Chapter 10. Introduction to Simple Experiments p.273-290
Control Variables
When researchers are manipulating an independent variable, they need to make sure they
are varying only one thing at a time à the potential causal force or proposed ‘’active
ingredient’’.
Therefore, besides the independent variable, researchers also control potential third
variables in their study by holding all other factors constant between the levels of the
independent variable.
Control variable = any variable that an experimenter holds constant on purpose.
à control variables are not really variables at all because they do not vary; experimenters
keep the levels the same for all participants.
à control variables allow researchers to separate one potential cause from another and
thus eliminate alternative explanations for results.
à Control variables establish external validity.
Control Groups, Treatment Groups and Comparison Groups
Control group = a group who receives no treatment, they are in neutral condition
Treatment group = group who gets a treatment. The other level or levels of the independent
variable.
Placebo group = when the control group is exposed to an inert treatment such as a sugar
pill.
à a comparison group does not have to be a control group.
Temporal precedence
By manipulating the independent variable, the experimenter virtually ensures that the cause
comes before the effect (or outcome).
Experiments unfold over time, and the experimenter makes sure the independent variable
comes first. Experimental research is, for that reason, superior to correlational studies.
Well-Designed Experiments Establish Internal Validity
Confounds = potential treats to internal validity.
For any given research question, there can be several possible alternative explanations, they
are called confounds. They tend to confuse about what is causing the change in the
dependent variable.
Design confounds
Design confounds = an experimenter’s mistake in designing the independent variable. It’s a
second variable that happens to vary systematically along with the intended independent
variable and therefore is an alternative explanation for the results.
à Threat to internal validity.
1
, Example: If all students in a ‘’using laptop group’’ had to answer more difficult questions
than the longhand group, that would be a design confound.
Systematic Variability is the problem à Not every potentially problematic variable is a
confound!
Example: It might be that some of the research assistants were generous and welcoming
and others were reserved. The attitude of the research assistants is a problem for internal
validity only if the shows systematic variability with the independent variable.
Selection Effects
Selection effects = when the kinds of participants in one level of the independent variable
are systematically different from those in the other.
- Experimenters let participants choose (select) which group they want to be in. A
selection effect may result if the experimenters assign one type of person (all women
or all who sign up early in the semester)
Avoiding selection effects with random assignment
Random assignment = giving respondents equal chance to get in a control/comparison
group.
Avoiding selection effects with Matched Groups
In the simplest type of random assignment, researchers assign participants at random to one
condition or another in the experiment.
Matched groups = To create matched groups from a sample, the researchers would fist
measure the participants on a particular variable that might matter to the dependent
variable.
à To be sure that the experimental groups are equal as possible before they administer the
independent variable.
à matching takes time because it’s an extra step before conducting the experiment.
Independent-Groups Design
Independent-Groups = in which different groups of participants are placed into different
levels of the independent variable. (between subjects or between-groups design).
Example: pasta bowl study: one group had a large bowl condition and other group medium
bowl condition.
Order effect
Within-groups design = there is only one group of participants, and each person is presented
with all levels of the independent variable.
- There is an alternative explanation because the outcome might be caused by the
independent variable, but it also might be caused by the order in which levels of the variable
are presented. We do not know if the independent variable has an actual effect pr whether
participants are just getting bored or tired.
Example: asking participants to take notes twice: first with using a laptop and then with pen
and paper.
2
Control Variables
When researchers are manipulating an independent variable, they need to make sure they
are varying only one thing at a time à the potential causal force or proposed ‘’active
ingredient’’.
Therefore, besides the independent variable, researchers also control potential third
variables in their study by holding all other factors constant between the levels of the
independent variable.
Control variable = any variable that an experimenter holds constant on purpose.
à control variables are not really variables at all because they do not vary; experimenters
keep the levels the same for all participants.
à control variables allow researchers to separate one potential cause from another and
thus eliminate alternative explanations for results.
à Control variables establish external validity.
Control Groups, Treatment Groups and Comparison Groups
Control group = a group who receives no treatment, they are in neutral condition
Treatment group = group who gets a treatment. The other level or levels of the independent
variable.
Placebo group = when the control group is exposed to an inert treatment such as a sugar
pill.
à a comparison group does not have to be a control group.
Temporal precedence
By manipulating the independent variable, the experimenter virtually ensures that the cause
comes before the effect (or outcome).
Experiments unfold over time, and the experimenter makes sure the independent variable
comes first. Experimental research is, for that reason, superior to correlational studies.
Well-Designed Experiments Establish Internal Validity
Confounds = potential treats to internal validity.
For any given research question, there can be several possible alternative explanations, they
are called confounds. They tend to confuse about what is causing the change in the
dependent variable.
Design confounds
Design confounds = an experimenter’s mistake in designing the independent variable. It’s a
second variable that happens to vary systematically along with the intended independent
variable and therefore is an alternative explanation for the results.
à Threat to internal validity.
1
, Example: If all students in a ‘’using laptop group’’ had to answer more difficult questions
than the longhand group, that would be a design confound.
Systematic Variability is the problem à Not every potentially problematic variable is a
confound!
Example: It might be that some of the research assistants were generous and welcoming
and others were reserved. The attitude of the research assistants is a problem for internal
validity only if the shows systematic variability with the independent variable.
Selection Effects
Selection effects = when the kinds of participants in one level of the independent variable
are systematically different from those in the other.
- Experimenters let participants choose (select) which group they want to be in. A
selection effect may result if the experimenters assign one type of person (all women
or all who sign up early in the semester)
Avoiding selection effects with random assignment
Random assignment = giving respondents equal chance to get in a control/comparison
group.
Avoiding selection effects with Matched Groups
In the simplest type of random assignment, researchers assign participants at random to one
condition or another in the experiment.
Matched groups = To create matched groups from a sample, the researchers would fist
measure the participants on a particular variable that might matter to the dependent
variable.
à To be sure that the experimental groups are equal as possible before they administer the
independent variable.
à matching takes time because it’s an extra step before conducting the experiment.
Independent-Groups Design
Independent-Groups = in which different groups of participants are placed into different
levels of the independent variable. (between subjects or between-groups design).
Example: pasta bowl study: one group had a large bowl condition and other group medium
bowl condition.
Order effect
Within-groups design = there is only one group of participants, and each person is presented
with all levels of the independent variable.
- There is an alternative explanation because the outcome might be caused by the
independent variable, but it also might be caused by the order in which levels of the variable
are presented. We do not know if the independent variable has an actual effect pr whether
participants are just getting bored or tired.
Example: asking participants to take notes twice: first with using a laptop and then with pen
and paper.
2
