Data Inerpretation PRACTICUM

Data Inerpretation PRACTICUM

Project description
Designing Experiments

This weeks instructions: .Please follow the specific instructions

This week, you will run descriptive statistics and a t test on your chosen dataset. This Application requires you to engage in data interpretation and to select the appropriate analyses for your hypotheses and for the data that you have at your disposal. Toward that end, you should consider which descriptive statistics will inform the reader and allow you to pursue your questions.

Your submission to your Instructor should include your SPSS output file of your descriptive statistics analysis in a Word document, along with each of the following elements: your SPSS output, including graphical representations; your narrative interpretation; the governing assumptions of the analyses you ran; the viable and nonviable hypotheses (null and alternative); and the relevant values (such as a P value indicating statistical significance or a lack thereof).

Information on the chosen dataset below:

The main data set selected from Week 1 resources from Green and Salkind (2014) provided key information that takes an in depth look at organizational behaviors. The quasi-experimental design was used to collect data from 51 warehouse managers selected from three different locations in Boston, Phoenix and Seattle. The managers in the study held diverse positions within their companies. The study examined the independent variables which were: safety climates and injury rates and the dependent variable was organizational risks. The quasi-experimental design was selected due to the fact that it demonstrates how the independent variables may cause the dependent variable to do interact. It demonstrates the cause and effect patterns. Subsequently, the quasi-experimental designs lack random assignment of participants to experimental groups (Shelley,2014) which made it feasible for my study. To assess organizational risk a comparison of means between injury rate and safety climate was conducted using two way ANOVA test.

The hypotheses selected to be tested is:

H0: The degree of employees compliance with safety climate has no significance relationship with injury rate.
H1: The degree of employees compliance with safety climate has a significance relationship with injury rate.

The analysis in this thesis was major descriptive statistics on the seven scales, comparison of means through one way two-way ANOVA, graphs and pie charts.
Table 1
Descriptive Statistics
NRangeMeanStd. DeviationVariance
NumEmps514024.027.49556.180
Hours Worked518320049960.7815590.236243055455.373
PerSafeBeh51.577.86582.138945.019
InjuryRate5176.92315.1757017.474677305.364
SafetyClimate5144.701.0351.071
Risk5164.592.0124.047
Experience Coded5121.96.824.678
Valid N (listwise)51

From table 1 above, there are an average number of employees of 24.02. These employees have worked on average 49960.78 hours having been exposed to an average 15.1757 injury rates.
Comparison of means between injury rate and safety climate was conducted using two way ANOVA test.
Table 2
ANOVAa
ModelSum of SquaresdfMean SquareFSig.
1Regression2.45312.453.008.930b
Residual15265.76549311.546
Total15268.21750
a. Dependent Variable: InjuryRate
b. Predictors: (Constant), SafetyClimate

With p=0.05, the hypothesis being tested was
H0: Safety climate has no significance relationship to injury rate
H1: Safety climate has a significance relationship to injury rate
Table 2 above gives p=0.93. The rule is if the calculated p value is smaller than the tabulated p=0.05, reject the null hypothesis. However, in this case p0.05, which imply that the null hypothesis is not rejected thus safety climate has no significant relationship with injury rate. This conclusion is well supported by the line graph in figure 1 below.
Figure 1. The plot of injury rate response to safety climate
The line graph shows high degree of variations between safety climate and injury rate. This implies that it was not easy to predict mean injury rate at any point in time based on safety climate. Safety climate scores as a proportion of the overall 7 points scale has a higher standard deviation that explain higher variability in the safety climate data collected. This indicates that, safety climate had a number of outliers that could be affecting the spread of the data and the mean. The result of such disparities is that it affects the relationship between safety climate and injury rate as a dependent factor to safety climate. A further test for possible outliers could clarify the disparity in the data. Although this research did not show any significant relationship between safety climate and injury rate, including more parameters in measuring the two aspects can help. The two parameters where measured in a wide spectrum thus could have failed to capture the key features needing to be addressed.
To check for the distribution of hours worked by employees, a pie chart was use.

Figure 2: Pie chart of hours worked by employees.
The highest hours worked were 45760. This indicates that the level of experience in this group of employees varied. Some had worked for longer while others had worked fewer hours. This imply that there was high competition and high turn overs for some while some enjoyed the privilege of having long-serving employees. This could also imply that the number of employees determine the hazard rate created to the environment. However, results shows that there is no significance relationship between hazard cause injuries and climate safety level

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