Huffman Trucking Company Data Collection

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Introduction

Huffman Trucking Company which is based in the United States of America is a transport and delivery company with its head offices in Cleveland. It has its branches spread all over the country for example in St Louis. The company has over 800 trailers and tractors. It employs 925 drivers and 425 support staff. Recent turnovers are 20% above the industry’s average.

Safety statistics are good though not good as those of its competitors. Several safety violations have been reported. This prompted the management of Huffman Trucking Company to recommend additional training for drivers. A new training meant to improve the drivers’ skills was implemented. The management also wanted to know the following before and after the training: 1.Did the training have a positive effect on the drivers?

What were the drivers’ attitudes on the training’s effectiveness, company policies, and job performance?

As a result, Huffman Trucking Company employed the services of a consultancy firm, Iwamoto • Crews • Coe. They were to design a data collection plan and perform data analysis so as to establish the effectiveness of the new training (Glaser & Strauss, 1967).

A data collection plan was drawn. It narrowed down the Huffman Trucking organization’s assessment study into three types of data namely demographic data, performance data, and assessment data.

Demographic data

The best way to collect the demographic data is through the personnel data in the human resource department. A structured data collection sheet should be formulated as the example below.

(Table 1)

Employed	Driver’s Gender	Region in which the driver is based	2010 Driver’s Salary

Since the targeted total population size is relatively large, about 925 cases and several sheets are required. The researcher should carefully go through all the personnel data and record the relevant data on the sheet (Shettleworth, 2010). A counter check of both the hardcopy and softcopy data is essential for verification purposes.

When the process of data collection is complete, identification of variables and their measurement scales is important for the purpose of analysis. The following table illustrates the variables identified and their measurements.

(Table 2)

Variable Name	Type	Measurement	Labels
Employed	Numeric Continuous values	Scale – interval	n/a
Gender	String Discrete values	Nominal	0 – Male 1 – Female
Region	String Discrete values	Nominal	0 – Cleveland 1 – Los Angeles 2 – St. Louis 3 – Bayonne
Salary	Numeric	Scale	n/a

Performance Data

The primary role of the performance data is to examine the effectiveness of the new training program. The data already exists in the company’s various sources for example, in personnel data, assignment logs, and the training data but it may not be recorded (Hergenhahn, 2005). The best way to record this data for the purpose of analysis will be through the following sheet.

(Table 3)

Number of years of service in the company	Driver’s skills level	Number of hours of training a driver received	Number of jobs assigned to a driver in the year 2010	Number of days the driver is on the road

With the sheet above, the following variables and measurement scales will be appropriate for the purpose of data analyzing.

(Table 4)

Variable Name	Type	Measurement	Labels
Number of Years_10	Numeric Discrete values	Scale – interval	n/a
Skills _level	String Discrete values	Nominal	0 – Driver Trainee 1 – Light Truck Driver 2 – Route Driver 3 – Heavy Truck Driver 4 – Long Distance Driver
Training_10	Numeric Discrete values	Scale Ratio values	n/a
Number of Jobs 10	Numeric Discrete values	Scale Ratio value	n/a
Travel 10	Numeric Discrete values	Scale Ratio value	n/a

Assessment data

A survey was conducted. A questionnaire was used. The questionnaire had four questions. The questions are:

Do I have the training necessary to do my job?
I am satisfied with my job?
Does the company place high importance on driver training?
Does the company place high importance on safety? The respondents were supposed to rate their responses with a five-point Likert-type scale:

Strongly agree
Agree
Neither neither agree nor disagree
Disagree
Strongly disagree

The following table illustrates variable definitions and the measurement units in the assessment data.

(Table 5)

Variable Name	Type	Measurement	Labels
Survey_1	Numeric Discrete values	Ordinal	Strongly agree Agree Neither agree nor disagree Disagree Strongly disagree
Survey_2	Numeric Discrete values	Ordinal	Strongly agree Agree Neither agree nor disagree Disagree Strongly disagree
Survey_3	Numeric Discrete values	Ordinal	Strongly agree Agree Neither agree nor disagree Disagree Strongly disagree
Survey_4	Numeric Discrete values	Ordinal	Strongly agree Agree Neither agree nor disagree Disagree Strongly disagree

Conclusion

Quantitative research enables the researcher to launch an objective assessment of a given case as statistical data of the variables is usually used (Given, 2008).This is important in getting accurate results. With the data collection executive summary, the Huffman Tucking Company will be able to properly analyze the data and hence examine it to realize if the objective the research was met.

References

Given, L. M. (2008). The Sage encyclopedia of qualitative research methods. Los Angeles, L.A, USA: Sage Publications.

Glaser, B. & Strauss, A. (1967). The discovery of grounded theory: Strategies for qualitative research. Chicago, Chgo: Aldine.

Hergenhahn, B.R. (2005). An introduction to the history of psychology. Belmont, CA, USA: Thomson Wadsworth.

Shettleworth, S. J. (2010). Cognition, Evolution and Behavior (2nd Ed). New York, NY: Oxford.

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