Bias in Experiments
Bias in experiments refers to systematic errors or inaccuracies that can occur due to flaws in the design, conduct, or analysis of the experiment. These biases can lead to misleading results and conclusions. It is important to identify and minimize biases in order to ensure the validity and reliability of the experimental findings.
6 Key excerpts on "Bias in Experiments"
eBook - ePubDesigning Social Inquiry
Scientific Inference in Qualitative Research, New Edition
- Gary King, Robert O. Keohane, Sidney Verba(Authors)
- 2021(Publication Date)
- Princeton University Press(Publisher)
...Our task is to find out what types of systematic measurement error result in which types of bias. In both quantitative and qualitative research, systematic error can derive from choices on the part of researchers that slant the data in favor of the researcher’s prior expectations. In quantitative work, the researcher may use such biased data because it is the only numerical series available. In qualitative research, systematic measurement error can result from subjective evaluations made by investigators who have already formed their hypotheses and who wish to demonstrate their correctness. It should be obvious that any systematic measurement error will bias descriptive inferences. 1 Consider, for example, the simplest possible case in which we inadvertently overestimate the amount of annual income of every survey respondent by $1,000. Our estimate of the average annual income for the whole sample will obviously be overestimated by the same figure. If we were interested in estimating the causal effect of a college education on average annual income, the systematic measurement error would have no effect on our causal inference. If, for example, our college group really earns $30,000 on average, but our control group of people who did not go to college earns an average of $25,000, our estimate of the causal effect of a college education on annual income would be $5,000. If the income of every person in both groups was overestimated by the same amount (say $1,000 again), then our causal effect—now calculated as the difference between $31,000 and $26,000—would still be $5,000. Thus, systematic measurement error which affects all units by the same constant amount causes no bias in causal inference...
eBook - ePubTaking Sides in Social Research
Essays on Partisanship and Bias
- Martyn Hammersley(Author)
- 2005(Publication Date)
- Routledge(Publisher)
...The effect of this is evident, for instance, in the claim that ‘the question is not whether the data are biased; the question is whose interests are served by the bias’ (Gitlin et al. 1989:245). Here, the recommendation is that research should be biased: in favour of serving one group rather than another. Of course, this is not the predominant sense of the term ‘bias’ as it is used in the social sciences. Instead, bias is generally seen as a negative feature, as something that can and should be avoided. Often, the term refers to any systematic deviation from validity, or to some deformation of research practice that produces such deviation. Thus, quantitative researchers routinely refer to measurement or sampling bias, by which they mean systematic error in measurement or sampling procedures that produces erroneous results. 3 The contrast here is with random (or haphazard) error: where bias tends to produce spurious results, random error may obscure true conclusions. The term ‘bias’ can also be employed in a more specific sense, to identify a particular source of systematic error. This is a tendency on the part of researchers to collect data, and/or to interpret and present them, in such a way as to favour false results that are in line with their pre-judgements and political or practical commitments. This may consist of a positive tendency towards a particular, but false, conclusion. Equally, it may involve the exclusion from consideration of some set of possible conclusions that happens to include the truth. This third interpretation of ‘bias’ will be our main focus. Such bias can be produced in a variety of ways. The most commonly recognised source is commitments that are external to the research process, such as religious or political attitudes, which discourage the discovery of uncomfortable facts and/or encourage the presentation of spurious ‘findings’. But there are also sources of bias that stem from the research process itself...
eBook - ePub- Joachim I. Krueger, Joachim I. Krueger(Authors)
- 2012(Publication Date)
- Psychology Press(Publisher)
...Overconfidence takes on meaning when a researcher identifies the points on the scale metric where measured hubris is linked to unacceptably high risks of teen pregnancy or drunk driving or budget overruns. In each case, interest in real-world outcomes pulls a researcher’s attention away from numbers and toward everyday life. It thereby adds a new twist to traditional debates over whether commonly measured forms of “irrationality” are adaptive (Taylor & Brown, 1988) or maladaptive (Colvin & Block, 1994), as it inextricably binds the assessment of rationality to its measureable effects. The Process Approach: Quantifying Erroneous Influences, Not Errors The psychometric hurdles reviewed here are only of concern when researchers wish to estimate accuracy and error. These can be circumvented, however, if a researcher instead focuses on modeling the antecedents and consequences of irrational thought process. This alternate approach is consistent with common uses of laboratory experimentation. Recall that experimental psychologists document errors by determining how experimental stimuli influence responses in ways not accounted for by idealized rational models. The same logic can be incorporated into measurement enterprises. Researchers can model the factors that influence judgments, after known rational influences have been statistically controlled. The factors that operate independent of known, rational influences can then be interpreted as biasing factors—factors that distort judgments away from what would be predicted on the basis of the rational model. For instance, in one idealized model, confidence in judgments will vary systematically as a function of the accuracy of these judgments (plus or minus random error). One can thereby identify factors that systematically bias judgmental confidence by modeling the variables that predict confidence ratings for a judgment or set of judgments, after judgmental accuracy has been statistically controlled...
- Bruce B. Frey(Author)
- 2018(Publication Date)
- SAGE Publications, Inc(Publisher)
...However, it is not the sole criterion for choosing a best estimator. Still, efforts are made to obtain better estimators by taking into account the unbiasedness. For example, the η 2 is a measure of effect size that represents the proportion of variance explained by the factor of interest to the total variation. The ω 2 is its bias-corrected version, although it is not completely unbiased. Use of an unbiased or less-biased estimator could matter when realized values of the estimator are averaged over multiple studies to obtain a single estimate of a population parameter as in meta-analysis. Relevance to Other Types of Bias Estimation bias is a purely statistical concept; it is theoretically derived from the statistical assumptions (i.e., the population model and the sampling scheme) and the choice of estimator. However, there are other sources that could cause systematic errors in real data. For example, selection bias occurs if the sample is not representative of the target population. Measurement bias takes place if one uses an ill-calibrated measurement instrument or scheme. Estimators that are supposedly unbiased could be invalid in these circumstances because the statistical assumptions from which the unbiasedness of those estimators is derived are likely violated. It is crucial to use an appropriate data collection design and statistical modeling so that one can reduce or separate possible bias that arises in the data collection process. In the scoring of an essay task, for example, a rater may produce consistently higher scores than the true scores (i.e., measurement bias). Such bias cannot be identified if only scores from that single rater are analyzed...
eBook - ePub- E. Bright Wilson(Author)
- 2012(Publication Date)
- Dover Publications(Publisher)
...In another part of the experiment with a pair of presumably similar bottles some undetermined difference similarly though less decisively biased the results. Randomization can reduce bias due to such effects if it is possible to randomize the troublemaking variables relative to the one of interest. Of course, if the effect is large, randomizing, though removing bias, will not prevent the effect, from reducing the sensitivity of the experiment. Bias in Instruments. Instruments and nonliving subjects are often biased by the association of other variables with the one under test. In particular, instruments seldom measure directly what they claim to measure, but rather some quantity related to the desired one by a theory. If the conditions of the theory are not fully met, the instrument may begin to record something quite different. For example, a voltmeter is supposed to convert voltage to a pointer reading, but if the impedance of the voltage source is much higher than that of the meter, the meter will not read true voltage because in fact voltmeters are current meters with an attached resistance and function properly only with sources of lower impedance. 4.7. Replication It is seldom that only one experiment is regarded as sufficient; usually repetitions are considered desirable in order to check the result and also to form a basis for estimating the precision obtained. This process of replication is especially necessary when the class under study is not too precisely defined and is therefore subject to wide individual variations. This applies almost always to biological, medical, and agricultural experiments, where large numbers of tests may be required, but it is also very desirable in physical observations to make check runs to catch mistakes. In the past there have been many ludicrous cases of conclusions drawn from an insufficient number of experiments. A story is told of an investigation in which chickens were subjected to a certain treatment...
eBook - ePubUsing Surveys to Value Public Goods
The Contingent Valuation Method
- Robert Cameron Mitchell, Richard T. Carson(Authors)
- 2013(Publication Date)
- RFF Press(Publisher)
...We intentionally excluded several types of bias, particularly hypothetical bias and information bias, 28 that have received considerable attention but which are not really meaningful categories of bias. Where possible, we drew upon the results of experiments conducted by CV researchers to detect the presence of one bias or another. We also called attention to the methodological problems that make some of these experiments less useful than they otherwise would be. 29 Our hope is that future bias experiments will be based on sample sizes that are large enough to allow meaningful conclusions to be drawn from their findings. 30 In our treatment of each type of bias we offered suggestions for avoiding or minimizing the bias effects whenever possible. Whether or not a study is vulnerable to one or more of these biases depends on a number of factors, including the survey method used, the nature of the amenity, and the purpose of the study. The researcher must carefully assess the potential sources of bias to which his or her study may be vulnerable. To do so will often require intensive preliminary research of a qualitative nature in order to better understand how and why potential respondents will react to the various scenario elements, and the inclusion of one or more bias experiments in the study design to verify that the most likely sources of bias are not present in the study. That the contingent valuation method is vulnerable to instrument effects and to miscommunication between what the interviewer says and what the respondent understands will come as no surprise to anyone familiar with survey research. The array of potential biases described here is a forceful reminder that the contingent valuation method cannot easily be applied in an off-the-shelf manner...
