Because the study population is 85 the load factor 06 KMO coefficient KMO is an

Because the study population is 85 the load factor 06

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However, the standard value of the load factor should depend on the study size. Because the study population is 85, the load factor > 0.6 KMO coefficient KMO is an indicator used to consider the suitability of factor analysis. 0.5 ≤ KMO ≤ 1: sufficient condition for factor analysis is appropriate KMO <0.5: factor analysis not likely to be relevant to the research data Bartlett test 16
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Use to consider whether observed variables in a factor are correlated or not. The necessary condition for factor analysis is that the observed variables reflect the different aspects of the factor that must be correlated with each other. If the test shows no statistical significance, factor analysis should not be applied to the variables under consideration. Sig Bartlett’s test <0.05: observed variables are correlated with each other in the population. Percentage variance extracted Represents the percentage change of the observed variables: if the variable is 100%, this value shows the extracted factors that are concretized as percentage and lost % of the observed variables. Percentage of variance> 50%: EFA model is suitable Eigenvalue values Is the criterion for determining the number of factor analysis in EFA factor analysis. Eigenvalue≥1: Factor will be retained in the analysis model. Any unsatisfactory variables are excluded from the model. In factor analysis, we will use Principal Components Analysis method and Varimax rotation. The method of extracting Principal Components Analysis is only retained for elements with Eigenvalue value ≥ 1. The Variance rotation will remove observations with a factor load factor <0.6 and retain observations with a factor load factor> 0.6. Rotate 2 times and produce the result, the variables will be divided into ... factors. We will name the group for the elements. 3.3.4. Multivariate regression model Multivariate regression models are used to identify factors that affect employee loyalty, and to assess the impact of these factors on their loyalty to the business they are working on. The selected analysis method is stepwise method, this is the widely used method. Multivariate regression model extends the two variable regression model by adding some independent variables to better explain the dependent variable. The model has the following form: Y=β0+β1.X1+β2.X2+... +βD.XD+ ei Y: dependent variable βi.Xi: expression of the value of the i independent variable βi: partial regression coefficient 17
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ei: a random independent variable with a normal distribution has a standard distribution of 0 and the variance is constant Indicators to use: R2: correlation coefficient, showing the actual model Adjusted R2: is used to better reflect the suitability of the multivariate regression model because it does not depend on the magnification deviation of R. Criteria for accepting the appropriateness of the regression correlation model are: - Testing F must have a value of sig. < 0.05 - Standards of acceptance of signs with Tolerance value > 0.0001 - Quantitative diagnosis of multi-collinear phenomena with VIF (Variance Inflation Factor) <10 - Durbin Watson coefficients are used to verify the correlation of adjacent errors (also known
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