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

EXPERIMENTAL DESIGNS

Criteria for Experiments Independent, Dependent, and

Confounding Variables Types of Experimental Designs Threats to Internal Validity Threats to External Validity

Criteria For Experimental Designs

Cause: experimenter manipulates a variable

Comparison: more than one condition Control: extraneous variables are

eliminated

Independent Variable

variable manipulated by the experimenter levels conditions

Dependent Variable variable measured to assess the effect

of the independent variable

Confounding Variable variable other than IV and DV which

changes between conditions control variable: potential confounding

variable that is controlled

Types of Experiments

between subjects matched groups within subjects

Threats to Internal Validity:Individual Differences

Systematic differences between individuals in different groups

Strategies random assignment matched groups within subjects design

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

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

Instrumentation

Changes in the measuring instrument or equipment

Strategy Use standardized administration

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

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

Demand Characteristics

Cues from the experimenter or research procedure about what behavior is desired

Strategy Single-blind procedure

Experimenter Effects

Experimenter’s expectations affect measurements

Strategy Double-blind procedure

Floor and Ceiling Effects

Measuring instrument is not sensitive enough Floor effects Ceiling effects

Strategy Check sensitivity of instrument prior to

experiment

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

Order Effects

Also called Testing or Repeated Testing Effects of repeated measurements

Fatigue effects Practice effects Carryover effects

Strategy Counterbalance order of conditions

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

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

Latin Square Counterbalancing

Each condition is presented in each position for an equal number of subjects

Controls for practice and fatigue effects

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

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)

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

Randomized Counterbalancing

Used when there are multiple stimuli tested for each condition

Put the stimuli in random order for each participant

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