THE PENALTY FOR NOT GUESSING<sup>1</sup>

Slakter, Malcolm J.

doi:10.1111/j.1745-3984.1968.tb00616.x

Cited by 47 publications

(17 citation statements)

References 13 publications

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“…Rational but cautious students may leave questions blank even though the expected score is positive. This theoretical result is consistent with the experimental evidence (Bliss, 1980;Cross & Frary, 1977;Ebel, 1968;Harden et al, 1976;Highham, 2007;Muijtjens et al, 1999;Sanderson, 1973;Sax & Collet, 1968;Sherriffs & Boomer, 1954;Slakter, 1968aSlakter, , 1968b. To introduce this idea in a formal model we use the theory of decision under uncertainty (e.g., Arrow, 1971;Pratt, 1964;von Neumann & Morgenstern, 1944) which explains why individuals may behave cautiously and for example be willing to buy insurance.…”

Section: Discussionsupporting

confidence: 67%

“…Comparing the free report condition to the forced report condition, Highham (2007) found that the decrease in the number of omissions increased both the raw and the corrected score and to a greater extent in the group facing the high penalty. These results are consistent with previous work on the accuracy of withheld answers (Bliss, 1980;Cross & Frary, 1977;Ebel, 1968;Harden, Brown, Biran, Dallas Ross, & Wakeford, 1976;Muijtjens, Mameren, & Hoogenboom, 1999;Sanderson, 1973;Sax & Collet, 1968;Sherriffs & Boomer, 1954;Slakter, 1968aSlakter, , 1968b). This evidence is not consistent with expected score maximizers.…”

supporting

confidence: 83%

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Optimal correction for guessing in multiple-choice tests

Espinosa

Gardeazábal

2010

Journal of Mathematical Psychology

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MCQ examinations are frequently used but there is no consensus as to whether a penalty for wrong answers should be used or not. For example, in some countries examinations for medical licensing include MCQ sections with penalty while in others there is no penalty for wrong answers. We contribute to this discussion with the analysis of the effects of penalties. In a partial knowledge model there is a trade-off between bias and measurement error that is affected by the level of penalties. We build on IRT and introduce students' optimal behavior. Our simulations indicate that the optimal penalty may be relatively high: even though it discriminates against risk averse students this effect is small compared with the measurement error that it prevents.

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Section: Discussionsupporting

confidence: 67%

supporting

confidence: 83%

Optimal correction for guessing in multiple-choice tests

Espinosa

Gardeazábal

2010

Journal of Mathematical Psychology

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“…Examinees were initially told to not guess at all, but these 'do-not-guess' instructions tend to provide an advantage to those who disobey the instructions and guess in spite of them (Diamond and Evans, 1973;Slakter, 1968;Votaw, 1936). Students achieve on average higher scores when they are required to answer all questions than when they respond under scoring rule instructions, even though the penalty for guessing is applied in both cases (Bliss, 1980;Cross and Fray, 1977).…”

Section: Scoring Rulesmentioning

confidence: 97%

Prospect theory analysis of guessing in multiple choice tests

Bereby‐Meyer

Meyer

Flascher

2002

Behavioral Decision Making

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The guessing of answers in multiple choice tests adds random error to the variance of the test scores, lowering their reliability. Formula scoring rules that penalize for wrong guesses are frequently used to solve this problem. This paper uses prospect theory to analyze scoring rules from a decision-making perspective and focuses on the effects of framing on the tendency to guess. In three experiments participants were presented with hypothetical test situations and were asked to indicate the degree of certainty that they thought was required for them to answer a question. In accordance with the framing hypothesis, participants tended to guess more when they anticipated a low grade and therefore considered themselves to be in the loss domain, or when the scoring rule caused the situation to be framed as entailing potential losses. The last experiment replicated these results with a task that resembles an actual test. Copyright # 2002 John Wiley & Sons, Ltd.key words multiple choice tests; framing effect; decision making; scoring rules Most standardized aptitude and achievement tests and many academic exams in schools and universities consist of multiple choice items. They are mainly used because their scoring is both easy and objective. Research on educational measurement has dealt extensively with scoring methods for these tests, aiming to increase their reliability and validity (Diamond and Evans, 1973;Frary, 1982; Rowley and Traub, 1977). We suggest that, in addition to the usual psychometric analyses, exams should also be considered as situations in which people face a series of decisions under uncertainty. Therefore the characteristics of behavioral decisionmaking processes that affect risk taking may shape people's behavior during tests. Individual differences in examinees' tendencies to take risks and the use of different scoring methods are likely to alter the patterns of responses systematically. This can provide advantages or disadvantages for certain groups of examinees, which may lower the tests' validity. The series of experiments reported here demonstrates that examinees' expectations about the outcome of a test and the scoring methods that are employed in an exam indeed create framing effects that affect the tendency to take risks and to guess in exams.

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“…For example, in the literature on formula-scoring, if students are asked to return to the test and give "best guesses" to questions for which answers were omitted, those answers tend to have higher-thanchance accuracy (e.g., Bliss, 1980;Cross & Frary, 1977;Ebel, 1968;Higham, 2007, Experiment 1;Muijtjens, van Mameren, & Hoogenboom, 1999;Sax & Collet, 1968;Sherriffs & Boomer, 1954;Slakter, 1968aSlakter, , 1968b. Although not UC in the typical sense of the term (i.e., actual accuracy exceeding assessed probability of correctness), it is UC in the sense that students were not confident enough to report these "best guesses."…”

Section: Overconfidence and Underconfidencementioning

confidence: 99%

Regulating accuracy on university tests with the plurality option

Higham

2013

Learning and Instruction

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THE PENALTY FOR NOT GUESSING¹

Cited by 47 publications

References 13 publications

Optimal correction for guessing in multiple-choice tests

Optimal correction for guessing in multiple-choice tests

Prospect theory analysis of guessing in multiple choice tests

Regulating accuracy on university tests with the plurality option

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THE PENALTY FOR NOT GUESSING1

Cited by 47 publications

References 13 publications

Optimal correction for guessing in multiple-choice tests

Optimal correction for guessing in multiple-choice tests

Prospect theory analysis of guessing in multiple choice tests

Regulating accuracy on university tests with the plurality option

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Product

Resources

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THE PENALTY FOR NOT GUESSING¹