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.

ABSTRACT. The first satellite-derived inventory of glaciers and rock glaciers in Chile, created from Landsat TM/ETM+ images spanning between 2000 and 2003 using a semi-automated procedure, is presented in a single standardized format. Large glacierized areas in the Altiplano, Palena Province and the periphery of the Patagonian icefields are inventoried. The Chilean glacierized area is 23 708 ± 1185 km 2 , including ∼3200 km 2 of both debris-covered glaciers and rock glaciers. Glacier distribution varies as a result of climatic gradients with latitude and elevation, with 0.8% occurring in the Desert Andes (17°30′-32°S); 3.6% in the Central Andes (32-36°S), 6.2% in the Lakes District and Palena Province (36-46°S), and 89.3% in Patagonia and Tierra del Fuego (46-56°S).Glacier outlines, across all glacierized regions and size classes, updated to 2015 using Landsat 8 images for 98 complexes indicate a decline in areal extent affecting mostly clean-ice glaciers (−92.3 ± 4.6 km 2 ), whereas debris-covered glaciers and rock glaciers in the Desert and Central Andes appear nearly unchanged in their extent. Glacier attributes estimated from this new inventory provide valuable insights into spatial patterns of glacier shrinkage for assessing future glacier changes in response to climate change.

During the present decade a large body of research has employed confirmatory factor analysis (CFA) to evaluate the factor structure of the Strengths and Difficulties Questionnaire (SDQ) across multiple languages and cultures. However, because CFA can produce strongly biased estimations when the population cross-loadings differ meaningfully from zero, it may not be the most appropriate framework to model the SDQ responses. With this in mind, the current study sought to assess the factorial structure of the SDQ using the more flexible exploratory structural equation modeling approach. Using a large-scale Spanish sample composed of 67,253 youths aged between 10 and 18 years ( M = 14.16, SD = 1.07), the results showed that CFA provided a severely biased and overly optimistic assessment of the underlying structure of the SDQ. In contrast, exploratory structural equation modeling revealed a generally weak factorial structure, including questionable indicators with large cross-loadings, multiple error correlations, and significant wording variance. A subsequent Monte Carlo study showed that sample sizes greater than 4,000 would be needed to adequately recover the SDQ loading structure. The findings from this study prevent recommending the SDQ as a screening tool and suggest caution when interpreting previous results in the literature based on CFA modeling.

The ADHD general factor explained most of the common variance. Given the low reliable variance ratios, the specific factors were difficult to interpret. However, in clinical samples, inattention acquired sufficient specificity and stability for interpretation beyond the general factor. Implications for research and clinical practice are discussed.

Quality of life, Quality of life models, Structural equation modeling, Validity, Social service recipients,