A pre-experimental design is a simple research process that happens before the actual experimental design takes place. The goal is to obtain preliminary results to gauge whether the financial and time investment of a true experiment will be worth it.
Pre-experimental design exampleA researcher wants to investigate the effect of a new type of meditation on stress levels in college students. They decide to conduct a small pre-experiment with 10 college students who are already interested in meditation.
The students are asked to participate in a 30-minute meditation session once a week for 4 weeks. The students’ stress levels are measured before and after the meditation sessions with a standardized questionnaire.
The researcher compares the outcomes and notices significant differences in stress scores. They decide to move forward with a more costly and time-consuming experiment where they take into account all criteria for an experimental design (e.g., random assignment of participants, control group, controlling for extraneous variables).
An experimental design diagram is a visual representation of the research design, showing the relationships among the variables, conditions, and participants. It helps researchers to:
Randomization: This principle involves randomly assigning participants to experimental conditions, ensuring that each participant has an equal chance of being assigned to any condition. Randomization helps to eliminate bias and ensures that the sample is representative of the population.
Manipulation: This principle involves deliberately manipulating the independent variable to create different conditions or levels. Manipulation allows researchers to test the effect of the independent variable on the dependent variable.
Control: This principle involves controlling for extraneous or confounding variables that could influence the outcome of the experiment. Control is achieved by holding constant all variables except for the independent variable(s) of interest.
Replication: This principle involves having built-in replications in your experimental design so that outcomes can be compared. A sufficient number of participants should take part in the experiment to make sure that randomization allows for groups with a similar distribution. This increases the chance of detecting true differences.
Randomization is a crucial component of experimental design, and it’s important for several reasons:
Prevents bias: Randomization ensures that each participant has an equal chance of being assigned to any condition, minimizing the potential for bias in the assignment process.
Controls for confounding variables: Randomization helps to distribute confounding variables evenly across conditions, reducing the risk of spurious correlations between the independent variable and the outcome.
Increases internal validity: By randomly assigning participants to conditions, you can increase the confidence that any observed differences between conditions are due to the independent variable and not some other factor.
A within-participant design, also known as a repeated-measures design, is a type of experimental design where the same participants are assigned to multiple groups or conditions. Some advantages of this design are:
Increased statistical power: By using the same participants across multiple conditions, you can reduce the number of participants needed to detect a significant effect, which can lead to increased statistical power.
Reduced between-participants variability: Since each participant is tested multiple times, the variability between participants is reduced, which can result in more accurate and reliable estimates of the effect.
Better control over extraneous variables: By using the same participants across multiple conditions, you can better control for extraneous variables that might affect the outcome, as these variables are likely to be constant across conditions.
Increased precision: Within-participant designs can provide more precise estimates of the effect size, as the same participants are used across all conditions.
Reduced sample size: Depending on the research question and design, a within-participant design can require fewer participants than a between-participants design, which can reduce costs and increase efficiency.
It’s important to note that within-participant designs also have some limitations, such as increased risk of order effects (where the order of conditions affects the outcome) and carryover effects (where the effects of one condition persist into another condition).
Treatment group: This group is exposed to the manipulated independent variable, and the researcher measures the effect of the treatment on the dependent variable.
Control group: This group is not exposed to the manipulated independent variable (the variable being changed or tested). The control group serves as a reference point to compare the results of the experimental group to.
In other words, the control group is used as a baseline to compare with the treatment group, which receives the experimental treatment or intervention.
Two groups in experimental design exampleYou want to test a new medication to treat headaches. You randomly assign your participants to one of two groups:
The treatment group, who receives the new medication
Cluster sampling usually harms internal validity, especially if you use multiple clustering stages. The results are also more likely to be biased and invalid, especially if the clusters don’t accurately represent the population. Lastly, cluster sampling is often much more complex than other sampling methods.
Cluster sampling is generally more inexpensive and efficient than other sampling methods. It is also one of the probability sampling methods (or random sampling methods), which contributes to high external validity.
In all three types of cluster sampling, you start by dividing the population into clusters before drawing a random sample of clusters for your research. The next steps depend on the type of cluster sampling:
Single-stage cluster sampling: you collect data from every unit in the clusters in your sample.
Double-stage cluster sampling: you draw a random sample of units from within the clusters and then you collect data from that sample.
Multi-stage cluster sampling: you repeat the process of drawing random samples from within the clusters until you’ve reached a small enough sample to collect data from.
Yes, stratified sampling is a random sampling method (also known as a probability sampling method). Within each stratum, a random sample is drawn, which ensures that each member of a stratum has an equal chance of being selected.
