Independent measure design / Between-Subjects Experiments
An independent measures design is a research method in which
multiple experimental groups are used and participants are only in one group. Each participant is
only in one condition of the independent variable during the experiment. For example, a
researcher with a sample of 100 university students might assign half of them to write about a
traumatic event and the other half write about a neutral event. Or a researcher with a sample of
60 people with severe agoraphobia (fear of open spaces) might assign 20 of them to receive each of three different treatments for that disorder. It is essential in a between-subjects experiment that
the researcher assigns participants to conditions so that the different groups are, on average,
highly similar to each other. Those in a trauma condition and a neutral condition, for example,
should include a similar proportion of men and women, and they should have similar average
intelligence quotients (IQs), similar average levels of motivation, similar average numbers of
health problems, and so on. This matching is a matter of controlling these extraneous participant
variables across conditions so that they do not become confounding variables.
Advantages of Independent measure design
1) No order effects
2) This design saves a great deal of time
3) Multiple variables, or multiple levels of a variable, can be tested simultaneously, and with
enough testing subjects, a large number can be tested.
Disadvantages of Independent measure design
1) Researcher cannot control the effects of participant variables (i.e. different characteristics
or abilities of each participant). This would cause a confounding variable.
2) Needs more design than the Repeated Measures Design in order to end up with the same
amount of data
3) They can be complex and often require a large number of participants to generate any
useful and reliable data.
4) Another major concern for between-group designs is bias. Assignment bias, observer-
expectancy and subjects- biases are common causes for skewed data results in between-
group experiments.
5) Some other disadvantages for between-group designs are generalization, individual
variability and environmental factors. Whilst it is easy to try to select subjects of the same
age, gender and background, this may lead to generalization issues, as you cannot then
extrapolate the results to include wider groups. At the same time, the lack of homogeneity
within a group due to individual variability may also produce unreliable results and
obscure genuine patterns and trends. Environmental variables can also influence results
and usually arise from poor research design [3]
Practice effect
A practice effect is the outcome/performance change resulting from repeated testing. This
is best described by the power law of practice: If multiple levels or some other variable variation
are tested repeatedly (which is the case in between-group experiments), the subjects within each
sub-group become more familiarized with testing conditions, thus increasing responsiveness and
performance.
Treatment and Control Conditions
Between-subjects experiments are often used to determine whether a treatment works. In
psychological research, a treatment is any intervention meant to change people’s behaviour for
the better. This intervention includes psychotherapies and medical treatments for psychological
disorders but also interventions designed to improve learning, promote conservation, reduce
prejudice, and so on. To determine whether a treatment works, participants are randomly
assigned to either a treatment condition, in which they receive the treatment, or
a control condition, in which they do not receive the treatment. If participants in the treatment
condition end up better off than participants in the control condition—for example, they are less depressed, learn faster, conserve more, express less prejudice—then the researcher can conclude
that the treatment works. In research on the effectiveness of psychotherapies and medical
treatments, this type of experiment is often called a randomized clinical trial.
There are different types of control conditions. In a no-treatment control condition,
participants receive no treatment whatsoever. One problem with this approach, however, is the
existence of placebo effects. A placebo is a simulated treatment that lacks any active ingredient
or element that should make it effective, and a placebo effect is a positive effect of such a
treatment. Many folk remedies that seem to work—such as eating chicken soup for a cold or
placing soap under the bedsheets to stop nighttime leg cramps—are probably nothing more than
placebos. Although placebo effects are not well understood, they are probably driven primarily
by people’s expectations that they will improve. Having the expectation to improve can result in
reduced stress, anxiety, and depression, which can alter perceptions and even improve immune
system functioning (Price, Finniss, & Benedetti, 2008). [4]
Placebo effects are interesting in their own right but they also pose a serious problem for
researchers who want to determine whether a treatment works.
Experimental Blinds
In order to avoid experimental bias, Experimental Blinds are usually applied in between-
group designs. The most commonly used type is the single-blind, which keeps the subjects blind
without identifying them as members of the treatment group or the control group. In a single-
blind experiment, a placebo is usually offered to the control group members. Occasionally, the
double-blind, a more secure way to avoid bias from both the subjects and the testers, is
implemented. In this case, both the subjects and the testers are unaware of which group subjects belong to. The double blind design can protect the experiment from the observer-expectancy
effect.
Random Assignment
The primary way that researchers accomplish this kind of control of extraneous variables
across conditions is called random assignment, which means using a random process to decide
which participants are tested in which conditions. Do not confuse random assignment with
random sampling. Random sampling is a method for selecting a sample from a population, and it
is rarely used in psychological research. Random assignment is a method for assigning
participants in a sample to the different conditions, and it is an important element of all
experimental research in psychology and other fields too.
In its strictest sense, random assignment should meet two criteria. One is that each
participant has an equal chance of being assigned to each condition (e.g., a 50% chance of being
assigned to each of two conditions). The second is that each participant is assigned to a condition
independently of other participants. Thus one way to assign participants to two conditions would
be to flip a coin for each one. If the coin lands heads, the participant is assigned to Condition A,
and if it lands tails, the participant is assigned to Condition B. For three conditions, one could use
a computer to generate a random integer from 1 to 3 for each participant. If the integer is 1, the
participant is assigned to Condition A; if it is 2, the participant is assigned to Condition B; and if
it is 3, the participant is assigned to Condition C. In practice, a full sequence of conditions—one
for each participant expected to be in the experiment—is usually created ahead of time, and each
new participant is assigned to the next condition in the sequence as he or she is tested. When the
procedure is computerized, the computer program often handles the random assignment.
An independent measures design is a research method in which
multiple experimental groups are used and participants are only in one group. Each participant is
only in one condition of the independent variable during the experiment. For example, a
researcher with a sample of 100 university students might assign half of them to write about a
traumatic event and the other half write about a neutral event. Or a researcher with a sample of
60 people with severe agoraphobia (fear of open spaces) might assign 20 of them to receive each of three different treatments for that disorder. It is essential in a between-subjects experiment that
the researcher assigns participants to conditions so that the different groups are, on average,
highly similar to each other. Those in a trauma condition and a neutral condition, for example,
should include a similar proportion of men and women, and they should have similar average
intelligence quotients (IQs), similar average levels of motivation, similar average numbers of
health problems, and so on. This matching is a matter of controlling these extraneous participant
variables across conditions so that they do not become confounding variables.
Advantages of Independent measure design
1) No order effects
2) This design saves a great deal of time
3) Multiple variables, or multiple levels of a variable, can be tested simultaneously, and with
enough testing subjects, a large number can be tested.
Disadvantages of Independent measure design
1) Researcher cannot control the effects of participant variables (i.e. different characteristics
or abilities of each participant). This would cause a confounding variable.
2) Needs more design than the Repeated Measures Design in order to end up with the same
amount of data
3) They can be complex and often require a large number of participants to generate any
useful and reliable data.
4) Another major concern for between-group designs is bias. Assignment bias, observer-
expectancy and subjects- biases are common causes for skewed data results in between-
group experiments.
5) Some other disadvantages for between-group designs are generalization, individual
variability and environmental factors. Whilst it is easy to try to select subjects of the same
age, gender and background, this may lead to generalization issues, as you cannot then
extrapolate the results to include wider groups. At the same time, the lack of homogeneity
within a group due to individual variability may also produce unreliable results and
obscure genuine patterns and trends. Environmental variables can also influence results
and usually arise from poor research design [3]
Practice effect
A practice effect is the outcome/performance change resulting from repeated testing. This
is best described by the power law of practice: If multiple levels or some other variable variation
are tested repeatedly (which is the case in between-group experiments), the subjects within each
sub-group become more familiarized with testing conditions, thus increasing responsiveness and
performance.
Treatment and Control Conditions
Between-subjects experiments are often used to determine whether a treatment works. In
psychological research, a treatment is any intervention meant to change people’s behaviour for
the better. This intervention includes psychotherapies and medical treatments for psychological
disorders but also interventions designed to improve learning, promote conservation, reduce
prejudice, and so on. To determine whether a treatment works, participants are randomly
assigned to either a treatment condition, in which they receive the treatment, or
a control condition, in which they do not receive the treatment. If participants in the treatment
condition end up better off than participants in the control condition—for example, they are less depressed, learn faster, conserve more, express less prejudice—then the researcher can conclude
that the treatment works. In research on the effectiveness of psychotherapies and medical
treatments, this type of experiment is often called a randomized clinical trial.
There are different types of control conditions. In a no-treatment control condition,
participants receive no treatment whatsoever. One problem with this approach, however, is the
existence of placebo effects. A placebo is a simulated treatment that lacks any active ingredient
or element that should make it effective, and a placebo effect is a positive effect of such a
treatment. Many folk remedies that seem to work—such as eating chicken soup for a cold or
placing soap under the bedsheets to stop nighttime leg cramps—are probably nothing more than
placebos. Although placebo effects are not well understood, they are probably driven primarily
by people’s expectations that they will improve. Having the expectation to improve can result in
reduced stress, anxiety, and depression, which can alter perceptions and even improve immune
system functioning (Price, Finniss, & Benedetti, 2008). [4]
Placebo effects are interesting in their own right but they also pose a serious problem for
researchers who want to determine whether a treatment works.
Experimental Blinds
In order to avoid experimental bias, Experimental Blinds are usually applied in between-
group designs. The most commonly used type is the single-blind, which keeps the subjects blind
without identifying them as members of the treatment group or the control group. In a single-
blind experiment, a placebo is usually offered to the control group members. Occasionally, the
double-blind, a more secure way to avoid bias from both the subjects and the testers, is
implemented. In this case, both the subjects and the testers are unaware of which group subjects belong to. The double blind design can protect the experiment from the observer-expectancy
effect.
Random Assignment
The primary way that researchers accomplish this kind of control of extraneous variables
across conditions is called random assignment, which means using a random process to decide
which participants are tested in which conditions. Do not confuse random assignment with
random sampling. Random sampling is a method for selecting a sample from a population, and it
is rarely used in psychological research. Random assignment is a method for assigning
participants in a sample to the different conditions, and it is an important element of all
experimental research in psychology and other fields too.
In its strictest sense, random assignment should meet two criteria. One is that each
participant has an equal chance of being assigned to each condition (e.g., a 50% chance of being
assigned to each of two conditions). The second is that each participant is assigned to a condition
independently of other participants. Thus one way to assign participants to two conditions would
be to flip a coin for each one. If the coin lands heads, the participant is assigned to Condition A,
and if it lands tails, the participant is assigned to Condition B. For three conditions, one could use
a computer to generate a random integer from 1 to 3 for each participant. If the integer is 1, the
participant is assigned to Condition A; if it is 2, the participant is assigned to Condition B; and if
it is 3, the participant is assigned to Condition C. In practice, a full sequence of conditions—one
for each participant expected to be in the experiment—is usually created ahead of time, and each
new participant is assigned to the next condition in the sequence as he or she is tested. When the
procedure is computerized, the computer program often handles the random assignment.
One problem with coin flipping and other strict procedures for random assignment is that
they are likely to result in unequal sample sizes in the different conditions. Unequal sample sizes
are generally not a serious problem, and you should never throw away data you have already
collected to achieve equal sample sizes. However, for a fixed number of participants, it is
statistically most efficient to divide them into equal-sized groups. It is standard practice,
therefore, to use a kind of modified random assignment that keeps the number of participants in
each group as similar as possible. One approach is block randomization. In block randomization,
all the conditions occur once in the sequence before any of them is repeated. Then they all occur
again before any of them is repeated again. Within each of these “blocks,” the conditions occur
in a random order. Again, the sequence of conditions is usually generated before any participants
are tested, and each new participant is assigned to the next condition in the sequence.
Challenges to Internal Validity
Randomly assigning intact groups to different conditions of the independent variable
creates a potential confounding due to pre-existing differences among participants in the intact
groups. Block randomization increases internal validity by balancing extraneous variables across
conditions of the independent variable. Whether extraneous variables are controlled by balancing
or by holding conditions constant influences the external validity and sensitivity of an
experiment. Subjective subject loss, but not mechanical subject loss, threatens the internal
validity of an experiment. Placebo control groups are used to control for the problem of demand
characteristics, and double-blind experiments control both demand characteristics and
experimenter effects.
they are likely to result in unequal sample sizes in the different conditions. Unequal sample sizes
are generally not a serious problem, and you should never throw away data you have already
collected to achieve equal sample sizes. However, for a fixed number of participants, it is
statistically most efficient to divide them into equal-sized groups. It is standard practice,
therefore, to use a kind of modified random assignment that keeps the number of participants in
each group as similar as possible. One approach is block randomization. In block randomization,
all the conditions occur once in the sequence before any of them is repeated. Then they all occur
again before any of them is repeated again. Within each of these “blocks,” the conditions occur
in a random order. Again, the sequence of conditions is usually generated before any participants
are tested, and each new participant is assigned to the next condition in the sequence.
Challenges to Internal Validity
Randomly assigning intact groups to different conditions of the independent variable
creates a potential confounding due to pre-existing differences among participants in the intact
groups. Block randomization increases internal validity by balancing extraneous variables across
conditions of the independent variable. Whether extraneous variables are controlled by balancing
or by holding conditions constant influences the external validity and sensitivity of an
experiment. Subjective subject loss, but not mechanical subject loss, threatens the internal
validity of an experiment. Placebo control groups are used to control for the problem of demand
characteristics, and double-blind experiments control both demand characteristics and
experimenter effects.
Really well done and explained! Thank you :)
ReplyDelete