Efficiency Is Key to Random Digit Sampling

Efficiency is key to random digit sampling

This article describes how sampling efficiency can affect research costs and highlights a new approach by Survey Sampling that identifies a large portion of the unproductive numbers in random-digit telephone samples without affecting the samples' integrity.

PrintArticle ID: 19900501 Published: May 1990 Author: Beth Wallace

Editor's note: Beth Wallace is manager of product marketing at Survey Sampling, Inc.

The 1990s will bring an increasingly cost-conscious and competitive environment for providers and buyers of survey research. Researchers will face additional pressure to maintain research quality while containing or reducing research costs.

One area that should be of top concern to researchers is the efficiency of the random digit samples they use to conduct telephone research. The efficiency level will significantly affect sampling and data collection costs and affect the overall research study budget. The more efficient the sample, the less the sample will cost to purchase and administer in the field.

Various techniques have been employed over the last decade to increase efficiency levels, including stratification techniques and purging business numbers. Recently, a new technology has been introduced by Survey Sampling, Inc. to push the efficiency level of random digit samples to 80% or better.

How sampling efficiency affects research costs

With random digit telephone samples, the level of efficiency refers to the proportion of sampling units that will reach residential households. This proportion is usually referred to as the "working phones rate" of the sample. Naturally, the nature of randomizing phone numbers to contact unlisted households will result in disconnected, unassigned, and business telephone numbers being created and included in your sample. It is precisely this group of unproductive telephone numbers that needs to be kept to a minimum which, conversely, will provide a high working phones rate.

The efficiency of random digit samples affects research costs in two main areas-initial sampling costs and data collection costs. Each of these areas requires some explanation.

Initial sampling costs are immediately affected by the working phones rate of the sampling methodology. The lower the working phones rate, the more sampling units will need to be purchased to complete the desired number of completed interviews. The hypothetical situation presented below illustrates the point. 

Sample Type I, having an expected working phones rate of 55%, will require 18% more sampling units than Sample Type II, which has an expected working phones rate of 65%. Clearly, a bigger sample is needed if a lower working phones rate is anticipated.

Data collection costs are directly affected by unproductive numbers included in the random digit sample. Dialing attempts made to unproductive numbers may cost more than many researchers think. Continuing with our hypothetical situation will demonstrate the waste associated with dialing bad numbers. 

Due to its lower working phones rate, Sample I will require 1358 more dialings to unproductive numbers than required with Sample II. Dialings to business numbers exacerbate the situation since consumer samples are usually dialed in the evening hours and business numbers will, in most cases, not be discovered to prevent wasted callbacks.