Causality, Etc. Prediction and Theoretical Explanation -

Causality, etc.

Prediction and theoretical explanation - Essentially, prediction may be anywhere from an informed "guess" to a well-reasoned hypothesis that is based on well-reasoned background knowledge but needs more specifics in order to be more accurate. Theoretical explanation, however, takes specific examples from theories that have already been proven, and provides a way of putting them into a context that allows for greater explanation. It seems that one can take a look at a problem or issue and find a continuum that allows for degrees of correctness based on either past knowledge or current experimentation. For instance, if point A at the left was "gut feeling" and point Z. On the right was "repeated and validated experimental data" then many forms of hypothetical thought would fall in between, depending on which stage or which actual type of prediction is being done. Prediction is a forecast that may or may not be based on experience or knowledge -- prediction a statement that an outcome is expected (students will graduate from high school) and a forecast a range (based on the data, we believe 80% of our students will graduate). A theoretical explanation, however, required more well-reasoned empirical data -- we had 1,000 students, 888 of whom have fulfilled all their graduation requirements; based on an error margin of 2% (illness, etc.) we expect that between 872 and 888 students will graduate, or a range of 87.2 to 88.8%. In addition, one might view prediction as a degree of informal guessing, which theoretical explanation as an explanation of data that has been rigorously tested (Rubin and Babbie, 2005).

Part 2 -- Essentially, causality refers to the relationship between an event (or the cause) and another event (the effect) in which the event is understood to be caused by the first event. This is typically temporal or chronological in explanation and can be:

Material -- the mass of which something is made is caused by or causes

Formal -- what thing is planned or intended (will be the cause)

Efficient -- what process or external item changes

Final -- what actions make something exist or cease to exist

For scholarship, most agree that the three conditions of causality or that A must precede B. In time and space; A must also still be present when B. reacts to A, and A must be absent when B. is non-reactive. More formally, these are called necessary, sufficient and contributory causes. If we observe a B, then we must have an A implied to cause B (necessary); if A is a sufficient cause of B, then there must be A and B, but there could also be C. And D (sufficient); and A contributes to B, but so might X ad infinitum (contributory). Social science uses this as a way to plan models or predict statistical events that may or may not occur for individuals or populations. Oftentimes, this is particularly valuable in order to go back to source or more original issues in order to find ways to handle population dynamics and/or issues. Finally, modeling and measuring models requires that we first understand the basis for causality within the structure of our experiment (Russo, 2008).