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Chapter 5

Analysis

5.0 DESCRIPTIVE STATISTICS

Descriptive Statistics
	N	Minimum	Maximum	Mean	Std. Deviation
reward	150	2.89	4.56	3.9015	.33010
motivation	150	1.80	5.00	3.8733	.49611
performance	150	2.75	5.00	3.9333	.45120
Valid N (list wise)	150

INTERPRETATION

Table shows the descriptive statistics that show the overall picture of all the 3 variables. There were scales of 5 responses that lead to the options (strongly disagree, disagree, neutral, agree, and strongly agree). Number of observation of each variable is 150. In above table, mean values and standard deviation values of 3 variables shown. Mean value provides the central tendency of values of variables. The average responses rates of these 3 variables are reward (mean: 3.90), motivation (mean: 3.87), and performance (mean: 3.93). If we observed the responses of all these variables lay within the option 3-4 (3 is for neutral and 4 is for satisfied). The minimum option that is ticked by respond is 1.50 and maximum option ticked by respond is 5.

Standard deviation gives the idea about the dispersion of the value of a variable from its mean value. The standard deviation of reward (S.D: 0.330) which is lowest in all variables values. Which shows most of the respondents answer were same for reward. The standard deviation of motivation (S.D: 0.496) which is highest in all variable values. Which shows most of the respondent answer were not the same and they don’t have consistency in their answers.

5.1 HISTOGRAMS

INTERPRETATION

The figure shows the graphical representation of the bars that is showing the response of the respondents regarding reward. Most of the participant’s lies within 3-4 (3 is for neutral and 4 is for agree). Small number of respondents were marked the very low and very high options. The bars in histogram from a distribution that is similar to the normal, bell shaped curve.

INTERPRETATION

The figure shows the graphical representation of the bars that is showing the response of the respondents regarding motivation. Most of the participant’s lies within 3-4 (3 is for neutral and 4 is for agree). Small number of respondents were marked the very low and very high options. The bars in histogram from a distribution that is similar to the normal, bell shaped curve.

INTERPRETATION

The figure shows the graphical representation of the bars that is showing the response of the respondents regarding performance Most of the participant’s lies within 3-4 (3 is for neutral and 4 is for agree). Small number of respondents were marked the very low and very high options. The bars in histogram from a distribution that is similar to the normal, bell shaped curve.

5.2 SCATTERPLOT

INTERPRETATION

This Figure shows the results of scatter plot matrix where we intend to have some idea about the relationship between reward and performance. The flow of line is from right to left which shows the positive relationship between reward and performance. This means that reward have the relationship with performance. And rewards increase the employee performance.

INTERPRETATION:

This Figure shows the results of scatter plot matrix where we intend to have some idea about the relationship between motivation and performance. The flow of line is from right to left which shows the positive relationship between motivation and performance. This means that motivation have the relationship with performance. And motivation increase the employee performance

5.3 CORRELATION

Correlation is used to check the mutual relationship among variables. For checking the relationship we will make two hypothesis; null hypothesis (Ho) and alternative hypothesis (H1). We interpret the findings on the acceptance and rejection of the hypothesis. We used correlation matrix to check the mutual relationship among variables. The hypotheses which we developed are given below;

Hypothesis 1:

H0: There is no relationship between reward and performance

H1: There is relationship between reward and performance.

Correlations
		reward	motivation	performance
reward	Pearson Correlation	1	.411**	.173*
	Sig. (2-tailed)		.000	.034
	N	150	150	150
motivation	Pearson Correlation	.411**	1	.226**
	Sig. (2-tailed)	.000		.005
	N	150	150	150
performance	Pearson Correlation	.173*	.226**	1
	Sig. (2-tailed)	.034	.005
	N	150	150	150
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).

INTERPRETATION

This table represents the table of correlations. Where motivation and performance are positively correlated with reward. (r=.411, p=.000 and r=.173, p=034)The magnitudes of this correlation are greater than 0.33 in the absolute terms, which shows the strong correlations between these variables. Reward and performance is positively correlated with motivation(   r= .411, p= .000 and r = 226, p=.005). The magnitude of correlation is more than 0.33 which shows the strong relationship between reward and motivation and the relationship between motivation and performance is weak.. Reward and motivation are positively correlated with performance(r=.173, p=.034 and r=.226, p=.005) which show the strong relationship between reward and performance and the relationship between motivation and performance is weak.  All the above correlations are statistically significant at less than five percent level of significance. In the case of these correlations the null hypothesis that were stated above of no correlation are rejected as the P-values are less than 0.05.  

5.4 REGRESSION

Variables Entered/Removedb
Model	Variables Entered	Variables Removed	Method
1	motivation, rewarda	.	Enter
a. All requested variables entered.
b. Dependent Variable: performance

Model Summary
Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
1	.243a	.059	.046	.44070
a. Predictors: (Constant), motivation, reward

ANOVAb
Model		Sum of Squares	df	Mean Square	F	Sig.
1	Regression	1.784	2	.892	4.593	.012a
	Residual	28.549	147	.194
	Total	30.333	149
a. Predictors: (Constant), motivation, reward
b. Dependent Variable: performance

The value of the coefficient of determination (R2) is (.059).. This shows that the correlation between the observed values of reward and fitted value of performance is 5%. The adjusted coefficient of determination (adj. R2) shows is adjusted for the degree of freedom. The value of adjusted coefficient of determination (adj. R2) is not affected. The value of adjusted coefficient of determination (adj. R2) is .046 which shows that 4% variation in employee perceived performance. The value of F-statistic is statistically significant at less five percent that exhibits that in the estimated model at least one of the partial regressions coefficients is different from zero

Coefficientsa
Model		Unstandardized Coefficients		Standardized Coefficients	t	Sig.
		B	Std. Error	Beta
1	(Constant)	2.761	.444		6.222	.000
	reward	.132	.120	.097	1.103	.272
	motivation	.169	.080	.186	2.119	.036
a. Dependent Variable: performance

Coefficient table presents the results of the regression analysis. The objective of the regression is to find the equation that could be used to find the impact of HR on employee perceived performance. The specific regression equation takes the following form:

Y = a + b (x1) + b (x2) + c (x3) ∑i

Per = 2.761 + .132 (reward) + .169 (motivation) + ∑i

Employee performance     = per

REWARD   = REW

MOTIVATION=MOT

The results show that independent variables significantly affect the employee performance, as shown above by the values of t-statistic and P-values. T-test is used to test the significance of the individual partial regression coefficient. Null hypothesis in this test is set as the partial regression coefficient is zero. This test shows that the coefficient of the predictor is statistically significant at less than five percent level of significance