Experiments was the topic of week 3’s lecture presented by David Arnott. We started with a classification of scientific investigation:

  • Descriptive studies
  • Correlation studies
  • Experiments

Importantly the anchor of these investigations is the research question.

Terms and concepts was the next sub-section:

  •  Subject (Participant by law in Aus where people are subjects) – The target of your experimentation
  • Variables (Independent variables, Dependent variables, Intermediate variables, Extraneous variables), these are self explanatory via dictionary definitions.
  • Variance/Factor  models – Aims to predict outcome from adjustment of predictor (independent?) variables, in an atomic time frame. That is my loose interpretation.
  • Process model -Aims to explain how outcomes develop over time (The difference between variance and process models appears to be moot and I feel somewhat irrelevant).
  • Groups -> experimentation group, control group -> ensuring group equivalence.
  • Hypothesis – Prediction about the effect of independent variable manipulation on dependent variables. One tailed, two tailed,  null hypothesis.
  • Significance – the difference between two descriptive statistics, to an extend which cannot be chance.
  • Reliability – Can the research method be replicated by another researcher
  • Internal Validity – How much is the manipulation of the independent variable responsible for the results in the dependent variable.
  • External validity – Can the results be generalized to entities outside of the experiment
  • Construct validity – extend to which the measures used in the experiment actually measure the construct?

Experimental Design followed:

  • Between-subject design vs Within-subject design -> are subjects manipulated in the same or differing ways.
  • After-only vs Before-after design -> testing of dependent variables at which stages..
  • Statistical tests must reflect the experimental design:

 

Statistical test to reflect the experimental design - Source week 3 lecture notes

When creating an experimental design it seems like a good idea just to make a check list.

The coffee/caffeine example covered next seemed a bit odd as it made the assumption that coffee caffeine are the same things. I recall same type assumption was made in regards to THC and marijuana which was later found to be fundamentally flawed. I did not understand the Decision support system example at all so was not really able to extrapolate much understanding from the two examples covered.