• Introspection - specially trained observers of their own thought processes, method fell out of favor in early 1900's
• Likert scales - strongly agree-agree-undecided-disagree-strongly disagree

• Choice/decision (sometimes timed)

• Physiological measures
  • Heart rate
  • GSR
  • Eye movements
  • Pupil size
  • EEG, PET scans, Functional MRI
• Behavioral measures
  • Reaction time
  • Accuracy
  • Dual task methodology

• A nominal scale consists of a set of categories that have different names. Measurements on a nomnal scale label and categorize observations, but do not make any quantitative distinctions between observations.
• An ordinal scale consists of a set of categories that are organized in an ordered sequence. Measurements on an ordinal scale rank observations in terms of size or magnitude.
• An interval scale consists of ordered categories where all of the categories are intervals of exactly the same size. With an interval scale, equal differences between numbers on the scale reflect equal differences in magnitude. However, ratios of magnitudes are not meaningful.
• A ratio scale is an interval scale with the additional feature of an absolute zero point. With a ratio scale, ratios of numbers DO reflect ratios of magnitude.

• Psychophysical - inputs are directly measurable, can relate the psychological dimensions directly with the physical dimension

  INPUTS Intense light Medium light ---> Weak light Construct "brightness" "seems very bright" ---> "seems sort of bright" "seems dim"

• Psychometric - inputs aren't directly measurable

  INPUTS Experience Genes ---> Environment Construct "depression" "I feel sad" ---> "I am unhappy" "nothing is going right"

• Reliability - if you measure the same thing twice (or have two measures of the same thing) do you get the same values?
  • Test-retest: give the same test before and after the treatment. However, the second test may be affected by the first test
  • Alternate form: have two tests that are designed to test the same thing, but are different (e.g., different questions)
  • Split-half: split one test "in half", give one half before the treatment and one half after (want to have both halves test the full range of variables)

• Validity - does your measure really measure what it is supposed to measure?
  • Face validity - at the surface level, does it look as if the measure is testing the construct?
  • Construct validity - usually requires multiple studies, a large body of evidence that supports the claim that the measure really tests the construct

• Floor effects - a value below which a response cannot be made
  • Imagine a task that is so difficult, that none of your participants can do it. As a result the effects of your IV (if there are indeed any) can't be seen.
• Ceiling effects - when the dependent variable reaches a level that cannot be exceeded
  • Imagine a task that is so easy, that everybody scores a 100% (imagine accuracy is your measure). So while there may be an effect of the IV, that effect can't be seen because everybody has "maxed out".

Possible Activities (if time permits)

#14, vol 4 Counting fidgets

purpose - to demonstrate the problems of observation and measurement (and perhaps inter-rater reliability)

# 10, vol 1 To err is human, especially in measurement

Day 2:

Single and Multiple (factorial) factor designs

So far we’ve covered a lot of the details of experiments, now let’s consider some specific experimental designs.

Single variable – one Factor

·      Two levels (t-test)

o      Basically you want to compare two groups

o      The statistics are pretty easy, a t-test

Disadvantages:

·      “True” shape of the function is hard to see

·      interpolation and extrapolation are not a good idea

·      more complex theories typically need more complex designs (more than two levels of one IV)

·      More than two-levels (ANOVA)

o      Gives a better picture of the relationship (function)

o      Requires more complex statistical analysis (analysis of variance and pairwise-comparisions)

o      Needs more resources (participants and/or stimuli)

Factorial – multiple factors

·      Two or more factors

o      2 x 4 design means two independent variables, one with 2 levels and one with 4 levels

o      “condition” or “groups” is calculated by multiplying the levels, so a 2x4 design has 8 different conditions

·      Main effects

·      Interaction effects

o      One should always consider the interaction effects before trying to interpret the main effects

So there are lots of different potential outcomes:

A = main effect of A

B = main effect of B

AB = interaction of A and B

With 2 factors there are 8 basic possible patterns of results:

1) No effects at all

2) A only

3) B only

4) AB only

5) A & B

6) A & AB

7) B & AB

8) A & B & AB

Returning to our anxiety and test performance example.  Let’s add another variable, test difficulty. 

Each of these different designs has advantages and disadvantages.