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Intro to Stats. Other tests. Multivariate ANOVA. More than one dependent variable/ outcome Often variables are related Need a procedure to estimate simultaneously. An example. MANOVA with gender (2 levels: male, female)

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intro to stats

Intro to Stats

Other tests

multivariate anova
Multivariate ANOVA
  • More than one dependent variable/ outcome
    • Often variables are related
    • Need a procedure to estimate simultaneously
an example
An example
  • MANOVA with
    • gender (2 levels: male, female)
    • Race (4 levels: caucasian, africanamerican, asianamerican, hispanic)
    • Grade (5 levels: 8, 9, 10, 11, 12)
  • DVs
    • Adolescent coping scale
      • Seek social support
      • Focus on solving the problem
      • Word hard and achieve
      • Worry
      • Invest in close friends
      • Seek to belong
      • Wishful thinking
      • Not coping
      • Tension reduction
      • Social action
      • Ignore the problem
      • Self-blame
      • Keep to self
      • Seek spiritual support
      • Focus on the positive
      • Seek professional help
      • Seek relaxing diversions
      • Physical reaction
multivariate anova1
Multivariate ANOVA
  • MANOVA Results With Demographics as Independent Variables
repeated measures anova
Repeated measures ANOVA
  • One factor on which participants are tested more than once
an example1
An example
  • Repeated measures ANOVA with
    • Gender (2 levels: male, female)
    • Interaction (2 levels: same sex, opposite sex)
    • Grade level as repeated measure
      • 11th grade
      • 12th grade
      • Multiple outcomes measured in the two grades
analysis of covariance
Analysis of Covariance
  • Can equalize initial differences among groups by including a covariate
  • Helps improve power by reducing problems with random assignment
an example2
An example
  • Women read scenarios about a woman who chooses to have sex or not
  • ANCOVA with
    • Relationship condition (4 levels: passion, passion+intimacy+no commitment, passion+intimacy, passion+intimacy+commitment)
    • Included ratings of acceptability of non-sexual scenario as covariate (to control for baseline ratings of protagonist)
    • DV: social acceptance (wanted to meet protagonist)
multiple regression
Multiple regression
  • Can include more than one predictor of an outcome
an example3
An example
  • Multiple regression
    • Outcome: child language skills
    • Predictors:
      • Mother literacy activities
      • Mother’s level of education
      • Mother’s age
      • Amount of shared reading
factor analysis
Factor analysis
  • How well items “hang” together and form clusters (factors)
  • Represent factors that are related to one another by a more general construct
an example4
An example
  • Interested in how experiences before 12 influence dating and peer relationships during adolescence
  • No scale of relationships
  • Administered 80 items with behaviors from self to partner or from partner to self
  • Conducted a factor analysis to see what types of behaviors were highly related with one another and formed “clusters” of related behaviors
meta analysis
Meta-analysis
  • Find all published studies that examine a particular relationship, then pull out and combine effects from all studies
an example5
An example
  • Examined whether elicited emotions (happiness, anger, sadness, anxiety) predict changes in cognitions, emotions, physiology, and behavior
  • Identified all published studies that included more than one emotion and at least one of the outcomes
  • Coded factors in each study: college students vs. community members, cover story or not
  • Also coded the effects – how large was the difference between 2 groups (heart rate in sad versus happy group)
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