The analytical approach used in this report was comparable to methods previously published 33 (link). Estimates were age adjusted to the 2010 US population to account for changes in population distribution between the two periods of data collection. Population estimates and standard errors using Taylor Series Linearization were calculated. Differences between groups were evaluated using a t-statistic at the p < 0.05 significance level. All results with a relative standard error (RSE) equal to or greater than 30% but less than 40% were reported but should be interpreted with caution. Estimates with a RSE > 40% were considered data statistically unreliable (DSU) and not reported. The RSE is equal to the standard error of a survey estimate divided by the survey estimate and then multiplied by 100 for expression to a percentage. Tests were conducted without adjustment for other socio-demographic factors, except for age adjustment as previously described. All analyses were undertaken using SAS 9.4 Survey Procedures (SAS Institute Inc.) except for the graphs illustrating individual toot loss, which were conducted in STATA/SE 14.2 and the Heatmaps, which were conducted in R. All differences discussed are statistically significant unless otherwise indicated.
Sas 9.4 survey procedures
Manufactured by SAS Institute
SAS 9.4 survey procedures is a suite of tools within the SAS software platform that enables users to analyze data collected through survey methodologies. The core function of these procedures is to provide statistical analysis and reporting capabilities specifically tailored for survey data, allowing users to generate descriptive statistics, perform hypothesis testing, and model relationships within survey datasets.
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Lab products found in correlation
4 protocols using sas 9.4 survey procedures
1
Age-Adjusted Population Estimates and Significance Testing
2
Neighborhood Cohesion and Walking Behavior
3
Latino Physical Activity Patterns
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Unadjusted means or frequencies and standard errors of participant characteristics (i.e., demographics, acculturation, and neighborhood social cohesion) and prevalence of aerobic activity were computed by Latino subgroup. Logistic regression models were used to estimate the adjusted odds ratios (OR) for the likelihood of meeting the aerobic physical activity guideline relative to not meeting the aerobic physical activity guideline. Model 1 estimates were unadjusted, and in Model 2 we adjusted for age, sex, education, acculturation, and Latino subgroup. Additionally, given prior evidence on health differences by Latino groups and the role of neighborhood environments in shaping physical activity, we stratified our analyses by Latino groups to assess if significant interactions or trends were present. We formally tested for this by including a Latino subgroup and neighborhood social cohesion interaction term in the fully adjusted model.
SAS 9.4 survey procedures (SAS Institute Inc., Cary, NC) were used in all analyses to account for the survey weights and the complex sampling design of NHIS.
SAS 9.4 survey procedures (SAS Institute Inc., Cary, NC) were used in all analyses to account for the survey weights and the complex sampling design of NHIS.
4
Metabolic Health, Obesity, and Physical Health-Related Quality of Life
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In descriptive analyses means, percentages and 95% confidence intervals were calculated in strata of metabolic health status and obesity categories. All analyses were stratified for sex. Linear regression analyses were performed with the PCS as dependent variable and categories of metabolic health and obesity as independent variable (Model 1). The group of MHNO was used as the reference category. Model 2 was additionally adjusted for age (squared). Model 3 additionally included alcohol consumption, educational status, physical activity, smoking, and comorbidity. In a sensitivity analysis linear regression analyses were performed with the physical functioning score as dependent variable because the association between BMI and HRQoL seems to be most pronounced in this domain [29 (link)]. In linear regression analyses gender differences in PCS were identified in categories of metabolic health and obesity. The level of statistical significance was set at p = 0.05 based on two-sided tests. SAS 9.4 survey procedures (SAS Institute Inc., Cary, NC) were used for all statistical analyses. To account for the clustered survey design specific survey procedures were used. Analyses were weighted using a weighting factor to correct for deviations of the net sample from the population structure in Germany and compensate for stratification.
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