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When a person's progress is evaluated using Goal Attainment Scaling their performance is scored on one or more scales with the values +2, +1, 0, -1 and -2, meaning "much better than expected", "better than expected", "expected", "not as well as expected" and "not nearly as well as expected" respectively.
If a person were to be evaluated on two scales then their score might be +1 on one scale and -2 on the other, in which case their overall score would be -1--in other words, just the sum of the two scores. You might find many ways of manipulating these total scores in the literature; however, in essence, GAS amounts to the scores on the individual scales plus the total of these scores.
There is no single 'Goal Attainment Scale' because the evaluation scales are made up depending on the needs of the person being evaluated. For example, a person with quite limited motor skills might be rated on their ability to climb steps in one scale, on their ability to take a drinking cup from a shelf in another scale, and on their ability to pour milk from a pitcher into a glass in another scale. Notice that the number of scales and the nature of the scales can vary.
Because the overall scale value is just a sum it is neither parametric nor nonparametric.
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What is the difference between data driven and goal driven?
Goal driven reasoning or backward chaining - an inference technique which uses IF THEN rules to repetitively break a goal into smaller sub-goals which are easier to prove. Data driven reasoning or forward chaining - an inference technique which uses IF THEN rules to deduce a problem solution from initial data.
What is the goal of the statistical theory of sampling?
Getting people to buy their products.
How do you use benchmarking?
Break progress toward a large goal down into smaller stages.
What does misunderestimate mean?
"Misunderestimate" is not a word. George W. Bush misspoke, and meant to just say "underestimate". "Misunderestimate" is the goal of a con artist, who depends on people not imagining how "low" he can sink.
Why inferential statistics is not required in census?
Inferential statistics is not required in a census because a census aims to collect data from every individual in a population, leaving no room for sampling error or uncertainty. The goal of a census is to provide an accurate count or measurement of a specific characteristic within a population, making the need for statistical inference unnecessary. In contrast, inferential statistics is used when data is collected from a sample of a population, and the goal is to make predictions or inferences about the larger population based on that sample.
