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.
Proportionate sampling in stratified sampling is a technique where the sample size from each stratum is proportional to the size of that stratum in the overall population.
This ensures that each stratum is represented in the sample in the same proportion as it is in the population, representing the population’s overall structure and diversity in the sample.
For example, the population you’re investigating consists of approximately 60% women, 30% men, and 10% people with a different gender identity. With proportionate sampling, your sample would have a similar distribution instead of equal parts.
Disproportionate sampling in stratified sampling is a technique where the sample sizes for each stratum are not proportional to their sizes in the overall population.
Instead, the sample size for each stratum is determined based on specific research needs, such as ensuring sufficient representation of small subgroups to draw statistical conclusions.
For example, the population you’re interested in consists of approximately 60% women, 30% men, and 10% people with a different gender identity. With disproportionate sampling, your sample would have 33% women, 33% men, and 33% people with a different gender identity. The sample’s distribution does not match the population’s.
Stratified sampling and systematic sampling are both probabilistic sampling methods used to obtain representative samples from a population, but they differ significantly in their approach and execution.
Stratified sampling involves dividing the population into distinct subgroups (strata) based on specific characteristics (e.g., age, gender, income level) and then randomly sampling from each stratum. It ensures representation of all subgroups within the population.
Systematic sampling involves selecting elements from an ordered population at regular intervals, starting from a randomly chosen point. For example, you have a list of students from a school and you choose students at an interval of 5. This is a useful method when the population is homogeneous or when there is no clear stratification. It’s much easier to design and less complex than stratified sampling.
