Other Comparative Designs

Review of Terms:

Random Selection/Sampling vs. Random Assignment
Random Selection = How do I get my sample? All people in your population have an equal chance of being selected
Random Assignment = Once you have your sample, how do you assign them to a group?

Internal Validity vs. External Validity
Internal Validity = control of the experiment
External Validity = generalizability of your experiment
You want to have the correct balance between the two

Independent, Dependent, & Extraneous Variables
Independent Variable = What you are manipulating
Dependent Variable = depends on the independent
Extraneous Variables = the things that mess everything up

Between vs. Within Subjects Variables
Between subjects variable = looking for a difference between subjects - they don't get the same experience in both groups - but you need to make sure that both groups are as similar as possible to confirm that the only differences between groups is your independent variable
Within subjects variable = Every individual gets both experiences in the experiment - ex. Pre vs. Post - Within is more ideal because you know your groups will be consistent for both independent variables

Factorial Design = measures the impact of more than one independent variable at once
Benefit: you can see if there is an interaction between the different independent variables
(No more than three independent variables, otherwise it gets too complicated and you can't tell what variables are interacting with which variables very clearly)

Experimental vs. Quasi-Experimental Designs
"True" Experimental Designs = involve random assignment to conditions manipulated by experimenter
Quasi-Experimental Designs = involve comparisions of groups in pre-selected conditions or groups - I design the study before I collect the data
Causal Comparative Designs = are ex post facto quasi-experimental designs; They involve comparison of pre-selected conditions/groups after the fact

Time-Series Design
Measure dependent variable a lot over time

Experimental Designs, cont.
Single Subject Designs
-Like mini longitudinal experimental designs, on individuals or small groups of individuals
-Similar to pretest/posttest designs; examines DV before and after IV
-Used when it doesn’t make sense to pool effects across individuals - ex. When working with children with special needs, the specific behaviors and needs of one child are not the same as others' - But tracking that one child over time may help develop strategies to help that specific child - you're not trying to generalize your findings, you're just trying to help that one individual
--Special populations
--Focus on success of specific interventions with specific individuals

Possible Single-Subject Designs
A-B Design = baseline, followed by intervention
A = Baseline
B = Intervention
But what happens after the intervention is removed? Does behavior go back to baseline?

A-B-A Withdrawal Design = baseline, followed by intervention, concluded with baseline
When the intervention is removed, does behavior go back to baseline?
Ethical issue: is it OK to intervene but then leave subjects back at baseline behavior, especially if we know that the intervention is needed?

A-B-A-B Design = one step further; instead of leaving subjects back at baseline, present intervention again (more ethical)

Multiple Baselines Designs = alternative to ABAB design; used when it’s not ethical to leave the subject at the baseline condition and when measures on multiple Dependent Variables (DVs) are taken.
-Taking baselines for multiple behaviors at same time – whether it’s one behavior in multiple individuals, multiple behaviors in one individual, one type of behavior in one individual in multiple settings, etc.
-Difficult to use, because must ensure that multiple DVs aren’t related to one another

Issues to Consider: Internal Validity
Is Treatment Standardized?
-# Variables Changing Across Conditions ? - Is the behavior changing because of my intervention, or is there another explanation?
Condition Length?
-Degree and Speed of Change? - You want to show a stable trend. Do you have enough data points to see a stable trend in the experiment results? - The "magic" number is 7 measurements to see a stable trend
Experimenter Effects?
-Because this is a one-on-one experiment, the experimenter is likely to have an impact on the individual
Practical Significance of Results?
-Practical significance is looked at more than statistical significance because of the single-subject design. - ie. Did it help the student improve?

Single Subject Designs and External Validity
It is very difficult to generalize the results of these designs beyond the sample studied – WHY?
-Because it was only designed for one person for a specific purpose. It is not necessarily meant to be generalized.
Thus, it is VERY IMPORTANT for this type of research to be replicated before strong conclusions are drawn.

*We now have all the information we need for quiz 3 if we want to take it now.