Measurement error is caused by difference between the information desired by the researcher and the information provided by the measurement process.
Experiments are designed to measure the impact of one or more independent variables on a dependent variable. Experimental error occurs when the effect of experimental situation itself is measured rather than the effect of independent variable. For example , a retail chain may increase the price of selected items constant in four similar outlets, in an attempt to discover the best pricing strategy. However, unique weather patterns, traffic conditions, or competitors’ activities may affect the sales at one set of stores and not the other. Thus, the experimental result will reflect the impact of variables other than price.
Population Specification Error
Population specification error is caused by selecting an inappropriate universe or population from which to collect data. This is a potentially serious problem in both industrial and consumer research. A firm wishing to learn the criteria that are considered most important in the purchase of certain machine tools might conduct a survey among purchasing agents. Yet, in many firms the purchasing agents don’t determine or necessary even know the criteria behind brand selections. These decisions may be made by the machine operators, by committee or high level executives. A study that focuses on the purchasing agent as the person who decides which brands to order may be subject to population specification error.
The sampling frame is the list of population members from which the sample units are selected. An ideal frame identifies each member of the population once and only once. Frame error is caused by using inaccurate or incomplete sampling frame.
For example, using the telephone directory as sampling frame for the population of a community contains a potential for frame error. Those families who don’t have listed numbers, both voluntarily or involuntarily, are likely to differ from those with listed numbers in such respects as income, gender and mobility.
Sampling error is caused by the generation of nonrepresentative sample by means of a probability sampling method. For example, a random sample of 100 university students could produce a sample of all families. Such a sample wouldn’t be representative of the over all student body. Yet it could occur in classic sampling technique. Sampling error is the focal point of concern in classical statistics.
Selection error occurs when a nonrepresentative sample is obtained by non probability sampling methods. For example, one of the authors talked with an interviewer who is afraid of dogs. In surveys that allowed any freedom of choice, this interviewer avoided home with dogs present. Obviously such practice may introduce error in to the survey results. Selection error is a major problem in nonprobablity samples.
Nonresponce error is caused by (1) failure to contact all members of a sample, and /or
(2) the failure of some contacted members of the sample to respond to all or specific parts of the measurement instrument. Individuals who are difficult to contact or who are reluctant to cooperate will differ, on at least some characteristics, from those who are relatively easy to contact or who readily cooperate. If these differences include variable of interest , nonresponse error has occurred.
For example, people who are more likely to respond to a survey on a topic that interests them. If a firm were to conduct a mail survey to estimate the incidence’s foot among adults, non response error would be of major concern. Why? Those most likely in athlete’s foot, and thus more likely to respond to the survey, are current or recent suffers of the problem. If the firm were to choose the percentage of those responding who report having athlete’s foot as an estimate of the total population having athlete’s foot, the company would probably overestimate the extent of the problem.