Data Analysis and Results Using Cronbachs Alpha - Free Essay Example

Sample details Pages: 6 Words: 1667 Downloads: 3 Date added: 2017/06/26 Category IT Essay Type Analytical essay Did you like this example? 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. Don’t waste time! Our writers will create an original "Data Analysis and Results Using Cronbachs Alpha" essay for you Create order There are 4 statistical tools will be carried out to analyse the results that are Cronbachs Alpha, Descriptive statistic, Person Correlation Coefficient and Simple Linear Regression. Firstly, reliability statistics will be analysed by using Cronbachs 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 Cronbachs Alpha N of Items .819 5 Section C Perceived Risk Cronbachs Alpha N of Items .826 5 Section D Perceived Usefulness Cronbachs Alpha N of Items .870 5 Section E Online Purchase Intention of Cinema Movie Ticket Cronbachs 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 Cronbachs Alpha in order to get the results of pilot test. As mentioned in chapter 3, the acceptable reliability value for Cronbachs 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 ticke t 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 cinem a 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.

Financial Analysis of Disney - 3827 Words

I. â€Å"I only hope we never lose sight of one thing- It was all started by a mouse,† Walt Disney. The Walt Disney Company has grown dramatically since its start in 1923. Its financial statements show that the company is in a great financial situation and looks to be continuing in that direction. II. A) The top management team is made up of John E. Pepper Jr., Robert A. Iger, and Thomas O. Staggs. Pepper, 69, is the chairman of the board while he is also the CEO of the National Railroad Freedom Center. He has also had several positions with Procter amp; Gamble. Iger, 56, is the president, CEO, and director of Walt Disney Company. He was COO for the company since 2000 and became CEO in October 2005. Iger has been with ABC since 1974 and has†¦show more content†¦He also produced propaganda and training films for the military. The Walt Disney Company had a hard time regaining its pre-war popularity. For several years, the Company released â€Å"package† films which contained groups of short cartoons. At this time, he also moved to working with live production with Song of the South and So Dear to My Heart. The studio had three major accomplishments in 1950: the first completely live action film, Treasure Island; the return to classic animation, Cinderella; and t he first Disney television show at Christmas time. With this Christmas special, Walt saw the potential in television and started the Disneyland anthology series, which featured Davy Crocket the first mini-series. The series played on all three networks and went through six name changes, but it is the longest running series, twenty-nine years. The Mickey Mouse Club premiered in 1955 and produced many stars. Walt wanted to create something new. He wanted a place where parents and children could have fun together. This was the groundwork for Disneyland. 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Audit Risk free essay sample

A. Define audit risk. Audit risk is the risk that the auditors may unknowingly fail to appropriately modify their opinion on financial statements that are materially misstated. B. Describe its components of inherent risk, control risk, and detection risk. The risk of material misstatement may be separated into two components-inherent risk and control risk. Both inherent risk and control risk exist independently of the audit of financial statements, or in other words, the risk of misstatement exists regardless of the audit being done or not. Inherent risk is the possibility of material misstatement of an assertion before considering the clients internal control. Factors that affect this are from the nature of the client and its environment, or from the nature of the account. The auditor should seriously consider whether to accept the engagement because inherent risk increases the overall risk of the audit. It is often useful to segregate transactions into three types- routine, non-routine, and estimation when assessing inherent risk. The assessment of what is material is a matter of professional judgment. In designing the audit plan, the auditor establishes an acceptable materiality level so as to detect quantitatively material misstatements. However, both the amount (quantity) and nature (quality) of misstatements need to be considered. Examples of qualitative misstatements would be the inadequate or improper description of an accounting policy when it is likely that a user of the financial statements would be misled by the description, and failure to disclose the breach of regulatory requirements when it is likely that the consequent imposition of regulatory restrictions will significantly impair operating capability. The auditor needs to consider the possibility of misstatements of relatively small amounts that, cumulatively, could have a material effect on the financial statements. For example, an error in a month end procedure could be an indication of a potential material misstatement if that error is repeated each month. The auditor considers materiality at both the overall financial statement level and in relation to individual account balances, classes of transactions, account balances, and disclosures. Materiality may be influenced by considerations such as legal and regulatory requirements and considerations relating to classes of transactions, account balances, and disclosures and their relationships 5. 6. 7. ISA 320 (CONFORMED) 2 AUDIT MATERIALITY (CONFORMED) individual financial statement account balances and relationships. This process may result in different materiality levels depending on the aspect of the financial statements being considered. 8. Materiality should be considered by the auditor when: (a) (b) Determining the nature, timing and extent of audit procedures; and Evaluating the effect of misstatements. The Relationship between Materiality and Audit Risk 9. When planning the audit, the auditor considers what would make the financial statements materially misstated. The auditor’s understanding of the entity and its environment establishes a frame of reference within which the auditor plans the audit and exercises professional judgment about assessing the risks of material misstatement of the financial statements and responding to those risks throughout the audit. It also assists the auditor to establish materiality and in evaluating whether the judgment about materiality remains appropriate as the audit progresses. The auditor’s assessment of materiality, related to specific account balances and classes of transactions, account balances, and disclosures helps the auditor decide such questions as what items to examine and whether to use sampling and substantive analytical procedures. This enables the auditor to select audit procedures that, in combination, can be expected to reduce audit risk to an acceptably low level. There is an inverse relationship between materiality and the level of audit risk, that is, the higher the materiality level, the lower the audit risk and vice versa. The auditor takes the inverse relationship between materiality and audit risk into account when determining the nature, timing and extent of audit procedures. For example, if, after planning for specific audit procedures, the auditor determines that the acceptable materiality level is lower, audit risk is increased. The auditor would compensate for this by either: (a) Reducing the assessed risk of material misstatementlevel of control risk, where this is possible, and supporting the reduced level by carrying out extended or additional tests of control; or Reducing detection risk by modifying the nature, timing and extent of planned substantive procedures. 0. (b) Materiality and Audit Risk in Evaluating Audit Evidence 11. The auditor’s assessment of materiality and audit risk may be different at the time of initially planning the engagement from at the time of evaluating the results of audit procedures. This could be because of a change in circumstances or because of a change in the auditor’s knowledge as a result of performing audit proceduresthe audit. For example, if the audit procedures are performed 3 ISA 320 (CONFORMED) AUDIT MATERIALITY (CONFORMED) is planned prior to period end, the auditor will anticipate the results of operations and the financial position. If actual results of operations and financial position are substantially different, the assessment of materiality and audit risk may also change. Additionally, the auditor may, in planning the audit work, intentionally set the acceptable materiality level at a lower level than is intended to be used to evaluate the results of the audit. This may be done to reduce the likelihood of undiscovered misstatements and to provide the auditor with a margin of safety when evaluating the effect of misstatements discovered during the audit. Evaluating the Effect of Misstatements 12. In evaluating whether the financial statements are prepared, in all material respects, in accordance with an applicable financial reporting framework, the fair presentation of the financial statements the auditor should assess whether the aggregate of uncorrected misstatements that have been identified during the audit is material. The aggregate of uncorrected misstatements comprises: (a) Specific misstatements identified by the auditor including the net effect of uncorrected misstatements identified during the audit of previous periods; and The auditor’s best estimate of other misstatements which cannot be specifically identified (i. e. , projected errors). 13. (b) 14. The auditor needs to consider whether the aggregate of uncorrected misstatements is material. If the auditor concludes that the misstatements may be material, the auditor needs to consider reducing audit risk by extending audit procedures or requesting management to adjust the financial statements. In any event, management may want to adjust the financial statements for the misstatements identified. If management refuses to adjust the financial statements and the results of extended audit procedures do not enable the auditor to conclude that the aggregate of uncorrected misstatements is not material, the auditor should consider the appropriate modification to the auditor’s report in accordance with ISA 700, â€Å"The Auditor’s Report on Financial Statements. If the aggregate of the uncorrected misstatements that the auditor has identified approaches the materiality level, the auditor would consider whether it is likely that undetected misstatements, when taken with aggregate uncorrected misstatements could exceed materiality level. Thus, as aggregate uncorrected misstatements approach the materiality level the auditor would consider reducing audit the risk by performing additional audit procedures or by 15. 16. ISA 320 (CONFORMED) 4 AUDIT MATERIALITY (CONFORMED) equesting management to adjust the financial statements for identified misstatements. Public Sector Perspective 1. In assessing materiality, the public sector auditor must, in addition to exercising professional judgment, consider any legislation or regulation which may impact that assessment. In the public sector, materiality is also based on the â€Å"context and nature† of an item and includes, for example, sensitivity as well as value. Sensitivity covers a variety of matters such as compliance with authorities, legislative concern or public interest. 5 ISA 320 (CONFORMED)

Microsoft Essays (1913 words) - Microsoft, Bill Gates, MS-DOS

Microsoft Introduction and History: There are very few people in the world today that can honestly say that they do not know who Bill Gates is. This Harvard dropout is quite possibly the most well known, and wealthiest person in the world. Even at the age of fourteen, Gates was on the road for success after he started his own computer programming company. This company, the Lakeside programming group, was credited for writing programs for his schools payroll account. Only after a year of higher learning, Gates and long time friend Paul Allen from the University of Washington, dropped out of college and moved to Albuquerque, New Mexico in 1974. Upon their arrival in New Mexico, Gates and Allen started Microsoft, which is now a multibillion dollar computer software company. Initially Microsoft was a small company that did programming from a computer company called MITS. One of Gates and Allen's first big accomplishments working for MITS was to write a programming language called BASIC. When MITS went under, Gates and Alle n had to look for other opportunities and relocated their company back to their home town of Seattle, Washington. At the same time the computer giant IBM realized how large the personal computer market was becoming, and decided to jump on the band wagon when they saw that they were considerably behind the competition in developing a PC product. IBM threw together a machine made mostly from other companies parts and tried to out source the software for the operating system. After being rejected twice by programming firms, including Microsoft, Bill Gates realized what a profitable opportunity it would be to move in on IBM's enormous market share. Gates borrowed fifty thousand dollars from his father, who was a prominent Seattle lawyer, and bought the rights to an operating system sold by Seattle Computer Products. An operating system is the language that links the keyboard, screen, printer, microprocessor and computer and allows the parts to communicate. Gates then approached IBM to s ign agreements to develop the systems for their PC's. This decision would later become legendary. Gates soon perfected this operating system and called it MS-DOS. MS-DOS promptly became the industry standard because of IBM's influence in the market. Microsoft became a wealthy company almost overnight, and is widely known as a leader in the software industry. Shortly after the company's inception, Microsoft started producing software programs, some of which are today, the most widely used everywhere. The famed software programs include Microsoft Word, Macintosh Word, and Excel. Microsoft forced its way into the market and has gradually added different programs to its team. In 1982 the developed Microsoft Multiplan for the business community, and in 1983 they developed their first word processing program to compete with word perfect. In addition to that, IBM urged Microsoft to make Microsoft Word available for the Macintosh. Then Microsoft decided they needed a to add a multitask operating system, which would allow the user to have word processing, spreadsheets, and several other computer applications working together. In 1984 Microsoft developed their biggest breakthrough in the operating systems market, a graphics based operatin g system called Windows. Windows allowed Microsoft to match the user friendliness of Apple Computers operating system, giving them another stronghold in the ever growing personal computer market. This development became extremely important for the company because Windows is now the standard system installed on new computers. In 1992, Microsoft started to dive into the emerging market of networking computers and developed Windows NT. As upgrades continued and the development of Windows 2000 became available, Microsoft became the largest and the strongest computer company in the world. With Gates at the helm steering his company into the future, by hard work and dedication, it is no wonder that he is the richest man in the world. Strengths and Weaknesses: Not mentioning Bill Gates as an incredible strength for Microsoft would be a great unjust for the company. Gates insight and decisions, has given Microsoft a virtual monopoly in the computer software industry. Microsoft had more than 20 projects in development in 1996, and though Microsoft usually is not the first to come out with a product is always, and undoubtedly proven to be the best. Revenues for