Monday, June 3, 2019
Employee Performance Analysis
Employee Performance AnalysisProject OutlineThis visualize for is slightly the Employee carrying into action in an organization. Data related to several factors such as Employee productiveness, customer delights win, verity pocks, learn and be on of Employees is taken into consideration. Statistical methods are employ to identify if there is any impact of develop and Experience of Employees on factors such as productivity, Customer blessedness and Accuracy.Theoretical FrameworkXYZ Corporation operating out of Illinois, US want to find out if the eld and witness of employees have an impact on his/her performance. They have hired an external consultant to study the impact of these two factors (age and reckon) on the performance metrics of the employees. According to the results of the research conducted by this external consultant, XYZ Corporate will design a strategy of recruiting the right talent which will have maximum performance. physical body and MethodologyDesi gn and Methodology employ by the external consultant include identifying the various performance factors common across different businesses within XYZ Corporation. The performance quantifys common for all businesses includedCustomer happiness ScoresAccuracy Scores productivityThe consultants decided to study the impact of age of employees and their experience on the to a higher place factors by using statistical methods.Details on participants and sampling methodsSampling MethodsSampling is the process of selecting a small number of elements from a larger defined take group of elements. Population is the total group of elements we want to study. Sample is the subgroup of the population we actually study. Sample would mean a group of n employees chosen haphazardly from organization of population N. Sampling is done in situations likeWe consume when the process involves destructive testing, e.g. taste tests, car crash tests, etc.We sample when there are constraints of time an d costsWe sample when the populations cannot be easily capturedSampling is NOT done in situations likeWe cannot sample when the events and products are incomparable and cannot be replicableSampling can be done by using several methods including Simple random sampling, Stratified random sampling, Systematic sampling and thud sampling. These are Probability Sampling Methods. Sampling can alike be done using methods such as Convenience sampling, model sampling, Quota sampling and sweet sand verbena sampling. These are non-probability methods of sampling.Simple random sampling is a method of sampling in which every unit has equal chance of being selected. Stratified random sampling is a method of sampling in which stratum/groups are created and then units are picked randomly. Systematic sampling is a method of sampling in which every nth unit is selected from the population. Cluster sampling is a method of sampling in which clusters are sampled every tth time.For the non-probabilit y methods, Convenience sampling relies upon convenience and access. Judgment sampling relies upon belief that participants fit characteristics. Quota sampling emphasizes representation of specific characteristics. Snowball sampling relies upon respondent referrals of others with like characteristics.In our research, the consultant organization used a Simple Random Sampling method to conduct the study where they chose about 75 random employees and gathered info of age, experience, their Customer Satisfaction scores, their Accuracy Scores and their Productivity scores.The employees were bifurcated into 3 age groups, namely, 20 30 days, 30 40 years and 40 50 years. Similarly, they were also bifurcated into 3 experience groups, namely, 0 10 years, 10 20 years and 20 30 years.Data AnalysisBelow are the different data analysis options used by the consultantImpact of mature on AccuracyImpact of Experience on AccuracyImpact of succession on Customer SatisfactionImpact of Experienc e on Customer SatisfactionImpact of Age on ProductivityImpact of Experience on ProductivityFor each of the above statistical analysis, we will need to use Hypothesis testing methods. Hypothesis testing tells us whether there exists statistically significant difference between the data sets for us to consider to represent different distribution. The difference that can be detected using theory testing isContinuous DataDifference in AverageDifference in VariationDiscrete DataDifference in Proportion DefectiveWe follow the below steps for Hypothesis testingStep 1 Determine appropriate Hypothesis testStep 2 State the Null Hypothesis Ho and assemble Hypothesis HaStep 3 Calculate Test Statistics / P-value against table value of test statisticStep 4 Interpret results Accept or reject HoThe apparatus of Hypothesis testing involves the followingHo = Null Hypothesis There is No statistically significant difference between the two groupsHa = Alternate Hypothesis There is statistically significant difference between the two groupsWe also have different types of errors that can be caused if we are using possibleness testing. The errors are as noted belowType I Error P (Reject Ho when Ho is true) = Type II Error P (Accept Ho when Ho is false) = P Value Statistical Measure which indicates the probability of making an error. The value ranges between 0 and 1. We normally work with 5% alpha risk, a p value lower than 0.05 means that we reject the Null hypothesis and accept alternate hypothesis.Lets talk a little about p-value. It is a Statistical Measure which indicates the probability of making an error. The value ranges between 0 and 1. We normally work with 5% alpha risk. should be specified forward the hypothesis test is conducted. If the p-value is 0.05, then Ho is true and there is no difference in the groups (Accept Ho). If the p-value is 0.05, then Ho is false and there is a statistically significant difference in the groups (Reject Ho).We will also d iscuss about the types of hypothesis testing1-Sample t-test Its used when we have Normal Continuous Y and Discrete X. It is used for canvas a population mean against a given standard. For example Is the mean Turn Around Time of thread 15 minutes.2-Sample t-test Its used when we have Normal Continuous Y and Discrete X. It is used for comparing means of two different populations. For example Is the mean performance of morning shift = mean performance of night shift.ANOVA Its used when we have Normal Continuous Y and Discrete X. It is used for comparing the means of much than two populations. For example Is the mean performance of supply A = mean performance of staff B = mean performance of staff C.Homogeneity Of Variance Its used when we have Normal Continuous Y and Discrete X. It is used for comparing the variance of two or more than two populations. For example Is the variation of staff A = variation of staff B = variation of staff C.Moods Median Test Its used when we have Non-no rmal Continuous Y and Discrete X. It is used for Comparing the medians of two or more than two populations. For example Is the median of staff A = median of staff B = median of staff C.Simple Linear Regression Its used when we have Continuous Y and Continuous X. It is used to see how output (Y) changes as the input (X) changes. For example If we need to find out how staff As true statement is related to his number of years spent in the process.Chi-square Test of Independence Its used when we have Discrete Y and Discrete X. It is used to see how output counts (Y) from two or more sub-groups (X) differ. For example If we want to find out whether defects from morning shift are significantly different from defects in the evening shift.Lets look at each of the analysis for our researchImpact of Age on AccuracyPractical ProblemHypothesisStatistical Tool Used closureIs Accuracy impacted by Age of EmployeesH0 Accuracy is fissiparous of the Age of EmployeesH1 Accuracy is impacted by Age of Employees unidirectional ANOVAp-value 0.05 indicates that performance standard of true statement is impacted by age factor unidirectional ANOVA Accuracy versus Age BucketSource DF SS MS F PAge Bucket 2 0.50616 0.25308 67.62 0.000Error 72 0.26946 0.00374Total 74 0.77562S = 0.06118 R-Sq = 65.26% R-Sq(adj) = 64.29% person 95% CIs For inculpate Based onPooled StDevLevel N entertain StDev ++++20 30 years 26 0.75448 0.06376 (*)30 40 years 26 0.85078 0.07069 (*)40 50 years 23 0.95813 0.04416 (*)++++0.770 0.840 0.910 0.980Pooled StDev = 0.06118Boxplot of Accuracy by Age Bucket closing curtain P-value of the above analysis 0.05 which indicates that we reject the vain hypothesis and thus, the performance footmark of accuracy is impacted by age of employees. As the age increases, we observe that the accuracy of the employees also increases.Impact of Experience on AccuracyPractical ProblemHypothesisStatistical Tool UsedConclusionIs Accuracy impacted by Experience of EmployeesH0 Accu racy is independent of the Experience of EmployeesH1 Accuracy is impacted by Experience of EmployeesOne-Way ANOVAp-value 0.05 indicates that performance measure of accuracy is impacted by experience factorOne-way ANOVA Accuracy versus Experience BucketSource DF SS MS F PExperience Bucke 2 0.53371 0.26685 79.42 0.000Error 72 0.24191 0.00336Total 74 0.77562S = 0.05796 R-Sq = 68.81% R-Sq(adj) = 67.94%Individual 95% CIs For Mean Based onPooled StDevLevel N Mean StDev -++++0 10 years 24 0.74403 0.05069 (*)10 20 years 23 0.84357 0.05354 (*)20 30 years 28 0.94696 0.06660 (*)-++++0.770 0.840 0.910 0.980Pooled StDev = 0.05796Boxplot of Accuracy by Experience BucketConclusion P-value of the above analysis 0.05 which indicates that we reject the null hypothesis and thus, the performance measure of accuracy is impacted by experience of employees. As the experience increases, we observe that the accuracy of the employees also increases.Impact of Age on Customer SatisfactionPractical Problem HypothesisStatistical Tool UsedConclusionIs Customer Satisfaction Score impacted by Age of EmployeesH0 Customer Satisfaction Score is independent of the Age of EmployeesH1 Customer Satisfaction Score is impacted by Age of EmployeesOne-Way ANOVAp-value 0.05 indicates that performance measure of Customer Satisfaction score is impacted by age factorOne-way ANOVA Customer Satisfaction versus Age BucketSource DF SS MS F PAge Bucket 2 49.51 24.75 18.92 0.000Error 72 94.23 1.31Total 74 143.74S = 1.144 R-Sq = 34.44% R-Sq(adj) = 32.62%Individual 95% CIs For Mean Based onPooled StDevLevel N Mean StDev ++++20 30 years 26 6.906 1.164 (-*)30 40 years 26 8.041 1.156 (*-)40 50 years 23 8.907 1.107 (*)++++7.20 8.00 8.80 9.60Pooled StDev = 1.144Boxplot of Customer Satisfaction by Age BucketConclusion P-value of the above analysis 0.05 which indicates that we reject the null hypothesis and thus, the performance measure of Customer Satisfaction Score is impacted by age of employees. As the age in creases, we observe that the Customer Satisfaction Score of the employees also increases.Impact of Experience on Customer SatisfactionPractical ProblemHypothesisStatistical Tool UsedConclusionIs Customer Satisfaction Score impacted by Experience of EmployeesH0 Customer Satisfaction Score is independent of the Experience of EmployeesH1 Customer Satisfaction Score is impacted by Experience of EmployeesOne-Way ANOVAp-value 0.05 indicates that performance measure of Customer Satisfaction score is impacted by experience factorOne-way ANOVA Customer Satisfaction versus Experience BucketSource DF SS MS F PExperience Bucke 2 51.20 25.60 19.92 0.000Error 72 92.54 1.29Total 74 143.74S = 1.134 R-Sq = 35.62% R-Sq(adj) = 33.83%Individual 95% CIs For Mean Based onPooled StDevLevel N Mean StDev ++++-0 10 years 24 7.035 1.277 (*)10 20 years 23 7.570 0.922 (*)20 30 years 28 8.948 1.160 (-*-)++++-7.20 8.00 8.80 9.60Pooled StDev = 1.134Boxplot of Customer Satisfaction by Experience BucketConclusio n P-value of the above analysis 0.05 which indicates that we reject the null hypothesis and thus, the performance measure of Customer Satisfaction Score is impacted by experience of employees. As the experience increases, we observe that the Customer Satisfaction Score of the employees also increases.Impact of Age on ProductivityPractical ProblemHypothesisStatistical Tool UsedConclusionIs Productivity impacted by Age of EmployeesH0 Productivity is independent of the Age of EmployeesH1 Productivity is impacted by Age of EmployeesOne-Way ANOVAp-value 0.05 indicates that performance measure of Productivity is impacted by experience factorOne-way ANOVA Productivity versus Age BucketSource DF SS MS F PAge Bucket 2 0.74389 0.37194 194.56 0.000Error 72 0.13765 0.00191Total 74 0.88153S = 0.04372 R-Sq = 84.39% R-Sq(adj) = 83.95%Individual 95% CIs For Mean Based onPooled StDevLevel N Mean StDev ++++20 30 years 26 0.93959 0.04287 (-*)30 40 years 26 0.81511 0.05831 (-*-)40 50 years 23 0.69 291 0.01747 (*-)++++0.720 0.800 0.880 0.960Pooled StDev = 0.04372Boxplot of Productivity by Age BucketConclusion P-value of the above analysis 0.05 which indicates that we reject the null hypothesis and thus, the performance measure of Productivity is impacted by age of employees. As the age increases, we observe that the Productivity of the employees decreases.Impact of Experience on ProductivityPractical ProblemHypothesisStatistical Tool UsedConclusionIs Productivity impacted by Experience of EmployeesH0 Productivity is independent of the Experience of EmployeesH1 Productivity is impacted by Experience of EmployeesOne-Way ANOVAp-value 0.05 indicates that performance measure of Productivity is impacted by experience factorOne-way ANOVA Productivity versus Experience BucketSource DF SS MS F PExperience Bucke 2 0.74024 0.37012 188.61 0.000Error 72 0.14129 0.00196Total 74 0.88153S = 0.04430 R-Sq = 83.97% R-Sq(adj) = 83.53%Individual 95% CIs For Mean Based onPooled StDevLevel N Mean StDev ++++-0 10 years 24 0.94474 0.03139 (*)10 20 years 23 0.83120 0.05754 (*-)20 30 years 28 0.70599 0.04118 (*-)++++-0.700 0.770 0.840 0.910Pooled StDev = 0.04430Boxplot of Productivity by Experience BucketConclusion P-value of the above analysis 0.05 which indicates that we reject the null hypothesis and thus, the performance measure of Productivity is impacted by experience of employees. As the experience increases, we observe that the Productivity of the employees decreases.Conclusion of the AnalysisAs Age and Experience increases, the Accuracy and Customer Satisfaction Scores of Employees increasesAs Age and Experience increases, the Productivity of Employees decreasesBibliographyThe data used in this analysis is self-created data using statistical software. Research inventory (Gantt Chart) of the Project
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