Using One – Way Analysis of Variance (ANOVA) Statistical techniques can be very useful in identifying and measuring the relationships shared by variables in an objective and an unbiased manner. Different statistics help us measure a verity of phenomena – for example – Correlation helps us measure the direction and intensity o the relationship shared by two or more variables; while the t-test helps to measure the generalisablity of a difference between two groups…
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The One – Way ANOVA is thus named, since it compares the variance of the different groups as a function of one Independent Variable. The ANOVA can only be conducted if certain conditions have been met – the data collected is either on an equal interval or ratio scale; the cases are independent of each other, the data comes from a normally distributed population, and that the population variance for the groups is equal. An example of a situation in which it would be possible to apply the One – Way ANOVA static would be one in which the efficacy of different training methods was to be measured. Suppose that a group of novices was to be taught a particular trade, and there were a number of ways in which the novices could be trained. It is possible to study the results of different kinds of training on group’s o novices and use the One – Way ANOVA to identify the most effective training method. ...
Thus the levels of the independent variable are categorical; and exclusive. For this example, it is possible to examine four types of training methods – Classroom teaching; On-the-job training; Guided Practice, and Simulation Training. Thus, the Independent variable – Type of Training – now has four levels - Classroom teaching; On-the-job training; Guided Practice, and Simulation Training. The effect of these techniques is tested on the way in which the individual completes a day of independent activity at the end of the training period. Thus, it is possible to say that at the end of the training period, the novices are tested to evaluate their level of learning. On the basis of this understanding it is possible to say that the Dependent Variable in this example is the Learning Exhibited by the novice, as measured by a test of skill. If this experiment were to be conducted; it would require the testing of a hypothesis. The null hypothesis for a One – Way ANOVA is – “There is no difference between the groups on learning that may be associated with the type of training”. Assuming that the study expected to find a difference in the learning exhibited; the alternative hypothesis “There will be a significant difference in the learning exhibited by novices as a function of the training method” may be put forth. In the event that there is a significant difference between the four groups – that is – the ANOVA static is significant at the chosen level; it becomes necessary to conduct a Post – Hoc test like the Tukey’s HSD. This is because, a significant ANOVA result indicates that there is a significant difference between the groups; but it does not indicate which groups differ significantly from each other. In order to ascertain this; i9t is
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