APPRAISAL OF FINANCIAL AND ADMINISTRATIVE FUNCTIONING OF PUNJAB TECHNICAL UNIVERSITY

APPRAISAL OF FINANCIAL AND ADMINISTRATIVE FUNCTIONING OF PUNJAB TECHNICAL UNIVERSITY In the previous chapters the budgets of the university have been analyzed using various techniques to understand the sources and application of the funds of the universities, the growth trend of the various sources of revenue and expenditure and the consistency level of the growth of revenue and expenditure. Along with these aspects of university financial system, it is also important to study the perceptions of the students, faculty and administrative staff regarding the functioning of university. To understand the perception of these groups, three different sets of questionnaire were designed that tried to solicit each of the categories view on five point scale, where 1= strongly disagree and 5= strongly agree. These questionnaires were administered to the students, teachers and administrative staff of the various colleges of Amritsar and Jalandhar city that are affiliated to Punjab Technical University. The distribution of the different segments of the sample is as follows: Students 207 Faculty 94 Administrative staff 46 Students were divided into two categories, the distribution of which is as follows: Scholarship holders 82 Non scholarship holders 125 Total 207 Similarly, for the purpose of study, teachers were also divided into the following categories: Teachers with less than three years of experience 54 Teachers with more than three years of experience 40 Total 94 203

Administrative staff were also categorized into two classes Class I employees 19 Class II employees 27 Total 46 6.1 ANALYSIS OF THE DATA The two techniques that have been used to analyse the data are: 1. Factor analysis 2. Independent sample t-test 1. Factor analysis This technique was used for reduction of data to a manageable level. 2. Independent sample t-test This test was used to find and study that whether any significant difference existed between the perception of different categories of students, faculty and administrative staff as far as the financial system of the university is concerned. The results of analysis have been discussed one by one for each group. 6.2 RESULTS OF FACTOR ANALYSIS FOR STUDENTS The questionnaire designed for the students consisted of the following 11 variables. Variable 1 (V1): The Financial Administration of academic activities in general is satisfactory. Variable 2 (V2): The Financial Administration in respect of students welfare is satisfactory. Variable 3 (V3): The present arrangement for remittance of tuition fees etc. is satisfactory. Variable 4 (V4): The procedure prescribed for remittance of fees needs no revision. 204

Variable 5 (V5): The Scholarships/ Fellowships to the students are paid within reasonable time. Variable 6 (V6): The procedure adopted for payment of scholarship/fellowship does not require any change. Variable 7 (V7): The funding agencies are providing funds in time for scholarships/fellowships. Variable 8 (V8): The colleges are submitting the scholarship/fellowship claims in time. Variable 9 (V9): The university s administrative section processing the papers for early payment of scholarships/fellowships. Variable 10 (V10): The finance section of the university is arranging for prompt payments of amounts due to the students. Variable 11 (V11): The present procedure for remitting examination fees and other charges is satisfactory. But variable 4 was dropped from the analysis as it was hindering the applicability of factor analysis. 6.2.1 KMO and Bartlett s Test The KMO measures the sampling adequacy which should be greater than 0.5 for conducing the factor analysis. In this case, the KMO statistic is 0.676 which shows that the data is suitable for factor analysis. Bartlett s test indicates the strength of the relationship among variables. This tests the null hypothesis that the correlation matrix is an identity matrix, that is, it is a matrix with all the diagonal elements as 1 and the value of all the non-diagonal elements is 0. It is clear from the Table 6.1, that the Bartlett s test of spherecity is significant as its associated probability is less than 0.05. The significance level is small enough to reject null hypothesis, that is, the correlation matrix is not an identity matrix. 205

Table 6.1: KMO and Bartlett s Test (Students) Kaiser-Meyer-Olkin Measure of Sampling Adequacy 0.676 Bartlett s test of shericity Approximate 853.393 chi square Df 45 Significance.000 6.2.2 Communalities The next item from the output is the table of communalities which shows how much of the variance in the variables has been accounted for by the extracted factors. It is clear from the table that the variable 9 which states that the finance section of the university arranges for prompt payments of amounts due to the students accounts for 78.2% of the variance. Table 6.2: Communalities (Students) Initial Extraction V1 1.000 0.584 V2 1.000 0.694 V 3 1.000 0.682 V5 1.000 0.566 V6 1.000 0.613 V7 1.000 0.728 V8 1.000 0.715 V9 1.000 0.683 V10 1.000 0.782 V11 1.000 0.758 Extraction Method: Principal Component Analysis 206

6.2.3 Total Variance Explained The next table shows all the factors extractable from the analysis along with their eigen values, the percent of variance attributable to each factor and the cumulative variance of the factor and the previous factors. Here factor 1 accounts for 37.96% of the variance, factor 2 accounts for 16.97% of the variance and factor 3 accounts for 13.12% of the variance. The remaining factors are not important. Also, we have to retain the factors with eigen values greater than 1. On the basis of this criteria, only first three factors would be retained for the analysis because they have eigen values greater than 1. Table 6.3: Total Variance Explained (Students) Component Initial Eigen values Extraction sum of squared loadings Rotation sum of squared loadings Total % of Cumulative Total % of Cumulative Total % of Cumulative variance % variance % variance % 1. 3.796 37.960 37.960 3.796 37.960 37.960 2.515 25.154 25.154 2. 1.698 16.975 54.935 1.698 16.975 54.935 2.391 23.905 49.059 3. 1.312 13.125 68.060 1.312 13.125 68.060 1.900 19.001 68.060 4. 0.902 9.024 77.084 5. 0.617 6.174 83.257 6. 0.470 4.698 87.955 7. 0.454 4.543 92.499 8. 0.306 3.058 95.557 9. 0.266 2.664 98.220 10. 0.178 1.780 100.000 Extraction Method: Principal Component Analysis 6.2.4 Scree Plot The screen plot is a graph of the Eigen values against all the factors. The graph helps to determine the number of factors to be retained for further analysis. In the graph, we have to look in for the point where the curve starts to flatten. Here the curve begins to flatten at the point between fourth and fifth factor. But factor 4 has an eigen value of less than 1. Thus, only three factors have been retained. 207

4 3.5 3 Eigen Values 2.5 2 1.5 1 0.5 0 1 2 3 4 5 6 7 8 9 10 Components Fig. 6.1: Scree Plot (Students) 6.2.5 Component (Factor) Matrix The table below shows the loadings of the 10 variables on the three factors extracted. The higher the absolute value of the loadings, the more the factor contributes to the variables. The gap on the table represents the loadings that are less than 0.5. This occurred because we have suppressed loading less then 0.5. Table 6.4: Component Factor Matrix a (Students) Component 1 2 3 V2 0.802 V5 0.732 V3 0.717 V8 0.691 V7 0.548 V1 0.52 V10 0.532-0.702 V11 0.560-0.640 V9 0.681 V6 0.530 Extraction Method: Principal Component Analysis; a. 3 components extracted. 208

6.2.6 Rotated Component (Factor) Matrix The idea of rotation is to reduce the number of factors on which the variables under investigation have high loadings. It actually makes the interpretation of the analysis easier. The table of the rotated components matrix can be interpreted as below: Statement (Variables) with high loadings on Factor 1 1. The funding agencies are providing funds in time for scholarships/fellowships. 2. The colleges are submitting the scholarship/fellowship claims in time. 3. The present arrangement for remittance of tuition fees etc is satisfactory. 4. The scholarships/fellowships to the students are paid within reasonable time. Statements/Variables with high loadings on Factor 2 1. The finance section of the university is arranging for prompt payment of amounts due to the students. 2. The present procedure for remitting examination fees and other charges is satisfactory. 3. The Financial Administration in respect of students welfare is satisfactory. Statements/Variables with high loadings on Factor 3 1. The university s administrative section processes the papers for early payment of scholarships/fellowships. 2. The procedure adopted for payment of scholarship/fellowship does not require any charge. 3. The Financial Administration of academic activities in general is satisfactory. 209

Table 6.5: Rotated Components Matrix a (Students) Component 1 2 3 V7.834 V8.825 V3.638 V5.603 V10.878 V11.849 V2.643 V9.771 V6.766 V1.675 Extraction method: Principal Component Analysis; Rotation Method Varimax with Kaiser Normalization; a. Rotation converged in 5 iterations The three factors so extracted have been named as: Factor 1: Factor 2: Factor 3: Payment of scholarships and remittance of fees Financial Management of the University University Administration The three factors extracted through the factor analysis were used for further analysis. 6.3 RESULTS OF INDEPENDENT SAMPLE T-TEST FOR STUDENTS After extracting these factors, independent sample t-test was applied to test whether there is any statistically significant difference between the perception of 210

scholarship and non-scholarship holders in relation to the factors. To test this, following hypothesis were framed. Null Hypothesis (H 0 ): There is no significant difference between the perception of scholarship holders and non-scholarship holders. Alternate Hypothesis (H 1 ): There is a significant difference between the perception of scholarship holders and non-scholarship holders. The hypotheses were tested and the following results were attained. 6.3.1 Group Statistics The group statistics table summarizes the mean, standard deviation and standard error. It is apparent from the table that there is a difference in the means of the two groups in relation to their perception with regard to the three factors. Table 6.6: Group Statistics (Students) SH N Mean Standard Deviation Standard Error Mean Payment of Scholarship and remittance of fees Non scholarship 125 2.9460 1.12906.10099 holders Scholarship holders 82 3.0579 1.12031.12372 Financial Management Non scholarship holders 125 2.8693 1.21807.10895 Scholarship holders 82 2.7927 1.24462.13745 University Administration Non scholarship holders 125 2.8693 1.01149.09047 Scholarship holders 82 2.9065 1.00106.11055 Further, we will test that whether this difference in the means of two categories is significant or not with reference to each of the factors. 211

6.3.2 Independent Sample Test Table 6.7: Independent Sample Test (Students) Levene s Test for Equality of Variances t-test for Equality of Means F Sig. T Df Sig. (2- tailed) Mean difference Std. Error difference Payment of scholarships and remittances of fees Equal variances assumed 0.066 0.798-0.700 205 0.485-0.11193 0.15996 Equal variances not assumed -0.701 174.341 0.484-0.11193 0.15970 Financial management University administration Equal variances assumed Equal variances not assumed Equal variances assumed Equal variances not assumed 0.254 0.615 0.439 205 0.661 0.07665 0.17460 0.437 170.734 0.663 0.07665 0.17539 0.53 0.818-0.260 205 0.795-0.03717 0.14316-0.260 174.655 0.795-0.03717 0.14285 Factor 1 The significance value under the column of Levene s Test is 0.798. This value is greater then 0.05 which implies that the two groups have equal variance. So, we will look for the value of t-statistic in the first row. The significance (2-tailed) value is 0.485 which is greater than 0.05. This shows that there is no significant difference in the perception of the two categories of students. Factor 2 The value of significance for Levene s Test is 0.615. This value being greater than 0.05, indicate that we have to read the results of t-test from the first row. The value of significance (2-tailed) is 0.661 and it is again greater then 0.05. This clearly shows that null hypothesis has to be accepted which means that there is no significant difference in the perception of the two categories of students in relation to this factor. 212

Factor 3 The significance value in the case of Levene s Test is more then 0.05 (0.818>0.05). Thus, first row has to be read for the purpose of analysis. The significance (2-tailed) value is 0.795 which exceeds 0.05. Hence, it will lead to the acceptance of null hypothesis and it can be stated that there is no significant difference in the perception of the students of both categories. 6.4 RESULTS OF FACTOR ANALYSIS FOR TEACHERS The questionnaire designed for the teachers comprised of following 12 statements. There were two statements (1 and 5) that were making the data unfit for factor analysis. So, these were left out for the analysis. Variable 1 (V1): The Financial Administrative system in respect of academic activities is satisfactory. Variable 2 (V2): The university has evolved suitable procedures to collect fees/other charges from the students. Variable 3 (V3): The colleges are making prompt claims for payment of scholarships/fellowships to the students. Variable 4 (V4): The university s administrative section is processing the papers for early payment of fellowships etc. Variable 5 (V5): The Finance section is also arranging for prompt payment of fellowships etc. Variable 6 (V6): The procedure adopted for claim and payment of TA bills needs no revision. Variable 7 (V7): The TA and DA are paid within reasonable time. Variable 8 (V8): The procedure for medical reimbursement is functioning satisfactorily. Variable 9 (V9): The present system of claiming project funds from funding agencies is working well. 213

Variable 10 (V10): The project expenditure details are well maintained in the colleges/university for early settlement of accounts. Variable 11 (V11): The procedure for salary disbursement is working well. Variable 12 (V12): The colleges and the university have developed better financial accounting procedure. 6.4.1 Kaiser-Meyer-Olkin (KMO) and Bartlett s Test of Sphercity The KMO statistic in this case is 0.663 which shows that he data is suitable for factor analysis. The Bartlett s test of sphericity is also significant as its associated probability is less than 0.05. Thus, it shows that correlation matrix is not an identity matrix. Table 6.8: KMO and Bartlett s Test (Teachers) Kaiser-Meyer-Olkin Measure of Sampling Adequacy 0.663 Approximate Chi Square 824.061 Bartlett s test of Shericity df 45 Significance.000 6.4.2 Communalities It is clear from the table of communalities that the variable which states that The university has evolved suitable procedures to collect fees and other charges from the students account for 91.2% of the variance whereas the variable 10 which states that the procedure adopted for claim and payment of TA bills needs no revision accounts for 69% of the variance. 214

Table 6.9: Communalities (Teachers) Initial Extraction V2 1.000.912 V3 1.000.761 V4 1.000.783 V6 1.000.554 V7 1.000.882 V8 1.000.771 V9 1.000.883 V10 1.000.897 V11 1.000.827 V12 1.000.690 Extraction Method: Principal Component Analysis 6.4.3 Total Variance Explained The table of the total explained variance shows that factor 1 accounts for 45.940% of the variance, factor 2 accounts for 21.796% of the variance, factor 3 accounts for 11.846% of the variance and rest of the factors are not significant. As we have to retain the factors with eigen values greater then 1, so only the first three factors will be used for further analysis. Table 6.10: Initial Total Variance Explained (Teachers) Component Initial Eigen Values Extraction sum of squared Loadings Total % of Variance Cumulative % Total % of Variance Cumulative % Total Rotation Sum of Squared Loadings % of Variance Cumulative % 1. 4.594 45.940 45.940 4.594 45.940 45.940 3.155 31.549 31.549 2. 2.180 21.796 67.736 2.180 21.796 67.736 2.877 28.772 60.329 3. 1.185 11.846 79.582 1.185 11.846 79.582 1.926 19.262 79.552 4..874 8.743 88.325 5..455 4.554 92.879 6..329 3.295 96.174 7..136 1.365 97.539 8..126 1.262 98.800 9..070.702 99.502 10..050.498 100.000 Extraction Method: Principal Component Analysis 215

6.4.4 Scree Plot In the scree plot, the curve begins to flatten at the point somewhat between fourth and fifth factor. But, these two factors have eigen Value less than 1. So, as per this criteria, only three factors would be retained. 5 4.5 4 3.5 Eigen Values 3 2.5 2 1.5 1 0.5 0 1 2 3 4 5 6 7 8 9 10 Components Fig. 6.2: Scree Plot (Teachers) 6.4.5 Component (Factor) Matrix In the table of the component (factor) Matrix, the higher absolute value of the loadings is an indication that the factor contributes more to the variables. As we have suppressed the loadings less than 0.5, so there is a gap in the table. 216

Table 6.11: Component Matrix a (Teachers) Component 1 2 3 V8 0.878 V10 0.848 V2.799 V9.785 V11.718 V12.674 V4.654.647.553 V6 -.731 V7.563 V3.552.557 Extraction Method: Principal Component Analysis a. 3 components extracted 6.4.6 Rotated Component Matrix The table of rotated component matrix has been interpreted below: Statements (Variables) with high loadings on Factor 1 1. The Colleges and the University have developed better financial accounting procedure. 2. The project expenditure details are well maintained in the colleges/university for early settlement of accounts. 3. The procedure adopted for claim and payment of TA bills needs no revision. Statements (Variables) with high loadings on Factor 2 1. The procedure for salary disbursement is working well. 2. The procedure for medical reimbursement is functioning satisfactory. 3. TA and DA are paid within reasonable time. 4. The present system of claiming project funds from funding agencies is working will. 217

Statements (Variables) with high loading on Factor 3 1. The colleges are making prompt claims for payment of scholarships/fellowships to the students. 2. The university has evolved suitable procedures to collect fees/other charges from the students. 3. The university s administrative section is processing the papers for early payment of fellowships etc. Table 6.12: Rotated Component Matrix a (Teachers) V12.899 V10.842 V6.815 Component 1 2 3 V11.923 V8.821 V7.705 V9.647 V3.826 V2.794 V4.659 Extraction Method: Principal Component Analysis; Rotation Method: Varimax with Kaiser Normalization; a. Rotation converged in 5 iteration The three factors so extracted have been named as: Factor 1: Factor 2: Factor 3: Effectiveness of the procedure for fee collection and payment to students Financial Management Accounting Procedure 218

6.5 RESULTS OF INDEPENDENT SAMPLE T-TEST FOR TEACHERS After extraction of the three factor independent sample t-test was applied to check whether there is any significant difference between the perception of the teachers who have less then 3 years of experience and the teachers who have more than 3 years of experience in relation to the three factors extracted. To test this the following hypothesis were framed: H 0 : There is no significant difference the perception of the teachers with less then 3 years of experience and more than 3 years of experience. H 1 : There is a significant difference in the perception of the teachers with less then 3 years of experience and more than 3 years of experience. 6.5.1 Group Statistics The group statistic table gives the summary of mean standard deviation and standard errors of mean. From the table it is apparent that there is a difference in the means of the two groups in context of their perception related to three factors. Effectiveness of the procedure of fee Collection and payments to students Financial management of the university Accounting procedure Table 6.13: Group Statistics (Teachers) Experience N Mean Standard Deviation Standard Error Mean Below 3 years 54 2.9460 1.12906 0.10099 Above 3 years 40 3.3333 1.13479 0.17943 Below 3 years 54 3.5046 0.93098 0.12669 Above 3 years 40 3.8250 0.86640 0.13699 Below 3 years 54 3.5988 0.85016 0.11569 Above 3 years 40 3.7417 0.79623 0.12431 6.5.2 Independent Sample Test The table of independent sample t-test will help us to understand whether the difference in the means of two groups is statistically significant or not. 219

Table 6.14: Independent Sample t-test (Teachers) Levene s Test for Equality of Variances t-test for Equality of Means Effectiveness of the procedure of fee collection and payments to students Financial management of university Accounting Procedure Equal variances assumed F Sig. T Df Sig. (2- tailed) Mean difference Std. Error difference 0.166 0.684-1.070 92 0.287-2.4691 0.23067 Equal variances not assumed -1.063 81.973 0.291-2.4691 0.23227 Equal variances assumed Equal variances not assumed Equal variances assumed Equal variances not assumed 0.177 0.675-1.699 92 0.093-0.32037 0.18862-1.717 87.267 0.090-0.32037 0.18659 0.015 0.903-0.832 92 0.408-0.14290 0.17183-0.841 87.508 0.402-0.14290 0.16982 Factor 1 For factor 1, the value of significance under the column Levene s test for equality of variances is 0.685, that is, it is greater then 0.05. Thus, it is assumed that the variances of the two groups are equal. Hence, we will read the first row for the results of t-test. The significance (2-tailed) value is 0.287 which is greater then 0.05. It leads to the acceptance of null hypothesis which states that there is no significant difference in the perception of two categories in relation to this factor. Factor 2 The value of significance for Levene s test for equality of variances is 0.675 which is greater than 0.05. So, we will read the upper row for the results of t-test. The significance (two tailed) value is greater then 0.05 (0.093>0.05) which shows that there is no significant difference in the perception of two groups as far as this factor is concerned. Factor 3 In case of factor 3, the value of significance for Levene s test statistic is 0.903. As this value is greater then 0.05, so the first row of t-test will be read. Here the 220

significance (two-tailed) value is 0.408. This, proves that there is no significant difference in the perception of two groups as this value is greater then 0.05. 6.6 RESULTS OF FACTOR ANALYSIS FOR ADMINISTRATIVE STAFF The questionnaire designed for the administrative staff consisted of the following variables out of which one variable (V7) has been dropped for the purpose of analysis as it was creating a hindrance in the application of factor analysis. Variable 1 (V1): The university administration is run in accordance with the provisions of University Act, Statutes and other Regulations. Variable 2 (V2): The flow of receipt of grant from the State Government and central agencies such as UGC is normal. Variable 3 (V3): Disbursal of salary to the teaching and non-teaching staff is on due date. Variable 4 (V4): The Financial Administration in respect of staff welfare activities is satisfactory. Variable 5 (V5): TA and DA are paid within reasonable time of submission of claim. Variable 6 (V6): Medical reimbursement is made within reasonable period after submission of claim. Variable 7 (V7): Students fee reimbursement is made within reasonable time of submission of claim Variable 8 (V8): Promotion avenues are available Variable 9 (V9): Functioning of complaint mechanism regarding women employees is satisfactory. Variable 10 (V10): Investments of employees provident fund is proper. 221

6.6.1 Kaiser-Meyer-Olkin (KMO) and Bartlett s Test of Sphericity The value of the KMO statistic in this case is 0.539. Also, the Bartlett s test of sphericity is also significant as its associated probability is less than 0.05. It implies that correlation matrix is not an identity matrix and the date is fit for conducting factor analysis. Table 6.15: KMO and Bartlett s test (Administrative Staff) Kaiser-Meyer-Olkin Measure of sampling adequacy 0.539 Bartlett s test of Sphercity Approximate chi square 83.044 df 15 Significant 0.000 6.6.2 Communalities The table of communalities make it clear that the variable which states that, The flow of receipt of grant from the State Government and Central agencies such as UGC in normal accounts for 92.6% of the variability. The variable which states that, Medical reimbursement is made within reasonable period after submission of claim accounts for 65.6% of the variability. Table 6.16: Communalities (Administrative Staff) Initial Extraction V1 1.000 0.926 V2 1.000 0.838 V3 1.000 0.813 V4 1.000 0.656 V5 1.000 0.862 V6 1.000 0.682 V8 1.000 0.802 V9 1.000 0.902 V10 1.000 0.828 Extraction method: Principal component analysis 222

6.6.3 Total Variance Explained The table for the total variance explained by the factors show that factor 1 accounts for 40.184% of the variance, factor 2 accounts for 21.544% of the variance and factor 3 accounts for 20.129% of the variance. The remaining factors are not significant. Also, factors with Eigen values greater than 1 have to be retained, so the first three factors would be retained for further analysis. Table 6.17: Total Variance Explained (Administrative Staff) Component Initial Eigen values Extraction sum of squared loadings Rotation sum of squared loadings Total % of Cumulative Total % of Cumulative Total % of Cumulative variance % variance % variance % 1. 2.411 40.184 40.184 2.411 40.184 40.184 2.340 39.003 39.003 2. 1.293 21.554 61.737 1.293 21.554 61.737 1.355 22.586 61.589 3. 1.208 20.129 81.866 1.208 20.129 81.866 1.217 20.277 81.866 4. 0.805 6.685 88.551 5. 0.763 5.400 93.951 6. 0.662 3.150 97.101 7. 0.553 1.710 98.811 8. 0.336 0.520 99.331 9. 0.200 0.669 100.000 Extraction Method: Principal Component Analysis 6.6.4 Scree Plot As depicted by the Scree Plot, the curve begins to flotten between fourth and fifth factor. But the Eigen value of these factors is less than 1. Hence, only three factors will be retained for the analysis. 223

3 2.5 Eigen Values 2 1.5 1 0.5 0 1 2 3 4 5 6 7 8 9 Components Fig. 6.3: Scree Plot (Administrative Staff) 6.6.5 Component Matrix The higher absolute values of the loadings in the table of Component Matrix indicate that the contribution of factor is more to the variables. There is a gap in the table as we have suppressed the loadings less then 0.5. Table 6.18: Component Matrix a (Administrative Staff) Component 1 2 3 V4 0.878 V6 0.801 V8 0.779 V1 0.501 V2 0.908 V9 0.887 V5-0.830 V10-0.765 0.876 V3 0.645 Extraction method: Principal component analysis; a. 3 components extracted 224

6.6.6 Rotated Component Matrix The interpretation of rotated component matrix has been given below: Statements (Variables) with high loadings on factor 1 1. The financial administration in respect of staff welfare activities is satisfactory. 2. Medical reimbursement is made within reasonable period after submission of claims. 3. Promotion avenues are available. 4. Functioning of complaint mechanism regarding women employees is satisfactory. Statements (Variables) with high loadings on factor 2 1. The flow of receipt of grant from the state governments and Central agencies such as UGC is normal. 2. The university administration is run in accordance with the provisions of University Act, statutes and other regulations. 3. Investments of employees provident fund is proper. Statements (Variables) with high loadings on factor 3 1. TA and DA are paid within reasonable time of submission of claim. 2. Disbursal of salary to the teaching and non-teaching staff is on due date. Table 6.19: Rotated Component Matrix a (Administrative Staff) Component 1 2 3 V4 0.923 V9 0.835 V6 0.804 V8 0.798 V2 0.958 V10 0.865 V1 0.647 V5 0.843 V3-0.704 Extraction method: Principal component analysis Rotation method: Varimax with Kaiser Normalization; a. Rotation converged in 5 iterations 225

The three factors, thus, extracted have been named as: Factor 1 : Staff welfare activities Factor 2 : Financial Management and administration Factor 3 : Effectiveness of the procedure for payments to staff After extraction of the three factors, independent sample t-test was applied to check whether there is any significant difference between the perception of the class I and Class II employees as regards to these three factors so extracted. In order to test this, the following hypothesis were framed: H 0 : H 1 : There is no significant difference in the perception of the Class I and Class II employees. There is a significant difference in the perception of the Class I and Class II employees 6.7 INTERPRETATION OF THE RESULTS OF t-test FOR ADMINISTRATIVE STAFF 6.7.1 Group Statistics The group statistics table summarises the mean, standard deviation and standard error of mean of the two groups. It is clear from the table that there is a difference in the means of the two categories in each case. Table 6.20: Group Statistics (Administrative Staff) N Mean Standard deviation Standard error mean Staff welfare Activities Financial management and administration Procedure for payment to staff Class 1 19 3.8421 1.01451 0.23275 Class 2 27 3.7037 1.09421 0.21058 Class 1 19 3.3158 0.94591 0.21701 Class 2 27 3.0926 1.00992 0.19436 Class 1 19 4.6053 0.56713 0.13011 Class 2 27 4.6111 0.52502 0.10104 226

6.7.2 Independent Sample Test The table for independent sample test will enable to understand the statistical significance of the difference in the means of two groups of administrative staff. Table 6.21: Independent Sample t-test (Administrative Staff) Staff welfare activities Financial management and administration Procedure for payment to staff Equal variances assumed Equal variances not assumed Equal variances assumed Equal variances not assumed Equal variances assumed Equal variances not assumed Levene s Test for Equality of Variances t-test for Equality of Means F Sig. T Df Sig. (2-tailed) Mean difference Std. Error difference 0.574 0.453 0.435 44 0.666 0.13840 0.31811 0.441 40.666 0.662 0.13840 0.31387 0.520 0.475 0.757 44 0.453 0.22320 0.29473 0.766 40.444 0.448 0.22320 0.29132 0.078 0.782-0.036 44 0.971-0.00585 0.16249-0.035 36.953 0.972-0.00585 0.16473 Factor 1 If we look at the column of Levene s test for equality of variances, it is clear that for Factor 1 as the significance value is greater than 0.05, variances are equal in both the conditions. Thus we have to look for the results of t-test in the first row. The significance (2 tailed) value is 0.666. This value is greater than 0.05. Hence, the null hypothesis is accepted in this case is, that is, there is no significant difference in the perception of class 1 and class 2 employees as regards factor 1. Factor 2 The significance value under the column of Levene s test is 0.475 which is again greater than 0.05. So, the results of t-test would be read from the first row as the 227

variances are assumed to be equal in both the cases. The significance (two tailed) value is 0.453. As this value is greater than 0.05, so it, leads to acceptance of null hypothesis which states that there is no significant differences in the perception of class 1 and class 2 employees in relation to this factor. Factor 3 The significance value under the column of Levene s test is 0.782. As this value is greater than 0.05, so we assure that variances are equal in both the conditions. Thus, we will read the results of t-test from the first row. In this case, the significance (two tailed) value is 0.971 which is greater than 0.05. Hence, the null hypothesis is accepted in this case also and hence we can say that there is no difference in the perception of the employees of two class as far as Factor 3 is concerned. When the results of both the tables are combined, then it can be said: (a) (b) (c) The employees agree that the administration of university in respect of staff welfare activities is good. The employees agree that the financial management and administration of the university is good. The employees strongly agree that the procedure for making payments to staff is satisfactory. 228