Reading Notes: Statistics 101 (Part 2)

Source: Andy Field
Correlational or cross-sectional research: observe what naturally goes on in the world without directly interfering with it.
- by either taking a snapshot of many variables at a single point in time, or
- by measuring variables repeatedly at different time points (known as longitudinal research).
== provides a very natural view of the question we’re researching because we are not influencing what happens and the measures of the variables should not be biased by the researcher being there (an important aspect of ecological validity).
== tells us nothing about the causal influence of variables.
- Variables are often measured simultaneously.
- The first problem with doing this is that it provides no information about the contiguity between different variables.
- The second problem with correlational evidence: the tertium quid (‘a third person or thing of indeterminate character’).
== E.g., a correlation has been found between having breast implants and suicide (Koot, Peeters, Granath, Grobbee, & Nyren, 2003).
== However, it is unlikely that having breast implants causes you to commit suicide – presumably, there is an external factor (or factors) that causes both; for example, low self-esteem might lead you to have breast implants and also attempt suicide.
== These extraneous factors are sometimes called confounding variables or confounds for short.

Experimental research: manipulate one variable to see its effect on another.
- Even when the cause–effect relationship is not explicitly stated, most research questions can be broken down into a proposed cause and a proposed outcome.
- Both the cause and the outcome are variables.
- The key to answering the research question is to uncover how the proposed cause and the proposed outcome relate to each other.

David Hume said that to infer cause and effect:
(1) cause and effect must occur close together in time (contiguity);
(2) the cause must occur before an effect does; and
(3) the effect should never occur without the presence of the cause.

- These conditions imply that causality can be inferred through corroborating evidence: cause is equated to high degrees of correlation between contiguous events.

- The shortcomings of Hume’s criteria led John Stuart Mill (1865) to add a further criterion: that all other explanations of the cause–effect relationship be ruled out.
== Mill proposed that, to rule out confounding variables, an effect should be present when the cause is present and that when the cause is absent the effect should be absent also.
== Mill’s ideas can be summed up by saying that the only way to infer causality is through comparison of two controlled situations: one in which the cause is present and one in which the cause is absent.

- This is what experimental methods strive to do: to provide a comparison of situations (usually called treatments or conditions) in which the proposed cause is present or absent.
- Example: the effect of motivators on learning about statistics. Randomly split some students into three different groups in which teaching styles vary in the seminars:
== Group 1 (positive reinforcement): praise participants
== Group 2 (punishment): give verbal punishment
== Group 3 (no motivator): give neither praise or punishment, i.e. give no feedback at all.

Manipulated variable or independent variable: the motivator (positive reinforcement, punishment or no motivator).
Interested outcome or dependent variable: statistical ability, to be measured via a statistics exam after the last seminar.
Assumption: the scores will depend upon the type of teaching method used (the independent variable).
Inclusion of the ‘no motivator’ group: proposed cause (motivator) is absent, and we can compare the outcome in this group against the two situations in which the proposed cause is present.

If the statistics scores are different in each of the motivation groups (cause is present) compared to the group for which no motivator was given (cause is absent) then this difference can be attributed to the type of motivator used.
In other words, the motivator used caused a difference in statistics scores.