Question

When considering statistical inference, it is important to ensure that the sample used is generalizable to the actual population being estimated. Sampling errors result may create biases that cause the actual error term to be much larger than the standard error. This, in turn, may lead to Type I and Type II errors even though the critical test statistics and p-values are outside the rejection region. Define and discuss non-response bias, undercoverage, and response bias. Identify specific scenarios in business research that could lead to these types of biases. Finally, explain how to prevent them from affecting the data collection.

Answer 1

Define and discuss non-response bias, undercoverage, and response bias.

A good sample is representative. This means that each sample point represents the attributes of a known number of population elements.

Bias often occurs when the survey sample does not accurately represent the population. The bias that results from an unrepresentative sample is called selection bias. Some common examples of selection bias are described below.

Undercoverage:

Undercoverage occurs when some members of the population are inadequately represented in the sample.

Here we give an example from Business research regarding undercoverage:

A classic example of undercoverage is the Literary Digest voter survey, which predicted that Alfred Landon would beat Franklin Roosevelt in the 1936 presidential election. The survey sample suffered from undercoverage of low-income voters, who tended to be Democrats.

How did this happen? The survey relied on a convenience sample, drawn from telephone directories and car registration lists. In 1936, people who owned cars and telephones tended to be more affluent. Undercoverage is often a problem with convenience samples.

Nonresponse bias:

Sometimes, individuals chosen for the sample are unwilling or unable to participate in the survey. Nonresponse bias is the bias that results when respondents differ in meaningful ways from nonrespondents. The Literary Digest survey illustrates this problem. Respondents tended to be Landon supporters; and nonrespondents, Roosevelt supporters. Since only 25% of the sampled voters actually completed the mail-in survey, survey results overestimated voter support for Alfred Landon.

The Literary Digest experience illustrates a common problem with mail surveys. Response rate is often low, making mail surveys vulnerable to nonresponse bias.

Response bias:

Response bias refers to the bias that results from problems in the measurement process. Some examples of response bias are given below.

Leading questions. In Business research, the wording of the question may be loaded in some way to unduly favor one response over another. For example, a satisfaction survey may ask the respondent to indicate where she is satisfied, dissatisfied, or very dissatified. By giving the respondent one response option to express satisfaction and two response options to express dissatisfaction, this survey question is biased toward getting a dissatisfied response.

In order to prevent them from affecting the data collection. First step, is that we should use appropriate technique of sampling according to Business research scenario. For example: if we have heterogenous population, simple random sampling can be biased and it is appropriate to use stratified random sampling. The second step is that we should use optimal sample size for Business research problem to avoid measurement errors due to non-response bias, undercoverage, and response bias.