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Cards In This Set
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What are the requirements for an experiment to be internally valid?
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All variables/conditions are held constant except for the manipulation
of the independent variable; individual differences in participants are
balanced across conditions (usually through random assignment); the
three requirements for making causal inferences are met (the IV and DV
covary; there is a time-order relationship; and possible alternative
explanations have been ruled out).
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What are the implications when an experiment is internally valid?
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The researcher is likely to be able to claim that the independent variable caused the observed changes in the dependent variable.
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When does an experiment involve a confounding?
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When the independent variable of interest and a potential independent variable are allowed to covary.
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What does it mean to hold conditions constant in an experiment? What is the purpose of holding conditions constant?
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That all materials, instructions, and experiences within the experiment are identical across the different conditions/levels of the IV, with the exception of the manipulation of the IV. Control technique to rule out other possible alternate explanations for the change in the dependent variable other than the manipulation of the independent variable.
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How and why do researchers balance participants’ individual differences across conditions of an experiment?
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Through random assignment, which works by generating groups of participants that are equivalent on average. Individual differences need to be balanced across experimental groups to rule out the possibility that some characteristic of the participants is actually responsible for the differences in the dependent variable rather than the manipulation of the independent variable.
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What is the logic behind establishing the independent variable as the cause of a change in the dependent variable in a random groups design experiment?
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Beginning with comparable groups, treating them differently and ending with non-comparable groups.
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What are intact groups, and why do they pose the potential problem of confounding?
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Groups that exist prior to the experiment, without the researcher randomly assigning participants to groups.
Potentially confounding because the previously established groups may differ on several characteristics that could influence the DV, so any differences observed between the two groups could be the result of those established differences rather than the IV. |
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What is subject loss and why does it threaten the internal validity of the experiment?
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When participants do not complete the experiment – mechanical or selective.
Mechanical is random and does not threaten the experiment’s internal validity. |
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Which type of subject loss poses the most serious threat to internal validity?
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The loss is selective in that some characteristic of the participant that is related to the outcome of the study is responsible.
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What are two procedures typically used to control for demand characteristics and experimenter effects in an experiment?
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Placebo control groups and double-blind procedures.
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What is effect size?
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A measure of the strength of the relationship between the independent and dependent variables that is independent of sample size.
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What is a meta-analysis?
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The statistical tool that is used to analyze the results of several independent experiments.
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What is the null hypothesis? How does null hypothesis testing work and what is the purpose?
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That there is no relationship between the IV and DV.
To determine whether the IV has a reliable effect on the DV. What is the probability that the effect observed is simply due to chance or error variation (p-value)? |
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What does it mean to say that the outcome of an experiment is statistically significant?
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That the outcome has a small likelihood of occurring if the null hypothesis is true (i.e., if there were truly no relationship between the IV and DV in the population, that there is only a 5% chance that the relationship would be observed in the experiment by chance. That the outcome is not simply the result of chance or error variation; the relationship between the IV and DV is most likely not a false positive (there is only a 5% chance that the result is a false positive).
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What are Type I and Type II error?
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Type I error = false positive; α or p. A Type I error occurs when the null hypothesis is really true and we claim the independent variable did have an effect on behavior. Type II error = false negative; β.
A type II error occurs when the null hypothesis is not true and we fail to reject it, or claim the independent variable did not have an effect on behavior. |
