Experimental Research Designs

Experimental research design: This research design is used to study causal relationships. One or more independent variables are manipulated, and their effect on one or more dependent variables is measured. Example: Determining the efficacy of a new vaccine plan for influenza.
1. True Experimental Design: True experimental designs, the most rigorous type of experimental design, offer the highest control and random assignment. Participants are randomly assigned to treatment or control groups.
Key Feature: Random assignment ensures that groups are equivalent before the experiment begins, minimizing bias.
Purpose: To establish cause-and-effect relationships by demonstrating a strong correlation between the independent and dependent variables.
2. Quasi-Experimental Design: Participants are not randomly assigned to groups. Quasi-experimental designs lack random assignment.
Key Feature: Often uses pre-existing groups or non-equivalent groups, making it difficult to establish causality as definitively as true experiments.
Purpose: Useful when random assignment is not feasible or ethical, such as in educational settings or when studying large populations.
3. Pre-experimental Design: The simplest and weakest form of experimental design, offering very little control over variables.
Key Features: One-shot case study: Involves a single group that receives a treatment, followed by an observation of the effect. One-group pretest-posttest: Measures a single group before and after a treatment to observe any changes. Static-group comparison: Compares a group that receives treatment with a control group that does not, but the groups are not randomly assigned.
Purpose: To provide preliminary insights and can be used when a full experiment is not possible, or to suggest the need for more rigorous research.
4. Other Types of Experimental Design
1. Experimental Research Design: Independent Measures
The independent measures design is an experimental design in which you only assign participants to one of your experimental conditions. This experimental design is also known as the between-subjects design. So, if you have two conditions (e.g. one involving getting three hours of sleep before an exam and one that involves getting eight hours), you will need two groups. Each of the participants taking part in your experiment would only be assigned to one of the conditions.
However, if you're comparing two different groups of people, you need to account for the potential individual differences between the groups to ensure your findings are valid. Randomly assigning participants to groups is one way to average out any between-group differences.
It might skew your results if your eight-hour sleep group is much more academically advanced than your three-hour sleep group. To control for this, you can allocate participants randomly to each of the two conditions. Random assignment means that each participant has a 50% chance of being assigned to either group. When you have two conditions, this could be done by flipping a coin when assigning each participant to the group or using a random number generator.
2. Experimental Research Design: Repeated Measures
In a repeated measures design, participants take part and are assessed in each experimental condition. Therefore, the data for each condition come from the same participants. This experimental design is also referred to as a within-subjects design. This approach eliminates the potential individual differences between groups, which is a confounding variable. This way, repeated measures design increases the study's validity.
Suppose the same group of students participate in our sleep and academic performance experiment conditions. In that case, we know our results won't be affected by differences in academic ability, intelligence, or motivation between the experimental groups. It also makes recruitment easier, as we need half the number of participants that we would have needed for the independent measures design.
3. Experimental Research Design: Matched-Pairs
Similarly to the independent-measures design, the matched-pairs design involves subjecting participants to only one experimental condition. However, in this design, the assignment process is more complex. Participants are first paired based on specific characteristics that could be potential confounding variables. Then each individual in the matched pair is randomly assigned to an experimental or control group.
In our experiment, we could first match the students we recruited based on their IQ and past academic performance. Let's say that Jess and Fiona performed similarly on both of these dimensions. We would flip a coin to decide which group Fiona would be assigned to and assign Jess to the other.

Видео Experimental Research Designs канала Think vista with Dr Aqeel and Nagina khan