Employee Performance Analysis

Employee Performance Analysis Project Outline: This research is about the Employee performance in an organization. Data related to several factors such as Employee Productivity, Customer Satisfactions Scores, Accuracy Scores, Experience and Age of Employees is taken into consideration. Statistical methods are used to identify if there is any impact of Age and Experience of Employees on factors such as Productivity, Customer Satisfaction and Accuracy. Theoretical Framework: XYZ Corporation operating out of Illinois, US want to find out if the age and experience 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 experience) 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. Design and Methodology: Design and Methodology used by the external consultant include identifying the various performance factors common across different businesses within XYZ Corporation. The performance measures common for all businesses included: Customer Satisfaction Scores Accuracy Scores Productivity The consultants decided to study the impact of age of employees and their experience on the above factors by using statistical methods. Details on participants and sampling methods: Sampling Methods: Sampling is the process of selecting a small number of elements from a larger defined target 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 randomly from organization of population ‘N’. Sampling is done in situations like: We sample when the process involves destructive testing, e.g. taste tests, car crash tests, etc. We sample when there are constraints of time and costs We sample when the populations cannot be easily captured Sampling is NOT done in situations like: We cannot sample when the events and products are unique and cannot be replicable Sampling can be done by using several methods including: Simple random sampling, Stratified random sampling, Systematic sampling and Cluster sampling. These are Probability Sampling Methods. Sampling can also be done using methods such as Convenience sampling, Judgment sampling, Quota sampling and Snowball 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-probability 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 data 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 years, 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 Analysis: Below are the different data analysis options used by the consultant: Impact of Age on Accuracy Impact of Experience on Accuracy Impact of Age on Customer Satisfaction Impact of Experience on Customer Satisfaction Impact of Age on Productivity Impact of Experience on Productivity For 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 hypothesis testing is: Continuous Data Difference in Average Difference in Variation Discrete Data Difference in Proportion Defective We follow the below steps for Hypothesis testing: Step 1 : Determine appropriate Hypothesis test Step 2 : State the Null Hypothesis Ho and Alternate Hypothesis Ha Step 3 : Calculate Test Statistics / P-value against table value of test statistic Step 4 : Interpret results – Accept or reject Ho The mechanism of Hypothesis testing involves the following: Ho = Null Hypothesis – There is No statistically significant difference between the two groups Ha = Alternate Hypothesis – There is statistically significant difference between the two groups We also have different types of errors that can be caused if we are using hypothesis testing. The errors are as noted below: Type I Error – P (Reject Ho when Ho is true) = ÃŽ ± Type II Error P (Accept Ho when Ho is false) = ÃŽ ² P Value – Statistical Measure which indicates the probability of making an ÃŽ ± 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. Let’s talk a little about p-value. It is a Statistical Measure which indicates the probability of making an ÃŽ ± error. The value ranges between 0 and 1. We normally work with 5% alpha risk. ÃŽ ± should be specified before 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 discuss about the types of hypothesis testing: 1-Sample t-test: It’s used when we have Normal Continuous Y and Discrete X. It is used for comparing a population mean against a given standard. For example: Is the mean Turn Around Time of thread à ¯Ã¢â‚¬Å¡Ã‚ £15 minutes. 2-Sample t-test: It’s 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: It’s used when we have Normal Continuous Y and Discrete X. It is used for comparing the means of more than two populations. For example: Is the mean performance of staff A = mean performance of staff B = mean performance of staff C. Homogeneity Of Variance: It’s 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. Mood’s Median Test: It’s used when we have Non-normal 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: It’s 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 A’s accuracy is related to his number of years spent in the process. Chi-square Test of Independence: It’s 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. Let’s look at each of the analysis for our research: Impact of Age on Accuracy Practical Problem Hypothesis Statistical Tool Used Conclusion Is Accuracy impacted by Age of Employees H0: Accuracy is independent of the Age of Employees H1: Accuracy is impacted by Age of Employees One-Way ANOVA p-value < 0.05 indicates that performance measure of accuracy is impacted by age factor One-way ANOVA: Accuracy versus Age Bucket Source DF SS MS F P Age Bucket 2 0.50616 0.25308 67.62 0.000 Error 72 0.26946 0.00374 Total 74 0.77562 S = 0.06118 R-Sq = 65.26% R-Sq(adj) = 64.29% Individual 95% CIs For Mean Based on Pooled StDev Level N Mean 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 0.980 Pooled StDev = 0.06118 Boxplot of Accuracy by Age Bucket Conclusion: 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 age of employees. As the age increases, we observe that the accuracy of the employees also increases. Impact of Experience on Accuracy Practical Problem Hypothesis Statistical Tool Used Conclusion Is Accuracy impacted by Experience of Employees H0: Accuracy is independent of the Experience of Employees H1: Accuracy is impacted by Experience of Employees One-Way ANOVA p-value < 0.05 indicates that performance measure of accuracy is impacted by experience factor One-way ANOVA: Accuracy versus Experience Bucket Source DF SS MS F P Experience Bucke 2 0.53371 0.26685 79.42 0.000 Error 72 0.24191 0.00336 Total 74 0.77562 S = 0.05796 R-Sq = 68.81% R-Sq(adj) = 67.94% Individual 95% CIs For Mean Based on Pooled StDev Level 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 0.980 Pooled StDev = 0.05796 Boxplot of Accuracy by Experience Bucket Conclusion: 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 Satisfaction Practical Problem Hypothesis Statistical Tool Used Conclusion Is Customer Satisfaction Score impacted by Age of Employees H0: Customer Satisfaction Score is independent of the Age of Employees H1: Customer Satisfaction Score is impacted by Age of Employees One-Way ANOVA p-value < 0.05 indicates that performance measure of Customer Satisfaction score is impacted by age factor One-way ANOVA: Customer Satisfaction versus Age Bucket Source DF SS MS F P Age Bucket 2 49.51 24.75 18.92 0.000 Error 72 94.23 1.31 Total 74 143.74 S = 1.144 R-Sq = 34.44% R-Sq(adj) = 32.62% Individual 95% CIs For Mean Based on Pooled StDev Level 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 9.60 Pooled StDev = 1.144 Boxplot of Customer Satisfaction by Age Bucket Conclusion: 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 increases, we observe that the Customer Satisfaction Score of the employees also increases. Impact of Experience on Customer Satisfaction Practical Problem Hypothesis Statistical Tool Used Conclusion Is Customer Satisfaction Score impacted by Experience of Employees H0: Customer Satisfaction Score is independent of the Experience of Employees H1: Customer Satisfaction Score is impacted by Experience of Employees One-Way ANOVA p-value < 0.05 indicates that performance measure of Customer Satisfaction score is impacted by experience factor One-way ANOVA: Customer Satisfaction versus Experience Bucket Source DF SS MS F P Experience Bucke 2 51.20 25.60 19.92 0.000 Error 72 92.54 1.29 Total 74 143.74 S = 1.134 R-Sq = 35.62% R-Sq(adj) = 33.83% Individual 95% CIs For Mean Based on Pooled StDev Level 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 9.60 Pooled StDev = 1.134 Boxplot of Customer Satisfaction by Experience Bucket Conclusion: 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 Productivity Practical Problem Hypothesis Statistical Tool Used Conclusion Is Productivity impacted by Age of Employees H0: Productivity is independent of the Age of Employees H1: Productivity is impacted by Age of Employees One-Way ANOVA p-value < 0.05 indicates that performance measure of Productivity is impacted by experience factor One-way ANOVA: Productivity versus Age Bucket Source DF SS MS F P Age Bucket 2 0.74389 0.37194 194.56 0.000 Error 72 0.13765 0.00191 Total 74 0.88153 S = 0.04372 R-Sq = 84.39% R-Sq(adj) = 83.95% Individual 95% CIs For Mean Based on Pooled StDev Level N Mean StDev ++++ 20 30 years 26 0.93959 0.04287 (-*) 30 40 years 26 0.81511 0.05831 (-*-) 40 50 years 23 0.69291 0.01747 (*-) ++++ 0.720 0.800 0.880 0.960 Pooled StDev = 0.04372 Boxplot of Productivity by Age Bucket Conclusion: 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 Productivity Practical Problem Hypothesis Statistical Tool Used Conclusion Is Productivity impacted by Experience of Employees H0: Productivity is independent of the Experience of Employees H1: Productivity is impacted by Experience of Employees One-Way ANOVA p-value < 0.05 indicates that performance measure of Productivity is impacted by experience factor One-way ANOVA: Productivity versus Experience Bucket Source DF SS MS F P Experience Bucke 2 0.74024 0.37012 188.61 0.000 Error 72 0.14129 0.00196 Total 74 0.88153 S = 0.04430 R-Sq = 83.97% R-Sq(adj) = 83.53% Individual 95% CIs For Mean Based on Pooled StDev Level 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 0.910 Pooled StDev = 0.04430 Boxplot of Productivity by Experience Bucket Conclusion: 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 Analysis: As Age and Experience increases, the Accuracy and Customer Satisfaction Scores of Employees increases As Age and Experience increases, the Productivity of Employees decreases Bibliography: The data used in this analysis is self-created data using statistical software.      Research Schedule (Gantt Chart) of the Project:

Carpe Diem Essay

Seizing the twenty-four hours has a profound significance behind it. in Latin it is known as Carpe Diem. Carpe Diem refers to holding no fright to make something. taking opportunities and hazards. In the film â€Å"The Dead Poets Society† a character named Knox has a crush on a miss. while she was already dating another cat. Knox was seeking to prehend the twenty-four hours by seeking to pass clip with her. There are many more illustrations of carpe diem in this exceeding film. but this I feel is the most pertinent. Many of the verse forms that we read in category were besides related to the unrecorded life like it is the last. The film and the other verse forms are cardinal in explicating what carpe diem truly means. â€Å"The Dead Poets Society† is a great illustration of prehending the twenty-four hours. Knox taking hazards. inquiring a football player’s girlfriend on a day of the month shows that he has no fright. He had no fright of the effects that would likely happen if he was of all time caught. In another illustration of the film. a character Neil defied his male parent by traveling into moving. Neil’s father told him that he did non desire Neil to travel into moving. and go a physician. Neil went against his male parent. and was the lead in a drama. Neil seized the twenty-four hours by making the drama. and hope that his male parent would happen forgiveness in him. These illustrations from â€Å"The Dead Poets Society† are great illustrations of what carpe diem signifies. Another illustration of carpe diem is the great figure of verse forms that we read in category. These verse forms besides stated the same as the film and that was to unrecorded life to the fullest every bit shortly as possible. The verse form such as â€Å"O captain. my captain† by Walt Whitman expressed carpe diem. which is why it was a major portion of the film â€Å"The Dead Poets Society† . This verse form was related to Lincoln and the civil war. about taking opportunities and to prehend the twenty-four hours. Other verse forms stressed that we are non on this Earth everlastingly and clip is traveling by fast. so now is the clip to prehend the twenty-four hours. Poems that we read stressed the significance of carpe diem. particularly the verse form by Walt Whitman. These illustrations of the verse forms that we read in category. and the illustrations that came out of the film are great illustrations of what prehending the twenty-four hours truly means. The illustration of Knox holding no fright whatsoever by dating another guy’s girlfriend. shows that he is taking opportunities and wants to prehend the twenty-four hours before it is excessively late. The verse form we read such as â€Å"o captain. my captain† besides show prehending the twenty-four hours as good. So overall carpe diem refers to make non wait until it is excessively late. if there is an ch ance take it and prehend twenty-four hours no affair what the hazards or effects.

In The Second Edition Of Servants Of Globalization Migration

In the Second Edition of Servants of Globalization: Migration and Domestic Work, Rhacel Parreà ±as examines all of the challenging aspects of the lives of migrant Filipino domestic workers. Throughout the interviews that are included in this novel, the author was able to analyze different cases filled with personal struggle and familial support using the perspectives of many determined women across the diaspora, mainly focusing on those to travel to work in Western Countries. In this paper, I will briefly summarize the first three chapters, bringing to light the most important aspects the Parreà ±as included. I will then discuss the methodology used in her convincing global ethnography, before I include both the advantages and disadvantages to†¦show more content†¦I will expand later on this paper about the arenas of constraint that the author wrote about for migrant workers in the conclusion of the first chapter, as it is a topic that needs to be unpacked greatly. The second chapter discussed the international division of reproductive labour and the reasons why women may choose to become migrant workers. Often times the common answer of why these Filipina women made the decision to enter the reproductive labour force is one based on both economic and gender inequality reasoning. Gender is now seen as the hidden cause of migration, where women are taking on the role of the income provider and it is a strategy that can â€Å"relieve women of their unequal division of labour with men in the family.† (Parreà ±as, 2015 – pg. 32) Through the three tier system there is honestly no gender-egalitarian way that the division occurs, because their own responsibilities are given to those who are less privileged for less benefits. Parreà ±as introduces the dynamic of postmodern familial struggles that inevitably come with being a migrant worker. 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What Comes Naturally Miscegenation Law And The Making Of...

In What Comes Naturally: Miscegenation Law and the Making of Race in America, Peggy Pascoe examines the history of miscegenation and how it laid the foundation of white supremacy in the United States. While visible forms of white supremacy such as segregation helped mask the importance of miscegenation laws, Pascoe argues that miscegenation laws was a national movement tied inseparably to gender and sexuality that went beyond the Black/White dynamic, which courts and bureaucracies of local marriage officials used to produce race in America. Pascoe goes on to argue that the core of miscegenation laws reached beyond the realm of romance as courts began to condemn the respectability of interracial relationships by equating them with illicit sex rather than marriage. Thus, this idea of unrespectable, unnatural, and immoral relationships became women into the fabric of the American society. Pascoe traces the development of court decisions about miscegenation laws regarding the mixing of W hites, Blacks, Asians, Latinos, and American Indians in order to show the link between marriage and property, and the passages of laws against interracial marriage. For instances, Pascoe starts by showing how the collapse of slavery and the need to hold tight to white supremacy pushed miscegenation into the forefront of American society during Reconstruction. In Fort Bend County, Alfred Foster, a White slaveowner, redefined the relationship between race and gender in marriage law after he leftShow MoreRelatedWhat Comes Naturally : Miscegenation Law And The Making Of Race1048 Words   |  5 PagesStephen Tighe Book Report 4/3/15 Peggy Pascoe’s â€Å"What Comes Naturally: Miscegenation Law and the Making of Race in America,† published in 2012, is a historical and legal analysis that emphasizes the impact of racial segregation and desegregation in our society. 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Enquiries concerning reproduction outside the scope of the above should be sent to the Rights Department, Oxford University Press, at the address above You must not circulate What Comes Naturally Miscegenation Law And The Making Of... Stephen Tighe Book Report 4/3/15 Peggy Pascoe’s â€Å"What Comes Naturally: Miscegenation Law and the Making of Race in America,† published in 2012, is a historical and legal analysis that emphasizes the impact of racial segregation and desegregation in our society. The book primarily focuses on the roles of race and gender in these extremely significant legal happenings, though other important talking points are acknowledged as well. The main narrative of racial implication is the underlying theme in Pascoe’s writing. Pascoe presents the hard facts and inevitable truth about miscegenation laws in the United States during a time where society was torn on these very trying issues. Comparing the more modern laws to those established in the days of white and native American marriage issues, Pascoe presents the argument that these laws were established and maintained in order to preserve White supremacy and the patriarchal society it thrived in. These laws allowed for white men to thrive socially a nd economically while minority- and female-held positions in society became scarce. Pascoe clearly feels very strongly on these legal and historical matters, which led her to write such a bold book. Written from the point of view of an outsider looking in, Pascoe’s main point was to educate the reader of the significance of miscegenation laws in the United States. This book functions like a historical analysis, and was published posthumously. â€Å"What Comes Naturally† is targeted towardsShow MoreRelatedWhat Comes Naturally : Miscegenation Law And The Making Of Race882 Words   |  4 PagesIn What Comes Naturally: Miscegenation Law and the Making of Race in America, Peggy Pascoe examines the history of miscegenation and how it laid the foundation of white supremacy in the United States. While visible forms of white supremacy such as segregation helped mask the importance of miscegenation laws, Pascoe argues that miscegenation laws was a national movement tied insepar ably to gender and sexuality that went beyond the Black/White dynamic, which courts and bureaucracies of local marriageRead MoreWhiteness and Citizenship971 Words   |  4 Pagesnecessity to separate the slave-owners from their slaves, making slavery more palatable to the freedom loving Americans. 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Enquiries concerning reproduction outside the scope of the above should be sent to the Rights Department, Oxford University Press, at the address above You must not circulate