TRC

By Rajan Sambandam

Introduction

As market researchers, we use data gathered from surveys to make informed decisions and give recommendations to clients on ways to improve their sales and standing in the marketplace. Whether these recommendations are to invest resources to increase satisfaction with customer support or to introduce a new product to the market, we need to feel confident that the results from many crosstabs and multivariate analyses are speaking the truth. The confidence we have in our results stems from the quality of our data. In today’s market research industry, we do a lot to help ensure our data meet certain standards: screener questions target the specific audience we want, online panels take many steps to ensure their samples contain the target we need, we weight respondents to match specific population demographics, etc. [Refer to white paper Situational Use of Data Weighting for more details] However, one of the most over-looked problems is that of non-response bias.

Non-Response Bias

In data collection, there are two types of non-response: item and unit non-response. Item non-response occurs when certain questions in a survey are not answered by a respondent. Unit non-response takes place when a randomly sampled individual cannot be contacted or refuses to participate in a survey. The bias occurs when answers to questions differ among the observed and non-respondent items or units. A general formula for measuring bias is:

Bias = P ( O – N )

where
P is the proportion of non-respondents from the targeted sample (i.e. non-response rate)

O is the answer based on observed responses

N is the answer based on non-respondents only

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