The demarcation between what is currently regarded as qualitative or quantitative research has been blurring for some time now. So-called non-traditional methods of research are on the rise. Two apparently opposing undercurrents dominate current developments, or so it would seem. On one hand, researchers are aiming for more proximity with the consumer by closely observing actual behaviour in the home and in-store. On the other hand, there is also a tendency to move away from physical contact with the consumer and this is leading to the growing use of remote research. However, a more pragmatic attitude has slowly become more accepted in the world of research with the view that it is possible to obtain sound evidence through different methods. Furthermore, the essential feature of research is and remains the number, the N: the definition of the random sample size. An acceptable number of perceptions is a prime condition for good quality interaction and its dissemination through the interviewer - or should we call these co-workers something else? Relevance determines what is reasonable. What remains certain is that reports cannot go only in the direction of statements based on N=i or the projection of primarily the researchers prejudice. The objective is deep consumer understanding, not the expression of ones own opinion.
In survey research it is very rare for all respondents in a given population to be interviewed. We usually take a sample of that population. The reason why we can do this is because a sample can give us, not necessarily the accuracy of a census (or full count), but sufficient accuracy for prediction purposes. This is true if the sample is representative of the population from which it is drawn. There are various sampling methods that can be used if we wish to obtain a representative sample. Such samples can give, depending mainly on the size of the sample, results to given levels of precision.
How should I decide on the sample size for a survey? That is a question often posed by survey researchers to statisticians. It is difficult to answer simply as in market research we carry out surveys which more often than not carry a large number of different questions. There may be questions which are more important than others and hence need to be answered with a higher level of precision. A good starting point therefore is to consider the most important item to be measured by a proposed survey. For the moment we will assume that the survey is to be carried out using a Simple Random Sample and that the survey result is a percentage.
The point of this paper is that based on the experience hitherto gained, the problems concerning representativeness in market research for investment goods should be studied more closely, and that stimuli should be provided for the improvement of the statistical basic data as well as for the further methodical development of industrial market research. Finally it should be born in mind that the industrial market research has by far not reached its point of culmination and that, to the author's conviction, it has ample possibilities for a bright future.
This paper has argued that the sample size necessary in a survey does not primarily depend upon the size of the population from which the sample is to be drawn, but from other factors concerning the basic characteristics of the population and the quality of the information required from the sample. Hence the idea of a constant percentage sample size is a myth; there never has been, or will be, a sampling plan that requires a constant percentage of the population to be sampled, valid for all population sizes. To determine the correct sample size, for defined precision and confidence, some knowledge is required of P for attribute sampling or S for measured variates. This knowledge is not always available and thus, it may sometimes be argued, the procedures outlined earlier are vitiated. But, whilst it is often true that P or S are not precisely known, an approximate value is commonly available and this may well be good enough. Furthermore, a small error in P or S may not vitally affect the value of N obtained. If a big error is found, this would become clear as the sampling progressed and, should circumstances warrant it, a re-calculation of N can made and the remaining balance of the freshly estimated sample size N obtained on a statistical basis. Finally, this paper has only discussed straightforward single sample plans. Whilst the basic thesis of the paper holds whatever the kind of sampling plan envisaged, e.g. double sampling, or stratified sampling, the determination of the necessary sample size becomes more involved and the reader must be referred to the more specialised standard works on sampling, whether for the general background, the mathematical theory, or the survey design and analysis necessary for rigorous sampling.