Several things can be done with the raw data to see what it can say about the hypotheses (Neuman, 2003). Inspection of the raw data can be performed using descriptive statistics to detect obvious coding errors. The minimum and maximum values ​​of each variable must be within the allowable range. Pairwise correlations show that all relationships should be in the expected direction. Meanwhile, listwise removal of missing values ​​indicates that the data can be used for analysis. An outlier is an unusually small or large observation. Outliers help researchers detect coding errors. According to Bagozzi and Baumgartner (1994), it is not recommended to systematically exclude outliers from further analysis. The collected data was analyzed using three approaches:1. Cronbach's alpha (a) was used to test reliability. Cronbach's alpha indicates the extent to which the elements of a set are positively correlated with each other. This is to ensure that the scales are free from random or unstable errors and produce consistent results over time (Cooper & Schindler, 1998);2. Descriptive statistics where the researcher used the mean, standard deviation and variance to get an idea of ​​how the respondents responded to the questionnaire items. The main concern of descriptive statistics is to present information in a practical, usable and understandable form (Runyon and Audry, 1980). A descriptive summary, including frequency and descriptiveness, was used to filter the dataset. Some of the basic statistics to use were mean, median, mode, sum, variance, range, minimum, maximum, skew, and kurtosis.3. Inferential statistics consists of generalizing from a sample to make estimates and inferences about a larger population (Neuman, 2003...... middle of article....... i.e. -say more than 30 (Hair et al., 2006) Sekaran (2003) suggests that approximation of normality of observed variables could be investigated by inspecting the data using histograms, stem and leaf displays, of probit plots and by calculating univariate and multivariate measures of skewness and kurtosis leaf and probit plots indicate the symmetrical distribution of variables or sets of variables. skewness and kurtosis is equal to zero if the distribution of a variable is normal and Chou and Bentler (1995) emphasize the absolute values ​​of univariate asymmetry indices greater than 3 can be. described as extremely skewed At the same time, a kurtosis threshold value above 10 can be considered problematic and a value above 20 can be considered serious problems (Hoyle, 1995; Kline, 1998).