Abstract
Background: Results of patient satisfaction questionnaires can contain a spike at the value corresponding to a complete satisfaction. A possible interpretation is that there are two types of respondents, those who are willing to provide a negative evaluation to one or more items proposed in the questionnaire and those who will always provide a completely positive evaluation irrespective of the item. The aim of the present study is to compare various statistical approaches to the analysis of such data using data from a rehabilitation patient survey of the German Statutory Pension Insurance Scheme as an example.Method: We used data from 272,806 respondents who participated in the survey from 2008 to 2011. We illustrate four models: linear regression, logistic regression, a two-part model based on the assumption of two underlying populations and quantile regression, which does not require any distributional assumptions. For each model we consider the relationship of the satisfaction score with two covariates.Results: While providing correct estimates of the mean values (marginal effects), the assumptions of the linear model are violated which can lead to false interpretations. A two-part regression which consists of a logistic regression followed by a linear regression conditional on not being fully satisfied is a useful alternative. For research questions focusing on specific parts of the distribution, logistic regression as well as quantile regression are to be considered.Discussion: Data with a spike represents a statistical challenge but a range of modeling approaches is available to provide sound interpretations and correct answers to research questions.
Highlights
Results of patient satisfaction questionnaires can contain a spike at the value corresponding to a complete satisfaction
In the presence of a large proportion of observations having the same value, assumptions of linear regression are violated because it is difficult to establish any linear relationship between the outcome and covariates
Dichotomising the data to fit a logistic regression model is an option frequently chosen for this scenario
Summary
Results of patient satisfaction questionnaires can contain a spike at the value corresponding to a complete satisfaction. Instruments consisting of several Likert items are employed, on which basis (sub-)scores on Survey Data With a Spike different dimensions are calculated. In such surveys, many respondents tend to report complete satisfaction [1,2,3] resulting in a spike in the distribution of the data at the value corresponding to being fully satisfied. Dichotomising the data to fit a logistic regression model is an option frequently chosen for this scenario It has many disadvantages because all information about the distribution are lost and in regression models relationship between outcome and covariates may disappear [5, 6]
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