slide1 n.
Skip this Video
Loading SlideShow in 5 Seconds..
30 pts PowerPoint Presentation
Download Presentation
30 pts

Loading in 2 Seconds...

play fullscreen
1 / 23

30 pts - PowerPoint PPT Presentation

  • Uploaded on

Revised Grading Scheme. 30 pts. 20 pts. Assignments. vMWM. Drop lowest test score. 130. 105. 235. Chapters 12 & 15. And so much more. Large and Small N designs. Small N one or a few subjects Large N Greater than a few subjects (often multiple groups)

I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
Download Presentation

PowerPoint Slideshow about '30 pts' - viveka

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

Revised Grading Scheme

30 pts

20 pts



Drop lowest test score




chapters 12 15

Chapters 12 & 15

And so much more

large and small n designs
Large and Small N designs

Small N

  • one or a few subjects

Large N

  • Greater than a few subjects (often multiple groups)
  • most common technique used in research design
large n designs
Large N Designs
  • Gained in popularity after Sir Ronald Fisher invented the analysis of variance in the 1930s
  • Easier to generalize with more than one subject (greater external validity)
why even use small n
Why even use small N?
  • Precision – pooling or combining data can obscure the results of individual subjects
  • You may miss effects by pooling data across individuals.

Subject 1

Subject 2


why even use small n1
Why even use small N?
  • Another example where pooling data led to a misinterpretation of what subjects had or had not learned?
  • Hint: a series of water maze studies on the effects of partial reinforcement (PR)
    • How many subjects in the PR group?
    • What data was pooled?
    • What was discovered by de-aggregating the data?
    • What’s the big picture lesson?
the big picture lesson
The BIG PICTURE lesson
  • Large N’s aggregate over subjects.
  • Smaller N studies sometimes aggregate over time.
  • Both have the potential to loose fidelity

Mirriam-Webster Online

a: the quality or state of being faithful b: accuracy in details :exactness 2: the degree to which an electronic device (as a record player, radio, or television) accurately reproduces its effect (as sound or picture)

From Wikipedia, the free encyclopedia

High fidelity (disambiguation)

High fidelity or hi-fi is most commonly a term for the high-quality reproduction of sound or images

small n designs
Small N Designs
  • Also used for practical reasons
    • Only a few patients in clinical research for a rare disease, plenty with common ones
    • Animals may be expensive (especially those fancy rats)
  • So, it’s ideal for poor researchers with restricted or limited access to human patients and/or those that may lack motivation to collect acceptable amounts of data in order to do a real study deemed credible by other scientific peers!

Just the crowd I want to hang around and get advice from

small n designs1
Small N Designs

Popular in:

  • Clinical and animal research
  • Laboratory and field studies
  • Psychophysics
  • Studies of learning
  • Used most extensively in operant conditioning research
aba design
ABA Design
  • The return to baseline in the ABA design tests whether B had an effect or whether another extraneous variable confounded the study.
  • Thus, the effect of B, the experimental treatment, must be reversible
  • it is also called a reversal design
variations of the aba design
Variations of the ABA Design
  • ABABA – two treatments and two returns to baseline – can detect cumulative effects of the treatment
  • ABACADA – multiple experimental conditions - B, C and D represent different treatments
  • AB design – sacrifice the return to baseline if it would harm the subject (e.g., behavior modification worked in reducing self-injurious behavior)
variations of the aba design1
Variations of the ABA Design

A Swedish design that only made sense in the drug-induced haze of the 70s disco era.

variations of the aba design2
Variations of the ABA Design
  • Multiple baseline design – a series of baselines and treatments are compared, but once a treatment is established it is not withdrawn (e.g. AAABBB no more As)
  • Discrete trials design – does not rely on baselines at all, but compares performance across treatment conditions (e.g. BCDE) a BC design would be analogous to what large N design?
variations of the aba design3
Variations of the ABA Design

AC/DC – a.k.a, the “Indiscrete trials design”

  • After “A”, never return to baseline
  • skip all the boring B condition stuff and go right for the CDC conditions that put you on a fast track to the land down-under…
  • Apply thunderbolt between C and D.
b f skinner
B. F. Skinner
  • Studied changes in the rate of behavior (e.g., a rat lever pressing for food)
  • by careful,continuous measurement of a single subject over time.

The control and experimental conditions are given to the same subject at different times







evaluating the experiment
Evaluating the Experiment
  • Internal validity – was the experiment free of confounding?
  • Manipulation check – assesses how successfully the experimenter manipulated the situation she or he intended to produce.
  • Pact of ignorance – subjects who have guessed the hypothesis might try to hide the fact because they know that their data might be discarded.
statistical problems
Statistical problems
  • Statistical conclusion validity – are conclusions about the statistical results valid?
  • Did you use an appropriate test?
  • Too many a priori tests – increases the chance of making a Type 1 error.
  • Small effect size – the results can be significant but not very meaningful if the effect size is small.
external validity
External validity
  • Two requirements:
    • The experiment is internally valid
    • And can be replicated

What form of validity is a prerequisite for another form of validity?

research significance
Research significance
  • Are the results consistent with prior studies?
  • Do the results extend our knowledge of the problem?
  • Are there any implications for broader theoretical issues?
multivariate designs
Multivariate Designs
  • Involve multiple variables studied concurrently
    • MANOVA (multiple DVs)
    • Multiple correlation
    • Factor analysis
unobtrusive measures
Unobtrusive measures
  • Specific procedures for measuring a subjects behavior without them knowing that their behavior is being measured
    • Greater external validity because the behavioral data is similar to behavior occurring outside the experiment
    • E.g., a field experiment
      • Manipulate antecedent conditions
      • Observe outcomes in natural setting
nonsignificant results
Nonsignificant results

You should reconsider:

  • The experimental hypothesis
  • The procedures used in the study
  • The possibility that a Type 2 error occurred