8 2 Non-Equivalent Groups Designs Research Methods in Psychology

Table Of Content

• Between-Subjects vs. Within-Subjects Study Design
• What’s the difference between a within-subjects versus a between-subjects design?
• Examples of between-subjects study design
• Experiment 2b: Between-Subject Design With Confidence Ratings
• V. Chapter 5: Experimental Research
• 2 Non-Equivalent Groups Designs
• II. Chapter 2: Overview of the Scientific Method

This is done in order to prevent participants from unintentionally or deliberately changing the test results. Users have no previous experience and pass the test immersing themselves as much as possible in the task. Your study wants to determine if a new Logo will increase positive reviews on a company’s website. The company Logo will be the independent variable, while the positive reviews will be the dependent variable. Next, you compare the findings from the dependent variables between groups to determine if manipulation that was done on the independent variable was effective.

Between-Subjects vs. Within-Subjects Study Design

A within subjects design can also help reduce errors due to individual differences. In the within subjects design perspective, where individuals are arbitrarily assigned task conditions, fundamental differences between groups can influence the outcome. On the within subjects design, people are subject to all levels of conditions, and therefore the results are not distorted by individual differences.

What’s the difference between a within-subjects versus a between-subjects design?

In addition, you can link certain group characteristics to certain design features. Then, you would administer the same test to all participants and compare test scores between the groups. 1Two words (time and inhabitant) were excluded from this calculation in the former case because the frequency was extreme (i.e., 1,599) and in the latter case because it did not have a corresponding Kučera-Francis word frequency. However, words and all instructions were presented via the participants’ web-browser in lowercase 14-point font. All experimental procedures were presented using custom software developed in the Python programming language () with the Pygame development library () loaded on a 24-inch iMac computer running Mac OSX Snow Leopard, version 10.6. Words and fixation stimuli were presented at centre in Arial size 42-point font against a black background.

Examples of between-subjects study design

Having established that the within-subject production effect is observed for both recollection and familiarity we next explored our claim that manipulating production between-subjects would result in a production effect only for know judgments. To accomplish this we replicated the methods from Experiment 1a with the exception that production was manipulated between-subjects, meaning that participants either read silently or read aloud all study items. Like as we have seen earlier, the primary way that researchers control for extraneous variables across conditions is called random assignment , which means using a random process to decide which participants are tested in which conditions. 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. (Does the attractiveness of one person depend on the attractiveness of other people that we have seen recently?) But when they are not the focus of the research, carryover effects can be problematic.

Characterising the unity and diversity of executive functions in a within-subject fMRI study Scientific Reports - Nature.com

Characterising the unity and diversity of executive functions in a within-subject fMRI study Scientific Reports.

Posted: Tue, 17 May 2022 07:00:00 GMT [source]

Another is that the inferential statistics that researchers use to decide whether a difference between groups reflects a difference in the population takes the “fallibility” of random assignment into account. Yet another reason is that even if random assignment does result in a confounding variable and therefore produces misleading results, this confound is likely to be detected when the experiment is replicated. The upshot is that random assignment to conditions—although not infallible in terms of controlling extraneous variables—is always considered a strength of a research design. Between-subjects and within-subjects designs can be combined in a single study when you have two or more independent variables (a factorial design). In a mixed factorial design, one variable is altered between subjects and another is altered within subjects. In the introduction we predicted that the strategic use of distinctive information at test was a recollective phenomenon and would occur only within-subjects, whereas the influence of production on familiarity would operate both within- and between-subjects.

But at the same time there is a nonequivalent control group that is given a pretest, does not receive the treatment, and then is given a posttest. The question, then, is not simply whether participants who receive the treatment improve, but whether they improve more than participants who do not receive the treatment. When choosing, think about the advantages and disadvantages of each method as compared to your study design. The methods typically differ in the number of sessions required, how they can assess conditions you’re testing, the setup of the experiment, and the length of each session. A between-subjects design typically requires a larger sample size to achieve the same statistical power as a within-subjects design.

2 Non-Equivalent Groups Designs

The decision to opt for a between-subjects and a within-subjects study design depends on your research questions, your stakeholders, your data collection requirements, and logistical considerations. Additionally, you can use this type of study design when you have participants that vary on a certain subject characteristic, such as a demographic factor. This could help you gain insight into how their subject factor affected their response to the exposure. One is to include a placebo control condition, in which participants receive a placebo that looks much like the treatment but lacks the active ingredient or element thought to be responsible for the treatment’s effectiveness. When participants in a treatment condition take a pill, for example, then those in a placebo control condition would take an identical-looking pill that lacks the active ingredient in the treatment (a “sugar pill”). In research on psychotherapy effectiveness, the placebo might involve going to a psychotherapist and talking in an unstructured way about one’s problems.

Attractiveness for clients

Evidence from a long-term experiment that collective risks change social norms and promote cooperation - Nature.com

Evidence from a long-term experiment that collective risks change social norms and promote cooperation.

Posted: Wed, 15 Sep 2021 07:00:00 GMT [source]

This guarantees that these variables will not be confounded across the experimental conditions. For instance, if we want to determine whether expressive writing affects people’s health then we could start by measuring various health-related variables in our prospective research participants. We could then use that information to rank-order participants according to how healthy or unhealthy they are. Next, the two healthiest participants would be randomly assigned to complete different conditions (one would be randomly assigned to the traumatic experiences writing condition and the other to the neutral writing condition). The next two healthiest participants would then be randomly assigned to complete different conditions, and so on until the two least healthy participants. This method would ensure that participants in the traumatic experiences writing condition are matched to participants in the neutral writing condition with respect to health at the beginning of the study.

When each participant is tested in more than one treatment or condition, it is considered a different type of research design, within-subjects design, which we will look at later on. Going back to between-subjects design, as an 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. In summary, our experiments show that whereas the production effect in recognition memory is supported by both recollection and familiarity in within-subject designs, it is supported by familiarity alone in between-subjects designs. We interpret these results in the context of a dual-process account of the production effect, which attributes the effect of production to differences in relative distinctiveness (as indexed by recollection) and differences in memory strength (as indexed by familiarity). This novel finding may explain why a significant between-subjects production effect has not always been found, and may lead to new ways of thinking about how production influences memory.

A carryover effect is an effect of being tested in one condition on participants’ behavior in later conditions. One type of carryover effect is a practice effect, where participants perform a task better in later conditions because they have had a chance to practice it. Another type is a fatigue effect, where participants perform a task worse in later conditions because they become tired or bored.

In a within-subjects study design, each participant experiences all conditions of the independent variable. This type of experiment is also called repeated measures design or dependent groups because the measures are all collected from the same subject group. You can use this type of experiment in longitudinal studies, where participants may have data collected over time. One way to differentiate different research designs is based on how many treatment(s) or condition(s) a participant receives.

Then I’ll just implement a within-subjects design every time.”  Be careful; there’s a catch. If you decide to save participants by having everyone try an apple and an orange, you may run into order effects. In this case, participants who try oranges first may perceive the taste of apples differently from those who do not, causing unwanted manipulations of your data.

If the resulting measures show a significant difference between the groups, you can deduce that the manipulation was responsible for the difference. Alternatively, you can have numerous groups with a key differentiating variable, like ethnicity, sexuality, or gender identity. While a between-subjects design has fewer threats to internal validity, it also requires more participants for high statistical power than a within-subjects design. To counter this in a between-subjects design, you can use matching to pair specific individuals or groups in your sample. That way, the groups are matched on specific variables (e.g., demographic characteristics or ability level) that may affect the results. With this in mind, a logistic regression model applied to the data for nonremember trials is simply a more flexible method of estimating the same population parameter.

In contrast, data collection in a within-subjects design takes longer because every participant is given multiple treatments. However, despite the data collection duration per participant taking longer, you need fewer participants compared to between-subjects design. You should also use masking to make sure that participants aren’t able to figure out whether they are in an experimental or control group. If they know their group assignment, they may unintentionally or intentionally alter their responses to meet the researchers’ expectations, and this would lead to biased results. A between-subjects design is also called an independent measures or independent-groups design because researchers compare unrelated measurements taken from separate groups. In experiments, you test the effect of an independent variable by creating conditions where different treatments (e.g. a placebo pill vs a new medication) are applied.