5: Basic Designs Flashcards
what do descriptive statistics do
describing the data
what are the measures of central tendency
arithmetic mean (average), harmonic mean, geometric mean, mode, median
what are measures of variability
standard deviation (SD), variance, standard error (SE), confidence intervals (CI), least significant differences (LSD’s), coefficient of variation (CV), range
what are inferential statistics
confirming or not the hypothesis or hypotheses (t-tests, ANOVA’s, etc.)
what are factorial designs
when more than one independent variable is used (independent variable = factor)
When two or more independent variables are used (bivalent or multivalent experiments), and each variable has 2 or more levels we talk about factorial
designs.
ANOVA’s (or “analysis of variance”) inferential statistics are needed for the data analysis of these experiments
what are the three main types of factorial designs
Between-subjects factorial designs
Within-subjects factorial designs
Combination of within and between subjects designs
what are the combinations of within and between subjects designs
Mixed (between and within) factorial design
Nested factorial designs
types of between subjects (groups) designs
Type 1: Completely randomized designs.
Type 2: Matched groups designs (more later).
For t-tests: “t-test for independent groups” or unpaired t-test
types of within subjects designs
Also called: Repeated measures designs or randomized- blocks designs
For t-tests: “t-test for dependent groups” or paired t-test or “related t-test” or “correlated t-test
what are mixed designs
within and between components. Also called split-plot designs. Analysis: SPANOVA. Also: Nested designs.
three main types of within subject designs
The same subject is observed under all treatment conditions.
The same subject is observed before and after a treatment (pretest-posttest design).
Subjects are matched on a subject variable* and then randomly assigned to the treatments. In fact, this is a between subject design requiring a within subject analysis of variance.
- = organismic variable or individual differences variable
advantages of within-subject designs
Each level of the independent variable is applied to all subjects. So we can evaluate how each level of the independent variable affects each subject. Each subject is its own control.
Excellent for assessing experiments on learning, transfer of training, practice effects.
May help increase statistical sensitivity or statistical power (subjects are not divided into groups, all subjects are involved in all conditions)
disadvantages of within-subject designs
Practice effects
Differential carryover effects
Violation of statistical assumptions
what are practice effects
If not the focus of study, it becomes a problem. Solution: Appropriate counterbalancing
procedures can counteract practice effects. Also: Make the treatment order an independent variable
what are differential carryover effects
Lingering effects of one or more treatment conditions. Often an issue in drug studies. Solution: Recovery periods (intervals)
what are violations of statistical assumptions
Covered in inferential statistics course. Solution: Use a more strict significance level (e.g., .025 instead of .05)
what are practice and carryover effects
Practice/learning: increase in performance via practice. Sometimes considered a carryover effect. Not necessarily a problem in some experiments.
Carryover effects:
* Fatigue: Decreased performance with time.
* Contrast: Treatments are compared by subjects.
* Habituation or sensitization.
* Adaptation*: e.g., tolerance in drug studies.
- Note: habituation is often considered under conscious control, adaptation is not
interactions between variables
Interactions between variables/factors = interconnectivity
Variables (factors): x, y, z
* Main effects: x, y, z
* Interactions: xy, xz, yz, xyz
You have an interaction when the effect of two or more variables is not simply additive
- Interactions make the interpretation of experimental data more challenging: A significant interaction will often mask the significance of main effects
examples of nested designs
Spatial:
* Multiple samples of a single tissue type within a rat
* Estuaries are unique to each river
Temporal
* Sub-samples in time can only be sampled at one time and not another
interactions between variables
Interactions between variables/factors = interconnectivity
* Variables (factors): x, y, z
* Main effects: x, y, z
* Interactions: xy, xz, yz, xyz
You have an interaction when the effect of two or more variables is not simply additive
Interactions make the interpretation of experimental data
more challenging: A significant interaction will often mask the significance of main effects
There is an interaction between two variables if the effect of one independent variable changes with different values of the second independent variable
Completely randomized factorial design
Different participants are in each treatment condition.
Each group of participants is independent of every other group
in a graph, how do you know that there is no interaction
lines are perfectly parallel
antagonistic interactions
certain kinds of interactions can cancel out the main effects
caution: factor and levels
limits the number of levels for each factor
limit the number of factors
this limits the number of potential interactions (as they may be hard to explain)
