Ayles Networks, an IT networking company, has a workforce of over 3,000 employees in the Southwestern United States. Despite being centrally located, the Human Resources (HR) office is situated up to 500 miles away from several corporate offices. The HR department has been assigned the task of analyzing the effectiveness of current staffing, training, and HR assessments using statistical techniques (University of Phoenix, 2011). The department will determine the necessary data and discuss the application of t-test, ANOVA, and regression analysis. Additionally, other techniques will also be reviewed. The research is based on testing hypotheses and obtaining statistical findings.
According to the text, the null hypothesis is considered true by default unless statistical testing provides evidence otherwise. When the null hypothesis is rejected, the alternative hypothesis is accepted instead. In order to evaluate training and staffing programs, specific d
...ata needs to be collected. This includes information on current and desired levels of staff, labor availability, as well as skillsets required for each position and location. Additionally, assessments related to hiring and promotions such as pre-employment tests, selection procedures, mandatory training records, and performance evaluation scores play a crucial role in analysis.
The t-test is a statistical test that compares the means of two groups, whether they are independent or dependent (StatSoft, Inc. 2011, para. 1). The dependent variable is influenced by the independent or predictor variable. Numeric levels of measurement in a t-test involve values represented by numbers, while nominal levels involve numbers assigned to names, categories, groups, or levels (Hopkins, 2000, para. 1; McIntire & Miller, 2007, p.).
The t-test is applicable for analyzing the effectiveness of training, HR assessment, and staffing on a per-location basis. Th
dependent variable, the mean score of each training program and HR assessment, will be determined. Subsequently, t-tests will be executed for each specific location.
The assessment of scores and effectiveness for each location involves examining the individual variation or deviation from the average. This information can be valuable in making adjustments to training, HR evaluations, and staffing. These adjustments aim not only to enhance training effectiveness but also to ensure appropriate staffing levels and selection of suitable candidates at each location. It is important to note that this approach is specifically designed for evaluating individual locations and cannot be utilized for comparing different locations across the entire organization.
To analyze multiple groups simultaneously, Analysis of Variance (ANOVA) is a statistical method used. ANOVA compares the variance between and within these groups while focusing on measuring differences in means among them rather than variances. ANOVA assumes consistency in standard deviation across all groups (Hopkins, 2000, para. 3).
ANOVA testing assumes that the dependent variable is metric and the independent variable is categorical (Ghauri & Gronhaug, 2005, p. 180). It can be used to analyze the effectiveness of training, HR assessment, and staffing on a total organizational level. For instance, it assumes that locations with similar size and staffing have the same group means and variances in the effectiveness of hiring and promotion assessment, training, and staffing. ANOVA testing would assess the variance within each group and between all groups.
According to Ghauri and Gronhaug 2005, regression analysis is a useful tool for examining relationships between variables (p. 183). If the null hypothesis is true, testing has confirmed that there is no variation in the effectiveness. This analysis
involves one dependent variable to be explained and one or more independent variables.
The analysis of numeric data against numeric data assumes that dependant and independent variables are metrics (Gharui & Gronhaug, 2005, p. 183; Hopkins, 2000). Regression analysis utilizes a method called data best-fit, which involves fitting a straight line that minimizes the squared vertical deviations from that line (Gharui & Gronhaug, 2005, p. 183, para. 3).
The straight line, also known as the calculated regression line, represents the correlation between the dependent and independent variables. It shows the unexplained variation through dots that are not on the line. The proximity of the dots to the line indicates the strength of the relationship between variables. Regression analysis enables HR to forecast future outcomes using present data. Effectiveness in hiring and promotion assessments can be used as an indicator of the ability to meet staffing needs efficiently. When data points are near or within the accepted range, there is a direct correlation between hiring and promotion assessments and effective staffing.
However, if there is a high level of variation, it would indicate that there is no correlation between assessments and effective staffing. Regression analysis can also be utilized to measure how effective training is based on performance evaluation scores. Effective training should result in an increase in performance evaluation scores and this would be reflected by minimal variance from the regression line. Another statistical method that can be used is Cross Tabulation or Contingency Table, which allows simultaneous analysis of two or more categorized variables. The chi-squared test is employed to determine the relationship between variables (Hopkins, 2000, para.
2). According to Ghauri & Gronhaug (2005, p. 167), the
cross-tabulation displays the dependent variable categories in the rows and the independent variable categories in the columns. It is important to include the frequencies in the cross-tabulation and also include the total sample size for reliability and additional statistical testing purposes.
Comparing the number of employees who undergo a promotion assessment to the actual number of promotions per position provides valuable information about the effectiveness of the assessment, training, and staffing levels. If there is a significant difference between the percentage of promoted employees and those who took the assessment, further investigation is needed to determine why. Frequency distributions present data for groups in tables or histograms, summarizing their distribution patterns and offering insights into specific ranges or categories (McIntire & Miller, 2007, p. 140).
2). The method outlined in this paragraph can be used to analyze the pattern (i.e. number and percentage) of employees who scored within certain test score ranges. This method is efficient in summarizing distribution values, which helps identify possible training and assessment issues. By doing so, timely and proactive solutions can be implemented instead of reactive and potentially inappropriate adjustments. In conclusion, evaluating the effectiveness of current staffing, training, and HR assessments is crucial for both individual and organizational success.
The text focuses on the data and statistical techniques utilized for testing in the case of Ayles Networks. The mentioned techniques include t-test, ANOVA, regression analysis, cross-tabulation, and frequency distribution. Various statistical models can be employed for research purposes, each yielding different results; hence it is crucial to choose the appropriate model. Moreover, comprehending how to analyze and interpret the outcomes plays a vital role in evaluating program effectiveness and making organizational
decisions.
References
- Ghauri, P., & Gronhaug, K. (2005).
- Research methods in business studies: A practical guide (3rd ed). New Jersey: Prentice-Hall.
- Hopkins W.G. (2000). A new view of statistics.
Retrieved from http://www.sportsci.org/resource/stats/ttest.html
McIntire, S. A.
, & Miller, L. A. (2007). Foundations of psychological testing: A practical approach (2nd ed.). London: Sage StatSoft, Inc.
The Electronic statistics textbook was published in 2011 and is located in Tulsa, OK. It can be accessed at http://www. statsoft.
com/textbook/ University of Phoenix (2011) Week Six syllabus. Retrieved from University of Phoenix HRM/558—Research in Human Resource Management course website.
