Multivariate Analysis Technique
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Several analytical techniques will be used to test the hypothesis of whether knowledge hoarding in the organization has contributed to poor investment decisions in the team. Multivariate analysis is one of them. According to Easterby-Smith et al. (2012), the multivariate analysis has several independent variables. This analytical technique argues that some of the independent variables are more critical than the others used in the multivariate analysis. While investigating the problem of knowledge hoarding in the investment team, several independent variables will be used. One of these variables is the culture of the organization and its contribution to the accumulation of knowledge in the investment team. Another independent variable, which can be used in the analysis, is the organizational structure, which contributes to knowledge hoarding in the whole organization. The possibility of using simplification through design will be considered as a tool for analyzing the data collected. This technique concentrates on making the sample size of each population group equal in the process of data analysis. It is important to make the same sample size of each population group under the study in order to eliminate the chances of interdependence between the independent variables used in the multivariate analysis.
Measuring of the data causality collected from the research will also be considered. Causality seeks to establish whether there is a strong relationship between one or more independent variables and the dependent variable. This will help ensure that only the relevant data is used in the statistical findings. The research process will be strengthened by this fact. Measuring of the interaction on the data collected from the members of the investment team will be considered, as well. It depends on the fact that the main reasons of knowledge hoarding in the investment team are investigated. According to Easterby-Smith et al. (2012), the concept of interaction determines whether the relationship between the dependent variable and the independent variable in a certain research relies on a value of the other predictive variable.
Several limitations can appear as these analytical techniques are conducted on the collected data. Since the multiple regression technique to analyze the data collected from the members of the investment team with the problem of knowledge hoarding in the organization is used, the challenge of the strong correlation existed between the used independent variables can be observed. This will make the data gathered from the research be unreliable. Another possible limitation in the analysis on the data collected is that some of the interviewed employees may fail to give adequate information, which will be used in the research. However, some methods can be practiced in order to resolve any data that may be excluded from a particular study. One of the methods of dealing with such data is to omit all the cases under the study, which are missing information. The researcher may also consider the replacement of the average variable containing the missing values (Grandacolas, Rettie & Maruskenkso 2003).
The procedures carried out during the analytical techniques, which are used in answering the research question, will have several assumptions. It is assumed that all the residuals in the research will have a normal probability distribution pattern, which includes a mean of zero (Kader, Adams & Mouratidis 2010). In addition, it is expected that homoscedasticity of the data collected will be hold, when the variance of the dependent variable is equal to different values of the independent variable. Violation of the homoscedasticity principle does not lead to any bias on the coefficient of regression.