10 Experimental Control Examples

experimental control examples, explained below

Experimental control refers to the practice of isolating the effects of a single variable in an experiment to ensure that this variable is the only factor affecting the results.

Generally, it involves identifying all possible confounding variables and controlling for them so you can truly see whether the independent variable is causing the expected effect on the dependent variable.

This methodology allows the researcher to draw definitive conclusions about cause-and-effect relationships.

This approach is considered the gold standard for determining cause-and-effect in scientific research.

Experimental Control Examples

The following are some methods for controlling variables in experimental design:

1. Random Assignment

In experimental research, random assignment is an often-employed method for controlling variables. Its purpose is to minimize bias and ensure that participant characteristics are evenly distributed across all groups.

The method involves assigning participants to groups randomly rather than based on specific characteristics. This ensures that the resulting observations are due to the intervention or test variable, not to differences between groups.

Random assignment therefore helps in controlling for confounding variables and establishing causal relationships.

Random Assignment Example: Suppose you’re investigating a new math teaching method’s effectiveness. You’d start off by collecting 100 volunteer students. Every student gets a number, 1 to 100, and a computer algorithm randomly assigns each student to either Group A or B. Group A is taught math using the new method, while Group B continues with the traditional teaching style. After a period of testing, you’d gauge the difference between Group A and B’s math scores to assess the new method’s effectiveness.

2. Blinding

Blinding is another commonly used technique in experimental research for the control of variables. It serves to eliminate bias, specifically observer and subject bias.

In a blinded study, one or more parties involved do not have information about the interventions received by each participant.

There are various degrees of blinding, from single-blind, where the participant doesn’t know their group assignment, to double-blind, where both the participant and the investigator are oblivious.

Blinding Example: Let’s envision a scenario where a researcher is studying the impact of a new exercise routine on weight loss. In this case, the participants wouldn’t know if they were part of the control group doing routine exercises or the experimental group trying the new routine—this is a single-blind experiment. If we advance by one step to a double-blind experiment, not only would the participants be in the dark, but the investigators assessing the results wouldn’t know which group each participant belongs to either. Thus, blinding keeps conscious and subconscious biases at bay and ensures the reliability of the results.

3. Negative Control Groups

Utilization of negative control groups is a fundamental method for controlling variables in experimental research. Control groups enable researchers to isolate the effects of the variable they are studying.

A negative control group consists of participants who do not receive the treatment or intervention being studied. They provide a baseline against which to measure the effects of the treatment on the experimental group.

Implementing a control group helps maintain the integrity and validity of the results by eliminating the chance that outside factors are causing the observed effects.

Negative Control Group Example: Let’s investigate a new dietary supplement’s effectiveness on improving memory. You would divide your participants into an experimental group and a control group. The experimental group would take the supplement regularly, but the control group would be given a placebo – a capsule identical in appearance but without the active ingredient. After the trial period, you’d compare data on memory capabilities between the two groups. If the experimental group showed substantial improvements compared to the control group, then it’s possible to conclude that the supplement positively affects memory.

4. Positive Control Groups

Positive control groups serve as another crucial method of controlling variables in experimental research. These groups help validate the outcome of an experiment by ensuring that it produces a known result.

A positive control group is given a treatment that is already known to produce the expected effect. This helps researchers ensure the experiment is working as designed and demonstrates the capability of the experimental setup to generate positive results.

In essence, positive control groups contribute to the reliability of experimental results by confirming that the research protocol is effective.

Positive Control Group Example: For instance, in a study designed to test a new antibiotic’s effectiveness, you might create a positive control group that receives a current, widely-accepted antibiotic known to effectively treat the condition. The experimental group gets the new antibiotic. If both groups show improved health, you have evidence that your experimental setup (i.e., ability to gauge improvement) works and that the new antibiotic could be effective. If the positive control group doesn’t show improvement, it may indicate issues with the process or the antibiotic used for the positive control.

chrisFurther Readings:For more on negative and positive controls, and a list of examples, see my article: negative vs positive controls in research.

5. Matching

Matching is a statistically sound method often used to control variables in experimental research. This approach aims to create comparable groups in terms of specific characteristics that may influence the study’s outcome.

In a matched study, each participant in the experimental group has a direct counterpart in the control group with similar demographics or other traits. These characteristics could include age, gender, ethnicity, body mass index, disease severity, and more.

Matching helps ensure that any differences between the experimental and control groups are due to the independent variable under study, not other confounding variables.

Matching Example: Let’s assume you’re researching the effects of a new rehabilitation program for stroke patients. Matching might be used to ensure each participant in the group receiving the new rehabilitation is paired with a participant in the regular care group of the same age, gender, and severity of stroke. By doing so, you ensure that the differences observed are likely due to the rehabilitation program and not other unrelated factors.

6. Counterbalancing

Counterbalancing is often applied as a method for controlling variables in experimental research, significantly in studies involving repeated measures or sequences. It is used to manage the potential impact of order effects such as learning, habituation, or fatigue.

In counterbalancing, researchers alternate the order in which participants experience conditions or treatments. The aim is to balance the potential impact of the condition order across participants, neutralizing confounding influences tied to the sequence.

By using this method, researchers can ensure that the observed results are not biased due to the order of presentation.

Counterbalancing Example: Suppose you’re investigating the effect of different types of background noise (music, silence, or white noise) on concentration levels. All participants are tested under these conditions, but the order in which they experience them differs. Some participants might first work with music playing, then in silence, and finally with white noise. Others could start with silence, then white noise, and finally music. By varying and balancing these conditions, you ensure that you aren’t just measuring whether the ability to concentrate changes simply because of the order in which the variables are experienced.

7. Parallel Groups Design

Parallel groups design is all about conducting studies in the exact same time period to control for changes that might take place across different time periods.

This approach is widely used in clinical trials and offers an effective means of reducing potential biases.

In this setup, participants are assigned to different groups, and each group receives a different treatment or intervention simultaneously. All groups are measured during the same time period, reducing the effects of time-dependent covariates.

This method helps ensure that observed variations are due to the different treatments rather than other external factors.

Parallel Groups Design Example: Consider a trial in which you’re testing the effectiveness of two different vaccines against a disease. You have two groups of participants that are fairly similar in terms of age, health, etc. Group A receives Vaccine 1, and Group B receives Vaccine 2. This testing happens parallelly. After a defined period, you measure the health status of participants in both groups to determine which vaccine was more effective. Since both groups were tested during the same time period, any differences can largely be attributed to the difference in vaccines, not an evolution in time or other outside factors.

8. Factorial Design

In a factorial design, all combinations of the various levels of the independent variables investigated are tested. This allows us to understand not just the main effects of our variables, but also any potential interaction effects between them.

Imagine you want find out how the amount of sunlight and water given to a plant affects how fast it grows. here, you have two variables (sunlight and water) that will interact in a variety of ways, so you’d want to study each interaction:

  1. A lot of sunlight and a lot of water
  2. A lot of sunlight but a little water
  3. A little sunlight but a lot of water
  4. A little sunlight and a little water

This is a factorial design – it allows you to see how different combinations of sunlight and water affect how fast sunflowers grow.

Implementing this method helps in increasing the efficiency and scope of experiments without requiring significantly more subjects or resources.

Factorial Design Example: Suppose you’re studying the effects of diet and exercise on weight loss. You have two levels of diet (low calorie, normal calorie), and two levels of exercise (regular exercise, no exercise). So, in a 2×2 factorial design, you have four conditions: low calorie diet with regular exercise, low calorie diet with no exercise, normal calorie diet with regular exercise, and normal calorie diet with no exercise. Participants would be randomly assigned to one of these four conditions, allowing you to examine the separate effects of diet and exercise and their combined effect on weight loss.

9. Crossover Design

In crossover design, each participant acts as their control by receiving both the treatment and the placebo at different times.

The crossover design has the advantage of eliminating the potential variance between individuals because the same subjects are subjected to both the treatment and the control condition.

This method requires fewer subjects, increases statistical power, and is beneficial when the effects of the treatment are temporary and when no carry-over effects take place.

However, it’s crucial to include a ‘wash-out’ period between treatments to reduce potential carry-over effects.

Crossover Design Example: In a study testing the effect of a new painkiller on headache relief, participants would be randomly allocated to receive either the painkiller or a placebo first. After a predefined time period, there would be a wash-out phase during which no medications are administered. Following the wash-out phase, those initially given the painkiller would get a placebo, and those first given the placebo would receive the painkiller. In the end, researchers compare the data from when each group received the painkiller and placebos, not confounded by individual variance as each participant received both treatment and control conditions.

10. Stratified Random Sampling

Stratified random sampling entails dividing the population into subgroups or strata based on specific characteristics, and then randomly selecting participants from each subgroup.

This method ensures that the sample accurately represents the overall population. It is beneficial when researchers believe certain characteristics (like age, sex, or ethnicity) in the population group could influence the outcome of the study.

Stratification results in increased statistical precision and ensures that all segments of a population are represented, helping to achieve variable control.

Stratified Random Sampling Example: For instance, if you’re carrying out a health survey, you could divide the population into age categories (under 20, 20-29, 30-39, and so on). From each age group you can then randomly select participants. This ensures that your sample encompasses all age groups and when you share your research, you can share credible conclusions about specific age categories as well as the wider population.

Conclusion

Experimental research methodologies necessitate rigorous controls to ensure valid and reliable results. Techniques like random assignment, blinding, and usage of control groups manage confounding variables, enhancing the integrity of the findings. Researchers have developed a range of methods for increasing control over the experiment and better measuring cause and effect. But it’s important to be judicious in knowing which experimental controls to use in which circumstance, based largely on the study design and research question.

Chris
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Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]

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