Data Analysis and Results Using Cronbach’s Alpha

Published: 2021-06-22 15:30:06
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CHAPTER 4 DATA ANALYSIS AND RESULTS 4.1 Introduction Chapter 4 will discuss the findings of the research that obtained from the questionnaire. SPSS Version 22 will be used to analyse the data and summarise all of the results that collected from respondents. The function of data analysis is to facilitate in testing hypotheses. There are 4 statistical tools will be carried out to analyse the results that are Cronbach’s Alpha, Descriptive statistic, Person Correlation Coefficient and Simple Linear Regression. Firstly, reliability statistics will be analysed by using Cronbach’s Alpha. Meanwhile, demographic information of respondents will be described by descriptive statistic. In addition, the relationship between independent variable and dependent variable will be analysed by using Pearson Correlation. Simple Linear Regression will be used to investigate the relationship between independent variable and dependent variable. 4.2 Cronbach’s Alpha Table 1: Reliability Statistics in Section B, C, D and E for Pilot Test Section B Web Security



Cronbach’s Alpha
N of Items


.819
5



Section C Perceived Risk



Cronbach’s Alpha
N of Items


.826
5



Section D Perceived Usefulness



Cronbach’s Alpha
N of Items


.870
5



Section E Online Purchase Intention of Cinema Movie Ticket



Cronbach’s Alpha
N of Items


.853
5



Table 1 showed the reliability statistic of all variables in this research. There are 25 respondents from SEGi College Subang Jaya will be used in the Cronbach’s Alpha in order to get the results of pilot test. As mentioned in chapter 3, the acceptable reliability value for Cronbach’s Alpha is 0.7 and above. Meanwhile, there are total 5 items in each of the variable. The reliability value of Section B is 0.819, Section C is 0.826, Section D is 0.870 and Section E is 0.853. However, the reliability value of all variables is above 0.7. Thus, the relationship between independent variable and dependent variable is reliable for further research. 4.2 Demographic Information of Respondents Table 2: Gender




Frequency
Percent
Valid Percent
Cumulative Percent


Valid
Male
58
58.0
58.0
58.0


Female
42
42.0
42.0
100.0


Total
100
100.0
100.0












According to Table 2, result showed the majority respondents of this research are male. However, male account 58% of the respondents. Meanwhile, the remaining 42% respondents are female.However, male is more willing to participate this survey. Table 3: Age




Frequency
Percent
Valid Percent
Cumulative Percent


Valid
Less than 18 years old
6
6.0
6.0
6.0


18 to 20 years old
40
40.0
40.0
46.0


21 to 23 years old
48
48.0
48.0
94.0


Above 23 years old
6
6.0
6.0
100.0


Total
100
100.0
100.0












As shown in Table 3, the majority age of respondents are 21 to 23 years old. However, 21 to 23 years old of respondents occupy 48%. Moreover, the age between 18 to 20 years old of respondents account 40%. Other than that, the percentage of respondents those less than 18 years old and above 23 years old are same. Both of these respondents also account 6%. Table 4: Ethnic Group




Frequency
Percent
Valid Percent
Cumulative Percent


Valid
Malay
29
29.0
29.0
29.0


Chinese
53
53.0
53.0
82.0


Indian
14
14.0
14.0
96.0


Others
4
4.0
4.0
100.0


Total
100
100.0
100.0












Table 4 showed 53% of ethnic group is Chinese in this research. Next, 29% of the respondents are Malay. In addition, Indian occupies 14% of the respondents in this research. Others ethnic group made up by small portion that is 4%. Table 5: Highest Education Level




Frequency
Percent
Valid Percent
Cumulative Percent


Valid
Certificate
5
5.0
5.0
5.0


Diploma
34
34.0
34.0
39.0


Degree
56
56.0
56.0
95.0


Master
3
3.0
3.0
98.0


PhD
2
2.0
2.0
100.0


Total
100
100.0
100.0












As refer to Table 5, the majority highest education level of respondents is Degree level and account 56%. On the other hand, Diploma level occupies 34%. Followed by is certificate that occupies 5%. 3% of respondents are Master level. Meanwhile, the PhD level of respondents occupy 2%. Table 6: Your monthly income




Frequency
Percent
Valid Percent
Cumulative Percent


Valid
Below RM1000
82
82.0
82.0
82.0


RM1001 to RM2000
12
12.0
12.0
94.0


RM2001 to RM3000
4
4.0
4.0
98.0


Above RM3000
2
2.0
2.0
100.0


Total
100
100.0
100.0












According to table 6, the majority respondents monthly income are below RM1000. However, these respondents account 84% of the total respondents.12% of the total respondents income level are RM1001 to RM 2000. On the other hand, there are 4% of the total respondents income level are RM2001 to RM3000.Only 2% of respondents income level are above RM3000. 4.3 Pearson Correlation Coefficient Table 7: Correlation between Web Security and Customer Online Purchase Intention of Cinema Movie Ticket




Total_IV1
Total_DV


Total_IV1
Pearson Correlation
1
.680**


Sig. (2-tailed)

.000


N
100
100


Total_DV
Pearson Correlation
.680**
1


Sig. (2-tailed)
.000



N
100
100


**. Correlation is significant at the 0.01 level (2-tailed).









Pearson correlation coefficient analysis is always used to measure the strength of the linear relationship between independent and dependent variable. Based on the rules of Pearson correlation coefficient, the larger coefficient identified the stronger relationship between independent and dependent variable.Table 7 showed the correlation of web security is significant at 0.01 level (2-tailed) with customer online purchase intention of cinema movie ticket. The correlation coefficient of web security is 0.680. Thus, web security has a positive significant relationship with customeronline purchase intention of cinema movie ticket. In addition, Hypotheses 1 (H1) is supported as result indicates there is a positive relationship between web security and customer online purchase intention of cinema movie ticket. Table 8: Correlation between Perceived Risk and Customer Online Purchase Intention of Cinema Movie Ticket




Total_IV2
Total_DV


Total_IV2
Pearson Correlation
1
.698**


Sig. (2-tailed)

.000


N
100
100


Total_DV
Pearson Correlation
.698**
1


Sig. (2-tailed)
.000



N
100
100


**. Correlation is significant at the 0.01 level (2-tailed).









According to table 8, the correlation of perceived risk is significant at 0.01 level (2-tailed) with customer online purchase intention of cinema movie ticket. The correlation coefficient of perceived risk is 0.698. Therefore, perceived risk has a positive significant relationship with customer online purchase intention of cinema movie ticket. Compared with web security and perceived usefulness, perceived risk is the strongest significant with customer online purchase intention of cinema movie ticket.Furthermore, Hypotheses 2 (H2) is supported as result indicates there is a positive relationship between perceived risk and customer online purchase intention of cinema movie ticket. Table 9: Correlation between Perceived Usefulness and Customer Online Purchase Intention of Cinema Movie Ticket




Total_IV3
Total_DV


Total_IV3
Pearson Correlation
1
.642**


Sig. (2-tailed)

.000


N
100
100


Total_DV
Pearson Correlation
.642**
1


Sig. (2-tailed)
.000



N
100
100


**. Correlation is significant at the 0.01 level (2-tailed).









As shown in Table 9, the correlation of perceived usefulness is significant at 0.01 level (2-tailed) with customer online purchase intention of cinema movie ticket. The correlation coefficient of perceived usefulness is 0.642. Hence, perceived risk has a positive significant relationship with customer online purchase intention of cinema movie ticket. Compared with web security and perceived risk, perceived usefulness is the weakest significant with customer online purchase intention of cinema movie ticket. Moreover, Hypotheses 3 (H3) is supported as result indicates there is a positive relationship between perceived usefulness and customer online purchase intention of cinema movie ticket. 4.4 Simple Linear Regression Table 10: Model Summary



Model
R
R Square
Adjusted R Square
Std. Error of the Estimate


1
.680a
.462
.456
2.44884


a. Predictors: (Constant), Total_IV1










Table 11: Coefficientsa



Model
Unstandardized Coefficients
Standardized Coefficients
t
Sig.


B
Std. Error
Beta


1
(Constant)
1.328
1.118

1.188
.238


Total_IV1
.829
.090
.680
9.171
.000


a. Dependent Variable: Total_DV












R Square is to measure the percentage of variance in customer online purchase intention of cinema movie ticket (Dependent Variable) can be explained by the predictor (Independent Variable). Standard Coefficient is to determine the relationship between independent variable and dependent variable is positive or negative. P-Value is to identify the significant of the relationship between independent variable and dependent variable. According to Table 10 and Table 11, the result of hypotheses testing is based on simple linear regression analysis. Web security is labeled as Total_IV1 and customer online purchase intention of cinema movie ticket is labeled as Total_DV. According to Table 10, the R Square value is 0.462. This mean that 46.2% of the variation in customer online purchase intention of cinema movie ticket can be explained by web security. As shown in Table 11, there is a positive relationship between web security and customer online purchase intention of cinema movie ticket when refer to Standard Coefficient (Beta=0.829). The P-Value is 0.00 that is less than 0.05. Therefore, the data is significant in statistically. Table 12: Model Summary



Model
R
R Square
Adjusted R Square
Std. Error of the Estimate


1
.698a
.487
.482
2.39112


a. Predictors: (Constant), Total_IV2










Table 13: Coefficientsa



Model
Unstandardized Coefficients
Standardized Coefficients
t
Sig.


B
Std. Error
Beta


1
(Constant)
2.739
.922

2.969
.004


Total_IV2
.743
.077
.698
9.644
.000


a. Dependent Variable: Total_DV












R Square is to measure the percentage of variance in customer online purchase intention of cinema movie ticket (Dependent Variable) can be explained by the predictor (Independent Variable). Standard Coefficient is to determine the relationship between independent variable and dependent variable is positive or negative. P-Value is to identify the significant of the relationship between independent variable and dependent variable.According to Table 12 and Table 13, the result of hypotheses testing is based on simple linear regression analysis. Perceived risk is labeled as Total_IV2 and customer online purchase intention of cinema movie ticket is labeled as Total_DV. Table 12 showed the R Square value is 0.487. This mean that 48.7% of the variation in customer online purchase intention of cinema movie ticket can be explained by perceived risk. As stated in Table 13, there is a positive relationship between perceived risk and customer online purchase intention of cinema movie ticket when refer to Standard Coefficient (Beta=0.743). The P-Value is 0.00 that is less than 0.05. Hence, the data is significant in statistically.Compared with web security and perceived usefulness, perceived risk is the most powerful factor that influencing customer online purchase intention of cinema movie ticket. Table 14: Model Summary



Model
R
R Square
Adjusted R Square
Std. Error of the Estimate


1
.642a
.412
.406
2.55970


a. Predictors: (Constant), Total_IV3










Table 15: Coefficientsa



Model
Unstandardized Coefficients
Standardized Coefficients
t
Sig.


B
Std. Error
Beta


1
(Constant)
3.848
.938

4.100
.000


Total_IV3
.666
.080
.642
8.287
.000


a. Dependent Variable: Total_DV












R Square is to measure the percentage of variance in customer online purchase intention of cinema movie ticket (Dependent Variable) can be explained by the predictor (Independent Variable). Standard Coefficient is to determine the relationship between independent variable and dependent variable is positive or negative. P-Value is to identify the significant of the relationship between independent variable and dependent variable. As refer to Table 14 and Table 15, the result of hypotheses testing is based on simple linear regression analysis. Perceived usefulness is labeled as Total_IV3 and customer online purchase intention of cinema movie ticket is labeled as Total_DV. Based on Table 14, the R Square value is 0.412. This mean that 41.2% of the variation in customer online purchase intention of cinema movie ticket can be explained by perceived usefulness. Table 15 showed there is a positive relationship between perceived usefulness and customer online purchase intention of cinema movie ticket when refer to Standard Coefficient (Beta=0.666). The P-Value is 0.00 that is less than 0.05. Thus, the data is significant in statistically.

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