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Hr Statistical Techniques

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HR Statistical Techniques


Ayles Networks is an established IT networking company that employs over 3,000 employees across the Southwestern United States. The CEO has asked Human Resources (HR) to use statistical techniques to assess the staffing, training, and HR assessments that the company currently has in place.

Data Types

Both quantitative and qualitative data types of research will be employed in order to assess the effectiveness of staffing, training, and HR assessments at Ayles Networks. Under the quantitative research type, surveys will be conducted where a researcher made questionnaire will be used. The questionnaires will be of two sub-sets; one for management and another for subordinates. The intention of having two sub-sets of questionnaires is to get two points of view.

For the qualitative research type, interviews and observations will be conducted. This data type is intended to gather qualitative data on the three human resource functions - recruitment, training, and performance appraisal. The instruments will be subject to validation and reliability procedures to ensure that quality data will be obtained. Documents on the levels of productivity of the workers will also be obtained.

Statistical Treatment of Data

To facilitate the analysis and interpretation of data the following statistical tools will be used:

Measures of Central Tendency (Mean, Median, Mode)

Measures of central tendency will be used in order to determine the typical or common characteristics of the employees and those who are performing the various human resource functions. They will be used to describe them along various profile variables such as age, position, number of years in the company, gender, etc.

Measures of Variability (Standard Deviation, Variance)

The measure of variability measures the distance of the productivity level and that of other variables of the respondents from the mean. Hence, they will be used to measure whether the performance of a particular employee is within, over, or below the average production level of the teams or the unit where he belong.


This statistical technique will be used to determine whether there is a significant difference in the scores of the subjects before and after a given training program. Computed t-values will be compared with the critical values, at 95% level of significance, to determine whether the hypothesis will be rejected or cannot be rejected. Large gain scores, the difference between post-test and pre-test scores, would also indicate effectiveness of a training program implemented. T-test of difference between means is applied when there are only two groups compared (example: pre-test post-test scores; scores of male as against female employees, etc.)


This statistic is also a test of differences. However, it is applied if there are three groups compared. In the case at hand, ANOVA will be used in comparing the productivity levels of employees as grouped according to employment status; full time, part time, and project-based.

Regression Analysis

This statistical technique will be used to determine how effectively one variable may be used to predict another variable (dependent variable). An example



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