response bias

Examples of response bias in the following topics:

• How Well Do Probability Methods Work?

  • Failure to use probability sampling may result in bias or systematic errors in the way the sample represents the population.
  • This is especially true of voluntary response samples--in which the respondents choose themselves if they want to be part of a survey-- and convenience samples--in which individuals easiest to reach are chosen.
  • A third example of bias is called response bias.
  • Careful training of pollsters can greatly reduce response bias.
  • Finally, another source of bias can come in the wording of questions.

• Telephone Surveys

  • As some people do not answer calls from strangers, or may refuse to answer the poll, poll samples are not always representative samples from a population due to what is known as non-response bias.
  • In this type of bias, the characteristics of those who agree to be interviewed may be markedly different from those who decline.
  • However, if those who do not answer have different opinions, then the results have bias.
  • In terms of election polls, studies suggest that bias effects are small, but each polling firm has its own techniques for adjusting weights to minimize selection bias.
  • Undercoverage is a highly prevalent source of bias.

• Sampling from a population

  • This introduces bias into a sample.
  • The act of taking a simple random sample helps minimize bias, however, bias can crop up in other ways.
  • Even when people are picked at random, e.g. for surveys, caution must be exercised if the non-response is high.
  • This non-response bias can skew results.
  • Due to the possibility of non-response, surveys studies may only reach a certain group within the population.

• A Closer Look at the Gallup Poll

  • Gallup still has to deal with the effects of nonresponse bias, because people may not answer their cell phones.
  • Because of this selection bias, the characteristics of those who agree to be interviewed may be markedly different from those who decline.
  • Response bias may also be a problem, which occurs when the answers given by respondents do not reflect their true beliefs.
  • Finally, there is still the problem of coverage bias.

• Adjusted R2 as a better estimate of explained variance

  • We first used R2 in Section 7.2 to determine the amount of variability in the response that was explained by the model:
  • 8.11: In multiple regression, the degrees of freedom associated with the variance of the estimate of the residuals is n−k−1, not n−1.For instance, if we were to make predictions for new data using our current model, we would find that the unadjusted R2 is an overly optimistic estimate of the reduction in variance in the response, and using the degrees of freedom in the adjusted R2 formula helps correct this bias.$ R^{2}_{adj} = 1 \frac{23.34}{83.06}\times\frac{1411}{14141}= 0.711.$

• Sampling Bias

  • This section discusses various types of sampling biases including self-selection bias and survivorship bias.
  • A common type of sampling bias is to sample too few observations from a segment of the population.
  • Gains in stock funds is an area in which survivorship bias often plays a role.
  • Therefore, there is a bias toward selecting better-performing funds.
  • There is good evidence that this survivorship bias is substantial (Malkiel, 1995).

• Chance Error and Bias

  • Chance error and bias are two different forms of error associated with sampling.
  • In statistics, sampling bias is a bias in which a sample is collected in such a way that some members of the intended population are less likely to be included than others.
  • Self-selection bias, which is possible whenever the group of people being studied has any form of control over whether to participate.
  • Exclusion bias, or exclusion of particular groups from the sample.

• Principles of experimental design

  • Randomizing patients into the treatment or control group helps even out such differences, and it also prevents accidental bias from entering the study.
  • The more cases researchers observe, the more accurately they can estimate the effect of the explanatory variable on the response.
  • Researchers sometimes know or suspect that variables, other than the treatment, influence the response.

• Distorting the Truth with Descriptive Statistics

  • Descriptive statistics can be manipulated in many ways that can be misleading, including the changing of scale and statistical bias.
  • Bias is another common distortion in the field of descriptive statistics.
  • The following are examples of statistical bias.
  • Analytical bias arises due to the way that the results are evaluated.
  • Exclusion bias arises due to the systematic exclusion of certain individuals from the study

• The Salk Vaccine Field Trial

  • The responses of a treatment group of subjects who are given the treatment are compared to the responses of a control group of subjects who are not given the treatment.
  • Two serious issues arose in this design: selection bias and diagnostic bias.