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Chapter 7

# PSYB01H3 Chapter Notes - Chapter 7: Social Exclusion, F-Test, Analysis Of Variance

Department
Psychology
Course Code
PSYB01H3
Professor
David Nussbaum
Chapter
7

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Chapter 7: True Experiments II
The emotional salience of the human face reflects an intricate interaction among distinct
features of the face
Complex experimental designs - which are used when a researcher seeks to manipulate
two or more independent variables at the same time
oAre more realistic
oAre more efficient than experimental analysis limited to a one-at-a-time variable
manipulation
These complex experimental designs = factorial designs
oA factor is simply another name for an independent variable
Multifactorial = experiments that vary two or more independent variables
Multifactorial Designs
All independent variables must have two or more levels
Single-factor experiments must incorporate one and only on independent variable with
two or more levels
oA researcher can always add a new level to an existing independent variable
In a multifactorial experiment, an altogether new independent variable is devised and
incorporated into the research design
oEach independent variable can be studied either between subjects or within
subjects
within subject design = all participants go through all conditions of the
experiment
Between subject design = participants are separated to each be
exclusively assigned to one of the conditions
A multifactorial design provides a more realistic model of rich psychological phenomena
oOffers an economical and efficient design to evaluate and to test the separate and
joint influences of one or more independent variables
Notation and Terms
The simplest multifactorial experiment design contains two independent variables, each
with two levels or values
Factorial experiments are typically designated or identified by a numbering notation
oExample: 2 x 2
The number of treatment groups can be calculated by multiplying the
number notations= 4 groups (2 x 2 = 4)
The number of numbers tells how many factors or independent variables
there are = 2 independent variables (2 terms, 2 & 2)
Number of values tells how many levels of the independent variable there
are = each IV has 2 levels (each term's numerical value = 2)
oAllows us to examine all these conditions
Theory and Experimentation
Theory provides us with the conceptual framework for how particular psychological
phenomena might work in real life
oExample: theory of embodied cognition = conceptual model for us to understand
how we experience, perceive, and communicate emotional information
We perceive and think about emotion, we re-enact or re-experience its
perceptual, somatovisceral, and motoric features
Theory and experimentation are closed intertwined
Theory guides how an experiment is designed, how independent variables are measured,
and what specific hypotheses are tested
A Complex Within-Subjects Experiment
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Approach-avoidance motivation - eye gaze direction and facial expressions can both
share informational values as signals of approach, propelling behaviour forward, setting the
stage for direct expression, confrontation or flight, conversely, they may fuel avoidance,
triggering inhibition, culminating in flight from negative stimuli, of potential danger and
threat
Social signaling system - proposal that facial expression of emotions and gaze
direction operate and governs our basic evolved behavioural tendencies for approach or
avoidance
Decoding Facial Expressions of Emotion
Experimentation often deconstructs complex phenomena
In a within-subject design, each participant serves as his or her own control, these
individual differences are effectively held constant
oMore economical than between subject designs
Fewer participants are needed to perform the experimental task
oFewer participants require that you collect more observations per participant
Reliability of a test can often be increased by simply adding more items
Same principle is good to keep in mind in light of the reduced number of participants that
is commonly used in within-subjects designs
Main Effects
In a factorial experiment, the effects of each independent variable on a dependent
variable are referred to as the main effect of that independent variable
oA main effect can be calculated for each independent variable
Interactions
A statistical interaction occurs when the effects of one level of the independent variable
depend on the particular level of the other independent variable
Interpreting Results of Multifactorial Designs
Factorial designs provide two distinct sources of information:
1. Main effect of each independent variable by itself and
2. Any interaction effect of the independent variables
Main effects are statistically independent of interaction effects but still have significant
interactions effects or vice versa
To interpret results generated from a multifactorial 2 x 2 experiments - look to see if
there is an interaction between the independent variables, and if so, we interpret the
interaction first, before the main effects
An interaction indicates any interpretation of a main effect of an independent variable by
itself will be misleading
oThe interaction tells us that the effects of the independent variable depend upon
the particular level of the independent variable
oTherefore cannot interpret a main effect of an independent variable without first
taking into consideration whether that effect interacts with another independent
variable
We often graph main effects and interactions as a way to help us to understand and
interpret our data
A simple main effect compares the influences of one level of an independent variable at a
given level of another independent variable
2 x 2 Logic
Simplest of the complex designs, yet not that simple
Proves a direct test of the main effect of each of the two independent variables as well a
direct test of the interaction effect between them
Either of the two independent variables may or may not affect the dependent variable
and an interaction between the two independent variables may or may not be present
Additivity and no interaction = synonymous
oIf you have two main effects but no interaction, we can say the values of one
independent variable exert a similar, additive effect on the values of the independent
variable
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