How can You Establish Experimental Design in Your Dissertation?

When you deal with experiments, you investigate the causal relationship between variables. What you fundamentally do is manipulate one or more than one independent variable (x) to determine their effect on dependent variables (y).


How can You Establish Experimental Design in Your Dissertation?

Introduction to experimental design

When you deal with experiments, you investigate the causal relationship between variables. What you fundamentally do is manipulate one or more than one independent variable (x) to determine their effect on dependent variables (y).

Suppose you wish to establish a sound experimental design. In that case, you must understand the system you want to study. Therefore, the bottom line is that you develop several procedures to test a hypothesis in the experimental design. While designing an experiment, the following five issues are relevant?

  1. Reflect on your variables and explain the association between them

  2. Pen a down-to-earth hypothesis that can be tested

  3. Design experimental treatments in such a way that you can modify your independent variables

  4. Conduct a treatment assignment for your units or subjects. This step determines whether you have between-subjects or within-subject designs

  5. Have a clear plan and methodology for measuring your dependent variables

If you want to have valid conclusions, your sample should be representative; that is, it should represent the population and curb any extraneous variables that might affect your results. Suppose you cannot randomly assign participants to control and treatment groups; then, you must ponder on an observational study.

Stage 1: Describing your variables

An essential issue is to start with a particular research question. You can translate your research question into an experimental hypothesis provided that you specify the primary variables and predict how they are associated. Listing independent and dependent variables will substantially help. Then the next step involves considering possible extraneous and confounding variables and how you can curb them in your experiment. Ultimately, you can produce a diagram with all variables. Depicting the potential relationships between variables and including signs to indicate the expected direction of the connections are valuable and required.

Stage 2: Pen your hypothesis

After a solid conceptual understanding of the system you are exploring, you can pen a particular, testable hypothesis. This hypothesis addresses the research question you wish to investigate. Assume you explore whether using an increased amount of fiber improves the yogurt texture. In that case, the null hypothesis (H0) suggests that no treatment effect exists. That is to say, the fiber level does not significantly affect yogurt texture. Nonetheless, the alternative hypothesis (Ha) poses that the treatment effect is present. In other words, higher fiber levels imply improved yogurt texture.

Designing a controlled experiment covers the following steps. When you discuss a controlled experiment, you must ensure that the following issues are considered:

i. You have successfully manipulated independent variable(s) to have a systematic design. 

ii. You have measured the dependent variable(s) precisely.

ii. You have curbed potential confounding variables.

Suppose that your experimental system does not match the above, you can resort to other research types to address your research question.

Stage 3: Engineering your experimental treatments

Handling the independent variable affects the experiment’s external validity. It suggests you generalize your results and imply them to the broader domain. However, it would help if you decided what would be the limit of the independent variable. Assume you have started adding fibers to the yogurt; you will certainly have a limit value beyond which your product may be rendered inedible. Thus, a limit for your independent variables may exist, and you must consider it accordingly.

Stage 4: Allocating treatments to your units or subjects

Having reliable and valid results primarily depends on how you apply your treatments to your test units or subjects. The first thing you need to heed is the size of your study. Begin by asking the question to yourself; how many units or subjects do I need to have a statistically sound experimental design? Overall, increasing the unit or subject size boosts the experiment’s statistical power suggesting that you can rely on your results. What follows is randomly assigning treatments to units or subjects. It implies that each unit or subject receives a level of the dependent variable. Your design must also have a control treatment. Accordingly, you can tell what would have occurred if the units or subjects had not been subjected to treatment. While assigning your units to treatments, you may employ a completely randomized design ora randomized block design. Moreover, a between-subject or within-subject design may also be your design choice in some other circumstances.

Stage 5: Metering your dependent variable

Ultimately, how you gather data substantially affects your dependent variable outcomes. To minimize research bias, you must employ approved and valid measurements. Suppose you have variables such as humidity or pressure. In that case, you can precisely measure these variables. However, you may wish to detect the depression level of people. Should it be the case, you may need to employ a developed scale like the Beck Depression Inventory.

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Experimental Design in Your Dissertation

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