Module 11: Complex Experimental Designs

Question 1

Basic experimental designs

Answer 1

Research design featuring one independent and one dependent variable

Question 2

Complex designs

Answer 2

experiments using more than one variable (either independent ot more than 1 dependent variable)

Question 3

Factorial design

Answer 3

a type of complex design with more than 1 independent variable

Question 4

Multivariate design

Answer 4

more than one dependent variable

Question 5

What is the 2 option if you want to investigate two independent variables?

Answer 5

1. conduct separate experiments
2. manipulate each independent variable of interest or manipulate 2 independent variables in the same experiment

Question 6

What us the benefit of combining two seperate experiments into one?

Answer 6

More individual tests of significance one runs, the greater the likelihood of finding false positive results

Question 7

2X2 factorial design (3)

Answer 7

• Includes 2 independent variables and 1 dependent variable
• You have 2 independent variables and each has 2 levels
• results in 4 treatment groups

Question 8

3 X 2 factorial design (2)

Answer 8

• you have 2 independent variables with one variable having 3 levels and the other 2 levels
• Results in 6 treatment groups

Question 9

Small sample sizes—– the power of a study

Answer 9

lower

Question 10

confirmattory hypothesis

Answer 10

The researcher specify what they expect to find
Ex: “Playing adventure type games will result in higher conigtive functioning”

Question 11

Exploratory Hypothesis

Answer 11

The researcher is not interested in confirming a prediction but only in investigating how two variables are related

Question 12

Main effects

Answer 12

A main effect in experimental design and statistics refers to the impact or influence of a single independent variable on a dependent variable, while holding all other independent variables constant. The main effect of variable A represents the average difference in the dependent variable C between levels A1 and A2, while disregarding the levels of B.
Main effects refer to the differences in mean scores between the levels of each independent variable across the values of the other independent variable (i.e., the marginal means).

Question 13

Marginal mean

Answer 13

the marginal means of one variable are the means for that variable averaged across every level of the other variable.

Question 14

Interaction

Answer 14

The effect of one independent variable depends on the level of another independent variable.
Interactions help identify situations where the effect of one factor (e.g., A) on the dependent variable (C) is different depending on the level of the other factor (B).

Question 15

Researchers have 2 ways of conclusing wether they have found an interaction effect in a study:

Answer 15

1. First way is by referring to the ANOVA summary table looking for the interaction effect and deterining its p value (<= 0.05)
2. Plotting the interaction on a graph

Question 16

When we plot an interaction graph how do we know we have an interaction?

Answer 16

The lines cross one another

Question 17

effect size

Answer 17

Question 18

meta analysis

Answer 18

a systematic review of previous studies in the same topic

Question 19

One common way to increase the liklihood of finding statistical significance is to—– but—-

Answer 19

increase sample size
but
the effect size may be so small as to have minor practical value

Question 20

Dr. Delgado’s computations showed that the eta squared value for one of the main effects in her study was quite large. This might mean that she has demonstrated:

Answer 20

a large effect size

Question 21

Type 1 error

Answer 21

finding false positives or rejectinv a null hypoethesis that is true

Question 22

What is the purpose of a post-hoc test after a statistically significant main effect finding for an independent variable with three levels?

Answer 22

to determine between which levels of the independent variable significance was found

Question 22

Higher order factorial design

Answer 22

a type of factorial design in which the researcher manipulates 3 or more independent variable

Question 23

When we have 3 independenr variables, we know we could have up to— statistically significgant main effects

Answer 23

3

Question 24

How do you know you have a three way interaction?

Answer 24

When the 2 way interaction plot differ for each level of the third factor
- You know there is a three-way interaction because the plots in C1 and C2 are different.

Question 25

Power

Answer 25

Power is the probability that you can detect an effect that is present. In other words, this is the probability that you will not make a type II error, or fail to reject a false null hypothesis. Power is usually stated as 1 - Β. Beta is usually set at 0.2, which results in the typical 80% power. The higher your power, the less likely you are to make a type II error.

Question 26

In between subject designs, a simple way to gain power is to

Answer 26

increase sample size

Question 27

When a study show statistically significant results yet tiny effect sizes, this means

Answer 27

that even though an independent variable might significantly affect a dependent bariable, its effect may explain very little of the varince in that dependent variable

Question 28

80% power means

Answer 28

you will detect a real affect 80% of the time and miss a real effect 20% of the time

Question 29

Mixed Designs+ ex (2)

Answer 29

includes elements of both between subject and repeated measures design
- ex: in a 2x2 mixed factorial design, one of the independent variables will be a repeated measures variable (exposed to both playing alone ot with eachother) and the other a between subject variable (what type of game)

Question 30

Covariate designs

Answer 30

statistically control for a rival explanatory variable by running a statitical test to tease out the covariate.
- ex: when analyzing the dependency between a blood protein and a given diagnosis, age can be a covariate.

Question 31

Multivariate Design

Answer 31

Question 32

disordinal interaction:

Answer 32

In a disordinal interaction, the graph will show the lines depicting the one independent variable cross one another.

Question 33

ordinal interaction:

Answer 33

In an ordinal interaction the graph will show that the lines depicting one independent variable do not cross, but are not parallel lines.