copy: Chapter 10: Using Between-Subjects and Within-Subjects Experimental Designs (copy)
Experiment:
Randomly sampling: choosing participants from a population - external validity
Randomly assigning subjects: randomly assigning participants from the population into groups - internal validity
Quasi-experiment:
Can't or don't randomly assign subjects
Experimental Designs
Between-subjects: each treatment administered to a different group (ex., comparing a control group and a treatment group)
Different groups of subjects in different groups
Ex. hot room participants vs cold room participants
Within-subjects: one group is exposed to all treatments (ex., one group takes a pretest and a posttest, and their pretest scores are compared to their posttest scores)
One group with 2+ variables
Ex. Tiffany's score in the hot room vs her score in the cold room
Cons
Practice effect - create strategies
Single-subject: one participant exposed to all treatments
Problem of Error Variance
Remember error variance (discussed earlier)
Ex. SAT scores = prep or not did affect scores
Ability to deal with stress, previously taken it before, quality of school, IQ, room temperature, etc. = error variance
Extraneous variables: variables not considered in the study; sometimes, you can control them.
The more things you can make the same, the better.
Subject variables: the difference between people; if you feel tired, your mood, if you do word searches a lot,
To prevent, give instructions, match people, compare yourself to yourself (within subject design)
Variance in the
Handling Error Variance
Reducing Error Variance
Within-subject design (not always)
Increasing the Effectiveness of Your Independent Variable (I don't understand)
ABA therapy vs sensory integration therapy
Randomizing Error Variance Across Groups
Control for h
Within-subjects designs decrease error variance by controlling for subject characteristics
still have extraneous variables as a source of error variance
Within-Subjects Designs
Single-Factor Two-Level Design [2 options for how to do this]
Option 1:
Randomly select participants from the population of interest
Expose all participants to 1 level of the IV
Measure the DV
Expose all participants to the other level of the IV
Measure the DV
Run a dependent samples t-test to determine if there are differences in the DV across levels of the IV
Ex: caffeine (2 levels) and concentration (continuous)
Option 2- Counterbalancing:
Randomly select participants from the population of interest
Randomly assign participants to 2 groups
Expose participants in group 1 to one level of the IV and participants in group 2 to the other level of the IV
Measure DV
Swap group exposure (expose participants in group 1 to the second level of the IV and participants in group 2 to the first level of the IV
Measure the DV
Run a dependent samples t-test to determine if there are differences in the DV across levels of the IV
Ex: caffeine (2 levels) and concentration (continuous)
Correlation design
Randomly sample from the population of interest
Have them fill out measures on at least 2 different variables
Run the correlational analysis
Ex. hypothesis correlation between room temperature and grades on a concentration test
The hotter it is, the higher the grade - positive correlation
key terms:
between-subjects design
An experimental design in which different groups of subjects are exposed to the various levels of the independent variable
Ex. study about sleep & memory. Group 1: 8hrs of sleep; Group 2: 4hrs of sleep; memory recall is compared
within-subjects design
An experimental design in which each subject is exposed to all levels of an independent variable
Ex: all participants get tested on their reaction time after drinking coffee and after drinking decaf
single-subject design
An experimental design that focuses on the behavior of an individual subject rather than groups of subjects.
Ex: therapists track one child over weeks to see if the new behavioral intervention is effective
Error variance
Variability in the value of the dependent variable that is related to extraneous variables and not to the variability in the independent variable.
Ex: in a study about stress, the participants' initial anxiety level adds error variance to the results
randomized two-group design
A between-subjects design in which subjects are assigned to two groups randomly.
Ex: participants get randomly assigned to 2 groups. 1 is exposed to CBT, and the other is not, then depression scores are compared.
parametric design
An experimental design in which the amount of the independent variable is systematically varied across several levels.
Ex: study on the impact of caffeine; different groups receive different amounts of caffeine.
nonparametric design
An experimental research design in which levels of the independent variable are represented by different categories rather than different amounts.
Ex: study on problem-solving skills across different types of learning (visual, auditory, or kinesthetic)
multiple control group design
Single-factor, experimental design that includes two or more control groups.
Ex: testing a new antidepressant; 1 group gets the drug, 1 gets a placebo, and one gets regular therapy
matched-groups design
Between-subjects experimental design in which matched sets of subjects are distributed, at random, one per group across groups of the experiment.
Ex: before testing a new teaching method, students are matched on GPA, then randomly assigned to the traditional method or the new method.
matched-pairs design
A two-group matched groups design.
Ex: 2 participants with similar IQ are paired, then one does mindfulness training and the other doesn't
carryover effect
A problem associated with within-subjects designs in which exposure to one level of the independent variable alters the behavior observed under subsequent levels.
Ex: in a mood study, participants who listened to happy music first might stay cheerful, which can affect their performance after listening to sad music
Counterbalancing
A technique used to combat carryover effects in within-subjects designs. Counterbalancing involves assigning the various treatments of an experiment in a different order for different subjects.
Ex: half of the participants listen to happy music while the other half listens to sad music to balance order effects
factorial design
An experimental design in which every level of one independent variable is combined with every level of every other independent variable.
Ex: study on caffeine (none vs 200mg) and sleep (8hrs vs 4hrs). 2x2 factorial
main effect
The independent effect of one independent variable in a factorial design on the dependent variable. There are as many main effects as there are independent variables
Ex: from the example above, if the group with 8hrs of sleep performed better regardless of caffeine, sleep is the main effect
Interaction
When the effect of one independent variable on the dependent variable in a factorial design changes over the levels of another independent variable.
Ex: from the example above, if caffeine only improved alertness with 4hrs of sleep, there is an interaction between sleep & caffeine
simple main effect
In a factorial analysis of variance (ANOVA), the effect of one factor at a given level (or combination of levels) of another factor (or factors).
Ex: from the example above, analyzing the effect of caffeine only for participants who slept 4 hrs would test a simple main effect
higher-order factorial design
An experimental design that includes more than two independent variables (factors).
Ex: A psychologist tests how sleep (4 vs. 8 hrs), caffeine (none vs. 200 mg), and music (quiet vs. upbeat) together influence concentration. 2×2×2 design.