Quantitaive Method Assignment Sample

Introduction

The present study mainly focuses on the relationship between human capacities and individual levels of outcomes to get better productivity. Psychological capital indicates self-efficiency, resilience, optimism, as well as hope that directly influence employee attitude, behaviours and performance effectively. SPSS analysis has been conducted in the section that helps to analyze the demographic information, descriptive analysis of employee’s psychological capacity and other research variables. Correlation and regression analysis also have been conducted to measure the relationship between all research variables to get desired research outcomes. Reliability analysis also may help to measure the scale of each variable to conduct this research smoothly. All the techniques of SPSS analysis will help to measure all the research variables regarding employees’ psychological capital, behaviour, and attitude that impacts the working output of employees.

Data findings

Descriptive analysis

Descriptive Statistics
  N Range Minimum Maximum Sum Mean Std. Deviation Variance
Statistic Statistic Statistic Statistic Statistic Statistic Std. Error Statistic Statistic
Confidence1 349 6 1 7 1835 5.26 .064 1.188 1.410
Confidence2 349 6 1 7 1840 5.27 .080 1.494 2.233
Confidence3 349 6 1 7 1729 4.95 .085 1.584 2.509
Confidence4 349 6 1 7 1857 5.32 .074 1.386 1.920
Hope1 349 6 1 7 1851 5.30 .065 1.222 1.494
Hope2 349 6 1 7 1619 4.64 .084 1.572 2.473
Hope3 349 6 1 7 1867 5.35 .062 1.159 1.343
Hope4 349 6 1 7 1704 4.88 .073 1.369 1.874
Resilience1 349 6 1 7 1880 5.39 .056 1.041 1.083
Resilience2 349 6 1 7 1667 4.78 .077 1.443 2.082
Resilience3 349 6 1 7 1868 5.35 .066 1.241 1.539
Resilience4 349 6 1 7 1865 5.34 .067 1.247 1.554
Optimism1 349 6 1 7 1483 4.25 .077 1.436 2.061
Optimism2 349 6 1 7 1640 4.70 .077 1.440 2.073
Optimism3 349 6 1 7 1612 4.62 .083 1.543 2.380
Optimism4 349 6 1 7 1579 4.52 .070 1.314 1.727
Inno1 349 6 1 7 1573 4.51 .079 1.473 2.170
Inno2 348 6 1 7 1662 4.78 .072 1.346 1.811
Inno3 346 6 1 7 1718 4.97 .067 1.244 1.547
Inno4 349 6 1 7 1433 4.11 .085 1.588 2.520
Job_sat1 349 6 1 7 1645 4.71 .082 1.533 2.349
Job_sat2 349 6 1 7 1782 5.11 .071 1.321 1.744
Job_sat3 349 6 1 7 1793 5.14 .071 1.328 1.763
Job_sat4 349 6 1 7 1651 4.73 .080 1.500 2.249
Perf1 347 6 1 7 1795 5.17 .080 1.484 2.201
Perf2 348 6 1 7 1917 5.51 .063 1.175 1.380
Perf3 348 6 1 7 1909 5.49 .066 1.223 1.495
Perf4 346 6 1 7 1894 5.47 .067 1.244 1.549
Tenure_post_code 338 5.00 1.00 6.00 880.00 2.6036 .06605 1.21425 1.474
Tenure_org_code 336 5.00 1.00 6.00 1029.00 3.0625 .07918 1.45139 2.107
Gender 331 1 0 1 166 .50 .028 .501 .251
Age 341 56 17 73 15784 46.29 .696 12.853 165.205
Valid N (listwise) 313                

Table 1: Descriptive analysis

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(Source: SPSS)

Reliability analysis

 
Cronbach’s Alpha N of Items
.776 32

 

Table 2: Reliability analysis

(Source: SPSS)

Correlation analysis

 
  Confidence1 Confidence2 Confidence3 Confidence4 Hope1 Hope2 Hope3 Hope4
Confidence1 Pearson Correlation 1 .574** .553** .460** .587** .353** .557** .499**
Sig. (2-tailed)   .000 .000 .000 .000 .000 .000 .000
Sum of Squares and Cross-products 490.791 354.501 362.126 263.117 296.665 229.493 266.539 282.573
Covariance 1.410 1.019 1.041 .756 .852 .659 .766 .812
N 349 349 349 349 349 349 349 349
Confidence2 Pearson Correlation .574** 1 .772** .731** .535** .426** .466** .548**
Sig. (2-tailed) .000   .000 .000 .000 .000 .000 .000
Sum of Squares and Cross-products 354.501 777.140 636.355 526.513 340.146 348.298 280.791 390.160
Covariance 1.019 2.233 1.829 1.513 .977 1.001 .807 1.121
N 349 349 349 349 349 349 349 349
Confidence3 Pearson Correlation .553** .772** 1 .739** .491** .426** .516** .530**
Sig. (2-tailed) .000 .000   .000 .000 .000 .000 .000
Sum of Squares and Cross-products 362.126 636.355 873.266 564.135 330.860 369.223 329.593 400.120
Covariance 1.041 1.829 2.509 1.621 .951 1.061 .947 1.150
N 349 349 349 349 349 349 349 349
Confidence4 Pearson Correlation .460** .731** .739** 1 .424** .453** .424** .570**
Sig. (2-tailed) .000 .000 .000   .000 .000 .000 .000
Sum of Squares and Cross-products 263.117 526.513 564.135 668.057 249.983 343.436 236.848 376.158
Covariance .756 1.513 1.621 1.920 .718 .987 .681 1.081
N 349 349 349 349 349 349 349 349
Hope1 Pearson Correlation .587** .535** .491** .424** 1 .392** .649** .497**
Sig. (2-tailed) .000 .000 .000 .000   .000 .000 .000
Sum of Squares and Cross-products 296.665 340.146 330.860 249.983 519.805 262.269 319.946 289.453
Covariance .852 .977 .951 .718 1.494 .754 .919 .832
N 349 349 349 349 349 349 349 349
Hope2 Pearson Correlation .353** .426** .426** .453** .392** 1 .442** .641**
Sig. (2-tailed) .000 .000 .000 .000 .000   .000 .000
Sum of Squares and Cross-products 229.493 348.298 369.223 343.436 262.269 860.510 280.046 480.198
Covariance .659 1.001 1.061 .987 .754 2.473 .805 1.380
N 349 349 349 349 349 349 349 349
Hope3 Pearson Correlation .557** .466** .516** .424** .649** .442** 1 .519**
Sig. (2-tailed) .000 .000 .000 .000 .000 .000   .000
Sum of Squares and Cross-products 266.539 280.791 329.593 236.848 319.946 280.046 467.352 286.332
Covariance .766 .807 .947 .681 .919 .805 1.343 .823
N 349 349 349 349 349 349 349 349
Hope4 Pearson Correlation .499** .548** .530** .570** .497** .641** .519** 1
Sig. (2-tailed) .000 .000 .000 .000 .000 .000 .000  
Sum of Squares and Cross-products 282.573 390.160 400.120 376.158 289.453 480.198 286.332 652.183
Covariance .812 1.121 1.150 1.081 .832 1.380 .823 1.874
N 349 349 349 349 349 349 349 349
 

 

 
  Resilience1 Resilience2 Resilience3 Resilience4 Hope1 Hope2 Hope3 Hope4
Resilience1 Pearson Correlation 1 .611** .740** .630** .606** .370** .593** .490**
Sig. (2-tailed)   .000 .000 .000 .000 .000 .000 .000
Sum of Squares and Cross-products 376.779 319.172 332.421 284.582 267.997 210.739 248.808 242.860
Covariance 1.083 .917 .955 .818 .770 .606 .715 .698
N 349 349 349 349 349 349 349 349
Resilience2 Pearson Correlation .611** 1 .730** .672** .558** .362** .487** .523**
Sig. (2-tailed) .000   .000 .000 .000 .000 .000 .000
Sum of Squares and Cross-products 319.172 724.567 454.490 420.819 342.691 285.840 283.266 359.837
Covariance .917 2.082 1.306 1.209 .985 .821 .814 1.034
N 349 349 349 349 349 349 349 349
Resilience3 Pearson Correlation .740** .730** 1 .693** .629** .412** .584** .515**
Sig. (2-tailed) .000 .000   .000 .000 .000 .000 .000
Sum of Squares and Cross-products 332.421 454.490 535.650 372.708 331.642 279.407 292.003 304.450
Covariance .955 1.306 1.539 1.071 .953 .803 .839 .875
N 349 349 349 349 349 349 349 349
Resilience4 Pearson Correlation .630** .672** .693** 1 .576** .354** .509** .526**
Sig. (2-tailed) .000 .000 .000   .000 .000 .000 .000
Sum of Squares and Cross-products 284.582 420.819 372.708 540.739 305.553 241.324 256.052 312.097
Covariance .818 1.209 1.071 1.554 .878 .693 .736 .897
N 349 349 349 349 349 349 349 349
Hope1 Pearson Correlation .606** .558** .629** .576** 1 .392** .649** .497**
Sig. (2-tailed) .000 .000 .000 .000   .000 .000 .000
Sum of Squares and Cross-products 267.997 342.691 331.642 305.553 519.805 262.269 319.946 289.453
Covariance .770 .985 .953 .878 1.494 .754 .919 .832
N 349 349 349 349 349 349 349 349
Hope2 Pearson Correlation .370** .362** .412** .354** .392** 1 .442** .641**
Sig. (2-tailed) .000 .000 .000 .000 .000   .000 .000
Sum of Squares and Cross-products 210.739 285.840 279.407 241.324 262.269 860.510 280.046 480.198
Covariance .606 .821 .803 .693 .754 2.473 .805 1.380
N 349 349 349 349 349 349 349 349
Hope3 Pearson Correlation .593** .487** .584** .509** .649** .442** 1 .519**
Sig. (2-tailed) .000 .000 .000 .000 .000 .000   .000
Sum of Squares and Cross-products 248.808 283.266 292.003 256.052 319.946 280.046 467.352 286.332
Covariance .715 .814 .839 .736 .919 .805 1.343 .823
N 349 349 349 349 349 349 349 349
Hope4 Pearson Correlation .490** .523** .515** .526** .497** .641** .519** 1
Sig. (2-tailed) .000 .000 .000 .000 .000 .000 .000  
Sum of Squares and Cross-products 242.860 359.837 304.450 312.097 289.453 480.198 286.332 652.183
Covariance .698 1.034 .875 .897 .832 1.380 .823 1.874
N 349 349 349 349 349 349 349 349
 

Table 3: Correlation analysis

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(Source: SPSS)

Regression analysis

ANOVA
Model Sum of Squares df Mean Square F Sig.
1 Regression 211.099 4 52.775 58.808 .000b
Residual 308.706 344 .897    
Total 519.805 348      
 

Model Summary
Model R R Square Adjusted R Square Std. An error in the Estimate
1 .491a .241 .232 1.378
 
Table 4: Regression analysis

(Source: SPSS)

Data analysis

Descriptive analysis

Based on the mean value for most of the questions is 1 and the maximum value of most of the questions is 7. The mean value generally indicates the lower responses and on the other hand, maximum value indicates higher responses from respondents (Sonmez Cakir and Adiguzel, 2020). Mean and maximum value indicates how respondents give responses regarding psychological capital of employees that impact employees’ behavior and attitude that gives positive working outcomes. The mean value of the first 4 questions is 5.26, 5.27, 5.2, 4.95, and 5.32 which indicates respondents agree that the psychological stability of employees increases self-confidence and gives better working output. The standard deviation of these questions is also 1.188, 1.494, 1.584, and 1.386 therefore, it can be said that responses are well distributed over the responses given by responders in this survey.

The mean value of questions 5th, 6th, 7th, and 8th are 5.30, 4.64, 5.35, and 4.88, indicating that respondents agree that management needs to focus on employee’s hope to make them satisfied and develop their psychological state within the organization. The standard deviation of 5th and 6th questions is 1.494, 2.473, hence it can be said that most of the responses from responders are well distributed. The mean value of all the questions regarding employee resilience is more than 4.78 and less than 5.39, Therefore respondents strongly agree that good psychological capital indicates better employee resilience and positive outcomes in working performance.

Based on the descriptive analysis it has been found that the mean value of the 13th, 14th, 15th, and 16th questions are 4.25, 4.70, 4.62, and 4.52, indicating respondents strongly agree that good psychological health of employees increase working performance and reduce organizational cost-effectively. The standard deviation rate of these questions is 2.061, 2.073, 2.380, and 1.727. Therefore, it can be understood that all responses are well distributed over the responses given by responders (Ahmad et al. 2019). The mean value of 17th number question is 4.51 therefore it can be understood that survey participants strongly agree that good psychological capital one employees bring innovation. The standard deviation of this question is 2.170 which indicates responses are well distributed over the given responses by responders in their survey. The mean value of job satisfaction-related questions is 4.71-5.14. It indicates that job satisfaction increases the working performance of employees and respondents strongly agree with that. The standard deviation rate of 21st, 22nd, and 23rd number questions are 2.349, 1.744, and 1.763, Therefore from this data, it can be said that responses are well distributed over the responses collected from the responder.

Reliability analysis

Based on the above table in this SPSS analysis, the value of Cronbach’s Alpha is .776 that indicates, all items have good reliability with the given topic in this present research. As per the views of Astivia and Zumbo (2019), values of alpha ranging from 0.6-0.7, indicate moderate reliability and on the other hand range between 0.7-0.8, indicating good reliability, as well as, values above 0.8 shows excellent reliability of items with the research scenario. All research items are mostly related to the employee’s psychological capital that impacts employee’s attitude and behaviours. Research members found suitable information regarding job satisfaction and self-confidence that directly affects employees’ effectiveness and working performance.

Correlational analysis

The correlation value of confidence-1 with hope-1, hope-2, hope-3 is .587, .353, .557, .499 that indicates that all variables are highly significant at 0.01 level. On the other hand, it has been seen that the correlation value between confidence-2 and hope-1, hope-2 and hope-3 is .535, .426, .466, .548. It also indicates that confidence-2 is highly significant with an employee’s hope. Therefore, correlational analysis helps to find out the significance level between two variables such as hope and confidence in this research (Guo and Hou, 2021). Based on the above table, it has been found that confidence-3 and confidence-4 are also well connected and highly significant with hope-1, hope-2, and hope-3.

Regression analysis

Based on the above table of regression analysis it has been found that the value of R is .491 and the value of R square is .241. Based on the Anova analysis it has been found that the total sum of squares is 860.510, where the value of the mean square of regression is 51.904. The regression analysis also helps to measure the relationship between research variables and research modules to get desired research outcomes effectively (Kafle, 2019).

Discussion     

Based on the above table it has been found, that respondents strongly agree that psychological capital of employees directly impacts self-confidence of employees (Mishra et al. 2019). Therefore it can be said that organizations need to develop mental stability for reducing psychological stress to enhance working performance. Employee attitude, behaviors can be affected by an employee’s psychological capacity within an organization (Hu, 2020). The mean value of hope is 5.30 that indicate that employee’s hope plays an important role to improve working efficiency and get better working performance from employees within an organization. Based on the above analysis it has been seen that most of the respondents stated that employee resilience depends on their psychological capital (Luthans et al. 2019). Good psychological capital and psychological capacity of each employee plays important roles to increase performance and working ability.

Based on the mean value of descriptive analysis respondents strongly agree that employee’s hope is an important factor in maintaining their psychological capacity effectively (Novitasari et al. 2020). Management needs to focus on employee’s hope to make them satisfied and encourage them to get better working output. The mean value of employees resilience is 5.39 with standard error 0.56. It can be said from the above data statistics that maintaining employee resilience depends on their behaviors, attitude, and psychological capacity (Wang et al. 2018). Based on the correlation analysis it has been seen that relation between two variables such as confidence and hope is highly significant. It can be said that an employee’s confidence level is effectively connected with the employee’s hope. Therefore, management needs to focus on maintaining employee confidence to make them satisfied and fulfill their hope within the organization (Burhanuddin et al. 2019). The variable of confidence-4 is highly significant with hope-1, hope-2 and hope-3.

The standard deviation value of each variable such as confidence, job satisfaction, hope, resilience indicates that responses are well distributed over the responses provided by respondents in the research. High correlation values between employee confidence and employee’s hope are effectively interconnected. As cited by Nolzen (2018), management needs to increase employee confidence level to manage their hopes and needs within an organization. It has been seen that all the variables have positive mean values and it indicates all respondents strongly agree for each response in this research. The mean value of job satisfaction is 4.71, indicating that the psychological state of an employee helps to get satisfaction in a job within an organization. Good job satisfaction level helps to increase the working ability as well as performance of employees (Sameer, 2018). Through this analysis, the relation between employee satisfactions, hope, and confidence with organizational performance has been discussed in an effective manner.

Conclusion

Based on the SPSS analysis it has been seen that most of the responses are well distributed over the responses given by the responders in this survey. Respondents strongly agree that psychological capital increases self-confidence, hope, job satisfaction, and innovation within an organization. The analysis of mean, median value through SPSS helps to understand how respondents agree, disagree, and strongly agree with each variable in this present research. Through the regression analysis, correlational analysis researchers found the value of R and R-square to understand the relation between waxing variables. The positive mean value indicates, respondents agree that employee’s behavior, attitude, job satisfaction, and hope is dependent on the psychological capital effectively. Therefore, SPSS analysis helps to understand the survey responses and measure the relationship between each research variable for achieving desired research outputs.

References

Ahmad, I., Danish, R.Q., Ali, S.A., Ali, H.F. and Humayon, A.A., 2019. A comparative study of banking industry based on appraisal system, rewards and employee performance. SEISENSE Journal of Management2(1), pp.1-11.

Astivia, O.L.O. and Zumbo, B.D., 2019. Heteroskedasticity in Multiple Regression Analysis: What it is, How to Detect it and How to Solve it with Applications in R and SPSS. Practical Assessment, Research, and Evaluation24(1), p.1.

Burhanuddin, N.A.N., Ahmad, N.A., Said, R.R. and Asimiran, S., 2019. A systematic review of the psychological capital (PsyCap) research development: Implementation and gaps. International Journal of Academic Research in Progressive Education and Development8(3), pp.133-150.

Guo, B. and Hou, Q., 2021. RETRACTED: Research on obstacles of socialization of old residential district management under the theory of community conflict—Regression analysis based on SPSS software. The International Journal of Electrical Engineering & Education, p.0020720920983548.

Hu, Z., 2020, August. Based on multiple linear regression analysis of ability of medical postgraduate students to express SPSS experiment results with three-line table. In Journal of Physics: Conference Series (Vol. 1592, No. 1, p. 012060). IOP Publishing.

Kafle, S.C., 2019. Correlation and regression analysis using SPSS. OCEM Journal of Management, Technology & Social Sciences, pp.126-132.

Luthans, K.W., Luthans, B.C. and Chaffin, T.D., 2019. Refining grit in academic performance: The mediational role of psychological capital. Journal of Management Education43(1), pp.35-61.

Mishra, P., Bhatnagar, J., Gupta, R. and Wadsworth, S.M., 2019. How work–family enrichment influence innovative work behavior: Role of psychological capital and supervisory support. Journal of Management & Organization25(1), pp.58-80.

Nolzen, N., 2018. The concept of psychological capital: a comprehensive review. Management Review Quarterly68(3), pp.237-277.

Novitasari, D., Siswanto, E., Purwanto, A. and Fahmi, K., 2020. Authentic Leadership and Innovation: What is the Role of Psychological Capital?. International Journal of Social and Management Studies1(1), pp.1-21.

Sameer, Y.M., 2018. Innovative behavior and psychological capital: Does positivity make any difference?. Journal of Economics & Management32, pp.75-101.

Sonmez Cakir, F. and Adiguzel, Z., 2020. Analysis of leader effectiveness in organization and knowledge sharing behavior on employees and organization. SAGE Open10(1), p.2158244020914634.

Wang, D., Wang, X. and Xia, N., 2018. How safety-related stress affects workers’ safety behavior: The moderating role of psychological capital. Safety science103, pp.247-259.

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