To investigate causal linkages, experiments are utilized. While conducting experiments, one or more independent variables are changed, and their influence over one or more dependent variables is observed.

The phrase “experimental design” refers to developing a set of tools for systematically evaluating a hypothesis. A solid understanding of the system under research is required for a good experimental design.

You must follow these five steps to creating an effective experimental design:

1. First, consider the relationships between your variables.
2. Second, make a concrete, testable hypothesis.
3. Third, create experimental treatments that will influence your independent variable.
4. Fourth, distribute subjects among “between-subjects” or “within-subjects” groupings.
5. Finally, make a plan for measuring your dependent variable.

You must also pick a representative sample and regulate any confounding variables that may impact your outcomes to draw accurate conclusions.

1. Defining variables Begin by formulating a precise research question. Then, we’ll look at two different research questions, one from public health and the other from ecology:

Question 1: What is the relationship between smartphone use and sleep patterns?
You’re curious to study the effect of smartphone use before bed on sleeping patterns. So you explicitly inquire as to how many minutes a person spends on their smartphone before going to bed and how it influences their sleep quality.

Questions 2: Impact of temperature on soil respiration
You’re curious to know more about the effect of temperature on soil respiration. You specifically inquire about the effect of higher ambient temperature near the soil surface on the concentration of co2 respired by the earth.

You must specify the essential variables and make inferences about how they are connected to turn your research objective into an experimental hypothesis.

Begin by writing down all of the independent and dependent variables.

Now you must evaluate any potential extraneous or confounding factors and minimize these variables in your experiment.

Finally, you may create a flowchart using these variables. Again, incorporate signs to reflect the predicted direction of the connections and use pointers to show the possible links between variables.

2. Write your hypothesis
You should be able to construct a particular, testable hypothesis that addresses your research question provided that you have a sound theoretical knowledge of the system you’re examining.

The following steps will show you how to create a controlled experimental design. But, first, you must be able to do the following in a controlled study:

• ✓ First, manipulate the independent variables systematically and accurately.
• ✓ Then, measure the dependent variables with precision.
• ✓ Any potential confounding variables should be minimized

If your experimental design does not meet these requirements, you can utilize alternative types of research to address your research question.

3. Designing experimental treatments
The experiment’s external validity – that is, the amount to which the results may be extended and applied to the larger world – is influenced by how you manipulate your independent variable.

You need first to determine how much you want to modify your independent variable.

Experiment with soil warming
You can opt to raise the temperature of the air:

• just a speck above the normal range for your research area
• to a broader range of temperatures to simulate future warming
• spanning an extensive range, well beyond any natural fluctuation

Next, you may need to decide how finely your independent variable should be varied. Again, your experimental system may make this decision for you, but it is up to you, and how much you can deduct from your data will be affected by it.

Experiment with cell phones
You have the option of treating phone use as follows:

• A categorized variable: either binary; yes/no or factor levels; no, low and high smartphone use.
• A Continuous variable: Minutes of phone use are monitored each night as a continuous variable.

4. Assigning subjects to treatment groups
For accurate and trustworthy findings, you must apply your experimental treatments to your test participants in a certain way.

To begin, think about the magnitude of the study: how many people will be involved in the experiment? Generally, the more individuals you enrol, the better the statistical power of your experiment, which influences how confident you may be with your findings.

Then you must allocate your subjects to treatment groups at random. Each group receives treatments at a different intensity.

Include a control group that does not get any therapy. The control group describes what would have resulted if your test participants had not been subjected to the experiment.

When it comes to dividing your subjects into groups, you have two options:

1. Randomized block design vs Completely randomized design
2. Within-subjects design vs Between subjects’ design

Randomization
A study can be entirely randomized or randomized within blocks (also known as strata):

Every subject is allocated to a treatment group randomly in a completely randomized design.

A randomized block design which is also known as a stratified random design groups subjects based on a shared trait before randomly assigning them to treatments inside those groups.

Researchers construct partially random or even non-random experimental designs when randomization isn’t realistic or moral. A quasi-experimental design is an experimental design in which treatments are not allocated at random.

The difference between the “Between-subjects” and “within-subjects” approach
Individuals receive just one of the possible levels of an experimental treatment in a between-subjects design. It is also known as an independent measures design.

You may also use matched pairs in your between-subjects design in medical or social studies to ensure that each treatment group has the same number of test subjects in the same percentages.

Every subject gets each of the experimental treatments one after the other in a within-subjects design, and their responses to each treatment are assessed.

Within-subjects or repeated measurements relate to an experimental design in which an impact occurs over time, and individual responses are assessed to track this influence.

Within-subject experimental designs frequently involve counterbalancing (randomizing or reversing the sequence of treatments across subjects) to guarantee that the sequence of treatment administration does not impact the experiment’s outcomes.

5. Measuring your dependent variable
The final step of your experimental design is selecting how you will gather data on the results of your dependent variable. Again, you must strive for consistent and precise measurements with slight bias or inaccuracy.

Variables like temperature, for example, maybe measured reliably using scientific tools. However, others would require operationalization to become quantifiable data.

Smartphone use and sleep patterns example
You might assess your dependent variable in your experiment about smartphone use and sleep in one of two methods:

• Participants should be asked to wear a sleep monitor.• Instruct participants to keep track of when they go to bed and wake up each day.
• Participants should be asked to wear a sleep monitor.

The types of statistical analysis you might perform on your data are also influenced by how accurately you measure your dependant variable.

Experiments are always dependent on the context, and an effective experimental design will consider all of your study system’s specific factors to create appropriate and reliable data to your research question.

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