Scientific experiments are designed to test hypotheses and collect data. Experiments can be conducted in labs or in the field on humans or animals.
Generally speaking, experiments use two groups of research subjects, an experimental group and a control group. This allows researchers to isolate the effect of one independent variable on a dependent variable.
Table of Contents
Often called the “gold standard” of research designs, true experiments seek to test cause-and-effect relationships using a control group that doesn’t receive a treatment and an experimental group that does. This approach, which is used both in laboratory settings and in real-life situations, provides the most reliable evidence about cause-and-effect relationships of all research designs. It also offers the greatest protection against threats to internal validity, such as extraneous variables and contamination.
A key feature of a true experiment is random selection and random assignment, which ensure that all participants have an equal chance of being assigned to groups or treatments. This process of assigning participants to groups and treatments before they have any effect on the dependent variable helps researchers avoid bias, which can occur when people’s behavior is influenced by factors that are not related to the experiment’s manipulations.
Another important aspect of a true experiment is a pretest and a posttest. The pretest gives the researcher a measure of baseline performance and allows her to determine if the experimental treatment had a significant impact on the outcome. The posttest shows whether or not the independent variable had a direct effect on the dependent variable.
The third important feature of a true experiment is the ability to control extraneous variables. This is a challenge, especially in real-life situations where it is not always possible to isolate the effects of an independent variable. A true experiment must be able to identify any outside influences and compensate for them by adjusting the independent variable or by using different controls in the experiment’s design.
One example of a natural experiment is the Oregon Health Study, in which the state randomly assigned low-income adults to receive Medicaid or not. Researchers could then observe the impact of this policy change over time by tracking those who participated in the lottery and those who didn’t. By carefully controlling the independent variable in this way, they were able to identify and quantify the impact of Medicaid. This allowed them to draw meaningful conclusions about the impact of this policy on participant health.
There are many ways to conduct a quasi-experiment. A common method is to split participants into two groups, administer a pre-test to both groups, implement the treatment, and then perform a post-test to see if there are any differences in the outcome. This type of research is often used in cases where it would be unethical to randomly assign people to different groups. For example, a company may hire men and women in equal numbers to participate in leadership training, but it would be unethical to give this training to only one group of employees. Instead, this company could use a regression discontinuity design to determine the impact of their training program on productivity by comparing the groups after they complete their course.
This type of research is also often used in situations where it would be impossible or impractical to carry out a true experiment. For example, it might be difficult to find enough volunteers to participate in a study on the effectiveness of different types of therapy. Instead, this research uses data from previous studies that have already been conducted to test the hypothesis.
The most important difference between true experimental and quasi-experimental studies is that quasi-experiments do not involve randomly selecting or assigning subjects to different treatment conditions. This lack of randomization can introduce bias into the results, making a dependable argument about causality more challenging.
Another way to distinguish between true experiments and quasi-experiments is by examining the groups that researchers create for their research. Quasi-experiments generally create comparable groups by using a mixture of criteria, such as gender, parenting styles, and SES demographics. In contrast, true experiments use only a single variable as the basis for their groupings.
While true experiments have higher internal validity than other research methods, they are not always possible or ethical to conduct in real-world settings. This is why some researchers choose to employ quasi-experiments, which are similar to true experiments but do not require the use of participants who have been randomly assigned to treatment conditions. This allows researchers to investigate their hypotheses in situations where a true experiment would be unethical or too expensive.
Controlled experiments are the most reliable way of testing a hypothesis. They typically compare a control group to an experimental group, and all variables apart from the independent variable are kept the same in both groups. By doing so, researchers can eliminate uncertainty about what caused the result and be confident that their results are valid.
Controlled experiment research is most often performed in laboratory settings, though it can also be done at home or in the workplace. The most common controlled experiment involves two groups: a control group that will not experience any changes and an experimental group that will be exposed to the independent variable being tested.
The control group is crucial to the effectiveness of any experiment because it provides a baseline against which the researcher can measure the effect of the independent variable. The researcher will then take the results of the comparison between the experimental and control groups and be able to determine if the difference is statistically significant.
There are a few things that need to be taken into account in order to conduct a successfully controlled experiment, such as masking. The purpose of masking is to prevent the participants from knowing which group they are in. This helps to ensure that the participants are not influenced by their own beliefs or opinions and that they are evaluating the experiment objectively.
In addition to masking, extraneous variables should be controlled whenever possible. This can be done by making sure that all of the participants are being tested in the same environment and using standard measurement procedures. It can also be accomplished by limiting the number of participants who are included in your experiment by using appropriate inclusion and exclusion criteria, such as age or gender.
Another important factor in a successful controlled experiment is to make sure that the independent variable being tested is the only variable that is manipulated. This is usually accomplished by randomly assigning participants to either the control or experimental group. This will help to avoid bias in the experiment and ensure that all of the participants are being treated equally. It is also a good idea to monitor the participants during the experiment and look out for any factors that might impact the independent variable, such as fatigue or boredom.
Natural experiments are research methods that take place in real-life settings. They are based on observation and involve no direct manipulation of participants by the researcher. They have high ecological validity as they study real-world problems and are, therefore, more likely to be generalizable to similar situations. For example, a psychologist might observe people after a traumatic event to see how it affects their mental health and use this as a model for further research.
While they have high ecological validity, natural experiments do have some limitations. For example, it may be difficult to find the right participants for a natural experiment, and some variables are often unknown and cannot be controlled. This can cause issues with the reliability and validity of the results. Also, it can be difficult to control extraneous variables such as demand characteristics and participant differences (these can act as confounding factors).
Another issue is that participants in a natural experiment may not know they are being studied, which can lead to bias in the results. This is sometimes known as participant-induced selection bias, and it can reduce the validity of the results.
Other times, it may be impossible to test a hypothesis using a controlled experiment for ethical or practical reasons. For example, a scientist testing a hypothesis about viral infection wouldn’t be able to test it by infecting one group of healthy people while leaving the other group uninfected, as this would not be safe or ethical. In these cases, scientists might make predictions about the patterns that should be seen in nature if their hypothesis is correct. They then collect and analyze data to see if these patterns are actually present.
As a result of these limitations, it is important to choose the right research method for each type of question. Experimental research can help us answer many different scientific questions, but it is not always possible or even desirable to conduct experimental studies for every question. Non-experimental research (correlations, observations, interviews, and questionnaires) is useful in a variety of situations, but it cannot provide the same level of confidence as experimental or quasi-experimental research.