confidence interval calculator degrees of freedom

#CONFIDENCE INTERVAL CALCULATOR DEGREES OF FREEDOM HOW TO#
#CONFIDENCE INTERVAL CALCULATOR DEGREES OF FREEDOM SOFTWARE#

Because DEFFs are highly variable for different variables within each 2-year cycle of the continuous NHANES, it is difficult to set a single minimum sample size for analysis. Within the continuous NHANES survey, DEFF can be very different for different variables due to differences in variation by geography, by household intra class correlation, and by demographic heterogeneity. For example, standard errors for a variable in NHANES 1999-2000 will be approximately 70% greater than for the corresponding variable in NHANES III (or when combining three cycles of the continuous NHANES).ĭesign effects for a variable can be different for race/ethnicity or age groups. Due to smaller sample sizes in each 2-year cycle of the continuous NHANES, data are subject to larger sampling variation. Moving from a 6-year (i.e.,NHANES III) to a 2-year data release in the continuous NHANES, the sample size for the survey is smaller for both the number of persons sampled and the number of geographic areas (PSUs) sampled. Another way to think about clustering is that there is a loss of precision and a reduction in the effective sample size because individuals are chosen within clusters instead of being sampled randomly throughout the population. The effective sample size is calculated by dividing the sample size in a subgroup by the DEFF. When the DEFF is greater than 1, the effective sample size is less than the number of sample persons but greater than the number of clusters. The DEFFs for NHANES are typically greater than 1. If the DEFF is 1, the variance for the estimate under the cluster sampling is the same as the variance under simple random sampling. It is defined as the ratio of the variance of a statistic which accounts for the complex sample design to the variance of the same statistic based on a hypothetical simple random sample of the same size.ĭesign Effect = Variance estimate (cluster) / Variance estimate (simple random sample) The impact of the complex sample design upon variance estimates is measured by the design effect (DEFF).

#CONFIDENCE INTERVAL CALCULATOR DEGREES OF FREEDOM SOFTWARE#

Warning: Standard statistical software packages that assume simple random sampling calculate variance estimates that are generally too low and biased because they do not account for differential weighting and the correlation among sample persons within a cluster. In a complex sample survey setting such as NHANES, variance estimates computed using standard statistical software packages that assume simple random sampling are generally too low (i.e., significance levels are overstated) and biased because they do not account for the differential weighting and the correlation among sample persons within a cluster. The sample size in each PSU is roughly equal and it is intended to yield about 5,000 examined persons per year. However, because of operational limitations (e.g., cost of moving the survey MECs, geographic distances between primary sampling units, etc.) NHANES can only sample 30 PSUs within a 2-year survey cycle. To achieve this, you want to sample fewer people within each cluster but sample more clusters. When working with a complex sample, you ideally want to decrease the amount of correlation between sample persons within clusters.

Typically, individuals within a cluster (i.e., county, school, city, census block) are more similar to one another than those in other clusters and this homogeneity of individuals within a given cluster is measured by the intra cluster correlation. NHANES survey design affects variance estimatesĪs stated in the module on sampling in NHANES, the NHANES has a complex, multistage, probability cluster design. In general, using statistical weights that reflect the probability of selection and propensity of response for sampled individuals will affect parameter estimates. Incorporating the attributes of the complex sample design (i.e., differential weighting, clustering and stratification) will affect variance estimates (estimated standard errors and thereby test statistics and confidence intervals). This first task describes the importance of accounting for the complex sampling structure of NHANES when estimating variances. Task 1: Explain Variance Estimation within NHANES In addition, there will be specific examples using the NHANES-CMS linked data.

#CONFIDENCE INTERVAL CALCULATOR DEGREES OF FREEDOM HOW TO#

You will learn how the complex survey design of NHANES and clustering of the data affect variance estimation, which methods are appropriate to use when calculating variance for NHANES data, and how to calculate degrees of freedom and construct confidence limits for NHANES estimates. This module introduces the basic concepts of variance (sampling error) estimation for NHANES data.

Confidence interval calculator degrees of freedom