Eyes with an IOL with a continuous 360-degree square edge had significantly less PCO than eyes with an IOL with a square edge that was interrupted at the optic-haptic junction.

PurposeSelecting appropriate variables for analytical studies is critical for the validity of analysis. It is the same with data envelopment analysis (DEA) studies. In this study, for benchmarking using DEA, the paper seeks to suggest a novel framework based on return on assets (ROA), which is popular and user‐friendly to managers, and demonstrate it by use of an example.Design/methodology/approachThe paper demonstrates the selection of variables using the elements of ROA and applies DEA for measuring and benchmarking the comparative efficiency of companies in the same industry.FindingsIt is frequently impossible to obtain internal data for benchmarking from competitors in the same industry. In this case, annual reports may be the only source of data for publicly traded companies. The framework demonstrated with an example is a practical approach for benchmarking with limited data.Research limitations/implicationsThis study employs financial data and is subject to the limitations of accounting practices.Originality/valueThe approach is applicable to various studies for performance measurement and benchmarking with minor modifications. Contributions of the study are twofold: first, a framework for selecting variables for DEA studies is suggested; second, the applicability of the framework with a real‐world example is demonstrated.

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The relationships of cognition, health status, and age to depression among alcoholics were studied. Eighty male alcoholics in an alcohol treatment unit were rated on health status and given a battery of psychological tests and scales. These included the Hooper VOT (cognition) and several self-rated depression indices: MMPI-Depression, MCMI-Dysthymic, BDI-Total (BDI-Somatic and BDI-Psychological), and the Geriatric Depression Scale (GDS). Results showed that depression was present in this sample at mild clinical levels. Regression analyses showed that age was the most influential factor on the MMPI-D and BDI-Somatic. Age also was most influential on the GDS, with health status contributing. However, except for somatic depression, the amount of explained variance was small. Despite this, it is argued that age is a contributing factor to depression among alcoholics, especially the somatic elements of depression.

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