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40 Cards in this Set

  • Front
  • Back
means the value of one variable is associated with the value of another variable
ceiling effect
a measurement cannot take on a value higher than some limit or "ceiling", which is imposed not by the phenomenon being measured, but by the finite nature of the measuring instrument
floor effect
refers to when data cannot take on a value lower than some particular number, called the "floor"
occurs when a treatment remains in effect when another treatment is given
Examples of "carryover"
-Drinking red wine before white wine changes the response
-A drug does not completely wear off before another treatment is given
occurs when a treatment "spills over" into another treatment that is administered nearby
Examples of "spillover"
-Water flows from one plot of land to another
-Patients in a ward share drugs
- Students copy during an exam (or experiment)
the extent to which a conclusion logically follows the premises
Internal Validity
the extent to which it shows a cause-effect relationship between the independent and dependent variable
External Validity
addresses the ability to generalize your study to other people and other situations; application of outcome logically follows the method
Construct Validity
degree to which the constructs allow us to conclude what we concluded
Statistical Conclusion Validity
the extent to which the conclusions drawn by the researcher logically follow from the statistical test he/she employed
Name the six sources of internal validity
-History (Local or Global)
-Mortality or Attrition
Threat: History
changes in the environment
Threat: Local History
Refers to the environmental effects that are closely linked to the administration of the treatment itself
Threat: Global History
Refers to the relatively gradual changes in the subject's world or to major events in his/her environment that coincide with treatment by chance
Threat: Maturation
over time, changes may occur within the subject for reasons unrelated to treatment and may confound the comparison
Example: Threat: Maturation
non living: radioactive decay of substance; rust

living: aging or growth; hunger; boredomg
Threat: Mortality or Attrition
subjects may be unavailable for measurement at posttest or observations may be lost or ruined
Example: Threat: Mortality or Attrition
-patients who think they've been cured or helped leave treatment program
-students who are failing drop a course
-treatment may kill a subject
Threat: Instrumentation
change in the observer or measuring instrument may confound the comparison

nonliving: spring in scale may weaken
living: instructor shifts grading methods
Threat: Testing
the acts of observation may itself change the subject so that subsequent observations would be different with or without treatment

Ex: answering ques on an attitude scale may change attitudes
Threat: Regression
when the experimenter selects a time when a response is unusually high (or low, stable, etc), relative to a long term average, the response will ten to change in the direction of its average

Ex: aspirin when a headache occurs
Placebo Effect
the tendency for a participant to behave according to what they expect the treatment to do
Participant Bias
the tendency of subjects to behave differently than they would if they were not participating in a study
Experimenter Bias
refers to any effects the experimenters themselves may have on the outcome of a study
Experimenter Effects
occurs when a characteristic of the researcher influences the behavior of the participant

Ex: gender/lab coat
Compensatory Equalization
occurs when the researcher offers or provides additional treatment to those in the control or "inferior" group
Compensatory Rivalry
occurs when the participant is made aware that he/she is in the control group or that they have been given the treatment that is expected to result in poorer performance; increased effort
Resentful Demoralization
related to compensatory rivalry; instead, participant's reaction is to give up or behave as expected with poor performance
experiment whose design consists of two or more factors, each with discrete possible values or "levels", and whose experimental units take on all possible combinations of these levels across all such factors
Main Effect
the effect of an independent variable on a dependent variable averaging across the levels of any other independent variables
when the effect of one IV depends on the level of another IV
Simple Effect
the effect at a single level of another variable. Often simple effects are computed following a significant interaction
Experiments conducted with a counterbalanced measures design are one of the best ways to avoid the pitfalls of standard repeated measures designs, where the subjects are exposed to all of the treatments
Between Subjects
each participant participates in one and only one group
(bayer or tylenol, NOT both)
Within Subjects
type of experimental design in which all participants are exposed to every treatment or condition (before/after, etc)
Type I Error
report of an effect that does not exist
Type II Error
failure to detect an effect
Threats to External Validity
incorrect generalization (people, places, or times)