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

(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

(Source: SPSS)

Regression analysis

ANOVA

Model

Sum of Squares

Mean Square

Sig.

Regression

211.099

52.775

58.808

.000^b

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	.491^a	.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 Management, 2(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 Evaluation, 24(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 Development, 8(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 Education, 43(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 & Organization, 25(1), pp.58-80.

Nolzen, N., 2018. The concept of psychological capital: a comprehensive review. Management Review Quarterly, 68(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 Studies, 1(1), pp.1-21.

Sameer, Y.M., 2018. Innovative behavior and psychological capital: Does positivity make any difference?. Journal of Economics & Management, 32, 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 Open, 10(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 science, 103, pp.247-259.

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