Advances in the assessment of self-determination: internal structure of a scale for people with intellectual disabilities aged 11 to 40.

BACKGROUND: Advances in theoretical frameworks of self-determination require the development of new assessment instruments. This study examines the dimensional structure of a self-determination scale and analyses the factorial invariance of its measurement across age and gender. METHOD: The AUTODDIS Scale was used to assess the self-determination of 541 people with intellectual disabilities aged from 11 to 40. RESULTS: Different models (correlational and hierarchical structures) of the scale were tested. The correlational model obtained from the exploratory structural equation model approach provided the best fit for the data. The results also supported measurement invariance across youths (aged 11 to 21 years) and adults (aged 21 to 40 years) and across genders. CONCLUSIONS: This study contributes to international research on self-determination and the development of assessment tools in this field, offering a better understanding of this multifaceted and complex construct. The results provide construct validity evidence regarding a new measurement tool tested across people aged 11 to 40, using information from third parties. However, further research is needed to explore the best ways to understand and assess the different factors related to self-determination.

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

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.