Page 246 - Solid Waste Analysis and Minimization a Systems Approach

224 DATA COLLECTION

Major group 87: Engineering, accounting, research, management, and services
Major group 88: Private households

Division J: Public administration
Major group 91: Executive, legislative, and general government, except finance
Major group 92: Justice, public order, and safety
Major group 93: Public finance, taxation, and monetary policy
Major group 94: Administration of human resource programs
Major group 95: Administration of environmental quality and housing programs
Major group 96: Administration of economic programs
Major group 97: National security and international affairs
Major group 99: Non-classifiable establishments

14.3 Data Collection Process

A seven-step process was utilized to collect the required data. The data was directly
collected using a national survey that was conducted in August 2002 through
September 2004 of manufacturing companies, service providers, and government
agencies located in the United States. The seven-step process was developed by
Creative Research Systems and revised in November of 2001 (www.surveysystem
.com). Below is an overview of the seven-step survey process:

1 Establish the goals of the survey.
2 Determine the sample and sample size.
3 Develop the sampling methodology.
4 Create the questionnaire.
5 Pretest the questionnaire.
6 Distribute the survey (data collection).
7 Analyze the data (survey results).

The following subsections expand on each step of the survey process and describe
how each was applied for this research.

14.3.1 GOAL OF THE SURVEY
A goal of the survey was to balance cost of collecting the data versus the required
accuracy. This was balanced by the number of observations included for the survey. If
a sample is too large, time, talent, and money are wasted (the cost of collecting the
data increases as the number of surveys increases). Conversely, if the number of obser-
vations included in the sample is too small, inadequate information may be collected
for the population (the data may not be accurate enough to base any decisions or create
any models).