# Data AnalysisDescriptive Analysis The applications of descriptive analysis are used to summarizing data and describe it based on table and histogram

May 4, 2019 Critical Thinking

Data AnalysisDescriptive Analysis
The applications of descriptive analysis are used to summarizing data and describe it based on table and histogram. The mean and standard deviation will present in table form for futher elaboration.

Reliability Test
Table 1 summarizes the reliability test of the measures. As shown, the Cronbach Alphas of the measures were all comfortably above the lower limit of acceptability that is ? >0.60 (Sekaran et al., 2000). Hence, all the measures were highly reliable. . The tool to assess the reliability test is Cronbach’s Alpha. The result of the test denotes in the number among 0 and 1 thus the best result obtained would be as high as possible.

Table3.3 Range of Cronbach’s Alpha Coefficient
Strength of association Alpha coefficient range
Excellent >0.90
Good 0.80 – 0.89
Acceptable 0.70 – 0.79
Questionable 0.60 – 0.69
Poor 0.50 – 0.59
Unacceptable <0.50
Source: Matkar (2012)
Inferential analysis
Pearson correlation analysis
Based on Statstutor (2011), Pearson correlation coefficient is a statistical linear relationship measurement between two variables, to determine the strength of both variables. Hair, Bush and Ortinau (2006) indicated correlation coefficient interval will affect the level of association between both variables. In order to get higher in strength of correlation, the correlation coefficient must be closer to the 1. If the correlation coefficient is zero, it means that are not association between two variables. Besides, the relationship of both variables will base on either positive or negative of correlation coefficient. If the result showed is negative, mean the increase of DV will decrease of the IV. Table 3.4 is the range of correlation coefficient shown.

Table 3.4 Correlation Coefficient Range
Strength of correlation Correlation Coefficient
very strong ±0.81 to ±1.0
Strong ±0.61 to ±0.80
Moderate ±0.41 to ±0..60
Weak ±0.21 to ±0..40
None ±0.00 to ±0.20
Source: Hair, Bush and Ortinau (2006)
Multiple Regressions
According to Uyan?k and Güler (2013) urged that regression analysis help to explain the influence of IVs toward DV. Multiple regression analysis was applied in this paper because there are more than one variable to be analyse. Multiple regression analysis was able to knowing how the IVs such as subjective norm, perceived behavioural control, perceive moral obligation, environmental awareness, that will influence toward DV referred as consumer intention to stay.
Conclusion
As a whole, discussions stated from above justify about the style of the research and all approaches that used to conduct the research were mentioned. Next, chapter 4 will be the interpretation the data.