The Effect of Survey Design on Extreme Response Style: Rating Job Satisfaction

Author(s):  
Luisa Corrado ◽  
Majlinda Joxhe
2021 ◽  
Vol 12 ◽  
Author(s):  
Xue Zhang ◽  
Chunyang Zhao ◽  
Yuqiao Xu ◽  
Shanhuai Liu ◽  
Zhihui Wu

Teachers play an important role in the educational system. Teacher self-efficacy, job satisfaction, school climate, and workplace well-being and stress are four individual characteristics shown to be associated with tendency to turnover. In this article, data from the Teaching and Learning International Survey (TALIS) 2018 teacher questionnaire are analyzed, with the goal to understand the interplay amongst these four individual characteristics. The main purposes of this study are to (1) measure extreme response style for each scale using unidimensional nominal response models, and (2) investigate the kernel causal paths among teacher self-efficacy, job satisfaction, school climate, and workplace well-being and stress in the TALIS-PISA linked countries/economies. Our findings support the existence of extreme response style, the rational non-normal distribution assumption of latent traits, and the feasibility of kernel causal inference in the educational sector. Results of the present study inform the development of future correlational research and policy making in education.


2011 ◽  
Author(s):  
Jorg-Henrik Heine ◽  
C. Tarnai ◽  
F. Hartmann

2013 ◽  
Vol 74 (1) ◽  
pp. 116-138 ◽  
Author(s):  
Kuan-Yu Jin ◽  
Wen-Chung Wang

2019 ◽  
Vol 45 (1) ◽  
pp. 86-107
Author(s):  
Dirk Lubbe ◽  
Christof Schuster

Extreme response style is the tendency of individuals to prefer the extreme categories of a rating scale irrespective of item content. It has been shown repeatedly that individual response style differences affect the reliability and validity of item responses and should, therefore, be considered carefully. To account for extreme response style (ERS) in ordered categorical item responses, it has been proposed to model responder-specific sets of category thresholds in connection with established polytomous item response models. An elegant approach to achieve this is to introduce a responder-specific scaling factor that modifies intervals between thresholds. By individually expanding or contracting intervals between thresholds, preferences for selecting either the outer or inner response categories can be modeled. However, for a responder-specific scaling factor to appropriately account for ERS, there are two important aspects that have not been considered previously and which, if ignored, will lead to questionable model properties. Specifically, the centering of threshold parameters and the type of category probability logit need to be considered carefully. In the present article, a scaled threshold model is proposed, which accounts for these considerations. Instructions on model fitting are given together with SAS PROC NLMIXED program code, and the model’s application and interpretation is demonstrated using simulation studies and two empirical examples.


2008 ◽  
Vol 45 (1) ◽  
pp. 104-115 ◽  
Author(s):  
Martijn G de Jong ◽  
Jan-Benedict E.M Steenkamp ◽  
Jean-Paul Fox ◽  
Hans Baumgartner

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