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Chapter 7

PSYB01 Chapter 7 notes + key terms for exam prep.doc

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David Nussbaum

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 o Are more realistic o Are more efficient than experimental analysis limited to a one-at-a-time variable manipulation • These complex experimental designs = factorial designs o A 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 o A 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 o Each 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 • o Offers 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 o Example: 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) o Allows 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 o Example: 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 • 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 o More economical than between subject designs • Fewer participants are needed to perform the experimental task o Fewer 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 o A 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 o The interaction tells us that the effects of the independent variable depend upon the particular level of the independent variable o Therefore 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 o If 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 A Complex Between-Subjects Experiment • True experiments, whether single or multifactorial, require independent variables that we can directly manipulate • Many variables of interest to psychologists are not easy to manipulate Staged Event Manipulations • A staged or event manipulation that can be employed when the primary goal is to introduce a particular psychological state in research participants 2 x 3 Design • A between subjects design requires more participants than a within subjects design Manipulation Check Sound experimental design often includes a manipulation heck • Main Effects and Interactions • Statistical interactions should be examined by graphing simple main effects; consider any main effect in a multifactorial experiment of two or more independent variables only after examining for interactions Interpreting Results • Between subjects designs are at the mercy of random assignment to control for neutralize individual differences A pretest measure would have provided additional control for any individual differences • that might have occurred by chance but still could have influenced the findings Statistical Analysis of Complex Designs • The analysis of variance (ANOVA) which is known as the F-test The analysis of variance is a general statistical procedure that is used to evaluate the • likelihood that results obtained in a study are due to chance • Used when there are more than two levels of an independent variable • Simple analysis of variance evaluates whether the obtained results of the study are statistically significant, meaning tat they are unlikely due to chance o Also called a one-factor analysis of variance or a simple ANOVA • Researchers are often interested in varying or manipulating two or more independent va
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