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EXPERIMENTAL DESIGNS Criteria for Experiments

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Presentation on theme: "EXPERIMENTAL DESIGNS Criteria for Experiments"— Presentation transcript:

1 EXPERIMENTAL DESIGNS Criteria for Experiments
Independent, Dependent, and Confounding Variables Types of Experimental Designs Threats to Internal Validity Threats to External Validity

2 Criteria For Experimental Designs
Cause: experimenter manipulates a variable Comparison: more than one condition Control: extraneous variables are eliminated

3 Independent Variable variable manipulated by the experimenter levels

4 Dependent Variable variable measured to assess the effect of the independent variable

5 Confounding Variable variable other than IV and DV which changes between conditions control variable: potential confounding variable that is controlled

6 Types of Experiments between subjects matched groups within subjects

7 Threats to Internal Validity: Individual Differences
Systematic differences between individuals in different groups Strategies random assignment matched groups within subjects design

8 History Events outside the experiment
Most likely when conditions are measured at different times with long delays Strategies Decrease time between conditions Add a control group measured at same times

9 Maturation Physical changes related to aging
Particular problem for within-subjects designs Strategies decrease time between measurements add a control group measured at same times

10 Instrumentation Changes in the measuring instrument or equipment
Strategy Use standardized administration

11 Attrition Participants drop out of the study at different rates for the different conditions Strategies Check attrition rates across groups Compare participants who drop out to those who stay in

12 Diffusion of Treatment
Information about the purpose of the study is shared with future participants Strategies Short time span between participants Use debriefing to request that participants do not share information about the study

13 Demand Characteristics
Cues from the experimenter or research procedure about what behavior is desired Strategy Single-blind procedure

14 Experimenter Effects Experimenter’s expectations affect measurements
Strategy Double-blind procedure

15 Floor and Ceiling Effects
Measuring instrument is not sensitive enough Floor effects Ceiling effects Strategy Check sensitivity of instrument prior to experiment

16 Regression to the Mean When measured twice, scores on the second testing tend to be closer to the mean Statistical phenomenon due to chance Strategy Don’t select participants for groups based on extreme scores Use an equivalently selected control group that does not get the treatment

17 Order Effects Also called Testing or Repeated Testing
Effects of repeated measurements Fatigue effects Practice effects Carryover effects Strategy Counterbalance order of conditions

18 How Counterbalancing Works
Change the order of conditions Order effects will still exist but will affect all conditions equally This prevents order effects from being confounding

19 Complete Counterbalancing
Each possible order of conditions is used for an equal number of subjects If your conditions are A,B, and C, 1/6 of participants will get each order: ABC CAB ACB CBA BAC BCA

20 Latin Square Counterbalancing
Each condition is presented in each position for an equal number of subjects Controls for practice and fatigue effects

21 Example Latin Square 1st 2nd 3rd 4th 1/4 get A B C D 1/4 get B C D A
1/4 get C D A B 1/4 get D A B C

22 Balanced Latin Square Latin square with additional requirement that each condition precedes and follows every other condition equally often Controls practice and fatigue effects Controls simple carryover effects (involving effect of a single condition)

23 Balanced Latin Square 1st 2nd 3rd 4th 1/4 get A B D C 1/4 get B C A D
1/4 get C D B A 1/4 get D A C B

24 Randomized Counterbalancing
Used when there are multiple stimuli tested for each condition Put the stimuli in random order for each participant

25 Threats to External Validity
Unrepresentative Sample use random or stratified random sampling do exact or systematic replications Artificiality use a more realistic setting do systematic or conceptual replications

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