How experimental design is different from non experimenter design?
Answers
Answer:
Non-experimental research does not mean nonscientific. Non-experimental research means there is a predictor variable or group of subjects that cannot be manipulated by the experimenter. ... Experimental design, on the other hand, allows for researchers to manipulate the predictor variable and subjects.
Hope it helps you mark as brainliest please
Answer:
An experiment deliberately imposes a treatment on a group of objects or subjects in the interest of observing the response. This differs from an observational study, which involves collecting and analyzing data without changing existing conditions. Because the validity of a experiment is directly affected by its construction and execution, attention to experimental design is extremely important.
Treatment
In experiments, a treatment is something that researchers administer to experimental units. For example, a corn field is divided into four, each part is 'treated' with a different fertiliser to see which produces the most corn; a teacher practices different teaching methods on different groups in her class to see which yields the best results; a doctor treats a patient with a skin condition with different creams to see which is most effective. Treatments are administered to experimental units by 'level', where level implies amount or magnitude. For example, if the experimental units were given 5mg, 10mg, 15mg of a medication, those amounts would be three levels of the treatment.
(Definition taken from Valerie J. Easton and John H. McColl's Statistics Glossary v1.1)
Factor
A factor of an experiment is a controlled independent variable; a variable whose levels are set by the experimenter.
A factor is a general type or category of treatments. Different treatments constitute different levels of a factor. For example, three different groups of runners are subjected to different training methods. The runners are the experimental units, the training methods, the treatments, where the three types of training methods constitute three levels of the factor 'type of training'.
(Definition taken from Valerie J. Easton and John H. McColl's Statistics Glossary v1.1)
, then the effect of the treatment cannot be accurately assessed. For this reason, double-blind experiments are generally preferable. In this case, neither the experimenters nor the subjects are aware of the subjects' group status. This eliminates the possibility that the experimenters will treat the placebo group differently from the treatment group, further reducing experimental bias.
Randomization
Because it is generally extremely difficult for experimenters to eliminate bias using only their expert judgment, the use of randomization in experiments is common practice. In a randomized experimental design, objects or individuals are randomly assigned (by chance) to an experimental group. Using randomization is the most reliable method of creating homogeneous treatment groups, without involving any potential biases or judgments. There are several variations of randomized experimental designs, two of which are briefly discussed below.
Completely Randomized Design
In a completely randomized design, objects or subjects are assigned to groups completely at random. One standard method for assigning subjects to treatment groups is to label each subject, then use a table of random numbers to select from the labelled subjects. This may also be accomplished using a computer. In MINITAB, the "SAMPLE" command will select a random sample of a specified size from a list of objects or numbers.
Randomized Block Design
If an experimenter is aware of specific differences among groups of subjects or objects within an experimental group, he or she may prefer a randomized block design to a completely randomized design. In a block design, experimental subjects are first divided into