Response styles, the tendency to respond to Likert-type items irrespective of content, are a widely known threat to the reliability and validity of self-report measures. However, it is still debated how to measure and control for response styles such as extreme responding. Recently, multiprocess item response theory models have been proposed that allow for separating multiple response processes in rating data. The rationale behind these models is to define process variables that capture psychologically meaningful aspects of the response process like, for example, content-and response style-related processes. The aim of the present research was to test the validity of this approach using two large data sets. In the first study, responses to a 7-point rating scale were disentangled, and it was shown that response style-related and content-related processes were selectively linked to extraneous criteria of response styles and content. The second study, using a 4-point rating scale, focused on a content-related criterion and revealed a substantial suppression effect of response style. The findings have implications for both basic and applied fields, namely, for modeling response styles and for the interpretation of rating data.

Even though there is an increasing interest in response styles, the field lacks a systematic investigation of the bias that response styles potentially cause. Therefore, a simulation was carried out to study this phenomenon with a focus on applied settings (reliability, validity, scale scores). The influence of acquiescence and extreme response style was investigated, and independent variables were, for example, the number of reverse-keyed items. Data were generated from a multidimensional item response model. The results indicated that response styles may bias findings based on self-report data and that this bias may be substantial if the attribute of interest is correlated with response style. However, in the absence of such correlations, bias was generally very small, especially for extreme response style and if acquiescence was controlled for by reverse-keyed items. An empirical example was used to illustrate and validate the simulations. In summary, it is concluded that the threat of response styles may be smaller than feared.

IRTree models decompose observed rating responses into sequences of theory-based decision nodes, and they provide a flexible framework for analysing trait-related judgements and response styles. However, most previous applications of IRTree models have been limited to binary decision nodes that reflect qualitatively distinct and unidimensional judgement processes. The present research extends the family of IRTree models for the analysis of response styles to ordinal judgement processes for polytomous decisions and to multidimensional parametrizations of decision nodes. The integration of ordinal judgement processes overcomes the limitation to binary nodes, and it allows researchers to test whether decisions reflect qualitatively distinct response processes or gradual steps on a joint latent continuum. The extension to multidimensional node models enables researchers to specify multiple judgement processes that simultaneously affect the decision between competing response options. Empirical applications highlight the roles of extreme and midpoint response style in rating judgements and show that judgement processes are moderated by different response formats. Model applications with multidimensional decision nodes reveal that decisions among rating categories are jointly informed by trait-related processes and response styles.

When measuring psychological traits, one has to consider that respondents often show content-unrelated response behavior in answering questionnaires. To disentangle the target trait and two such response styles, extreme responding and midpoint responding, Böckenholt ( 2012a ) developed an item response model based on a latent processing tree structure. We propose a theoretically motivated extension of this model to also measure acquiescence, the tendency to agree with both regular and reversed items. Substantively, our approach builds on multinomial processing tree (MPT) models that are used in cognitive psychology to disentangle qualitatively distinct processes. Accordingly, the new model for response styles assumes a mixture distribution of affirmative responses, which are either determined by the underlying target trait or by acquiescence. In order to estimate the model parameters, we rely on Bayesian hierarchical estimation of MPT models. In simulations, we show that the model provides unbiased estimates of response styles and the target trait, and we compare the new model and Böckenholt's model in a recovery study. An empirical example from personality psychology is used for illustrative purposes.

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