Common use of Variance Estimation Clause in Contracts

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 1996 data. Variables needed to implement a ▇▇▇▇▇▇ series estimation approach 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 VARSTR96 and VARPSU96, respectively. Specifying a “with replacement” design in a computer software package such as SUDAAN (▇▇▇▇, 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. The following illustrates these concepts using two examples from Section 4.2. Using a ▇▇▇▇▇▇ series approach, specifying VARSTR96 and VARPSU96 as the variance estimation strata and PSUs (within these strata) respectively and specifying a “with replacement” design in the computer software package SUDAAN will yield an estimate of standard error of $136 for the estimated mean of out-of-pocket payment.

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 1996 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 VARSTR96 VARSTR99 and VARPSU96VARPSU99, respectively. Specifying a “with replacement” design in a computer software package such as SUDAAN (▇▇▇▇, 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. The following illustrates these concepts using two examples from Section section 4.2. Using a ▇▇▇▇▇▇ series Series approach, specifying VARSTR96 VARSTR99 and VARPSU96 VARPSU99 as the variance estimation strata and PSUs (within these strata) respectively and specifying a “with replacement” design in the a computer software package SUDAAN will yield an estimate of standard error estimates of $136 0.4609 and 0.0067 for the estimated mean of out-of-pocket paymentpayment and the estimated mean proportion of total expenditures paid by private insurance respectively.

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 1996 1997 data. Variables needed to implement a ▇▇▇▇▇▇ series estimation approach 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 VARSTR96 VARSTR97 and VARPSU96VARPSU97, respectively. Specifying a “with replacement” design in a computer software package such as SUDAAN (▇▇▇▇, 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. The following illustrates these concepts using two examples from Section 4.2. Example 2 from Section 4.2 Using a ▇▇▇▇▇▇ series approach, specifying VARSTR96 VARSTR97 and VARPSU96 VARPSU97 as the variance estimation strata and PSUs (within these strata) respectively and specifying a “with replacement” design in the computer software package SUDAAN will yield an estimate of standard error of $136 85.51 for the estimated mean of out-of-pocket payment.

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 1996 1997 data. Variables needed to implement a ▇▇▇▇▇▇ series estimation approach 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 VARSTR96 VARSTR97 and VARPSU96VARPSU97, respectively. Specifying a “with replacement” design in a computer software package such as SUDAAN (▇▇▇▇, 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. The following illustrates these concepts using two examples from Section 4.2. Example 2 from Section 4.2 Using a ▇▇▇▇▇▇ series Series approach, specifying VARSTR96 VARSTR97 and VARPSU96 VARPSU97 as the variance estimation strata and PSUs (within these strata) respectively and specifying a “with replacement” design in the computer software package SUDAAN will yield an estimate of standard error of $136 6.01 for the estimated mean of out-of-pocket payment.

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 1996 data. Variables needed to implement a ▇▇▇▇▇▇ series estimation approach 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 VARSTR96 and VARPSU96, respectively. Specifying a “with replacement” design in a computer software package such as SUDAAN (▇▇▇▇, 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. The following illustrates these concepts using two examples from Section 4.2. Example 2 from Section 4.2 Using a ▇▇▇▇▇▇ series Series approach, specifying VARSTR96 and VARPSU96 as the variance estimation strata and PSUs (within these strata) respectively and specifying a “with replacement” design in the computer software package SUDAAN will yield an estimate of standard error of $136 2.71 for the estimated mean of out-of-pocket payment. Example 3 from Section 4.2 Using a ▇▇▇▇▇▇ Series approach, specifying VARSTR96 and VARPSU96 as the variance estimation strata and PSUs (within these strata) respectively and specifying a “with replacement” design in the computer software package SUDAAN will yield an estimate of standard error of 0.0118 for the weighted mean proportion of total expenditures paid by private insurance.

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 1996 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 VARSTR96 and VARPSU96, respectively. Specifying a “with replacement” design in a computer software package such as SUDAAN (▇▇▇▇, 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. The following illustrates these concepts using two examples from Section section 4.2. Using a ▇▇▇▇▇▇ series approach, specifying VARSTR96 and VARPSU96 as the variance estimation strata and PSUs (within these strata) respectively and specifying a “with replacement” design in the computer software package SUDAAN will yield an estimate of standard error of $136 for the estimated mean of out-of-pocket payment.

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 1996 1997 data. Variables needed to implement a ▇▇▇▇▇▇ series estimation approach 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 VARSTR96 VARSTR97 and VARPSU96VARPSU97, respectively. Specifying a “with replacement” design in a computer software package such as SUDAAN (▇▇▇▇, 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. The following illustrates these concepts using two examples from Section 4.2. Using a ▇▇▇▇▇▇ series approach, specifying VARSTR96 VARSTR97 and VARPSU96 VARPSU97 as the variance estimation strata and PSUs (within these strata) respectively and specifying a “with replacement” design in the computer software package SUDAAN will yield an estimate of standard error of $136 0.92 for the estimated mean of out-of-pocket payment.

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 1996 1997 data. Variables needed to implement a ▇▇▇▇▇▇ series estimation approach 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 VARSTR96 VARSTR97 and VARPSU96VARPSU97, respectively. Specifying a “with replacement” design in a computer software package such as SUDAAN (▇▇▇▇, 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. The following illustrates these concepts using two examples from Section 4.2. Example 2 from Section 4.2 Using a ▇▇▇▇▇▇ series Series approach, specifying VARSTR96 VARSTR97 and VARPSU96 VARPSU97 as the variance estimation strata and PSUs (within these strata) respectively and specifying a “with replacement” design in the computer software package SUDAAN will yield an estimate of standard error of $136 23.08 for the estimated mean of out-of-pocket payment. Example 3 from section 4.2 Using a ▇▇▇▇▇▇ Series approach, specifying VARSTR97 and VARPSU97 as the variance estimation strata and PSUs (within these strata) respectively and specifying a “with replacement” design in the computer software package SUDAAN will yield an estimate of standard error of 0.0132 for the weighted mean proportion of total expenditures paid by private insurance.

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 1996 1998 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 VARSTR96 VARSTR98 and VARPSU96VARPSU98, respectively. Specifying a “with replacement” design in a computer software package such as SUDAAN (▇▇▇▇▇ et al, 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. The following illustrates these concepts using two examples from Section 4.2. Using a ▇▇▇▇▇▇ series Series approach, specifying VARSTR96 VARSTR98 and VARPSU96 VARPSU98 as the variance estimation strata and PSUs (within these strata) respectively and specifying a “with replacement” replacement design in the a computer software package SUDAAN will yield an estimate of standard error estimates of $136 2.93 and 0.0080 for the estimated mean of out-of-pocket paymentpayment and the estimated mean proportion of total expenditures paid by private insurance respectively.

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 1996 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 VARSTR96 VARSTR99 and VARPSU96VARPSU99, respectively. Specifying a “with replacement” design in a computer software package such as SUDAAN (▇▇▇▇, 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. The following illustrates these concepts using two examples from Section 4.2. Using a ▇▇▇▇▇▇ series Series approach, specifying VARSTR96 VARSTR99 and VARPSU96 VARPSU99 as the variance estimation strata and PSUs (within these strata) respectively and specifying a “with replacement” Awith replacement@ design in the a computer software package SUDAAN will yield an estimate of standard error estimates of $136 2.50 and 0.0075 for the estimated mean of out-of-pocket paymentpayment and the estimated mean proportion of total expenditures paid by private insurance respectively.

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 1996 1998 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 VARSTR96 VARSTR98 and VARPSU96VARPSU98, respectively. Specifying a “with replacement” design in a computer software package such as SUDAAN (▇▇▇▇, 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. The following illustrates these concepts using two examples from Section 4.2. Using a ▇▇▇▇▇▇ series Series approach, specifying VARSTR96 VARSTR98 and VARPSU96 VARPSU98 as the variance estimation strata and PSUs (within these strata) respectively and specifying a “with replacement” replacement design in the a computer software package SUDAAN will yield an estimate of standard error estimates of $136 5.57 and 0.0130 for the estimated mean of out-of-pocket paymentpayment and the estimated mean proportion of total expenditures paid by private insurance respectively.

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 1996 1998 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 VARSTR96 VARSTR98 and VARPSU96VARPSU98, respectively. Specifying a “with replacement” design in a computer software package such as SUDAAN (▇▇▇▇, 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. The following illustrates these concepts using two examples from Section 4.2. Using a ▇▇▇▇▇▇ series Series approach, specifying VARSTR96 VARSTR98 and VARPSU96 VARPSU98 as the variance estimation strata and PSUs (within these strata) respectively and specifying a “with replacement” design in the a computer software package SUDAAN will yield an estimate of standard error estimates of $136 48.38 and 0.0359 for the estimated mean of out-of-pocket paymentpayment and the estimated mean proportion of total expenditures paid by private insurance respectively.

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 1996 2000 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 VARSTR96 VARSTR00 and VARPSU96VARPSU00, respectively. Specifying a “with replacement” design in a computer software package such as SUDAAN (▇▇▇▇, 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. The following illustrates these concepts using two examples from Section section 4.2. Using a ▇▇▇▇▇▇ series Series approach, specifying VARSTR96 VARSTR00 and VARPSU96 VARPSU00 as the variance estimation strata and PSUs (within these strata) respectively and specifying a “with replacement” design in the a computer software package SUDAAN will yield an estimate of standard error estimates of $136 0.4237 and 0.0074 for the estimated mean of out-of-pocket paymentpayment and the estimated mean proportion of total expenditures paid by private insurance respectively.

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 1996 2003 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 VARSTR96 VARSTR and VARPSU96VARPSU, respectively. Specifying a “with replacement” design in a computer software package such as SUDAAN (▇▇▇▇, 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. The following illustrates these concepts using two examples examp les from Section section 4.2. Using a ▇▇▇▇▇▇ series Series approach, specifying VARSTR96 VARSTR and VARPSU96 VARPSU as the variance estimation strata and PSUs (within these strata) respectively and specifying a “with replacement” design in the a computer software package SUDAAN will yield an estimate of standard error estimates of $136 0.3927 and 0.0052 for the estimated mean of out-of-pocket paymentpayment and the estimated mean proportion of total expenditures paid by private insurance respectively.

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 1996 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 VARSTR96 VARSTR99 and VARPSU96VARPSU99, respectively. Specifying a “with replacement” design in a computer software package such as SUDAAN (▇▇▇▇, 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. The following illustrates these concepts using two examples from Section 4.2. Using a ▇▇▇▇▇▇ series Series approach, specifying VARSTR96 VARSTR99 and VARPSU96 VARPSU99 as the variance estimation strata and PSUs (within these strata) respectively and specifying a “with replacement” design in the a computer software package SUDAAN will yield an estimate of standard error estimates of $136 5.31 and 0.0128 for the estimated mean of out-of-pocket paymentpayment and the estimated mean proportion of total expenditures paid by private insurance respectively.

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 1996 data. Variables needed to implement a ▇▇▇▇▇▇ series estimation approach 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 VARSTR96 and VARPSU96, respectively. Specifying a “with replacement” design in a computer software package such as SUDAAN (▇▇▇▇, 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. The following illustrates these concepts using two examples from Section 4.2. Example 2 from Section 4.2 Using a ▇▇▇▇▇▇ series Series approach, specifying VARSTR96 and VARPSU96 as the variance estimation strata and PSUs (within these strata) respectively and specifying a “with replacement” design in the computer software package SUDAAN will yield an estimate of standard error of $136 18.96 for the estimated mean of out-of-pocket payment. Example 3 from section 4.2 Using a ▇▇▇▇▇▇ Series approach, specifying VARSTR96 and VARPSU96 as the variance estimation strata and PSUs (within these strata) respectively and specifying a “with replacement” design in the computer software package SUDAAN will yield an estimate of standard error of 0.0159 for the weighted mean proportion of total expenditures paid by private insurance.

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 1996 data. Variables needed to implement a ▇▇▇▇▇▇ series estimation approach 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 VARSTR96 and VARPSU96, respectively. Specifying a “with replacement” design in a computer software package such as SUDAAN (▇▇▇▇, 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. The following illustrates these concepts using two examples from Section 4.2. Example 2 from section 4.2 Using a ▇▇▇▇▇▇ series approach, specifying VARSTR96 and VARPSU96 as the variance estimation strata and PSUs (within these strata) respectively and specifying a “with replacement” design in the computer software package SUDAAN will yield an estimate of standard error of $136 0.59 for the estimated mean of out-of-pocket payment. Example 3 from Section 4.2 Using a ▇▇▇▇▇▇ Series approach, specifying VARSTR96 and VARPSU96 as the variance estimation strata and PSUs (within these strata) respectively and specifying a “with replacement” design in the computer software package SUDAAN will yield an estimate of standard error of 0.0091 for the weighted mean proportion of total expenditures paid by private insurance.