Essay On Noise Reduction

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Signal Enhancing and Noise Reducing Experimental Design to Increase Quality Design and analysis of a research study are necessary to an answer to the research problem and can be a simple two group design or multiple variables and variable level design to collect the necessary data to derive a supporting or opposing hypothesis (Jackson, 2011). An experiment and its design should 1) reduce or eliminate influencing factors, 2) randomly assign the treatments and 3) use multi-level design to increase the quality of the outcome of the research study (Wiley, 2009).
This paper will focus on the factorial and covariance or blocking designs used in complex research studies to improve the quality of the research and its outcome
Improving quality
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An example of a factorial design would be an educational study to determine if a particular setting delivers better test scores. The design would take on a 2x2 structure where the two factors to be considered are 1) setting where the education takes place and 2) the length of time the education takes place. The levels of the factors would be 1) setting is in a classroom and 2) setting is online and time of education would be 1) four hours a week and 2) eight hours a week. The 2x2 structure then allows for the variables to be measured against one another for outcomes. The study would consist of four groups; 1) in a classroom for 4 hours a week, 2) in a classroom for 8 hours a week, 3) online for 4 hours a week and 4) online for 8 hours a week. The test scores collected for each of the four groups measure for main and interaction effects. Main effects measure at the level of the variable, and the interaction effects measure at the factor (Jackson, 2011; Trochim, Donnelly & Arora, 2016). In other words, the main effect would be graphically represented with the mean results of the four groups visually showing the impact of the results in each of the groups (Jackson, 2011; Trochim, Donnelly & Arora, 2016). The interaction effect measures the impact of the time and setting at the factor level (Jackson, 2011; Trochim, Donnelly & Arora, 2016).
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