Accomplishing the daily office goals at XYZ Relations takes complete teamwork from all employees. Being able to attend to all potential applicants in a timely manner demands dedication, effort and understanding. The tedious work required, presses employees to focus on tasks and depend on one another to accomplish goals. With an abundance amount of paperwork to deal with, XYZ Relations has been able to keep a steady pace by hiring the right employees and completing all required screening before employees begin work.
Inferential Statistics and Findings Review
XYZ Relation’s HR Department had some of their team member’s research inferential statistics; a statistical hypothesis is a statement about the numerical value of a population parameter.” (McClave, Benson, & Sincich, 2011). The question at hand, is there a relationship between the speed of onboarding a new employee and the days it takes to complete required background tests? . “The average time-to-fill for companies of 1,000 employees and more is 43 days, compared with 29 days for companies having fewer employees.” (Lytle, 2013) Surprisingly, all the team members came back with same search results using a t-test and random sampling. Based on the teams finds, unanimously, the decision was to reject the null hypothesis due to the calculated p values being lower than the error () value. Using a t-test has many advantages. One is that the understanding of the output is easy to translate and interpret statistical differences. Two, even with a small sample set; the t-test is still relevant and needs one value from each test subject.
Since many people today are not forth coming with personal information using a small sample set was not an issue for Survey’s 2000 as they knew the data is still relevant regardless of the sample size. Three, the formula for a t-test is simple and easy; not requiring statistical training. The down side to a t-test is that the confidence interval can be manipulated to attain a desired outcome. Skewing with data is unethical and XYZ Relations needed to ensure that the date was not going to be manipulated, hence why XYZ hired Survey’s 2000. Another weakness of the t-test is outcomes are only correct with normal populations. We all know real populations are not typically “normal”. There are always exceptions. The happy path is never a true test. Business Research Report
The research question posed for XYZ Relations was: is there a relationship between the speed of onboarding a new employee and the days it takes to complete required background tests? After reviewing the below testing, we do find that the speed of onboarding is hampered by the time it takes to complete the background checks. The hypothesis presented is that it takes longer than the average human resources company (45 days) to receive results from background checks. H0: Days to Hire ≤ 45; HA: Days to Hire > 45. The most appropriate statistical tool to test the hypothesis is the t-test to compare the data set from XYZ Relations to the average of other HR corporations. The hypothesis test will use a 95% confidence level with an upper tail test performed. The data computed with the 95% confidence level allows the rejection of the null hypothesis due to the calculated p values being lower than the error () value. Hypothesis Test: Paired Observations
The data collected for this research was normally distributed and appears to be in agreement with the hypothesis that there is a direct relationship between the date of hire to date to begin work and the time taken to complete the employee background check.
While it appears the days to hire are distributed normally, plotting the for days it takes for background checks shows that the longer amount of time it takes for the background investigation then the longer it takes for a person to on-board.
Observations and Recommendations
The conclusions that were derived from this study were: XYZ Relations will proudly outsource pre-employment screenings of the hiring process, “these are normally contracted services through an external vendor or government agency” (Mueller & Baum, 2011); XYZ Relations will examine the current in-house practices; data mining and survey sampling were the best two options used that produced prime solutions in the end; and both sampling styles brought different aspects but also displayed how each method could be used appropriately. Recommendations based on the results are as follows:
1) Statically audit the background processes;
2) Get quotes from different drug testing companies with faster response times;
3) Use technology to speed up the process;
4) Find and embrace new technologies;
5) Question our company’s assumptions and reevaluate our methods; and
6) Shorten the amount of time between steps in the hiring process.
The observations (reflection) on the business problem and its solution with the RQ of : Is there a relationship between the speed of hiring a new employee and the required background tests? Team C found that it takes more than 45 days to process an employee due to background checks, which includes a drug test which are outsourced to a company that takes more time than other drug testing companies. XYZ Relations knows what to do when determining each phase of the hiring process. XYZ also has more insight of how directly this process influences the time it takes to fill employment agencies. With exceptional calculations, YXZ Relations will be able to fill their vacant positions with qualified employees. Team C experienced quite a bit of research challenges with the data collections method. All of the methods were analyzed to determine which would provide the most accurate and relevant data for use in addressing the hypothesis. As the research continued, we experienced these challenges with the following data collections methods: Cluster Sampling
From all the different type of probability sampling, this technique is both the least representative of the population and has the highest possibility of sampling error. (Explorable.com, 2009). Data Mining
Data mining eliminates the time constraints of the survey method but is limited to information already captured without the benefit of any amplifying remarks should they be required for explanation. Again, with only what is contained in the record, critical pieces of information might be missed.
XYZ Relations can reduce their challenges with Cluster Sampling by raising their sample size. Having a higher sample size will give XYZ more data to compute and allow more room for sampling errors. With a higher sample size/population, the sampling errors would be minimal in the overall conclusions of the findings. Focusing on given information can minimize the challenges with Data Mining. Using this method can be problematic but can prove to be very beneficial. Conclusion
XYZ Relations has collected data and completed a plethora of research studies in regards to our hypothesis question. This has formed many sub-questions, which drove XYZ to our conclusion to outsource pre-employment screenings of the hiring process. Outsourcing these steps in the hiring process to a vendor who maintains the industry average wait times will allow for XYZ to focus on other sectors of their day-to-day operations.
Lytle, T. (2013). Streamline hiring. HRMagazine, 58(4), 63-65. Retrieved from
http://search.proquest.com/docview/1346729623?accountid=35812 McClave, J. T., Benson, P. G., & Sincich, T. (2011). Statistics for Business and Economics (11th ed.). Boston, MA: Prentice Hall. Mueller, J. R., & Baum, B. (2011). The definitive guide to hiring right. The Journal of Applied Business and Economics, 12(3), 140-153. Retrieved from http://search.proquest.com/docview/885179591?accountid=458.