# Intro to Stats - PowerPoint PPT Presentation

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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

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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)

• Race (4 levels: caucasian, africanamerican, asianamerican, hispanic)

• Grade (5 levels: 8, 9, 10, 11, 12)

• DVs

• 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 ANOVA

• MANOVA Results With Demographics as Independent Variables

### Repeated measures ANOVA

• One factor on which participants are tested more than once

### An example

• Repeated measures ANOVA with

• Gender (2 levels: male, female)

• Interaction (2 levels: same sex, opposite sex)

• Grade level as repeated measure

• Multiple outcomes measured in the two grades

### Analysis of Covariance

• Can equalize initial differences among groups by including a covariate

• Helps improve power by reducing problems with random assignment

### 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

• Can include more than one predictor of an outcome

### An example

• Multiple regression

• Outcome: child language skills

• Predictors:

• Mother literacy activities

• Mother’s level of education

• Mother’s age

### 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 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

• Find all published studies that examine a particular relationship, then pull out and combine effects from all studies

### 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)