Structural Equation Modeling in Management Research: Twenty-Year Veterans and Federal Employment - Assignment Example

3. The total number of retired veterans in the different sectors was increasing steadily among all the categories and sectors for all the four years. At the end of the analysis, there will be a test on all the hypotheses to ascertain whether they are valid or not. Using SPSS, the tests will be run on various data samples to ascertain the level of significance of various variables using multivariate techniques. Ultimately, the tests of the hypotheses will be able to create the decision rules and the actual decisions as they are guided by the data samples.

From the decisions, the research process will be able to make an evaluation of the reliability and accuracy of the results by eliminating the abnormality that arises from the limitation of the analysis. Justification for Using SPSS SPSS (Statistical Package for Social Sciences) is the most appropriate statistical software for this analysis, since it is able to perform various options of analysis compared to STATA and other forms of software. It is very quick in producing output results, and it is easy to use in relation to data editing.

Naming the Variables The analysis is based on the variables relating to the various categories and for the range of years within the study period. The research variables are divided into two; dependent variables and independent variables. Dependent variables Number of Veteran Males (VM) Number of Veteran Females (VF) Number of Non Veteran Males (NVM) Number of Non Veteran Females (NVF) Number of Retired Veterans (RV) A sample data set for the variables is shown below: Figure 1: Sample Raw Data in SPSS Editor Independent Variables The only independent variable for this Structural Equation Modeling is the year, whose values range from 2009 to 2012.

In other data sets, the independent variables are the sectors under which the retired veterans are employed in the US Federal government. An example of the presentation of the retired veterans by the sectors is shown below: Category Federal State Local Total Average VM 190 14 7 211 70.33333 VF 12 7 872 891 297 NVM 98 216 225 539 179.6667 NVF 87 709 312 1108 369.3333 RV 105 165 462 732 244 Table 1: Categories of Retired Veteran and sectors of Government Figure 2: Various Sectors of US Government.

Test of Data Samples The test for the data samples is meant to prove the validity of the hypotheses. The research thus uses a number of relevant data sets as follows: Figure 3: Sample Data set YEAR VM VF NVM NVF RV Average 2009* 144 36 6366 6218 86 2570 2010* 148 620 6458 6389 97 2742.4 2011* 152 48 6548 6489 4098 3467 2012* 167 57 6687 6758 128 2759.4 Table 2: Sample Data set Charts and graphs in testing hypothesis The test results show the corresponding average number of veteran soldiers employed in the different years that the study is concerned with.

The graph below is to show the behavior of the numbers employed in different years from 2009 to 2012. S No YEAR Total 1 Y2009 12850 2 Y2010 13712 3 Y2011 17335 4 Y2012 13797 Table 3: Total number employed in the four years Figure 4: Total Number Employed . The behavior of these variables shows that there is a constant rise from 2009 to 2011. This proves the first hypothesis to be valid. For the second hypothesis, it uses average as the measure of trend. Practically, even though the average numbers increase up to 2011 and then falls in 2012, the linear average line is a steady positive gradient line.

The average line is a curved line of equation R2 = 0.176 while the linear averag