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Intro to Stats

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

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  1. Intro to Stats Other tests

  2. Multivariate ANOVA • More than one dependent variable/ outcome • Often variables are related • Need a procedure to estimate simultaneously

  3. 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

  4. Multivariate ANOVA • MANOVA Results With Demographics as Independent Variables

  5. Repeated measures ANOVA • One factor on which participants are tested more than once

  6. 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

  7. Analysis of Covariance • Can equalize initial differences among groups by including a covariate • Helps improve power by reducing problems with random assignment

  8. 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)

  9. Multiple regression • Can include more than one predictor of an outcome

  10. An example • Multiple regression • Outcome: child language skills • Predictors: • Mother literacy activities • Mother’s level of education • Mother’s age • Amount of shared reading

  11. Factor analysis • How well items “hang” together and form clusters (factors) • Represent factors that are related to one another by a more general construct

  12. 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

  13. Meta-analysis • Find all published studies that examine a particular relationship, then pull out and combine effects from all studies

  14. 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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