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The objective of the D-optimal design is to extract the maximum amount of information from the respondents subject to the number of attributes, attribute levels and other characteristics.e As a result of running the D-optimal design through Design-Expert 7.0 (Stat-Ease, Inc ., we created 22 choice sets.
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To maintain anonymity of survey respondents, subjects who took the survey online were not given incentives.
Among respondents, subjects who reported a previous knee injury (fractures involving the knee joint, meniscal lesions or cruciate ligament ruptures) were excluded (floor layers, n = 22; graphic designers, n = 32).
A major limitation is that people self-reported their own income and a high percentage of non-respondent subjects.
Indeed, we recorded a high percentage (30.7%) of non-respondent subjects who refused to declare or did not know their family income.
Usually study respondents, as subjects of research, are not authors of research papers.
District-level data enable crisp ascertainment of exposure to helmet laws, whereas state YRBS datasets do not identify municipalities, so respondents not subject to a state law may be inaccurately classified if they are still subject to a municipal law.
Both patient and physician survey respondents are subject to recall bias.
The study employed a purposive cross-sectional study design where 813 respondents were subjected to a structured interview as part of a screening procedure for an emergency medical intervention to identify respondents who required psychological, gynaecological and surgical treatment.
They interviewed subjects directly, when feasible, or surrogate respondents for subjects deceased or not present (41%).
SD, Standard deviation NS, Not significant BMI, Body mass index * Independent samples t test with equal variances ° Chi-square test †Among respondents; 4 subjects did not respond to the question about race.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com