What does measurement bias mean

Video Segment In this video segment, Professor Kader and participants discuss the presence of bias in two surveys about nuclear power, including the one presented in Problem B8.

You can find this segment on the session video approximately 16 minutes and 51 seconds after the Annenberg Media logo. In this Interactive Activity, you will have an opportunity to see how well you make two visual judgements. On a piece of paper, try drawing each of the graphics below, but with the length of segment BC equal to the length of AB. Then measure your segments to see how accurate your drawings are.

Would you say that the errors you made in the visual judgments of the Interactive Activity were due to random error, or to bias? Why or why not? Measurement processes may be biased due to human error. The Interactive Activity is based on a visual illusion known as the Muller-Lyer illusion, a phenomenon thoroughly studied by behavioral scientists.

In this illusion, two arrows with different arrowheads — one pointing out and one pointing in — are placed next to each other. Although the shafts are of equal lengths, the arrow with the outgoing head looks longer than the other arrow. If you are working with a group, recording the responses of each group member will make the bias demonstrated in the activity more clear.

There are two ways you might record results. Your results should show an obvious difference between pluses and minuses. With a little more effort you could measure the deviation of perceived length and actual length e. In the second part of the Interactive Activity, you are asked to judge the length of two lines with vertical bars at each end. The errors are mainly due to the bias in the presentation. A random error is just as likely to be an overestimate as an underestimate, whereas bias is a systematic error that consistently overestimates or underestimates the true length.

Most people who compare the lengths overestimate the length of BC, with its outward-pointing arrow, because of the optical illusion caused by the direction of the arrowheads.

They are less likely to make this error when the arrows are replaced by perpendicular lines. Consider statistics as a problem-solving process and examine its four components: asking questions, collecting appropriate data, analyzing the data, and interpreting the results.

This session investigates the nature of data and its potential sources of variation. Variables, bias, and random sampling are introduced.

Explore different ways of representing, analyzing, and interpreting data, including line plots, frequency tables, cumulative and relative frequency tables, and bar graphs.

Learn how to use intervals to describe variation in data. Learn how to determine and understand the median. Continue learning about organizing and grouping data in different graphs and tables. Examples from the field of health-related quality of life research illustrate the definitions. Results: Definitions of response shifts as special cases of either measurement bias or explanation bias clarify different interpretations of response shift and lead to different research methods.

Different structural equation models are suggested to investigate biases and response shifts in each of the two perspectives. Conclusion: It is important to distinguish between measurement and conceptual perspectives as they involve different ideas about response shift. Catalogue of Bias Collaboration. Heneghan C, Brassey J. Insensitive measure bias. Fanning J et al. Porta M et al. A dictionary of epidemiology.