Data collection, focus groups, surveying, polling, voting, performance analysis, scientific analysis and data analysis involve the collection of response data to determine correlations and develop insights into a data set across many variables. FIG. 1 illustrates a flowchart for a method of modeling relationships between query responses in a data set according to an exemplary embodiment.
The principles and structure underlying such systems are typically based on equality of inputs—either 1-person-1-vote (example: 20% of the voters agree strongly or show positivity, 10% disagree somewhat or show slight negativity, and 70% disagree strongly or show extreme negativity) or each query reply presented is of equal importance to each other (example respiration above normal, rapid heartbeat, low blood pressure).
Even with rapid acceleration in computer processing speed and scope, current platforms provide analysis based on finding direct relationships between equally important findings (as opposed to correlating unequally important factors).
The human cognitive process of reaching decisions is almost never based on equality and so the results of polling or retrieving data in the usual ways have been far from useful and instead often misleading and mistrusted. A person's use of inequality-based decision making is inherently complex and opaque. If done by a leader of a group of people, relying on equality also leaves the participants out of the process and unaware of the real factors that led to the leader's decision (for example a teacher decides to follow the advice of the better students in some situations in order to pursue certain goals—however there is no real-time visualization or feedback system in this process or way for the students to understand what happened and why).
In the case of data sets which incorporate varying degrees of enthusiasm or support, responses themselves are often given emphasis by the responder (for example: more satisfied vs. somewhat satisfied or strongly disagree vs. disagree). These levels of support are usually not seamlessly integrated and harmonized with the results in a transparent manner. To the contrary, the data sets corresponding to those who respond with the most passion or level of support typically overshadow data sets with lower levels of support, usually due the implementation architecture of a particular value scale or the calculation of analytic metrics.
Social engineering and thought experiments are not quantized on either an imposed inequality base (for example, giving responses of students with mediocre grades more weight) or expressed inequality base (for example, giving moderate responses more weight than passionate responses) and most resulting data analysis is inaccurate and/or too rigid.
When provided, data analysis reports about hypothetical situations resulting from multiple combined query response sets along with various imposed and expressed adjustments result in up to thousands large tables and graphics. As a result, it is very difficult to understand, explore, or navigate the analysis data in order to form new insights.
There are no currently no data structures which encode and encapsulate analysis conducted on response data according to various inequality adjustments. Additionally, there are currently no graphical structures or interfaces which transform analytical data based on response data according to various inequality adjustments into a user-accessible and navigable format.