The purposes of this methodologic paper are to (1) describe theoretical background in conducting research across different cultures; (2) address measurement issues related to instrument administration; and (3) provide strategies to deal with measurement issues.
A thorough review of the literature was conducted. A theoretical background is provided, and examples of administering instrument in studies are described.
When applying an instrument to different cultures, both equivalence and bias need to be established. Three levels of equivalence, i.e., construct equivalence, measurement unit equivalence, and full score comparability, need to be explained to maintain the same concept being measured. In this paper, sources of bias in construct, method, and item are discussed. Issues related to instrument administration in a cross-cultural study are described.
Researchers need to acknowledge various group differences in concept and/or language that include a specific set of symbols and norms. There is a need to question the philosophical and conceptual appropriateness of an assessment measure that has been conceptualized and operationalized in a different culture. Additionally, testing different response formats such as narrowing response range can be considered to reduce bias.
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The aim of this study was to compare statistical methods to control response bias in nursing activity surveys.
Data were collected at a medical unit of a general hospital. The number of nursing activities and consumed activity time were measured using self-report questionnaires. Descriptive statistics were used to identify general characteristics of the units. Average, Z-standardization, gamma regression, finite mixture model, and stochastic frontier model were adopted to estimate true activity time controlling for response bias.
The nursing activity time data were highly skewed and had non-normal distributions. Among the 4 different methods, only gamma regression and stochastic frontier model controlled response bias effectively and the estimated total nursing activity time did not exceeded total work time. However, in gamma regression, estimated total nursing activity time was too small to use in real clinical settings. Thus stochastic frontier model was the most appropriate method to control response bias when compared with the other methods.
According to these results, we recommend the use of a stochastic frontier model to estimate true nursing activity time when using self-report surveys.
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This study was conducted to provide basic data for developing education and health promotion programs for the prevention of cancer by identifying the relation between optimistic bias about cancer and cancer preventive behavior in Korean, Chinese, American, and Japanese residents in Korea.
Using a questionnaire administered by the researcher, data were collected from a convenience sample of 600, 19 to 64-yr-old male and female Korean, Chinese, American, and Japanese residents in Korea. Data was collected between February 6 and 28, 2009.
Scores for optimistic bias about cancer by nationality were: Koreans, -1.03; Chinese, -0.43; Americans, -0.23; and Japanese, 0.05. The cancer preventive behavior scores were: Koreans, 43.17; Chinese, 71.84; Americans, 71.71; and Japanese, 73.97. Optimistic bias about cancer and cancer preventive behavior showed a significantly positive correlation in all participants: Koreans (r=.223,
The greater the optimistic bias about cancer is, the lower the cancer preventive behavior. The findings suggest that nursing interventions are needed to reduce optimistic bias about cancer and to form a positive attitude towards cancer prevention because an optimistic bias about cancer adversely affects cancer preventive behavior.
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This study was performed to identify the relationship between optimistic bias about health crisis and health behavior of Korean adults in a crisis of health, and to prepare baseline data for developing a health education and promotion program.
Study subjects were 595 adults aged from 19 to 64 who live in Korea. Data were collected through questionnaires administered by one interviewer. Descriptive statistics and Pearson's correlation coefficient were calculated using the SPSS program.
The average score for optimistic bias about health crisis was 2.69, and that for health behavior was 107.05. The optimistic bias about health crisis showed a significantly positive correlation with health behavior (r=.187, p=.000).
To make our results more useful, it is necessary to identity the causal relationship between health attitudes as an explanatory variable and optimistic bias as an outcome variable. In addition, a relatively low score in optimistic bias from this research compared to other studies must be explained through further studies considering unique Korean cultural background. Moreover, research of the relationship between optimistic bias about health crisis and health behavior looking at people who don't have good health behaviors is needed.
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