TRC

By Rajan Sambandam

Introduction

The culmination of a quantitative market research project is some type of reporting of study findings. The validity of such findings is predicated on any number of elements including questionnaire design, proper sample frames, respondent selection, data collection methods and data cleaning. However, the most important criterion in determining the validity of findings is the extent to which the data collected represents the actual population under study. While every effort is made to obtain a representative sample of respondents, it is understood that often the final data set is not entirely representative of the population of interest. This may occur due to both reasons beyond the researcher’s control (non-response bias, deficiencies in sample availability) as well as due to the researcher’s intervention (quota sampling). Our final recourse before reporting findings is to take stock of the data set and determine if it needs to be altered in order to make it representative (or more representative). This alteration of the data is typically referred to as weighting. Given the importance of ensuring the most representative sample possible, it is critical that the researcher understand when weighting might be required and the various methods for providing viable weights. The purpose of this paper is to provide a guideline for identifying those situations where data weighting is to be considered as well as the considerations that go into the decision to alter the data.

Brief Review of Weighting

Before considering various situations where data weighting is appropriate please consider a brief review of the weighting concept. If we assume our data set is representative, then analysis proceeds under the concept that the respondents in the sample represent the members of the population in proper proportion (for example, the percentage of males, females, customers, non-customers, etc. are nearly equivalent in the sample and the population). Having achieved proportional representation in our sample, respondents are grouped according to various characteristics and attitudes and tabulated accordingly; with each respondent counting as one person.

If a data set contains specific groups of respondents that are either over-represented or under-represented, then the sample is not indicative of the population and analyzing the data as collected is not appropriate. Instead, the data should be redistributed (or weighted) so that we achieve proportional representation in our sample. Specifically, each data point will carry a weight and rather than each respondent counting equally as one sample member, will thereafter represent either more or less than one sample member when results are tabulated.

This process will be illustrated more clearly by example in the following section. The important concept to remember is that the goal of any study is to obtain a representative sample. If that is not achieved naturally, redistribution of the data is required to yield one. 

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