Psyc300 Notes- Experiments.docx

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PSYC 330
Andrea Chronis- Tuscano

Chapter 11: Factorial Experimental Designs One Two- way Design (2x2) - 4 conditions - Two Independent Variables- DrugAor Drug B (two levels in each variable) - DV- health outcome - DrugAworks regardless of Drug B, and Drug B works regardless of DrugA - Have 2 main effects plus interactions Main effect- when one variable has an effect regardless of other one - No interactions b/c effects are same Interaction - Drug a doesn’t help if don’t get drug b, but if do get drug b then drug a has effect Crossover Interaction- different/ reversed simple effects - Signifies very strong test - Drugs work together well/ taking nothing works but taking only DrugAor Drug B has neg effects Factorial Experimental Designs - More than one IV (Factor) and Factors normally crossed - More efficient and informative - Schematic Diagram >, < expected means of IV on DV are greater or less then in each condition Use different words for variables and levels - Physical appearance- attractive, unattractive - Effects of IV variable w/in one level of noise in comparison to other IV variable with other level Interaction- when moderator variable is working - In graph if lines aren’t parallel to each other then there is interaction b/c is a difference Chapter 12 Correlation between IV and DV while controlling for the other IV variables Label with operationalization rather than conceptual variable (instead of Description of Librarian: Consistent or Inconsistent) use (Traits: Librarian or Boozer) b- probability of making Type II error Factorial Experimental Designs Describe as factorial design, use more than one IV (call factors) Moderator variable changes relationship *Replication variable goes on left and moderator goes on top in schematic diagram Statistical analysis - Main effects- one for each factor, marginal means - Interaction o Line chart, as long as lines aren’t parallel then there is an interaction o Main effect, difference between the variables (compare middle values) - Simple effects( ^ or v in diagram, compares conditions within one level of moderator variable) - ANOVAsummary table 11/14/12 Explaining Schematic Diagram: expect to find that replication variable has effect in regular condition, but adding moderator variable makes effect change Start w/ explaining control condition (in absence of frustration the violent films created more aggression) but (in the presence of frustration, those who saw the nonviolent film showed more agg) 2x3 Two independent variables, six conditions Main effects compare marginal means Simple effects compare two different conditions (V or ^ in diagram) Pairwise Comparisons- any possible comparisons between 2 conditions Planned comparisons- make comparisons planned to make ahead of time, only compare planned ones Post- hoc Comparisons- make all comparisons want to make but adjust type 1 error so each test has more stringent significance test Complex Comparisons- if a lot of different comparisons, then use when have three conditions and compare all three at once CHAPTER 12 Invalidity - Construct invalidity - Statistical conclusion invalidity o Type 1 error- falsely rejected null o Type 2 error- fail to reject null when should’ve - Internal invalidity- - External invalidity Experimental Example: Experimental manipulation Conceptual IV  Conceptual DV Measured DV Manipulation Check - Operational DV- how long takes to pick up papers - Experimental manipulation causes IV o Comedy or Tragedy Film (manipulation) changes mood - Film  Mood (mood measure)  Helping  pick up papers - Manipulation check always conducted after experiment (very short, usually one question) Recruit students who want to get into law school and give them what they think is copy of exam Reduce Type 2 Errors - Increase the signal o Impact (experimental realism) o Strong manipulations o Manipulation checks - Reduce random error o Increase reliability o Standardize conditions o Limited- participant designs o Matched participant design Pepsi labeled 2 glasses M or Q, M was Pepsi and Q was coke - Experimental condition (one measure created not measured and is manipulated) o IV- type of drink, 2 levels o Within participant b/c tried both - Participants preferred glass M over Q - Can conclude that Pepsi was preferred to coke? Two variables are confounded that leaves alternative explanation open (not only are drinks different, but letters are so some people might like letter M more than Q) o Run again and switch letters, or not use letters 11/19/12 More internal validity in experiments - Does IV really cause DV - Reduce systematic error - Confounding: variables are mixed up, produces alternative explanations Alcohol study: those who drank alcohol found opposite more attractive can’t be determined because told what condition being in (placebo effect) - Alcohol and expectancy of alcohol are the variables - Dv- rated attractiveness
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