Indirect observation is a recent concept in systematic observation. It largely involves analyzing textual material generated either indirectly from transcriptions of audio recordings of verbal behavior in natural settings (e.g., conversation, group discussions) or directly from narratives (e.g., letters of complaint, tweets, forum posts). It may also feature seemingly unobtrusive objects that can provide relevant insights into daily routines. All these materials constitute an extremely rich source of information for studying everyday life, and they are continuously growing with the burgeoning of new technologies for data recording, dissemination, and storage. Narratives are an excellent vehicle for studying everyday life, and quantitization is proposed as a means of integrating qualitative and quantitative elements. However, this analysis requires a structured system that enables researchers to analyze varying forms and sources of information objectively. In this paper, we present a methodological framework detailing the steps and decisions required to quantitatively analyze a set of data that was originally qualitative. We provide guidelines on study dimensions, text segmentation criteria, ad hoc observation instruments, data quality controls, and coding and preparation of text for quantitative analysis. The quality control stage is essential to ensure that the code matrices generated from the qualitative data are reliable. We provide examples of how an indirect observation study can produce data for quantitative analysis and also describe the different software tools available for the various stages of the process. The proposed method is framed within a specific mixed methods approach that involves collecting qualitative data and subsequently transforming these into matrices of codes (not frequencies) for quantitative analysis to detect underlying structures and behavioral patterns. The data collection and quality control procedures fully meet the requirement of flexibility and provide new perspectives on data integration in the study of biopsychosocial aspects in everyday contexts.

AimsTo contribute new evidence to the controversy about the factor structure of the Eating Disorder Examination Questionnaire (EDE-Q) and to provide, for the first time, norms based on a large adolescent Mexican community sample, regarding sex and area of residence (urban/rural). MethodsA total of 2928 schoolchildren (1544 females and 1384 males) aged 11-18 were assessed with the EDE-Q and other disordered eating questionnaire measures.ResultsConfirmatory factor analysis of the attitudinal items of the EDE-Q did not support the four theorized subscales, and a two-factor solution, Restraint and Eating-Shape-Weight concern, showed better fit than the other models examined (RMSEA = .054); measurement invariance for this two-factor model across sex and area of residence was found. Satisfactory internal consistency (ω ≥ .80) and two-week test-retest reliability (ICCa ≥ .84; κ ≥ .56), and evidence for convergent validity with external measures was obtained. The highest attitudinal EDE-Q scores were found for urban females and the lowest scores were found for rural males, whereas the occurrence of key eating disorder behavioural features and compensatory behaviours was similar in both areas of residence. ConclusionsThis study reveals satisfactory psychometric properties and provides population norms of the EDE-Q, which may help clinicians and researchers to interpret the EDE-Q scores of adolescents from urban and rural areas in Mexico.

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Understanding how risk is perceived by workers is necessary for effective risk communication and risk management. This study adapts key elements of the psychometric perspective to characterize occupational risk perception at a worker level. A total of 313 Spanish healthcare workers evaluated relevant hazards in their workplaces related to biological, ergonomic and organizational factors. A questionnaire elicited workers' ratings of 3 occupational hazards on 9 risk attributes along with perceived risk. Factor and regression analyses reveal regularities in how different risks are perceived, while, at the same time, the procedure helps to summarize specificities in the perception of each hazard. The main regularity is the weight of feeling of dread/severity in order to characterize the risk perceived (β ranges from .22 to .41; p < .001). Data also suggest an underestimation of expert knowledge in relation to the personal knowledge of risk. Thus, participants consider their knowledge of the risk related to biological, ergonomic, and organizational hazards to be higher than the knowledge attributed to the occupational experts (mean differences 95% CIs [.10, .30], [.54, .94], and [0.52, 1.05]). We demonstrate the application of a feasible and systematic procedure to capture how workers perceive hazards in their immediate work environment.

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