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Psych Chp 11
Terms in this set (29)
one-group, pretest/posttest design
An experiment in which a researcher recruits one group of participants; measures them on a pretest; exposes them to a treatment, intervention, or change; and then measures them on a posttest
A threat to internal validity that occurs when an observed change in an experimental group could have emerged more or less spontaneously over time
A threat to internal validity that occurs when it is unclear whether a change in the treatment group is caused by the treatment itself or by an external or historical factor that affects most members of the group.
A threat to internal validity related to regression to the mean, a phenomenon in which any extreme finding is likely to be closer to its own typical, or mean, level the next time it is measured (with or without the experimental treatment or intervention)
regression to the mean
A phenomenon in which an extreme finding is likely to be closer to its own typical, or mean, level the next time it is measured, because the same combination of chance factors that made the finding extreme are not present the second time.
In a pretest/posttest, repeated-measures, or quasi-experimental study, a threat to internal validity that occurs when a systematic type of participant drops out of the study before it ends.
problem only if specific types of people are dropping out (ex.outliers)
In a repeated-measures experiment or quasiexperiment, a kind of order effect in which scores change over time just because participants have taken the test more than once; includes practice effects.
A threat to internal validity that occurs when a measuring instrument changes over time.
A threat to internal validity in which a historical or seasonal event systematically affects only the participants in the treatment group or only those in the comparison group, not both.
A threat to internal validity in which participants are likely to drop out of either the treatment group or the comparison group, not both
A bias that occurs when observer expectations influence the interpretation of participant behaviors or the outcome of the study.
A cue that leads participants to guess a study's hypotheses or goals; a threat to internal validity. Also called experimental demand.
A study in which neither the participants nor the researchers who evaluate them know who is in the treatment group and who is in the comparison group
A study design in which the observers are unaware of the experimental conditions to which participants have been assigned. Also called blind design.
A response or effect that occurs when people receiving an experimental treatment experience a change only because they believe they are receiving a valid treatment.
double-blind placebo control study
A study that uses a treatment group and a placebo group and in which neither the researchers nor the participants know who is in which group.
A finding that an independent variable did not make a difference in the dependent variable; there is no significant covariance between the two. Also called null result.
all scores are at the high end
all scores are at the low end
In an experiment, an extra dependent variable researchers can include to determine how well a manipulation worked.
Unsystematic variability among the members of a group in an experiment, which might be caused by situation noise, individual differences, or measurement error. Also called error variance, unsystematic variance.
The degree to which the recorded measure for a participant on some variable differs from the true value of the variable for that participant. Measurement errors may be random, such that scores that are too high and too low cancel each other out; or they may be systematic, such that most scores are biased too high or too low.
Unrelated events or distractions in the external environment that create unsystematic variability within groups in an experiment.
The likelihood that a study will show a statistically significant result when an independent variable truly has an effect in the population; the probability of not making a Type II error.
how to avoid regression to the mean
-use random assignment and add a comparison group
- trim outliers before you compute the mean so that outliers won't shift the mean
You want to RULE OUT that change is due to regression in the mean
Scientific/alternative hypothesis vs null hypothesis
scientific/alternative hypothesis: predicts an effect of the IV in the population
null hypothesis: predicts no effect in the population
determine likelihood of obtaining sample data when null hypothesis is true, the decision is whether to reject or fail to reject the null hypothesis
what if the independent variable does not make a difference?
perhaps there is not enough between groups difference.........ineffective manipulation, insensitive measures, ceiling/floor effects
or within-groups variability obscured the group differences....measurement error, individual differences, situation variability/noise
or maybe the IV just really doesn't affect the DV
why don't we publish null results?
weak manipulations..the IV, operational definition is important
insensitive measures...the DV
ceiling&floor effects.....poorly designed IVs and DVs
would rather say that an effect does NOT exist and be wrong (Type I) than to say that an effect exists and be wrong (Type II)
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