Week 2
(Chapter 8, 9)
Key points:
● Between-subjects experiments: advantages and disadvantages (8)
● Confounding variables (8)
● Within-subjects experiments: advantages and disadvantages (9)
● Between-subjects and within-subjects experiments compared (9)
Chapter 8: Experimental Designs: Between-Subjects Designs
BETWEEN-SUBJECTS DESIGN
Characteristics of Between-Subjects Designs
● The key element is that separate groups of participants are used for the different
treatment conditions.
● Independent scores: 30 in treatment A, 30 in treatment B = 60 total individual
scores
A between-subjects experimental design, also known as an independent measures
experimental design, requires a separate, independent group of individuals for each
treatment condition. As a result, the data for a between-subjects design contain only one
score for each participant. To qualify as an experiment, the design must satisfy all other
requirements of the experimental research strategy, such as manipulation of an independent
variable and control of extraneous variables.
Advantages and Disadvantages of Between-Subjects Designs
Advantages:
● the participant’s score is not influenced by such factors as:
○ practice or experience gained in other treatments.
○ fatigue or boredom from participating in a series of different treatments.
○ contrast effects that result from comparing one treatment to another (a
60-degree room might feel cold after a 70-degree room, but the same
60-degree room might feel warm after a 50-degree room).
● between-subjects designs can be used for a wide variety ofresearch questions
Disadvantages:
● between-subjects designs require a relatively large number of participants.
● Individual Differences
Individual Differences
The primary disadvantage of a between-subjects design stems from the fact that each score
is obtained from a unique individual who has personal characteristics that are different from
all of the other participants. (e.g. John is a 21-year-old white male vs. Mary is a 20-year-old
black female)
20
,The two major concerns are:
1. Individual differences can become confounding variables (threat to internal validity
and is called assignment bias)
2. Individual differences can produce high variability in the scores, making it difficult to
determine whether the treatment has any effect.
Concern 1: Confounding Variables
1. Confounding from individual differences, which is called assignment bias.
Individual differences are any participant characteristics that can differ from one
participant to another.
2. Confounding from environmental variables. Environmental variables are any
characteristics of the environment that may differ. For example, one group may be
tested in a large room and another group in a smaller room
Limiting Confounding by Individual Differences
Random Assignment (Randomization)
The goal is to ensure that all individuals have the same chance of being assigned to a group.
Because group assignment is based on a random process, it is reasonable to expect that
characteristics such as age, IQ, and gender are also distributed randomly across groups.
Thus, we minimize the potential for confounding because it is unlikely that any group is
systematically older, or smarter, or more feminine than another.
In restricted random assignment, the group assignment process is limited to ensure
predetermined characteristics (such as equal size) for the separate groups.
Matching Groups (Matched Assignment)
Instead of hoping that random assignment produces equivalent groups, a researcher can
use matching to guarantee that the different groups of participants are equivalent (or nearly
equivalent) with respect to intelligence.
Matching involves assigning individuals to groups so that a specific variable is balanced, or
matched, across the groups. The intent is to create groups that are equivalent (or nearly
equivalent) with respect to the variable matched.
21
, Holding Variables Constant or Restricting Range of Variability
For example, if a researcher suspects that gender differences between groups might
confound a research study, one solution is to eliminate gender as a variable. By using only
female participants, a researcher can guarantee that all of the groups in a study are
equivalent with respect to gender; all groups are all female.
→ external validity of the research is limited.
Concern 2: Individual Differences and Variability
In addition to becoming confounding variables, individual differences have the potential to
produce high variability in the scores within a research study. As we noted earlier, high
variability can obscure any treatment effects that may exist and therefore can undermine the
likelihood of a successful study.
Small Variance
When the individual differences are small, the variance is also small, and it is easy to see the
10-point treatment effect.
Large Variance
When the individual differences are large, the variance is also large, and it is not at all easy
to see the 10-point treatment effect.
Differences Between Treatments and Variance Within Treatments
Researchers typically try to increase the differences between treatments and to decrease
the variance within treatments. For example, if we were examining the effects of room color
on mood, it would not be wise to compare two rooms that were slightly different shades of
green. With only a subtle difference between the two colors, we would be unlikely to find a
noticeable difference in mood. Instead, the best strategy would be to maximize the difference
between room colors to increase our chances of finding a large difference in mood between
treatments.
22
(Chapter 8, 9)
Key points:
● Between-subjects experiments: advantages and disadvantages (8)
● Confounding variables (8)
● Within-subjects experiments: advantages and disadvantages (9)
● Between-subjects and within-subjects experiments compared (9)
Chapter 8: Experimental Designs: Between-Subjects Designs
BETWEEN-SUBJECTS DESIGN
Characteristics of Between-Subjects Designs
● The key element is that separate groups of participants are used for the different
treatment conditions.
● Independent scores: 30 in treatment A, 30 in treatment B = 60 total individual
scores
A between-subjects experimental design, also known as an independent measures
experimental design, requires a separate, independent group of individuals for each
treatment condition. As a result, the data for a between-subjects design contain only one
score for each participant. To qualify as an experiment, the design must satisfy all other
requirements of the experimental research strategy, such as manipulation of an independent
variable and control of extraneous variables.
Advantages and Disadvantages of Between-Subjects Designs
Advantages:
● the participant’s score is not influenced by such factors as:
○ practice or experience gained in other treatments.
○ fatigue or boredom from participating in a series of different treatments.
○ contrast effects that result from comparing one treatment to another (a
60-degree room might feel cold after a 70-degree room, but the same
60-degree room might feel warm after a 50-degree room).
● between-subjects designs can be used for a wide variety ofresearch questions
Disadvantages:
● between-subjects designs require a relatively large number of participants.
● Individual Differences
Individual Differences
The primary disadvantage of a between-subjects design stems from the fact that each score
is obtained from a unique individual who has personal characteristics that are different from
all of the other participants. (e.g. John is a 21-year-old white male vs. Mary is a 20-year-old
black female)
20
,The two major concerns are:
1. Individual differences can become confounding variables (threat to internal validity
and is called assignment bias)
2. Individual differences can produce high variability in the scores, making it difficult to
determine whether the treatment has any effect.
Concern 1: Confounding Variables
1. Confounding from individual differences, which is called assignment bias.
Individual differences are any participant characteristics that can differ from one
participant to another.
2. Confounding from environmental variables. Environmental variables are any
characteristics of the environment that may differ. For example, one group may be
tested in a large room and another group in a smaller room
Limiting Confounding by Individual Differences
Random Assignment (Randomization)
The goal is to ensure that all individuals have the same chance of being assigned to a group.
Because group assignment is based on a random process, it is reasonable to expect that
characteristics such as age, IQ, and gender are also distributed randomly across groups.
Thus, we minimize the potential for confounding because it is unlikely that any group is
systematically older, or smarter, or more feminine than another.
In restricted random assignment, the group assignment process is limited to ensure
predetermined characteristics (such as equal size) for the separate groups.
Matching Groups (Matched Assignment)
Instead of hoping that random assignment produces equivalent groups, a researcher can
use matching to guarantee that the different groups of participants are equivalent (or nearly
equivalent) with respect to intelligence.
Matching involves assigning individuals to groups so that a specific variable is balanced, or
matched, across the groups. The intent is to create groups that are equivalent (or nearly
equivalent) with respect to the variable matched.
21
, Holding Variables Constant or Restricting Range of Variability
For example, if a researcher suspects that gender differences between groups might
confound a research study, one solution is to eliminate gender as a variable. By using only
female participants, a researcher can guarantee that all of the groups in a study are
equivalent with respect to gender; all groups are all female.
→ external validity of the research is limited.
Concern 2: Individual Differences and Variability
In addition to becoming confounding variables, individual differences have the potential to
produce high variability in the scores within a research study. As we noted earlier, high
variability can obscure any treatment effects that may exist and therefore can undermine the
likelihood of a successful study.
Small Variance
When the individual differences are small, the variance is also small, and it is easy to see the
10-point treatment effect.
Large Variance
When the individual differences are large, the variance is also large, and it is not at all easy
to see the 10-point treatment effect.
Differences Between Treatments and Variance Within Treatments
Researchers typically try to increase the differences between treatments and to decrease
the variance within treatments. For example, if we were examining the effects of room color
on mood, it would not be wise to compare two rooms that were slightly different shades of
green. With only a subtle difference between the two colors, we would be unlikely to find a
noticeable difference in mood. Instead, the best strategy would be to maximize the difference
between room colors to increase our chances of finding a large difference in mood between
treatments.
22
