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37 Cards in this Set
 Front
 Back
Ambiguous temporal precedence

We don’t know which variable came first.
< internal > 

Selection

A difference in respondents could cause the effect.
< internal > 

History

Events occurring at the same time as the treatment could cause the effect.
< internal > 

Maturation

Naturally occurring change could cause the effect.
< internal > 

Regression to the mean

Respondents selected for extreme scores will score lower on other/later measures.
< internal > 

Attrition

Losing participants could affect the causal relationship.
< internal > 

Testing

Exposure to a test can affect other measures (e.g., practice).
< internal > 

Instrumentation

The nature of a measure may change over time based on definitions or new research.
< internal > 

Additive and interactive threats

selection x history/maturation/attrition
< internal > 

Inadequate explication of constructs

Lack of clarification/definition.
< construct > 

Construct confounding

Lack of distinction from a similar construct.
< construct > 

Monooperation bias

Only one operationalization used.
< construct > 

Monomethod bias

All operationalizations use the same method.
< construct > 

Confounding constructs with levels of constructs

When the construct being measured is one specific dimension of a more general construct (i.e., physical violence vs aggression)
< construct > 

Treatment sensitive factorial structure

When people exposed to a treatment see a measure differently than those who aren’t.
< construct > 

Reactive selfreport changes

Participant motivation to be in a treatment condition can affect selfreport.
< construct > 

Reactivity to the experimental situation

Simply being in the experimental situation could influence the relationship and should then be considered a part of the “treatment” definition.
< construct > 

Experimenter expectancies

The experimenter’s conveying of expectations could affect behavior and should then be considered a part of the “treatment” definition.
< construct > 

Novelty and Disruption effects

New innovations >> positive response.
Disruptions to routines >>negative response. Either should then be considered a part of the “treatment” definition. < construct > 

Compensatory equalization

When one condition receives a “treatment” and gets some benefit, and then the other condition receives a different benefit to equal out the playing field, this should be considered a part of the “treatment” definition.
< construct > 

Compensatory rivalry

Participants not receiving treatment may be motivated to show they can do as well as the treatment condition, which should be a part of the “treatment” definition.
< construct > 

Resentful demoralization

Not receiving treatment could lead to more negative selfreport which should be a part of the “treatment” definition.
< construct > 

Treatment diffusion

Participants may receive treatments in a condition that they weren’t assigned to.
< construct > 

Interaction of the causal relationship with units

An effect found with certain kinds of units might not hold if other kinds of units had been studied.
< external > 

Interaction of the causal relationship over treatment variations

An effect found with one treatment might not hold with other treatments.
< external > 

Interaction of the causal relationship with outcomes

An effect found with one kind of outcomes measure might not be found if another were used.
< external > 

Interaction of the causal relationship with settings

An effect found in one setting might not be found in another.
< external > 

Contextdependent mediation

An explanatory mediator of a causal relationship in one context may not mediate in another context.
< external > 

Low statistical power

Is a threat when sample sizes are too small or when alpha is set low. This is because low statistical power increases the likelihood of making a Type II error (accepting null when it is false).
< SCV > 

Violated assumptions of statistical tests

Is a threat when the assumptions underlying statistical tests (e.g., normality) are not met, biasing statistical tests.
< SCV > 

Fishing and error rate problem

Numerous multiple comparisons (when conducting a large number of statistical tests) increases chance of randomly finding significance  Type I error.
< SCV > 

Unreliability of measures

Unreliable scales cannot be relied on for detecting true differences or changes (less than .7.8).
< SCV > 

Range restriction

Floor/ceiling effects, or clustering around the mean. If you ain't got variance, you ain't got covariance.
< SCV > 

Unreliability of treatment implementation

Variations in treatment introduce error, decreasing the chance that a true difference will be detected.
< SCV > 

Random irrelevancies/extraneous variance in the experimental setting

Irrelevant nontreatment features of the experimental setting may influence scores on the dependent variable (e.g., excessive noise for one group but not another) by inflating error variance.
< SCV > 

Random heterogeneity of respondents

If respondents in any treatment group differ on factors that are also correlated with the DV, may cause certain kinds of respondents will be more affected by a treatment than others (a matter of external validity). Error variance may also be inflated.
< SCV > 

Inaccurate effect size estimation

Causes unreliable estimations of covariance < SCV >
