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Terms in this set (35)
confounding variable
a factor other than the independent variable that might produce an effect in the dependant variable
confound (variables)
a confounding variable is a variable other than the independent variable that causes a change in the dependent variable
Why confounds are problematic
they act directly on the dependent variable, making it hard to conclude definitively that manipulation of the independent variable caused changes in the dependent variable.
What type of research designs confounding variables are and are not problematic for
Confounding variables are problematic for experimental research since there is there is an independant variable being manipulated.
They are not problematic for correlational research
Explain the relationship between confounds and internal validity
When designing an experimental study, it is important to ensure high internal validity, which is a rating of confidence that only the manipulation of the independent variable, and not the presence of other variables called confounding variables, caused observed changes in the dependent variable.
Describe the elements of Mill's criteria for establishing causality: covariance, temporal precedence, eliminating alternative explanations
Covariance: The presence or level of one variable is related to the presence or level of another variable.
temporal precedence: A cause must come before the effect. Therefore, the independent variable must be manipulated before there is a measured difference in the dependent variable.
e.a.e : In other words, there is no confounding variable to explain the relationship between the independent and dependent variable
Identify which of the three Mills criteria are affected by compounds
Eliminate alternative hypothesis because It can be difficult to eliminate alternative explanations. Here, it is important, therefore to control confounding variables
participant variables
characteristics of research participants that are not of primary interest but can influence the results of study
participant variables examples
gender, age, religion, socioeconomic status, intelligence
why participant variables are potential confounds in an experiment
A researcher cannot, for example, manipulate a participant's age; she cannot make a participant older or younger. Therefore, participant variables are never independent variables. However, they can still affect the dependent variable
List the strategies used to reduce the confounding effects of participant variables
Random Assignment, Homogeneous Assignment, match-pair design, outside influences
Random Assignment: Describe the strategies used to reduce the confounding effects of participant variables
Helps neutralize the confounding effects of participant variables by distributing the variables evenly across groups
Homogeneous Assignment: Describe the strategies used to reduce the confounding effects of participant variables
One way to make groups equivalent is to use a homogeneous sample, which means that all participants are highly similar, regardless of their assigned group,
Match-pair design: Describe the strategies used to reduce the confounding effects of participant variables
Pairs participants with similar characteristics and then randomly assigning each person to the experimental or control group
outside influences: Describe the strategies used to reduce the confounding effects of participant variables
Equivalency for the groups outside experiences
List some other variables that could cause confounds in an experiment because all groups do not have equal experimental experiences
1) outside influences (if someone had a bad day, this could negatively influence the results)
2) Contamination by communication (when participants have foreknowledge about experiment from other participantss)
3) History effect (when events outside an experiment influence the outcome)
4) Study with repeated testing
5) Maturation (aging)
Name and describe the two areas of outside experience that can cause confounds in an experiment, and explain how to reduce these confounds
1) Contamination by communication (when participants have foreknowledge about experiment from other participantss)
2) History effect (when events outside an experiment influence the outcome)
explain how to reduce Contamination by communication
1. to explain to each participant the reason for not talking with others about the session and then ask for cooperation.
2. to recruit participants from different places in the hope that they will not know one another or have an opportunity to talk about the study,
explain how to reduce History Effect
It is difficult, if not impossible, for researchers to anticipate and control for history effects. However, if the researcher is aware of a potential history effect once the study is completed, she must acknowledge this limitation when she publishes the results of the study.
Name the three main types of change over time that can confound experiments
1. Maturation;
2) Regression to the Mean
3) testing effect
Maturation
1. Maturation; Sometimes, naturally occurring time-related changes in participants—called maturation—can influence the outcome of a study, thereby confounding it. This is more likely to occur in longitudinal studies
Regression to the Mean
the tendency for extremely high or extremely low scores to become more moderate (i.e., closer to the mean) upon retesting over time.
testing effect
If participants are tested multiple times, then practice or repetition of test items, tasks, or activities can influence participants' performance in subsequent tests. Generally, they either have a positive (subjects become better at performing the task) or negative (subjects become worse at performing the task) effect.
how to reduce maturation
Because this is a naturally occurring change, it typically cannot be prevented
How to reduce Regression to the Mean
To avoid this potential confound, a researcher should not select participants based on extreme scores.
How to reduce testing effect
Of course, the testing effect is most likely to occur in studies using pretest-posttest designs. Thus, by designing research using posttest-only designs, the testing effect is essentially eliminated
Placebo effect
experimental results caused by expectations alone; any effect on behavior caused by the administration of an inert substance or condition, which the recipient assumes is an active agent.
Describe how to control for the placebo effect
To control for the placebo effect, the researcher can give a placebo pill to the control group participants. If both groups of participants receive a pill and neither knows which condition they are in, then both groups will have the same expectations of the outcome
single-blind study
study in which the subjects do not know if they are in the experimental or the control group but experimenter knows
double-blind study
An experiment in which neither the participant nor the researcher knows whether the participant has received the treatment or the placebo
Describe how measurement instrumentation can cause a threat to internal validity
A change in the measuring device during the course of a study can be a confound if the instrument is poorly calibrated or malfunctions
what steps can be taken to prevent the problem with instrumentation
constantly monitor and properly calibrate any devices used for measurement
List variables that a within-groups research design eliminates as confounds
age, sex, etc
Name and describe the three types of threats to internal validity that apply to both between-groups and within-groups designs
1. Confounding variables. Could something else have happened between the control and experimental conditions that might have caused any differences in the outcome?
2. Maturation. Although the same participants are involved on multiple occasions, are they truly the same on the
second occasion as they were on the first, or have they changed in some way over time?
3. Testing effect. If participants are tested multiple times, is it possible that simply being tested served as "practice" that influenced their performance on a subsequent test?
Describe the main threat to internal validity in within-subjects designs and how it can be reduced.
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