5: Basic Designs

Question 1

what do descriptive statistics do

Answer 1

describing the data

Question 2

what are the measures of central tendency

Answer 2

arithmetic mean (average), harmonic mean, geometric mean, mode, median

Question 3

what are measures of variability

Answer 3

standard deviation (SD), variance, standard error (SE), confidence intervals (CI), least significant differences (LSD’s), coefficient of variation (CV), range

Question 4

what are inferential statistics

Answer 4

confirming or not the hypothesis or hypotheses (t-tests, ANOVA’s, etc.)

Question 5

what are factorial designs

Answer 5

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

Question 6

what are the three main types of factorial designs

Answer 6

Between-subjects factorial designs

Within-subjects factorial designs

Combination of within and between subjects designs

Question 7

what are the combinations of within and between subjects designs

Answer 7

Mixed (between and within) factorial design

Nested factorial designs

Question 8

types of between subjects (groups) designs

Answer 8

Type 1: Completely randomized designs.

Type 2: Matched groups designs (more later).

For t-tests: “t-test for independent groups” or unpaired t-test

Question 9

types of within subjects designs

Answer 9

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

Question 10

what are mixed designs

Answer 10

within and between components. Also called split-plot designs. Analysis: SPANOVA. Also: Nested designs.

Question 11

three main types of within subject designs

Answer 11

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

Question 12

advantages of within-subject designs

Answer 12

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)

Question 13

disadvantages of within-subject designs

Answer 13

Practice effects

Differential carryover effects

Violation of statistical assumptions

Question 14

what are practice effects

Answer 14

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

Question 15

what are differential carryover effects

Answer 15

Lingering effects of one or more treatment conditions. Often an issue in drug studies. Solution: Recovery periods (intervals)

Question 15

what are violations of statistical assumptions

Answer 15

Covered in inferential statistics course. Solution: Use a more strict significance level (e.g., .025 instead of .05)

Question 15

what are practice and carryover effects

Answer 15

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

Question 15

interactions between variables

Answer 15

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

Question 16

examples of nested designs

Answer 16

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

Question 16

interactions between variables

Answer 16

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

Question 17

Completely randomized factorial design

Answer 17

Different participants are in each treatment condition.

Each group of participants is independent of every other group

Question 18

in a graph, how do you know that there is no interaction

Answer 18

lines are perfectly parallel

Question 19

antagonistic interactions

Answer 19

certain kinds of interactions can cancel out the main effects

Question 20

caution: factor and levels

Answer 20

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)