assumed during the project. Bracketing for your attitude involves reconsidering the phenomenon, context, focus, methodological approach, and form of your natural language data, and comparing these to your derived revelatory statements and essential thematic structure. If your derived statements and thematic structure provide insightful and contextualized information that is conceptually aligned with the assumed attitude, then your analysis is likely complete.

If, however, there is misalignment between the attitude assumed and the results acquired, you should consider adapting and reforming your attitude, and then re-examine your existing data utilizing alternate methods, or using different states or classes of variables (or codes). Alternatively, you might design a follow-up project that more fully aligns your assumed attitude with the themes and structure of the natural language expression you are likely to acquire.

If the process of bracketing leaves substantial data unaccounted, repeat substeps 6.1-6.3 until you account for them.

If, after you have clarified and interpreted your raw data, you have not changed the original structure of the data in your spreadsheet, you have completed the steps in performing a Quantitative Phenomenology with Raven’s Eye. You can now download the results you have been otherwise manipulating online, and produce messages, tables, graphs, or other visualizations in your spreadsheet, word processing, or other programs.

If, however, you repartitioned your spreadsheet based on the considerations in this step, you will need to upload it to Raven’s Eye as a second project, categorize your columns, and select it for analysis¹. By default, you will arrive at the main table of your newly uploaded project. If this is the case, it is advisable to revisit step 2 and proceed from that point in the procedures.

¹ In this way, your major data transformations are demarcated by file, and thus facilitate easier replication and extension in subsequent projects.