Sheikh Sudais
Sunday, May 4, 2008
Thursday, May 1, 2008
Saturday, April 26, 2008
Learn From History
26 April 2008
Many people have inability to learn from history but people simply do not know how to learn from history where chance are complex, both successful and unsuccessful.
Many people have inability to learn from history but people simply do not know how to learn from history where chance are complex, both successful and unsuccessful.
30 April 2008
Null hypothesis (H0) - Cross Tabulation
In statistics, a null hypothesis (H0) is a hypothesis set up to be nullified or refuted in order to support an alternative hypothesis. When used, the null hypothesis is presumed true until statistical evidence, in the form of a hypothesis test, indicates otherwise — that is, when the researcher has a certain degree of confidence, usually 95% to 99%, that the data does not support the null hypothesis. It is possible for an experiment to fail to reject the null hypothesis. It is also possible that both the null hypothesis and the alternate hypothesis are rejected if there are more than those two possibilities.
The null hypothesis is a hypothesis about a population parameter. The purpose of hypothesis testing is to test the viability of the null hypothesis in the light of experimental data. Depending on the data, the null hypothesis either will or will not be rejected as a viable possibility.
H0 is a stated assumption that there is no difference in parameters (mean, variance) for populations. null hypothesis (H0) According to the null hypothesis, any observed difference in samples is due to chance or sampling error.
Example:
Consider a researcher interested in whether the time to respond to a tone is affected by the consumption of alcohol. The null hypothesis is that µ1 - µ2 = 0 where µ1 is the mean time to respond after consuming alcohol and µ2 is the mean time to respond otherwise. Thus, the null hypothesis concerns the parameter µ1 - µ2 and the null hypothesis is that the parameter equals zero. The null hypothesis is often the reverse of what the experimenter actually believes; it is put forward to allow the data to contradict it. In the experiment on the effect of alcohol, the experimenter probably expects alcohol to have a harmful effect. If the experimental data show a sufficiently large effect of alcohol, then the null hypothesis that alcohol has no effect can be rejected.
In statistics, a null hypothesis (H0) is a hypothesis set up to be nullified or refuted in order to support an alternative hypothesis. When used, the null hypothesis is presumed true until statistical evidence, in the form of a hypothesis test, indicates otherwise — that is, when the researcher has a certain degree of confidence, usually 95% to 99%, that the data does not support the null hypothesis. It is possible for an experiment to fail to reject the null hypothesis. It is also possible that both the null hypothesis and the alternate hypothesis are rejected if there are more than those two possibilities.
The null hypothesis is a hypothesis about a population parameter. The purpose of hypothesis testing is to test the viability of the null hypothesis in the light of experimental data. Depending on the data, the null hypothesis either will or will not be rejected as a viable possibility.
H0 is a stated assumption that there is no difference in parameters (mean, variance) for populations. null hypothesis (H0) According to the null hypothesis, any observed difference in samples is due to chance or sampling error.
Example:
Consider a researcher interested in whether the time to respond to a tone is affected by the consumption of alcohol. The null hypothesis is that µ1 - µ2 = 0 where µ1 is the mean time to respond after consuming alcohol and µ2 is the mean time to respond otherwise. Thus, the null hypothesis concerns the parameter µ1 - µ2 and the null hypothesis is that the parameter equals zero. The null hypothesis is often the reverse of what the experimenter actually believes; it is put forward to allow the data to contradict it. In the experiment on the effect of alcohol, the experimenter probably expects alcohol to have a harmful effect. If the experimental data show a sufficiently large effect of alcohol, then the null hypothesis that alcohol has no effect can be rejected.
Thursday, April 24, 2008
Importance of the study

Importance and benefits of research
The results of the study can generate new knowledge and gain better understanding of the roles of Learning Organisation in DSD as a coordinating agency in ensuring the implementation of NDTS and to make the private sector aware the benefit of the NDTS so that private sectors will make cooperation with the public sector, and to secure private sector support for the introduction of the system as well as the public sector’s in involvement in making it operational. To be specific, the findings of the study will;
•benefit DSD of The MOHR through the development of a new model of LO interventions for effective role in coordinating and implementing dual system training that could be used by many agencies involved across many part of countries globally . The functions of this department are to formulate, promote and co-ordinate the vocational and industrial skills training to fulfil the national economic development of the country would be enhanced with the development of curriculum development framework;
•gives input to training institutions and industries globally on the strategies and pertinent factors for strong and smart partnerships in dual system training. Create awareness among training institutions and industries about the new approach and benefit of the NDTS in Malaysia and other parts of the world;
•gives input to the DSD, MOHR in Malaysia as a coordinating agency on the approaches and measures needed to address all the challenges and constraints for sucessfull NDTS implementation;
•new knowledge and understanding of the factors and roles of organisations to develop into LO in Malaysia and other parts of the world; and
•gives input to industries and especially to the DSD, MOHR in Malaysia as a coordinating agency on the approaches and measures needed to address all the challenges and constraints in developing as LO.
Factor Analysis
With Frank Musekamp at room 2.40 ITB. Discussed about data analysis again. Factor analysis can be used to reduce the number of items. All 135 items dived by:
i. Policy = 25 items
ii. Processes = 16 items
iii. Procedure = 13 items
iv. ICT = 11 items
v. Leadership = 30 items
vi. Culture = 29 items
vii. Output = 11 items
viii Demography = 8 items
OUTCOME
The outcome of the study is a model of a new road map or cornerstone for the development of Learning Organisation which is an integrated and formulated new role of Learning Organisation in the Department of Skill Development, Ministry of Human Resource. This model will enable DSD to move forward not only as a coordinating agency but also as an effective body that could implement and manage NDTS sucessfully. The development of this model is based on the anticipated findings from the analysis of both the quantitative and qualitative data ie;
•The significant factors that contribute to the role of the learning organisation in DSD to enhance the implementation of NDTS;
•The critical success factors practiced by other agencies (such as BiBB and companies) that could be adapted in implementing dual training system in term of the roles of Learning Organisation; and
•A predictable relationship between the factors and the level of the effectiveness of the Learning Organisation in DSD.
i. Policy = 25 items
ii. Processes = 16 items
iii. Procedure = 13 items
iv. ICT = 11 items
v. Leadership = 30 items
vi. Culture = 29 items
vii. Output = 11 items
viii Demography = 8 items
OUTCOME
The outcome of the study is a model of a new road map or cornerstone for the development of Learning Organisation which is an integrated and formulated new role of Learning Organisation in the Department of Skill Development, Ministry of Human Resource. This model will enable DSD to move forward not only as a coordinating agency but also as an effective body that could implement and manage NDTS sucessfully. The development of this model is based on the anticipated findings from the analysis of both the quantitative and qualitative data ie;
•The significant factors that contribute to the role of the learning organisation in DSD to enhance the implementation of NDTS;
•The critical success factors practiced by other agencies (such as BiBB and companies) that could be adapted in implementing dual training system in term of the roles of Learning Organisation; and
•A predictable relationship between the factors and the level of the effectiveness of the Learning Organisation in DSD.
Tuesday, April 22, 2008
First Meeting
This morning I was with my supervisor, the discussion regarding Factor Analysis and reliability. The task is to find out the sub-dimension or sub-category of policy, procedure, processes, leadership, ICT and culture. The sub-dimension must relate with specific theory regarding learning or learning organization.
Second parts of it are to do micro correlation hypothesis regarding level of respondent, such as top management, middle management or non-management. To look at the involvement of which particular level will affect the effectiveness of the particular dimensions. See you on 7 May 2008, 10.00 am.
Second parts of it are to do micro correlation hypothesis regarding level of respondent, such as top management, middle management or non-management. To look at the involvement of which particular level will affect the effectiveness of the particular dimensions. See you on 7 May 2008, 10.00 am.
Saturday, April 19, 2008
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