Survey of Employee Satisfaction Essay Example

arraies to randomly choose a sample of 30 employees from the 363 records.

A transcript of the random figure tabular arraies used and the sample of 30 records can be found in the appendix of this assignment.

Undertaking 2 – Graphs and Calculation

Comparison of Employment Status and Recommend Organisation

Organization recommendation by employment type and figure of employees variables were chosen for comparing. These variables were taken from 30 employees random figure tabular array ( see appendix 2 ) . A chart was created utilizing those variables to visualize the comparing of Employment position and Recommendation.

The Charts above shows that 18 Employees who are Permanent and 6 who are Casual recommend yes. On the contrary, 4 Permanent and 2 Casual employees recommend no. There is besides a important difference can be visualised between positive and negative recommendation. The entire figure of positive recommendation is 24 ( 80 % ) out of 30. On the other manus, the combination of negative recommendation is 6 ( 20 % ) out of 30 ; which is one fifth of the entire participant.

Satisfaction with Working Conditionss

In order to cipher frequence, satisfaction of working status mark is used from a random figure tabular array which shows in appendix 2. The sores were taken into lowest to highest and bins were calculated in order. In order to cipher the frequence, a expression of frequence “=FREQUENCY ( G2: G31, G35: G40 ) ” was used. Centers were calculated by figure taken from tonss and bins by choosing their in-between figure manually. The histogram was created utilizing frequence Numberss in excel with 2D clustered column. Then from informations series spread breadth reduced to

0 % to make a complete histogram.

Frequencies utilizing Bins

Formula

Histogram

The histogram above shows employee figure of frequence distribution in relation to satisfaction with working status. Tonss between 2.5 to 3.5 and 4.0 to 4.5 are rather frequent. On the other manus, tonss between 3.5 to 4.0 and 4.5 to 5.0 are somewhat lower but every bit balanced. And score 2.0 to 2.5 is a really low graduated table. Finally, due to the big value, the histogram is somewhat skewed to the left, or negative skewed. Without this value, the histogram would be moderately symmetric.

Accumulative Frequency Curve for Satisfaction with Working Conditions

The cumulative frequence is the running sum of frequences. On a graph, it is represented by a cumulative frequence polygon, where consecutive lines connect to the cumulative frequence curve. These informations were used to pull a cumulative frequence polygon by plotting the cumulative frequences against the category. Accumulative frequence was calculated utilizing frequence Numberss which were created earlier.

Accumulative Frequency Curve

The graph above shows a smooth curve. The cumulative frequence graph above illustrates employees satisfaction with working conditions. By pulling horizontal lines it represents first quartile of the entire frequence, 2nd quartile of the entire frequence, 3rd quartile of the entire frequence, can read estimations of the lower quartile, average and upper quartile from the horizontal

axis. Though here a set N value nowadays, the undermentioned computation is done in order.

Table 1. Formula

Though there are 30 values ; n=30, is an even figure, from the undermentioned expression ;

• Lower quartile = 7.75 th value ; which would be 2.62
• Upper quartile = 23.25 th value ; which would be 3.84
• Median = 15.5 th value ; which would be 3.28 ( agencies median would be 15 Thursday and 16 Thursday values average ) .
• Interquartile scope ( 3.84 – 2.62 ) =1. 22 and
• semi-interquartile scope ( 1.22/2 ) =0.61

Statistical Calculations

The below statistical computations shows that mean 6.07 for length of services is exact reply, whereas median is 3. There is a big difference between mean and average because of utmost value e.g. 34

The mean and median for the satisfaction of working conditions are 3.79 and 3.85 severally. These are good tonss out of five. And 4.12 and 4.15 are the mean and median for the importance of working conditions at the same time. These are more good tonss out of five.

Remarks: consequences are dependable

Mean and Standard Deviation of a Frequency Distribution

Length of Service used for frequence distribution and organised lowest to highest order in order to bring forth their mid-point.

Formulas

used

The computation shows that the mean is 6.00 and the standard divergence is 6.51. The standard divergence measures the spread of the informations about the mean.

Drumhead Statisticss

Importance of Working Conditions allows to cipher drumhead statistics: mean, average, standard divergence, percentiles, etc.

The drumhead statistics show ;

• The assurance interval ( 95.0 % ) is 4.12 ± 0.21.

• Kurtosis and Skewness less than 2*0.105 ( standard mistake ) , so the information is normal. In this table Kurtosis 0.105 and Skewness -0.541 both are less than 2*0.105.

Undertaking 3 – t-test

T-test comparison satisfaction with working conditions for lasting and insouciant employees

Hydrogen₀: There is no important difference Null Hypothesis

Hydrogen₁: There is a important difference Alternative Hypothesis

Analysis and account

• From the findings, the average satisfaction mark of the lasting employees is 3.8 and the average satisfaction mark of the causal employees is 3.9

• The t-statistic is – 0.383 and the critical value is 2.048 for a two tailed trial.

• Ignore the subtraction mark and since 0.383 & A ; lt ; 2.048, so Accept H₀

• Alternatively, since the p-value = 0.704 & A ; gt ; 0.05 so Accept H₀

• The sample grounds indicates that there is no important difference in the satisfaction mark of the lasting and insouciant employees at the 5 % degree of significance. The sample besides grounds indicates Null Hypothesis can non reject because P & A ; gt ; 0.05

Undertaking 4 – Correlation and Regression

Scatter graph compared tonss in correlativity of satisfaction and importance of working conditions

The spread graph shows positive correlativity at the 5 % degree, which means within these two variables there is a

strong positive relationship. Arrested development used to find the quantitative relationship between two variables, and normally follows on from the constitution of important correlativity. The equation of the arrested development line is “y = a + bx” ; where a ( the intercept ) and B ( the incline ) are invariables.

Undertaking 5 – Written Analysis

Summary of findings:

• In this assignment 30 employees record was collected from 363 population as a sample of informations analysis.

• Datas used for multiple saloon chart comparing drawn between Employment position and Recommend of organisation which shows that 18 Employees who are Permanent and 6 who are Casual recommend yes. On the contrary, 4 Permanent and 2 Casual employees recommend no.

• From graphical computations shows in a histogram that frequence distribution at 2.25 represent a category 0-3 for length of services of 30 employees. On the other manus, frequence distribution at 4.25 represent category 4 -4.5.

• The drumhead statistics show that the assurance interval ( 95.0 % ) is 4.12 ± 0.21. Kurtosis and Skewness less than 2*0.105 ( standard mistake ) , so the information is normal. In this table Kurtosis 0.105 and Skewness -0.541 both are less than 2*0.105.

• The average satisfaction mark of the lasting employees is 3.8 and the average satisfaction mark of the causal employees is 3.9. The t-statistic is – 0.383 and the critical value is 2.048 for a two tailed trial.

• Ignore the subtraction mark and since 0.383 & A ; lt ; 2.048, so Accept H0
• Alternatively, since the p-value = 0.704 & A ; gt ; 0.05 so Accept H0

The sample grounds indicates that there is no important difference in the

satisfaction mark of the lasting and insouciant employees at the 5 % degree of significance.The sample besides grounds indicates Null Hypothesis can non reject because P & A ; gt ; 0.05

• The spread graph shows positive correlativity at the 5 % degree, which means within these two variables there is a strong positive relationship. Arrested development used to find the quantitative relationship between two variables, and normally follows on from the constitution of important correlativity. The equation of the arrested development line is y = a + bx ; where a ( the intercept ) and B ( the incline ) are invariables

Appendix 1 – Copy of random figure tabular arraies used