Running Head: HR STATISTICAL TECHNIQUES
HR Statistical Techniques
January 23, 2012
HR Statistical Techniques
Ayles Networks is an IT networking company employing over 3,000 people across the Southwestern United States. Although, centrally located, the Human Resources (HR) office is up to 500 miles from several corporate offices. The HR department has been tasked with using HR statistical techniques to assess the effectiveness of current staffing, training, and HR assessments (University of Phoenix, 2011). The HR department will identify the type of data needed, the application of t-test, ANOVA, and regression analysis statistical techniques will be discussed and additional techniques will be reviewed. Required Data
Testing of hypotheses is the basis for research and that results in statistical findings. A null hypothesis is presumed true until proven otherwise by statistical testing. If the null hypothesis is rejected then the alternative hypothesis is accepted. To begin statistical testing to determine the effectiveness of training and staffing programs requires several types of data including current and required staffing levels, labor availability, and skill sets data is required for each position and location. Results of hiring and promotion assessments such as pre-employment, selection, required training, and performance evaluation scores are also required. T-test
A t-test is used to evaluate the differences in means between two groups, which can be either independent or dependent (StatSoft, Inc., 2011, para. 1). The dependant variable is affected by the independent or predictor variable. Levels of measurement using a t-test include numeric, consisting of numbers as values and nominal, consisting of numbers assigned to names, categories, groups, or levels (Hopkins, 2000, para. 1; McIntire & Miller, 2007, p. 135).
T-test can be used to analyze training, HR assessment, and staffing effectiveness and on a per location basis. The dependent variable, which is the mean score for each training program and HR assessment, would be determined. Next, a series of t-tests would be conducted for each individual location. Scores and effectiveness for each location can be evaluated by the individual variance or deviation from the mean. This information than can used to adjust training, HR assessments, and staffing to not only develop effective training but to ensure proper staffing levels and the right person for the job at each location. This method is appropriate only for individual locations not for comparison of locations on a total organizational level. Analysis of Variance (ANOVA)
ANOVA is used to analyze more than two groups simultaneously and compares the ratio of variance between and within groups. This method measures the “differences between means of groups, not differences between variances” and is based on the assumption that the standard deviation is the same in all groups (Hopkins, 2000, para. 3). ANOVA testing assumes the dependent variable is metric and the independent variable is categorical (Ghauri & Gronhaug, 2005, p. 180).
ANOVA can be used to analyze training, HR assessment, and staffing effectiveness on a total organizational level. For example, the assumption is that locations of similar size and staffing have the same group means and the same variances in the effectiveness level of hiring and promotion assessment, training, and staffing. ANOVA testing would measure the variance within each group as well as between all groups. If the null hypothesis is true, then testing has proven that there is no variation in the effectiveness. Regression Analysis
According to Ghauri and Gronhaug 2005, regression analysis is useful when examining relationships between variables (p. 183). There is one dependant variable to be explained and one or more independent variables. Dependant and independent variables are assumed metric and therefore...
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