A question about education and big data analytics

 It is an interesting question. I wonder what field besides that of professional educators would use such fuzzy logic to classify a statement or proposition?

"Characterize the following research questions as: descriptive, causal, prescriptive, or normative. Briefly explain answer. What factors increase women’s political representation in developed democracies?"

Apparently descriptive questions imply a true or false answer is possible while a normative question suggests that some state of affairs ‘ought’ to be. It’s is a value judgment and subjective.

Causal questions might ask for a cause or reason that something occurs and since David Hume wrote to discredit the relation of cause and effect the notion idea has been troubled.

In my opinion proximal causes as in legal arguments concerning why some x occurred or was done by agent y are generalities and somewhat non-technical philosophically inasmuchas they cannot be absolutely, logically exhaustive description of the state of affairs.

Consider that the entire Universe exists be-cause of the expansion of a singularity (if cosmology is accurate and that is an assumption), therefore that is the original cause of Billy Bob robbing the 7–11 store last night? The cause would need to be advanced up the line to a closer space-time connection. As in ‘Billy Bob robbed the 7–11 last night because his girl needed a Covid 19 vaccination and he was trying to save her from certain death from the South African variant he read was going to arrive in town and he needed the cash to bribe a front-line worker to let her assume her fake identity and get the shot.’ That cannot be considered a very accurate cause in some senses, for instance if he hadn’t been forced out of work by an employer that didn’t like his political opinions he would have had the cash to bribe for a shot for his girl, therefore the former employer is at fault, or was the cause of the robbery?

Anyway causality is a tricky slippery slope. I think people use pragmatism about causality and just cancel out equal terms on both side of the formal logical equation to determine if what remains is the real or even meaningful cause of something.

In the field of big data analysis predictive and prescriptive analytics are use in processing and writing code evidently.

From predictive to prescriptive analytics (part 1) — the benefit of causal diagrams

Predictive analytics would seek to determine what will occur I guess while prescriptive analytics would attempt to shape events.

One example would be the riots at the capitol of January 6, 2021. The electorate could be considered the big part regarded as datums. Some might use big data analytics to predict the outcome of events about crowds of a given size, security at the capitol and other variables. A use of prescriptive analytics might be to determine how to shape events or to shape the description of events so the public considers the riot a faux pas by political tramps or an insurrection.

Big data analysis joined with mass media has several axis for utilizing analytics with all four of the elements mentioned.

On the question of how or why females can have political representation in a democracy, one would need to disambiguate the question initially to clarify the meaning of ‘representation’ before dropping it into the algorithmic engine of big data processing. That is- can a man be a representative politically for women or need the representative be a woman?

If one shapes the question well then using logical processes with it is possible. Personally I would like the generality of adding causes from historical review and put those into a logic box and test them against nations that had accomplished female political representation in a democratic environment.

When one use computer code for analysis, specific values or variables are required in flowing electrons or quantum states through test conditions, forks and so forth along the logic tree. There is a reiteration of specificity unless non-specificity is placed with another tree of branches, forks and test conditions, and that sort of thing is itself specific and determinative as a definite causal agent of further processing. Even if one tries prescriptive iterations to determine a result one might ask if the basic algorithmic method doesn’t imply that necessarily.

The idea of using will to make things happen is perhaps a less rigorous analytic or algorithmic tool. The main point of analysis is of course that it is accurate.