Intro to stats
This presentation is the property of its rightful owner.
Sponsored Links
1 / 14

Intro to Stats PowerPoint PPT Presentation


  • 65 Views
  • Uploaded on
  • Presentation posted in: General

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)

Download Presentation

Intro to Stats

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


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)


  • Login