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Experimental design is a scientific approach to data collection and measurement. The concept focuses on creating an intervention in a controlled environment with the purpose of establishing a relationship between the independent and dependent variable.
The process of the experimental design tests out a hypothesis using observational and statistical measurement skills to provide data that backs up the validity of the hypothesis. The researcher controls an independent variables to study the impact that it has on the dependent variables.
Experimental design starts by establishing a research purpose, state the hypothesis to be tested, identify target population, choose suitable methodology, define conditions under which the experiment intervention would take place, determine method of data recording and tools to be used for final analysis.
A thorough experimental research assists the derivation of intelligent conclusions that is used to prove or disprove the hypothesis. Steps are also to be taken to eliminate any bias or error that may skew or distort the final analysis , thus, leading to reduced actionability.
This principle adopts a random approach to assignment of treatments to experimental groups of people. It is considered one the most reliable mechanisms mainly because of its ability of creating an equal probability for every experimental group to receive any of the available treatments. This eliminates any bias or variation, thus making the experimental research more authentic.
For example: If a study involves 8 experimental groups that are to be assigned one out of four treatments: A, B, C and D; each to see its particular effect on the group as well as compare these effects with those of other experimental groups that have been assigned different treatments. The researcher uses a random number generators such as random number tables or drawing random number cards.
The necessity to use randomisation is basically used to make the study more reliable through elimination of any external variables in assignment. The artificial assignment of treatments can skew results in the favour of certain groups. The treatments must be allocated without any pre-requisite knowledge of the effects that the treatment will have on the group.
Replication emphasizes the repeated application of the basic experiment to multiple experimental units. This means that a basic experiment gets applied multiple times throughout the experimental research. The repeated application helps in measuring variability among different groups and pinpoints the reasons behind such a variation.
The underlined idea behind multiple application is to improve accuracy through wide measurement and observations.
For example: the school sports grading system using replication by asking all students to repeat same sport related activities to measure how good they are. One simple sport being high jump in which every student is asked to repeat the same activity (in this case, the treatment) to measure how high can each student jump. Measurements of each student’s maximum height of jump is recorded. Such numerical observations can be used to derive statistical inferences.
This principle is highly applicable because of certain benefits namely:
– reduction in experimental error: conducting experimental research using large samples helps in incorporating maximum observations leading to a reduction in margin of error.
– accurate measurement of mean effect of treatment:
Standard error of y = Sample standard deviation / ✓n
where n is the number of replications .
Local control focuses on increasing experimental precision through the exercise of control over extraneous variables. This is a refined technique that aims at reducing external influence to a much higher level than replication or randomisation. Local control restricts the study to variables which are relevant and imperative for the research purpose. The influence of other variables, that may not be a part of research characteristics, but have an affect on the treatment itself gets minimised.
Local control further consists of two techniques:
1) Balancing: Assignment of treatments to experimental groups in a way that the treatments are allocated in a balanced manner.
2) Blocking: The grouping of similar experimental units together to create homogenous groups for treatment purposes.
For example: a study to determine the ease of navigation and design interface of a website for customers of an electronics brand can be done by grouping customers within similar age groups together to create homogenous lots for nuanced experimental research. This facilitates concentrated observation of homogenous groups, formed using segmentation to create groups with similar characteristics.
Local control is used for
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There are 3 main principles of experimental design:
Although there are many principles to be considered while aiming to conduct an unbiased comprehensive research design, the 4 main principles that are highly crucial to the research design include: Replication, Validity, Reliability and Flexibility with timeliness.
Step 1: Identify research purpose and formulate hypothesis to be tested
Step2: Design experimental treatments
Step3: Identify target population and assign treatments to experimental groups
Step4: Conduct final analysis and derive conclusions
The elements involved in research design include: Research Objective, Sampling, Selection and assignment, Suitable methodology, Data collection and recording, Analytical treatment, Conclusions and Reporting.
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