While controversies regarding the use of midpoint in Likert scale measurements pervade in survey research, the existing literature mainly focuses on confirmatory analyses aiming to assess effects on scale reliability and factorial structure. The consequences of midpoint removal in predictive contexts – which serve as the primary foundation for practical interventions – remain underexplored. We investigate this concern by delving into the stability of the statistical properties of three prediction models built upon the Theory of Planned Behavior framework, by simulating scenarios with and without a midpoint in survey data originally measured on a 7-point Likert scale.
We conduct 5,000 partial-least-squares regression models to simulate the effects of the replacement of the midpoint responses in the original datasets based on several replacement rules with psychological plausibility. We compare the results of the original models with the results of the 5,000 simulations.
Reduced scales lead to a lower degree of measurement reliability and a lower explanatory power of the models. The statistical significance of predictors is affected under some replacement scenarios. The estimated coefficients also change, to the point that the importance of the independent variables in the original model differs from the one obtained in simulated data. Also, the statistical significance of the predictors, as recommended by the Theory of Planned Behavior, is unstable, and the estimated coefficients of the 5,000 simulation models tend to either overestimate or underestimate the original coefficients.
We raise awareness that although building survey research on a strong and stable theoretical background is necessary, it may not be sufficient in practical contexts. If the research design is not properly calibrated in terms of measurement, unreliable results may serve as grounds for costly, but likely inefficient, practical interventions.
Unlike previous research focused on testing the effects of the midpoint on the reliability of a scale and its factorial structure, all driven by confirmatory analysis aiming to test the conformity of the data with a specific theory, we explore potential effects of the midpoint removal in predictive settings, often used in practical interventions.
