Introduction
Training programs are believed to impact the way the employees consider themselves able to work or rather have the necessary training to complete their work. This research evaluates if such a relationship exists. This is achieved through a series of steps. It is important to note that this research begins by presuming that no such relationship exists and hence the level of training does not affect how employees feel they can do the job.
Research question
What is the effect of the training program on whether employees believe they have the training necessary to do their job?
Null hypothesis
There is no difference in employee perceptions of whether they have the training necessary to do their job before or after the training program.
Alternative hypothesis
There is a difference in employee perceptions of whether they have the training necessary to do their job before or after the training program.
Statistical procedures used in testing the null hypothesis
The study will evaluate whether changes in training levels will result in a similar change in the perception held by employees as to whether or not they have the necessary training to do their job. In essence, the research will evaluate whether there is a relationship between changes in employee perceptions as to whether they have the necessary training for the job and changes in the level of training over the same period.
Correlation analysis is therefore used to compare the relationship between the two. To achieve this, the difference between the number of hours of training an employee had in 2010 and the number of hours of training the employee had in 2009 are obtained. This gives the change in training level from 2009 to 2010. Additionally, the difference in perception of employees that they have adequate training in 2010 and 2009 are obtained. This forms the change in employee perception as to whether or not they are fit for the job.
More often correlation analysis is used in the evaluation of the relationship that exists between two variables and always lies amidst –1 and +1 (Stevens, 2002; Neuman, 2011). This is appropriate in this case since the relationship being evaluated is between two variables namely changes in employee perceptions as to whether they have the necessary training for the job and changes in the level of training. When the value returned is zero, it will be concluded that there is a difference in employee perceptions of whether they have the training necessary to do their job before or after the training program. On the other hand, the larger the r-value is, the stronger the association will be concluded to exist between the variables (Kiess & Green, 2010).
The null hypothesis will be accepted if the following mathematical condition is met:
This simply means that in case the value of r obtained is not equal to zero, then the correlation will be said to exist between the two variables. However, this is not sufficient to completely accept the null hypothesis. A further value, the p-value will be obtained to establish the significance of the obtained value and hence form a further basis for acceptance or rejection of the null hypothesis. In case the null hypothesis fails to hold, the null hypothesis stated will be instead accepted. The accepted p-value should be less than 0.05. If this condition in addition to the first one is met, the null hypothesis will be accepted.
Test results
Other than providing correlation analysis results, the test goes a step further to provide the means of each of the variables and hence present a generalized picture of the distribution of the data and the overall changes that occurs in each of the variables used in the study.
Correlation analysis is performed with the help of SPSS software and the output is as shown in the tables below:
The descriptive statistics results in present interesting findings. The change in employee training hours from 2009 to 2010 is 1 while the change in employee perception is rather small,.0294. At face value, no clear relationship can be concluded between the two.
The results as displayed in the SPSS output can be represented as follows: r (34) = -0.320, p =.065. Recalling our null hypothesis:
we affirm that there is a negative relationship between employee perceptions of whether they have the training necessary and the level of training.
Conclusion
Even though the conditions for the null hypothesis are not met as significance is obtained with the p-value being greater than.05 (Aron, Coups, & Aron, 2011). In this regard, we accept the null hypothesis and hence dispose of the alternative hypothesis. As a result, we conclude that there is no difference in employee perceptions of whether they have the training necessary to do their job before or after the training program.
The research set out to investigate the effect of changes in the level of training on employee perception as to whether or not they have the necessary training to do the work. Changes in the level are treated as the independent variable while the level of training on employee perception represents the dependent variable.
At the onset, the research question was stated as ‘What is the effect of the training program on whether employees believe they have the training necessary to do their job?’ To successfully respond to this question, correlation analysis was run in an attempt to establish if there exists a linear relationship between the two variables of interest. It was assumed that differences from one year to another reflected the changes. It was assumed that there is a linear relationship between the variables and hence the research set out to evaluate this. According to the findings, r (34) = -0.320, p =.065. The null hypothesis is expected and hence it is concluded that there is no difference in employee perceptions of whether they have the training necessary to do their job before or after the training program. The results fail to confirm the existence of a relationship between the two variables evaluated.
References
Aron, A., Coups, E. J., & Aron, E. N. (2011). Statistics for the behavioral & social sciences: A brief course. (5th ed.). Upper Saddle River, NJ: Prentice Hall.
Kiess, H. O., & Green, B. A. (2010). Statistical concepts for the behavioral sciences. (4th ed.). Boston, MA: Allyn and Bacon.
Neuman, W. L. (2011). Social research methods: Qualitative and quantitative approaches. (7th ed.). Boston, MA: Allyn and Bacon.
Stevens, J. P. (2002). Applied multivariate statistics for the social sciences (4th ed.). Mahwah, NJ: Lawrence Erlbaum Associates.