Status, legitimacy and ingroup bias in the context of an organizational merger

Adopting an intergroup perspective, the research was designed to examine predictors of employee responses to an organizational merger. Data were collected from 120 employees of a newly merged scientific organization. As predicted from social identity theory, the most negative responses to the merger were apparent among the employees of the low status premerger organization. There was also evidence of ingroup bias among both groups of employees involved in the merger—as expected, the bias was most marked on the status-irrelevant dimensions for the employees of the lower status organization, but most marked on the status-relevant dimensions for the employees of the high status organization. Also, in support of social identity theory, the perceived legitimacy of the basis for the status differentiation between the groups was associated with more positive responses to the merger among employees of the low status premerger organization, but with poorer responses among employees of the high status premerger organization. There was consistent evidence that the status by legitimacy interaction was mediated through the extent to which employees of the newly merged organization perceived a common ingroup identity

Postmenopausal Hormone Use, Screening, and Breast Cancer: Characterization and Control of a Bias

Previous investigators have suggested that screening-related biases may explain associations between postmenopausal hormone use and breast cancer. To investigate these biases, we studied postmenopausal women in the Nurses' Health Study from 1988 to 1994. Hormone use is associated with increased subsequent screening. Among women not screened in the previous 2 years, the probability difference, comparing current hormone users with others, for having mammography in the following 2 years is 19.5%; among women previously screened, the difference is 4.9%. These differences persist after control for other factors. If the increase in screening is causal, screening by mammogram could be intermediate in the causal pathway to breast cancer diagnosis. To deal with this problem, we restrict attention to a subset of the cohort in which the effect of postmenopausal hormone use on screening is small (women previously screened). In this subset, the rate ratio comparing breast cancer rates among current postmenopausal hormone users with others is 1.28. In a sensitivity analysis, the bias could not by itself plausibly account for the associations in our data. Our data provide evidence of an association between postmenopausal hormone use and breast cancer that is not solely the product of a detection bias.

Comparison of measured sleeping metabolic rate and predicted basal metabolic rate during the first year of life: evidence of a bias changing with increasing metabolic rate

Objective: To compare measurements of sleeping metabolic rate (SMR) in infancy with predicted basal metabolic rate (BMR) estimated by the equations of Schofield. Methods: Some 104 serial measurements of SMR by indirect calorimetry were performed in 43 healthy infants at 1.5, 3, 6, 9 and 12 months of age. Predicted BMR was calculated using the weight only (BMR-wo) and weight and height (BMR-wh) equations of Schofield for 0-3-y-olds. Measured SMR values were compared with both predictive values by means of the Bland-Altman statistical test. Results: The mean measured SMR was 1.48 MJ/day. The mean predicted BMR values were 1.66 and 1.47 MJ/day for the weight only and weight and height equations, respectively. The Bland-Altman analysis showed that BMR-wo equation on average overestimated SMR by 0.18 MJ/day (11%) and the BMR-wh equation underestimated SMR by 0.01 MJ/day (1%). However the 95% limits of agreement were wide: - 0.64 to - 0.28MJ/day (28%) for the former equation and - 0.39 to +0.41 MJ/day (27%) for the latter equation. Moreover there was a significant correlation between the mean of the measured and predicted metabolic rate and the difference between them. Conclusions: The wide variation seen in the difference between measured and predicted metabolic rate and the bias probably with age indicates there is a need to measure actual metabolic rate for individual clinical care in this age group.