Evaluate Gathered Data (SO3-AC3)

Analysing information involves examining it in ways that reveal the patterns, trends, etc. that can be found within it. That may mean subjecting it to statistical operations that can tell you not only what kinds of relationships seem to exist among variables, but also to what level you can trust the answers you’re getting. It may mean comparing your information to that from a control or comparison figures, etc. to help draw some conclusions from the data. The point, in terms of your evaluation, is to get an accurate assessment in order to better understand the overall situation.

There are two kinds of data you’re able to be working with, although not all evaluations will necessarily include both. Quantitative data refer to the information that is collected as or can be translated into, numbers, which can then be displayed and analysed mathematically. Qualitative data are collected as descriptions, sketches, opinions, quotes, interpretations, etc. and are generally either not able to be reduced to numbers, or are considered more valuable or informative if left as descriptions. As you might expect, quantitative and qualitative information needs to be analysed differently.

Quantitative data are typically collected directly as numbers. Some examples include:

• The frequency (rate, duration) of specific behaviours or conditions.
• Survey results (e.g. reported behaviour, or outcomes to environmental conditions; ratings of satisfaction, stress, etc.).

Qualitative data:

Unlike numbers or “hard data,” qualitative information tends to be “soft,” meaning it can’t always be reduced to something definite.

Qualitative data can sometimes be changed into numbers, usually, by counting the number of times specific things occur in the course of observations or by assigning numbers or ratings to dimensions (e.g. importance, satisfaction, ease of use).

The challenges of translating qualitative into quantitative data have to do with the human factor.

Quantitative analysis is considered to be objective – without any human bias attached to it – because it depends on the comparison of numbers according to mathematical calculations. Analysis of qualitative data is generally accomplished by methods more subjective – dependent on people’s opinions, knowledge, assumptions, and inferences (and therefore biases) – than that quantitative data.