logo
    The Association of Survey Setting and Mode with Self-Reported Health Risk Behaviors among High School Students
    91
    Citation
    29
    Reference
    10
    Related Paper
    Citation Trend
    Abstract:
    This study examined whether the prevalence of self-reported health risk behaviors among high school students varied by survey setting (school versus home) and mode of administration (paper and pencil versus computer). Students in grades 9 and 11 were assigned randomly to one of four conditions—school paper-and-pencil instrument (PAPI), school computer-assisted self-interview (CASI), home PAPI, and home CASI. During the spring of 2004, 4,506 students completed identically worded questionnaires based on the Youth Risk Behavior Survey questionnaire. Logistic regression analyses controlling for sex, grade, and race/ethnicity revealed that setting was associated significantly with the reporting of 30 of the 55 risk behaviors examined, and mode was associated significantly with the reporting of 7 of the 55 behaviors. For all behaviors with a significant setting main effect, the odds of reporting the behavior were greater among students who completed questionnaires at school than among students who completed questionnaires at home. For all behaviors with a significant mode main effect, PAPI mode students had lower odds of reporting the behavior than CASI mode students. Because social measurement research assumes that higher prevalence estimates are more valid than lower estimates, methodological factors shown to increase estimates, such as setting and mode, should be considered when planning surveys.
    Keywords:
    Odds
    Association (psychology)
    Mode (computer interface)
    The Heart Attack has produced an alarming rate of deaths and considered as one of the most deadly events for persons with hypertension, diabetes, with family history, a smoker and alcoholic. The study aims to produce a model using the Forward Stepwise Binary Logistic Regression Method for estimating the odds and probabilities when conceded with the given situations, and also the comparisons of the odds in which the factors are arranged by combinations of the significant variables that are included in the equation of every circumstances. Combination of covariates, gender aside, where the outcome of the study indicates that men are two times more likely to have heart attack than women. Hypertension and Diabetes, two covariates giving two of the highest odds among the factors, is 35 times more likely to trigger a heart attack than a person with none. Smoking plus the two mentioned covariates gives 114 times more. Alcohol added, has the ratio of 210 to 1 which is harshly a huge number. Summing up all the risks of the factors considered, including the family history as the last covariate, it shows the ratio 352 times that is, than a person with no complication nor any harmful vices.
    Odds
    An odds ratio (OR) is a measure of association between an exposure and an outcome. The OR represents the odds that an outcome will occur given a particular exposure, compared to the odds of the outcome occurring in the absence of that exposure. Odds ratios are most commonly used in case-control studies, however they can also be used in cross-sectional and cohort study designs as well (with some modifications and/or assumptions). Odds ratios and logistic regression When a logistic regression is calculated, the regression coefficient (b1) is the estimated increase in the log odds of the outcome per unit increase in the value of the exposure. In other words, the exponential function of the regression coefficient (e b1 ) is the odds ratio associated with a one-unit increase in the exposure.
    Odds
    Citations (755)
    Abstract The odds ratio is known to closely approximate the relative risk when the disease is rare. Logistic regression models are often used to estimate such odds ratios, but here a different model is used which avoids the assumptions implicit in logistic modelling; it also has the advantage of providing a test of homogeneity for odds rat os in situations where the logistic model cannot.
    Odds
    Citations (0)
    Logistic regression analysis, which estimates odds ratios, is often used to adjust for covariables in cohort studies and randomized controlled trials (RCTs) that study a dichotomous outcome. In case–control studies, the odds ratio is the appropriate effect estimate, and the odds ratio can
    Odds
    Citations (437)
    Logistic regression and odds ratios (ORs) are powerful tools recently becoming more common in the social sciences. Yet few understand the technical challenges of correctly interpreting an odds ratio, and often it is done incorrectly in a variety of different ways. The goal of this brief note is to review the correct interpretation of the odds ratio, how to transform it into the more easily understood and intuitive relative risk (RRs) estimate, and a suggestion for dealing with odds ratios or relative risk estimates that are below 1.0 so that perceptually their magnitude is equivalent of Ors or RRs greater than 1.0.
    Odds
    Diagnostic odds ratio
    Citations (82)
    Multiple logistic regression is an accepted statistical method for assessing association between an anticedant characteristic (risk factor) and a quantal outcome (probability of disease occurrence), statistically adjusting for potential confounding effects of other covariates. Yet the method has potential drawbacks which are not generally recognized. This article considers one important drawback of logistic regression. Specifically the so-called main effect logistic model assumes that the probability of developing disease is linearly and additively related to the risk factors on the logistic scale. This assumption stipulates that for each risk factor, the odds ratio is constant over all reference exposure levels, and that the odds ratio exposed to two or more factors is equal to the product of individual risk factor odds ratios. If the observed odds ratios in the data follow this pattern, the model-predicted odds ratios will be accurate, and the meaning of the odds ratio for each risk factor will be straightforward. But if the observed odds ratios deviate from the model assumption, the model will not fit the data accurately, and the model-predicted odds ratios will not reflect those in the data. Although satisfactory fit can always be achieved by adding to the model polynomial and product terms derived from the original risk factors, the odds ratios estimated by such an interaction logistic model are difficult to interpret, viz., the odds ratio for each risk factor depends not only on the reference exposure levels of that factor, but also on the exposure level in other factors.(ABSTRACT TRUNCATED AT 250 WORDS)
    Odds
    Diagnostic odds ratio
    Citations (51)