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What Are the Priming and Ceiling Effects of One Experience Measure on Another?

ABSTRACT: Patient-reported experience measures have notable ceiling effects which can hinder efforts to learn and improve. This study tested whether an iterative (Guttman-style) satisfaction questionnaire combined with instructions intended to give people agency to critique us primes responses on an ordinal scale and reduces ceiling effects. Among the 161 subjects randomly assigned to complete an iterative satisfaction questionnaire before or after an ordinal scale, there was no difference in mean satisfaction (no priming). The Guttman scale was more normally distributed and had slightly less ceiling effect when compared to the ordinal scale. Iterative satisfaction scales partially mitigate ceiling effects. The absence of priming suggests that attempts to encourage agency and reflection have limited ability to reduce ceiling effects, and alternative approaches should be tested.

SUBMITTER: Salman AA

PROVIDER: S-EPMC7786675 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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What Are the Priming and Ceiling Effects of One Experience Measure on Another?

Salman Aresh Al AA Kopp Benjamin J BJ Thomas Jacob E JE Ring David D Fatehi Amirreza A

Journal of patient experience 20200831 6

Patient-reported experience measures have notable ceiling effects which can hinder efforts to learn and improve. This study tested whether an iterative (Guttman-style) satisfaction questionnaire combined with instructions intended to give people agency to critique us primes responses on an ordinal scale and reduces ceiling effects. Among the 161 subjects randomly assigned to complete an iterative satisfaction questionnaire before or after an ordinal scale, there was no difference in mean satisfa ...[more]

PMID: 33457640

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Project description:Goals and methodsA simulation study investigated how ceiling and floor effect (CFE) affect the performance of Welch's t-test, F-test, Mann-Whitney test, Kruskal-Wallis test, Scheirer-Ray-Hare-test, trimmed t-test, Bayesian t-test, and the "two one-sided tests" equivalence testing procedure. The effect of CFE on the estimate of group difference and on its confidence interval, and on Cohen's d and on its confidence interval was also evaluated. In addition, the parametric methods were applied to data transformed with log or logit function and the performance was evaluated. The notion of essential maximum from abstract measurement theory is used to formally define CFE and the principle of maximum entropy was used to derive probability distributions with essential maximum/minimum. These distributions allow the manipulation of the magnitude of CFE through a parameter. Beta, Gamma, Beta prime and Beta-binomial distributions were obtained in this way with the CFE parameter corresponding to the logarithm of the geometric mean. Wald distribution and ordered logistic regression were also included in the study due to their measure-theoretic connection to CFE, even though these models lack essential minimum/maximum. Performance in two-group, three-group and 2 × 2 factor design scenarios was investigated by fixing the group differences in terms of CFE parameter and by adjusting the base level of CFE.Results and conclusionsIn general, bias and uncertainty increased with CFE. Most problematic were occasional instances of biased inference which became more certain and more biased as the magnitude of CFE increased. The bias affected the estimate of group difference, the estimate of Cohen's d and the decisions of the equivalence testing methods. Statistical methods worked best with transformed data, albeit this depended on the match between the choice of transformation and the type of CFE. Log transform worked well with Gamma and Beta prime distribution while logit transform worked well with Beta distribution. Rank-based tests showed best performance with discrete data, but it was demonstrated that even there a model derived with measurement-theoretic principles may show superior performance. Trimmed t-test showed poor performance. In the factor design, CFE prevented the detection of main effects as well as the detection of interaction. Irrespective of CFE, F-test misidentified main effects and interactions on multiple occasions. Five different constellations of main effect and interactions were investigated for each probability distribution, and weaknesses of each statistical method were identified and reported. As part of the discussion, the use of generalized linear models based on abstract measurement theory is recommended to counter CFE. Furthermore, the necessity of measure validation/calibration studies to obtain the necessary knowledge of CFE to design and select an appropriate statistical tool, is stressed.

Dataset Information

What Are the Priming and Ceiling Effects of One Experience Measure on Another?

Publications

What Are the Priming and Ceiling Effects of One Experience Measure on Another?

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