Data analysis is the process of systematically applying statistical and/or logical techniques to describe and illustrate, condense and recap and evaluate data.
2. What are the two methodologies for collecting data to measure short and long-term outcomes?
Short and long-term outcomes can be measured by using different methodologies for collecting data.
★ Cross-sectional study
★ Longitudinal study
3. What is long-term outcome in data analysis?
Long-term outcomes are persistence of behaviors and broader lifestyle changes.
4. What is medium-term outcome in data analysis?
Medium-term outcomes include changes in behavior and decision-making.
5. What is short-term outcome in data analysis?
Short-term outcomes are most likely include changes in skills, attitudes and knowledge.
6. Define confounding with example?
To rule out that a relationship between two events has been distorted by other, external factors, it is necessary to control for confounding. Confounding factors may actually be the reason we see particular outcomes, which may have nothing to do with what is being measured.
To rule out confounding, additional information must be gathered and analyzed. This includes any information that can possibly influence outcomes.
Example:
When mounting a campaign against alcohol-impaired driving, it is important to know whether other interventions aimed at road traffic safety are being undertaken at the same time. Similarly, if the campaign coincides with tighter regulations around BAC limits and with increased enforcement and roadside testing by police, it would be difficult to say whether any drop in the rate of drunk-driving crashes was attributable to the campaign or to these other measures.
7. Define causation with example?
A causal relationship exists when one event (cause) is necessary for a second event (effect) to occur. The order in which the two occur is also critical.
Example:
★ For intoxication to occur, there must be heavy drinking, which precedes intoxication.
★ Determining cause and effect is an important function of evaluation, but it is also a major challenge.
Causation can be complex:
★ Some causes may be necessary for an effect to be observed, but may not be sufficient; other factors may also be needed.
★ While one cause may result in a particular outcome, other causes may have the same effect.
Being able to correctly attribute causation is critical, particularly when conducting an evaluation and interpreting the findings.
8. Define association in data analysis with example?
An association exists when one event is more likely to occur because another event has taken place. However, although the two events may be associated. One does not necessarily cause the other. The second event can still occur independently of the first.
Example:
Some research supports an association between certain patterns of drinking and the incidence of violence. However, even though harmful drinking and violent behavior may co-occur, there is no evidence showing that it is drinking that causes violence.
9. Explain the difference between association, causation and confounding?
One of the most important issues in interpreting research findings is understanding how outcomes relate to the intervention that is being evaluated. This involves making the distinction between association and causation and the role that can be played by confounding factors in skewing the evidence.
10. List the evaluation criteria as the basis for organizing and analyzing data?
★ Relevance
★ Results/Impact
★ Sustainability
★ Effectiveness
★ Efficiency
