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Correlation between Length of Work Experience and the Time it Takes to Learn the System - Research Paper Example

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This research paper "Correlation between Length of Work Experience and the Time it Takes to Learn the System" discusses if there is a significant association or relationship between the length of work of experience of the respondents with their length of time to successfully use the system…
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Correlation between Length of Work Experience and the Time it Takes to Learn the System
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A - Correlation A Length of work experience as radiographer or radiologist Numerical value given Ten years or more 4 At least 6 years, but less than 10 years 3 At least 3 years, but less than 6 years 2 At least 1 year, but less than 3 years 1 Length of time it takes to successfully use the system Numerical value given 1-2 weeks 1 2-4 weeks 2 4-6 weeks 3 6-8 weeks 4 Remarks: The purpose of this analysis is to find out if there is a significant association or relationship between the length of work of experience of the respondents with their length of time to successfully use the system. The results of correlation analysis are the following: Table 1. Pearson Product Moment Correlation between length of work experience of the respondents and the time it takes to learn the system successfully Pearson Product Moment Correlation – Ungrouped Data Correlation Value -0.1507 Probability (Two-tailed) – P-value 0.3430 Probability (One-tailed) – P-value 0.1715 Interpretation of result: The computed correlation value closer to “1” or “-1” indicates higher degree of association or relationship between the two variables. This means that a certain variable can be a significant factor of the behaviour of the other variable and vice versa. In the case of the result, the two variables included are the length of work of the respondents and the time it took them to learn the system successfully. The result of the correlation is -0.1507 which is relatively far from -1. The maximum probability of this result to be significant is only 34% at two-tailed test. This means that testing for the relationship between the length of work experience of the respondents and the time it takes them to learn the system successfully cannot entirely give significant relationship. Thus, the ability of the respondents to adapt or to learn the new system has nothing to do with the length of time they have been radiographers or radiologists. This result can significantly conclude that there are other contributing factors affecting the ability of the respondents to learn the new system. The data: Length of work experience Length of time it took to learn the system 1 1 4 1 3 1 2 2 2 3 4 2 4 1 2 1 2 2 2 2 2 2 3 1 4 1 4 1 4 2 4 1 3 4 4 1 2 4 1 1 2 1 1 2 3 4 4 1 1 1 1 1 3 1 1 1 3 1 2 2 1 4 3 1 1 1 4 1 3 1 2 1 3 1 4 3 4 1 3 1 4 1 2 2 Assumption of the data: The data is assumed normally distributed so as to use Pearson Product Moment Correlation. A.2 Educational attainment of the respondents Numerical value given Less than a High School Diploma 1 High School Diploma 2 Post-Secondary Certificate 3 Associate’s Degree 4 Bachelor’s Degree 5 Master’s Degree 6 Doctoral Degree 7 Post-Doctoral Training 8 Length of time it takes to successfully use the system Numerical value given 1-2 weeks 1 2-4 weeks 2 4-6 weeks 3 6-8 weeks 4 Remarks: The purpose of this analysis is to find out if there is a significant association or relationship between the respondents’ educational attainment with their length of time to successfully use the system. The results of correlation analysis are the following: Table 2. Pearson Product Moment Correlation between respondents’ highest educational attainment and the time it takes to learn the system successfully Pearson Product Moment Correlation – Ungrouped Data Correlation Value -0.1099 Probability (Two-tailed) – P-value 0.4881 Probability (One-tailed) – P-value 0.2440 Interpretation of result: The computed correlation value closer to “1” or “-1” indicates higher degree of association or relationship between the two variables. This means that a certain variable can be a significant factor of the behaviour of the other variable and vice versa. In the case of the result shown in Table 2, the two variables included are the respondents’ highest educational attainment and the time it took them to learn the system successfully. The result of the correlation is -0.1099 which is relatively far from -1. The maximum probability of this result to be significant is at most 49% at two-tailed test. This means that testing for the relationship between the length of the respondents’ highest educational attainment and the time it takes them to learn the system successfully cannot entirely give significant relationship. Thus, the ability of the respondents to adapt or to learn the new system has nothing to do with their educational attainment. This result can significantly conclude that there are other contributing factors affecting the ability of the respondents to learn the new system. The data: Highest educational attainment Length of time it took to learn the system 8 1 5 1 4 1 8 2 4 3 8 2 8 1 4 1 4 2 4 2 3 2 8 1 6 1 5 1 3 2 5 1 5 4 4 1 5 4 5 1 5 1 4 2 5 4 4 1 4 1 5 1 5 1 4 1 4 1 3 2 4 4 5 1 6 1 4 1 5 1 5 1 3 1 6 3 6 1 4 1 8 1 4 2 Assumption of the data: The data is assumed normally distributed so as to use Pearson Product Moment Correlation. B. Overall Remarks and Conclusion: From the data set, it seems the most significant variables at first glance that most likely to contribute to respondents’ adaption to the new system are educational attainment and length of work experience as radiographer or radiologist. However, from the result of the correlation analysis, no significant association or relationship was obtained. This suggests that there must be some other factors affecting or contributing to the ability of respondents to adapt to the new system with respect to time. It can be personal at some point, but in an organisation, other factors can be significant indicators. The learning of the new system were evaluated by the respondents based on the level of communication, leadership support, end users’ involvement and training and evaluation imparted by the department. This can give remarkable idea on the result of the level of adaption to successfully use the system. Since there was a high average percentage of rating for training and education given by the respondents, it is implied that there must be a higher rate of result to successfully use the new system. This is implied so because in the first place, most of the respondents obtained high educational attainment and significant work experience. Thus, learning the new technology or technique would not be difficult at all. Thus, in order for the new system to be successfully implemented, an organisation must learn to improve its communication, leadership support, end user involvement and training and education. References (For Statistical Computation) Soper. D. S. (2010) ‘Statistics Calculator’. [Online] (http://www.danielsoper.com/statcalc/calc44.aspx (Accessed: 20 January 2010) Wessa, P. (2010), Free Statistics Software, Office for Research Development and Education, [Online] version 1.1.23-r5, URL http://www.wessa.net/ (Accessed: 20 January 2010) Read More

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