Literature DB >> 32462604

A little garbage in, lots of garbage out: Assessing the impact of careless responding in personality survey data.

Víctor B Arias1, L E Garrido2, C Jenaro3, A Martínez-Molina4, B Arias5.   

Abstract

In self-report surveys, it is common that some individuals do not pay enough attention and effort to give valid responses. Our aim was to investigate the extent to which careless and insufficient effort responding contributes to the biasing of data. We performed analyses of dimensionality, internal structure, and data reliability of four personality scales (extroversion, conscientiousness, stability, and dispositional optimism) in two independent samples. In order to identify careless/insufficient effort (C/IE) respondents, we used a factor mixture model (FMM) designed to detect inconsistencies of response to items with different semantic polarity. The FMM identified between 4.4% and 10% of C/IE cases, depending on the scale and the sample examined. In the complete samples, all the theoretical models obtained an unacceptable fit, forcing the rejection of the starting hypothesis and making additional wording factors necessary. In the clean samples, all the theoretical models fitted satisfactorily, and the wording factors practically disappeared. Trait estimates in the clean samples were between 4.5% and 11.8% more accurate than in the complete samples. These results show that a limited amount of C/IE data can lead to a drastic deterioration in the fit of the theoretical model, produce large amounts of spurious variance, raise serious doubts about the dimensionality and internal structure of the data, and reduce the reliability with which the trait scores of all surveyed are estimated. Identifying and filtering C/IE responses is necessary to ensure the validity of research results.

Keywords:  Careless responding; Data cleaning; Factor mixture modelling; Insufficient effort responding; Invalid response

Mesh:

Year:  2020        PMID: 32462604     DOI: 10.3758/s13428-020-01401-8

Source DB:  PubMed          Journal:  Behav Res Methods        ISSN: 1554-351X


  5 in total

1.  Machine learning to detect invalid text responses: Validation and comparison to existing detection methods.

Authors:  Ryan C Yeung; Myra A Fernandes
Journal:  Behav Res Methods       Date:  2022-02-17

2.  Studies in the Mentality of Literates: 3. Conceptual Structure and Nonsense of Personality Testing.

Authors:  Aaro Toomela; Delma Barros Filho; Ana Cecília S Bastos; Antonio Marcos Chaves; Marilena Ristum; Sara Santos Chaves; Soraya Jesus Salomão; Aleksander Pulver
Journal:  Integr Psychol Behav Sci       Date:  2022-08-01       Impact factor: 1.156

3.  Using Mokken scaling techniques to explore carelessness in survey research.

Authors:  Stefanie Wind; Yurou Wang
Journal:  Behav Res Methods       Date:  2022-09-21

4.  Social Networks Addiction (SNA-6) - Short: Validity of Measurement in Mexican Youths.

Authors:  Edwin Salas-Blas; César Merino-Soto; Berenice Pérez-Amezcua; Filiberto Toledano-Toledano
Journal:  Front Psychol       Date:  2022-01-12

5.  Detecting Careless Responding in Survey Data Using Stochastic Gradient Boosting.

Authors:  Ulrich Schroeders; Christoph Schmidt; Timo Gnambs
Journal:  Educ Psychol Meas       Date:  2021-04-19       Impact factor: 2.821

  5 in total

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