Variance Estimation Sample Clauses

Variance Estimation. MEPS has a complex sample design. To obtain estimates of variability (such as the standard error of sample estimates or corresponding confidence intervals) for MEPS estimates, analysts need to take into account the complex sample design of MEPS for both person-level and family- level analyses. Several methodologies have been developed for estimating standard errors for surveys with a complex sample design, including the ▇▇▇▇▇▇-series linearization method, balanced repeated replication, and jackknife replication. Various software packages provide analysts with the capability of implementing these methodologies. Replicate weights have not been developed for the MEPS data. Instead, the variables needed to calculate appropriate standard errors based on the ▇▇▇▇▇▇-series linearization method are included on this point-in-time file as well as all other MEPS public use files. Software packages that permit the use of the ▇▇▇▇▇▇-series linearization method include SUDAAN, Stata, SAS (version 8.2 and higher), and SPSS (version 12.0 and higher). For complete information on the capabilities of each package, analysts should refer to the corresponding software user documentation. Using the ▇▇▇▇▇▇-series linearization method, variance estimation strata and the variance estimation PSUs within these strata must be specified. The variables VARSTR and VARPSU on this MEPS data file serve to identify the sampling strata and primary sampling units required by the variance estimation programs. Specifying a “with replacement” design in one of the previously mentioned computer software packages will provide estimated standard errors appropriate for assessing the variability of MEPS survey estimates. It should be noted that the number of degrees of freedom associated with estimates of variability indicated by such a package may not appropriately reflect the number available. For variables of interest distributed throughout the country (and thus the MEPS sample PSUs), one can generally expect to have at least 100 degrees of freedom associated with the estimated standard errors for national estimates based on this MEPS database. Initially, MEPS variance strata and PSUs were developed independently from year to year, and the last two characters of the strata and PSU variable names denoted the rounds. However, beginning with the 2002 Point-in-Time PUF, the variance strata and PSUs were developed to be compatible with all future PUF until the NHIS design changed. As discussed, this chan...

Variance Estimation. (▇▇▇▇▇▇, VARSTR)

Variance Estimation. (VARSTR00, VARPSU00) Examples 2 and 3 from Section 4.2

Variance Estimation. To obtain estimates of variability (such as the standard error of sample estimates or corresponding confidence intervals) for estimates based on MEPS survey data, the complex sample design of MEPS for both person and family-level analyses must be taken into account. Various approaches can be used to develop such estimates of variance including use of the ▇▇▇▇▇▇ series or replication methodologies. Replicate weights have not been developed for the MEPS 1998 data. Using a ▇▇▇▇▇▇ Series approach, variance estimation strata and the variance estimation PSUs within these strata must be specified. The corresponding variables on the 1998 MEPS full year utilization database are VARSTR98 and VARPSU98, respectively. Specifying a “with replacement” design in a computer software package, such as SUDAAN, should provide standard errors appropriate for assessing the variability of MEPS survey estimates. It should be noted that the number of degrees of freedom associated with estimates of variability indicated by such a package may not appropriately reflect the actual number available. For MEPS sample estimates for characteristics generally distributed throughout the country (and thus the sample PSUs), there are over 100 degrees of freedom for the 1998 full year data associated with the corresponding estimates of variance.

Variance Estimation. (▇▇▇▇▇▇, VARSTR) 1. When pooling any year from 2002 or later, one can use the variance strata numbering as is. 2. When pooling any year from 1996 to 2001 with any year from 2002 or later, use the H36 file. 3. A new H36 file will be constructed in the future to allow pooling of 2007 and later years with 1996 to 2006.

Variance Estimation. To obtain estimates of variability (such as the standard error of sample estimates or corresponding confidence intervals) for estimates based on MEPS survey data, one needs to take into account the complex sample design of MEPS. Various approaches can be used to develop such estimates of variance including use of the ▇▇▇▇▇▇ series or various replication methodologies. Replicate weights have not been developed for the MEPS 1999 data. Variables needed to implement a ▇▇▇▇▇▇ series estimation approach are provided in the file and are described in the paragraph below. Using a ▇▇▇▇▇▇ Series approach, variance estimation strata and the variance estimation PSUs within these strata must be specified. The corresponding variables on the MEPS full year utilization database are VARSTR99 and VARPSU99, respectively. Specifying a “with replacement” design in a computer software package such as SUDAAN (Shah, 1996) should provide standard errors appropriate for assessing the variability of MEPS survey estimates. It should be noted that the number of degrees of freedom associated with estimates of variability indicated by such a package may not appropriately reflect the actual number available. For MEPS sample estimates for characteristics generally distributed throughout the country (and thus the sample PSUs), there are over 100 degrees of freedom associated with the corresponding estimates of variance.

Variance Estimation. Variance provides a means of assessing and reporting the precision of a point estimate, playing a critical role in the interval estimation, hypothesis testing, and power calculation. In this section, asymptotic variance formulas for different IRA statistics in the review are summarized, which allows approximate variance estimations for those IRA measures based ▇▇▇▇▇▇-corrected IRA statistics with ICC interpretations ▇▇▇▇▇▇▇ and ▇▇▇▇▇▇▇▇’s 𝑟11 Equal when 𝑛10 = 𝑛01 Mak’s 𝜌˜ ▇▇▇▇▇’▇ κ Equal when 𝑛10 = 𝑛01 ▇▇▇▇▇’▇ π Equal when 𝑛11= 𝑛00 Equal when 𝑛11 = 𝑛00 and 𝑛10 = 𝑛01 ▇▇▇▇▇▇▇▇▇▇▇▇’s α ▇▇▇ ▇▇▇▇’▇ 𝐼2 r Gwet’s 𝐴𝐶1 on the observed data from Table 1. e|κ ▇▇▇▇▇▇, ▇▇▇▇▇, and ▇▇▇▇▇▇▇ [47] proposed a variance estimator for ▇▇▇▇▇’▇ κ under the nonnull case of IRA, and Gwet [12] rewrote the variance formula under the two-rater case for better comparability with the estimated variances of several other IRA statistics. The estimated variance of κ is given as Vˆar(κ) = npˆ (1 − pˆ ) − 4(1 − κ) pˆ ωˆ + pˆ (1 − ωˆ) − κpˆ N (1 − pˆe|κ )2 11 00 + 4(1 − κ)2 pˆ ωˆ2 + 1 pˆ (2pˆ + pˆ )2 + 1 pˆ 01 01 10 00 (2pˆ + pˆ )2 + pˆ (1 − ωˆ)2 ,, where ωˆ = pˆ11 + pˆ10/2 + pˆ01/2. The performance of this large-sample variance of κ have been evaluated via Monte Carlo simulations in ▇▇▇▇▇▇▇▇▇ and ▇▇▇▇▇▇ [48] and Fleiss and ▇▇▇▇▇▇▇▇▇ [49]. ˆ For ▇▇▇▇▇▇▇ et al.’s S and the several equivalent IRA measures mentioned in the review, since the asymptotic variance estimator of percent agreement could be obtained as Var(pˆa) = pˆa(1 − pˆa)/N based on the binomial properties, we can get the variance estimator for S = 2pˆa − 1 as ˆ Var(S) = N pˆa(1 − pˆa). For ▇▇▇▇▇’▇ π, Gwet [12] proposed a nonparametric variance estimator with linearization techniques to remedy the formula proposed by ▇▇▇▇▇▇ [50], which was under the hypothesis of no agreement and was not valid for building up confidence intervals. Under our scenario of interest, the variance formula of π can be written as 1 N (1 − pˆe|π )2 11 Vˆar(π) = npˆ (1 − pˆ ) − 4(1 − π) pˆ 00 e|π ωˆ + pˆ (1 − ωˆ) − pˆ pˆ 11 ˆ + ( 4 10 01 00 + 4(1 − π)2 pˆ ω2 pˆ + pˆ ) + pˆ (1 − ωˆ)2 − pˆ2 ,. e|π Regarding Gwet’s AC1, Gwet [12] utilized a linear approximation that included all terms with a stochastic order of magnitude up to N−1/2 to derive a consistent variance estimator. The variance formula of AC1 is given by Vˆar(AC ) = npˆ (1 − pˆ ) − 4(1 − AC ) pˆ (1 − ωˆ) + pˆ ωˆ − pˆ pˆ N (1 − pˆe|AC1 )2 1 11 1 11 ˆ) + ( 4 + 4(1 − AC )2 pˆ

Variance Estimation. The MEPS is based on a complex sample design. To obtain estimates of variability (such as the standard error of sample estimates or corresponding confidence intervals) for MEPS estimates, analysts need to take into account the complex sample design of MEPS for both person-level and family-level analyses. Several methodologies have been developed for estimating standard errors for surveys with a complex sample design, including the ▇▇▇▇▇▇-series linearization method, balanced repeated replication, and jackknife replication. Various software packages provide analysts with the capability of implementing these methodologies. MEPS analysts most commonly use the ▇▇▇▇▇▇ Series approach. However, an option is also provided to apply the BRR approach when needed to develop variances for more complex estimators.