In this step, you will identify the essential structure of your natural language data. To do so, you will derive themes by first associating and clustering your revelatory statements. Then, you will differentiate those revelatory statements in each theme that appear transcendent from those aspects that appear confined. After this, you will utilize the theme's transcendent aspects to essentiallize it. Finally, you will utilize the dispersion of themes across geists and meaning units or parts of your natural language sample to identify and reconstruct the elements of its essential structure.

To associate your revelatory statements, you will be conducting activities similar to those described in step 4.2. Follow the directions in that step, except apply them to your revelatory statements. Select the main concept of your most frequent revelatory statement and briefly inspect the Word-in-Context window to get a sense of the way in which the selected concept is contextualized in the original natural language data. Then, select the Word Association feature from the drop-down menu to analyze up to 10 words surrounding the target word. After you have a general sense of the relations between the selected concept and those in other revelatory statements in the natural language sample, serially reduce in step-wise fashion the number of words included in each analysis to reveal those concepts that are more intimately related to the selected concept. Repeat this procedure for each of the revelatory statement's main concepts, noting their relations to main concepts from other revelatory statements.

Determine the ordered relations between revelatory statements by narrowing the Word Association to the words preceding, and then the words following, each target word. This will reveal the ordered relations and contingencies between revelatory statements. As needed, continue refining these associations through a step-wise reduction in the number of words included in each analysis.

Cluster those revelatory statements that share common or synonymous and overrepresented concepts into thematic groupings. The construction of such themes follows the same basic activities as those delineated in step 4.1 to construct concepts, and in 2.1 to construct gists. Choose a concept from within a revelatory statement and inspect your tables for synonyms and other terms associated with it. Scrolling downward in the table (or resorting it alphabetically) will generally reveal the presence of these. To verify that a synonym or other word is utilized in the same manner as the concept in question, select the synonym or other word in the table, and then inspect the use of the word via the CORVID, Word Association, or Word-In-Context features of the Advanced dropdown menu¹.

Those revelatory statements containing concepts used similarly in the natural language pool are clustered together into a common theme, which is labeled according to the most proportional of those overrepresented revelatory statements that constitute the cluster (and, from 2.1, it’s most proportional overrepresented inflection) in the natural language sample. Repeat this process until you have sufficiently accounted for those revelatory statements containing the most frequent and overrepresented concepts in your natural language pool.

Distinguish those revelatory statements that are used consistently across cases and geists or other variables, from those that are inconsistently used, or those that are highly associated with a particular variable or other geist. To do so, first examine the CORVID feature for each of the main concepts constituting the most frequent revelatory statement in each theme. Note those that are consistently frequent across states and classes of variables (or other geists), as well as those that appear confined or otherwise highly associated with one or more particular states or classes of variables. Next, compare the Variable Overrepresentation of the main concepts constituting the most frequent revelatory statement in each theme. Do so in a manner similar to that just performed on the CORVID feature (i.e., note those concepts that are consistently overrepresented across states and classes of variables, as well as those that appear confined or otherwise highly associated with one or more particular states or classes of variables). The revelatory statements in each theme whose concepts display consistent proportionality across states or classes of variables are considered to be transcendent or convergent (depending on the particularities of your attitude, project, and natural language text), while those whose concepts display very different levels of proportionality when compared across states or classes of variables are considered to be confined or divergent (again, depending on the particularities of your attitude, project, and natural language response set).

Essentialize each theme into its most direct form by setting aside confined or divergent revelatory statements, or those concepts that serve as elaborations on the theme. In this step, then, you are identifying and isolating a subset of the most universal, frequent, and intimate of the revelatory statements comprising each of your themes. This subset of revelatory statements form the essence of the theme, and—in the next step toward identifying the essential structure of your natural language pool—can now be associated.

Explicating the essential structure of your natural language based on the associations between the themes found within it consists of two activities: identifying elements of the essential structure that appear contained within meaning units or parts of your natural language, and identifying elements of the essential structure that may span meaning units. Both of these activities are performed in the same manner, which is similar to the activities performed in step 4.3.

Based on the context in which your themes are used, and the associations between them, identify their essential structure for each part (or meaning unit) of your data. If, through your previous use of the Word-in-Context and Word Association features of the Advanced tab, the association between themes are not already apparent, the CORVID feature will generally help to reveal or confirm them. Utilize the CORVID feature to delineate the relationships between themes as they are associated in the natural language pool being analyzed.

Based on the context in which your themes are used, and the associations between them, identify those elements of your natural language pool's essential structure that may span its meaning units or parts. Such elements may include repetitive thematic content, or associations between themes that are present in more than one meaning unit or part of your natural language pool. They may also include organizational or narrative commentary, verb tense, repetitive use of qualifiers or other idiosyncrasies of expression, or other elements that appear consistent across parts of the natural language pool. To facilitate this identification, it may also be useful to consider navigating to the Settings page and changing whether your analysis combines or separates qualitative columns, and then re-examine your themes.

Once you have associated your themes sufficiently to identify the essential structure(s) of the parts in your natural language sample, bracket to ensure that there are no unaccounted geists or other variables potentially influencing your structure. See step 2 for the specific procedures related to bracketing for geists and other influential variables. If one or more of your essential structures appears to span meaning units or parts in your natural language sample, also bracket to ensure that your meaning units are sufficiently distinguished or related. See step 3 for the specific procedures related to bracketing for meaning units and parts of your natural language sample.

If the process of bracketing appears to reveal additional functional associations between your themes, repeat substeps 5.1-5.4 until you account for them.

If, after you have associated your themes, you have not changed the original structure of the data in your spreadsheet, you may proceed. If, however, you repartitioned it based on the considerations in this step, you will need to upload it to Raven’s Eye as a separate project, categorize your columns, and select it for analysis². By default, you will arrive at the main table of your newly uploaded project. It is advisable to revisit step 2 and proceed from that point in the procedures.

Many researchers may find the needs of their projects met upon completion of this procedure. If such is the case in your project, you can download the results you have been otherwise visually inspecting, and produce messages, tables, graphs, or other visualizations in your spreadsheet, word processing, or other programs.

¹ Please review step 4 for detailed information on these features.

² In this way, your major data transformations are demarcated by file, and thus facilitate easier replication and extension in subsequent projects.