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37 Cards in this Set
- Front
- Back
Ambiguous temporal precedence
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We don’t know which variable came first.
< internal > |
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Selection
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A difference in respondents could cause the effect.
< internal > |
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History
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Events occurring at the same time as the treatment could cause the effect.
< internal > |
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Maturation
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Naturally occurring change could cause the effect.
< internal > |
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Regression to the mean
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Respondents selected for extreme scores will score lower on other/later measures.
< internal > |
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Attrition
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Losing participants could affect the causal relationship.
< internal > |
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Testing
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Exposure to a test can affect other measures (e.g., practice).
< internal > |
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Instrumentation
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The nature of a measure may change over time based on definitions or new research.
< internal > |
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Additive and interactive threats
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selection x history/maturation/attrition
< internal > |
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Inadequate explication of constructs
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Lack of clarification/definition.
< construct > |
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Construct confounding
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Lack of distinction from a similar construct.
< construct > |
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Mono-operation bias
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Only one operationalization used.
< construct > |
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Mono-method bias
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All operationalizations use the same method.
< construct > |
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Confounding constructs with levels of constructs
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When the construct being measured is one specific dimension of a more general construct (i.e., physical violence vs aggression)
< construct > |
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Treatment sensitive factorial structure
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When people exposed to a treatment see a measure differently than those who aren’t.
< construct > |
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Reactive self-report changes
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Participant motivation to be in a treatment condition can affect self-report.
< construct > |
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Reactivity to the experimental situation
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Simply being in the experimental situation could influence the relationship and should then be considered a part of the “treatment” definition.
< construct > |
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Experimenter expectancies
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The experimenter’s conveying of expectations could affect behavior and should then be considered a part of the “treatment” definition.
< construct > |
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Novelty and Disruption effects
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New innovations >> positive response.
Disruptions to routines >>negative response. Either should then be considered a part of the “treatment” definition. < construct > |
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Compensatory equalization
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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 > |
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Compensatory rivalry
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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 > |
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Resentful demoralization
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Not receiving treatment could lead to more negative self-report which should be a part of the “treatment” definition.
< construct > |
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Treatment diffusion
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Participants may receive treatments in a condition that they weren’t assigned to.
< construct > |
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Interaction of the causal relationship with units
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An effect found with certain kinds of units might not hold if other kinds of units had been studied.
< external > |
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Interaction of the causal relationship over treatment variations
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An effect found with one treatment might not hold with other treatments.
< external > |
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Interaction of the causal relationship with outcomes
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An effect found with one kind of outcomes measure might not be found if another were used.
< external > |
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Interaction of the causal relationship with settings
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An effect found in one setting might not be found in another.
< external > |
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Context-dependent mediation
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An explanatory mediator of a causal relationship in one context may not mediate in another context.
< external > |
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Low statistical power
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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 > |
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Violated assumptions of statistical tests
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Is a threat when the assumptions underlying statistical tests (e.g., normality) are not met, biasing statistical tests.
< SCV > |
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Fishing and error rate problem
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Numerous multiple comparisons (when conducting a large number of statistical tests) increases chance of randomly finding significance -- Type I error.
< SCV > |
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Unreliability of measures
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Unreliable scales cannot be relied on for detecting true differences or changes (less than .7-.8).
< SCV > |
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Range restriction
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Floor/ceiling effects, or clustering around the mean. If you ain't got variance, you ain't got covariance.
< SCV > |
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Unreliability of treatment implementation
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Variations in treatment introduce error, decreasing the chance that a true difference will be detected.
< SCV > |
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Random irrelevancies/extraneous variance in the experimental setting
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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 > |
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Random heterogeneity of respondents
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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 > |
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Inaccurate effect size estimation
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Causes unreliable estimations of covariance < SCV >
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