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Measurement versus prediction in the construction of patient-reported outcome questionnaires: can we have our cake and eat it?
by
Smits, Niels
, Conijn, Judith M.
, van der Ark, L. Andries
in
Clinical outcomes
/ Construction
/ Medicine
/ Medicine & Public Health
/ Methods
/ Patients
/ Public Health
/ Quality of Life Research
/ Questionnaires
/ Random variables
/ Sociology
/ SPECIAL SECTION: TEST CONSTRUCTION (BY INVITATION ONLY)
/ Validity
2018
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Measurement versus prediction in the construction of patient-reported outcome questionnaires: can we have our cake and eat it?
by
Smits, Niels
, Conijn, Judith M.
, van der Ark, L. Andries
in
Clinical outcomes
/ Construction
/ Medicine
/ Medicine & Public Health
/ Methods
/ Patients
/ Public Health
/ Quality of Life Research
/ Questionnaires
/ Random variables
/ Sociology
/ SPECIAL SECTION: TEST CONSTRUCTION (BY INVITATION ONLY)
/ Validity
2018
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Do you wish to request the book?
Measurement versus prediction in the construction of patient-reported outcome questionnaires: can we have our cake and eat it?
by
Smits, Niels
, Conijn, Judith M.
, van der Ark, L. Andries
in
Clinical outcomes
/ Construction
/ Medicine
/ Medicine & Public Health
/ Methods
/ Patients
/ Public Health
/ Quality of Life Research
/ Questionnaires
/ Random variables
/ Sociology
/ SPECIAL SECTION: TEST CONSTRUCTION (BY INVITATION ONLY)
/ Validity
2018
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Measurement versus prediction in the construction of patient-reported outcome questionnaires: can we have our cake and eat it?
Journal Article
Measurement versus prediction in the construction of patient-reported outcome questionnaires: can we have our cake and eat it?
2018
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Overview
Background Two important goals when using questionnaires are (a) measurement: the questionnaire is constructed to assign numerical values that accurately represent the test taker's attribute, and (b) prediction: the questionnaire is constructed to give an accurate forecast of an external criterion. Construction methods aimed at measurement prescribe that items should be reliable. In practice, this leads to questionnaires with high inter-item correlations. By contrast, construction methods aimed at prediction typically prescribe that items have a high correlation with the criterion and low inter-item correlations. The latter approach has often been said to produce a paradox concerning the relation between reliability and validity [1-3], because it is often assumed that good measurement is a prerequisite of good prediction. Objective To answer four questions: (1) Why are measurement-based methods suboptimal for questionnaires that are used for prediction? (2) How should one construct a questionnaire that is used for prediction? (3) Do questionnaire-construction methods that optimize measurement and prediction lead to the selection of different items in the questionnaire? (4) Is it possible to construct a questionnaire that can be used for both measurement and prediction? Illustrative example An empirical data set consisting of scores of 242 respondents on questionnaire items measuring mental health is used to select items by means of two methods: a method that optimizes the predictive value of the scale (i.e., forecast a clinical diagnosis), and a method that optimizes the reliability of the scale. We show that for the two scales different sets of items are selected and that a scale constructed to meet the one goal does not show optimal performance with reference to the other goal. Discussion The answers are as follows: (1) Because measurement-based methods tend to maximize inter-item correlations by which predictive validity reduces. (2) Through selecting items that correlate highly with the criterion and lowly with the remaining items. (3) Yes, these methods may lead to different item selections. (4) For a single questionnaire: Yes, but it is problematic because reliability cannot be estimated accurately. For a test battery: Yes, but it is very costly. Implications for the construction of patient-reported outcome questionnaires are discussed.
Publisher
Springer,Springer International Publishing,Springer Nature B.V
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