(copy) Module 11 - Complex Experimental Designs

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17 Terms

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Basic Experimental Designs

One independent and one dependent variable

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Complex Experimental Designs

  • Multiple variables

  • Factorial designs: multiple independent variables

  • Multivariate designs: multiple dependent variables

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Factorial Designs

  • Multiple independent variables

  • When it’s this design, tells you that the predictors are categorical

    • In a study you have a drug X drug Y and a placebo

      • Those are categories

  • The reason we would have a study

    with multiple predictors is almost always because we think that, the effect of one of the predictors on the outcome might depend on what's happening with the other predictor

    • Has main and interaction effects

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Multivariate Designs

Multiple dependent variables

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2 x 2 Between-subjects Factorial Design

  • Two independent variables in the same study

  • Different participants in the various conditions

  • 4 groups

  • (# x #) # refer to the number of levels in each factor

<ul><li><p>Two independent variables in the same study</p></li><li><p>Different participants in the various conditions</p></li><li><p>4 groups</p></li><li><p>(# x #) # refer to the number of levels in each factor </p></li></ul>
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Example of a Factorial Design

How does playing video games influence cognitive functioning?

  • IV#1: Game type (Adventure vs. RPG)

  • IV#2: Time played (3 hrs vs. 6 hrs)

  • Confirmatory hypotheses:

    • The researcher specifies what effects they expect to find

  • Exploratory hypotheses:

    • The researcher does not specify what results will be found

You think there's something about the effect of one of them might differ, depending on the other one

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Confirmatory/Directional Hypotheses

  • The researcher specifies what effects they expect to find

  • You have a very specific effect that you think you’re gonna find

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Exploratory Hypotheses

  • The researcher does not specify what results will be found

  • I think, there might be interaction between these 2 predictors, But I'm not sure what it's gonna be

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An Example of a Factorial Design: Contingency Table

  • Factorial designs have main effects and interaction effects

  • This shows cell means and marginal means

<ul><li><p>Factorial designs have <strong>main effects</strong> and <strong>interaction effects</strong></p></li><li><p>This shows cell means and marginal means</p></li></ul>
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Main Effects in Factorial Designs

  • Compare all differences between levels of one independent variable across levels of the other

  • When we just isolate the effect of one of the predictors

    • Just comparing the rpg game vs the adventure game (ignoring time spent playing)

    • Or just comparing playing for 3hrs vs playing for 6hrs (ignoring the type of game that was played)

<ul><li><p>Compare all differences between levels of one independent variable <em>across</em> levels of the other</p></li><li><p>When we just isolate the effect of one of the predictors</p><ul><li><p>Just comparing the rpg game vs the adventure game (ignoring time spent playing)</p></li><li><p>Or just comparing playing for 3hrs vs playing for 6hrs (ignoring the type of game that was played)</p></li></ul></li></ul>
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Interaction Effects in Factorial Designs

  • Combined Effects of independent variables

  • An effect of one IV on the DV depending on the level of the other IV

  • Describing main effects in reference to each other

  • There's a different effect of the game depending on this other variable here

Example: Ice Cream Preference

  • Ice cream you prefer on its own

    • = Main Effect

  • Preference for what ice cream you want, depending on what you're having (with Pie)

    • ________ ________

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Other Variations of Factorial Designs (Specifically 2Ă—3)

  • More than two levels for the independent variable

  • A main effect does not clarify which levels differ

  • Post-hoc tests:

    • Allow researchers to compare the means of the levels of the IV

<ul><li><p>More than two levels for the independent variable</p></li><li><p>A main effect does not clarify <em>which</em> levels differ</p></li><li><p><strong>Post-hoc tests:</strong></p><ul><li><p>Allow researchers to compare the means of the levels of the IV</p></li></ul></li></ul>
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Post-Hoc Tests

Allow researchers to compare the means of the levels of the IV

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Higher-Order Factorial Designs

  • Have additional main effects and interactions

  • A design with three or more independent variables (2Ă—2Ă—2)

    • 2 (three hours versus six hours)

    • x 2 (adventure versus RPG)

    • x 2 (low versus high delay of gratification) design has six conditions

<ul><li><p>Have additional main effects and interactions</p></li><li><p>A design with <em>three</em> or <em>more</em> independent variables (2Ă—2Ă—2)</p><ul><li><p>2 (three hours versus six hours)</p></li><li><p>x 2 (adventure versus RPG)</p></li><li><p>x 2 (low versus high delay of gratification) design has <em>six</em> conditions</p></li></ul></li></ul>
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Repeated Measures Complex Designs

  • Participants are exposed to all levels of the independent variable

  • Increased statistical power

  • Be wary of order effects, interference effects, fatigue, and other confounds

  • The same group of participants take part in all of the conditions

<ul><li><p>Participants are exposed to <em>all levels</em> of the independent variable</p></li><li><p>Increased statistical <strong>power</strong></p></li><li><p>Be wary of order effects, interference effects, fatigue, and other confounds</p></li><li><p>The same group of participants take part in all of the conditions</p></li></ul>
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Mixed Designs

Includes between-subjects and repeated-measures design features

  • Example:

    • We randomly assign participants to study in either a re-reading or retrieval practice condition and have all participants take a test in a hot and cold room.

    • This is a 2 (Between factor: re-reading or retrieval practice study method) x 2 (Within factor: hot and cold room temperature) between-within mixed design

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Best Practices in Open Science: How Many Participants Do We Really Need?

  • Complex designs require more participants for appropriate power

  • Interaction effects require more participants than main effects

  • Smaller effects require more participants

  • Researchers should focus on conducting larger, well-powered studies