Finally the CFI is a no centrality based index which tests the viability of the

Finally the cfi is a no centrality based index which

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Valavanis & Vachtsevanos, 2015). Finally, the CFI is a no centrality-based index, which tests the viability of the alternative hypothesis ( Półk & Kuziora, 2017). While the centrality based indices above test for rejecting the null hypothesis by comparing the results with a null chi-square, the CFI attempts to reject the alternative by looking at the non-central distribution ( Valavanis &
PROJECT PROPOSAL 10 Vachtsevanos, 2015). This distribution is created under the assumption that the alternative is true for the population. A CFI score of greater than .9 is the criterion for accepting the model as a good fit ( Dodge, 2015). The intent of the research is to verify if the previously identified knowledge management factors and areas are evident through structural equation modeling for the organization being evaluated ( Półk & Kuziora, 2017). The null hypotheses will be rejected if two of the three indices described earlier meet the recommended thresholds for a good model fit ( Półk & Kuziora, 2017). An example would be, if the four lessons learned factors had NNFI and CFI scores of greater than .9, then the conclusion would be to reject H1 0 regardless of the SRMR score ( Półk & Kuziora, 2017). If two of the three indices do not meet the criteria for accepting the model as a good fit, then there would be insufficient evidence to reject the null hypotheses. Other types of quantitative testing, such as the t test or correlation tests, do not fully examine the existence of factors and areas the way that structural equation modeling does ( Valavanis & Vachtsevanos, 2015). Using the indices listed above provides a spectrum of testing parameters that will allow this research to conclude the existence or lack of KMC factors and areas in the aerospace institute being studied ( Półk & Kuziora, 2017). The modeling parameters, coding scheme, and indices used to test the hypotheses are the most appropriate for this type of study ( Półk & Kuziora, 2017). The first main assumption of this study is the fact that population will be representing aerospace field of using the UAVs. It is rational to assume that the population of the people affected with calamities is 120. This assumption explain the individual that understand how drones are used for humanitarian aid ( Valavanis & Vachtsevanos, 2015). The other assumption that will be involved is that the data collected on the online publication sources is
PROJECT PROPOSAL 11 accurate and true ( Valavanis & Vachtsevanos, 2015). The limitation of this study is that there is no involvement of physical research and hence it is difficult to understand if the data provided from the articles are correct. The other assumption made is that the sample population that are aware of the Aerial systems truly understand the systems and not just making assumption of what they do not know in writing. ( Valavanis & Vachtsevanos, 2015).

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