Weighting and Statistical Significance
Because Kaiser/HRET selects firms randomly, it is possible through the use of statistical weights to extrapolate the results to national (as well as regional, industry, and firm size) averages. These weights allow Kaiser/HRET to present findings based on the number of workers covered by health plans, the number of total workers, and the number of firms. Specific weights were created to analyze the HDHP plans that are offered along with HRAs or are HSA qualified. These weights represent the proportion of employees enrolled in each of these arrangements.
Calculation of the weights follows a common approach. First, the basic weight is determined, followed by a nonresponse adjustment. As part of this nonresponse adjustment, Kaiser/HRET again conducted a small follow-up survey of those firms with 3-49 workers that refused to participate in the full survey. We applied an additional nonresponse adjustment to the weight to reflect the findings of this survey.
Next we trimmed the weights in order to reduce the influence of weight outliers. First, the weights were ranked from largest to smallest based on their proportion of the total weight sum. Next, we identified trimming cut points such that the observations to be trimmed contribute no more than five percent towards the total weight sum. We also minimized the number of nontrimmed observations that exceed the cut point after the trimming adjustment. This method reduced the variability in the weights and maintained, with few exceptions, the ordinal integrity of the observation weights.
Finally, we applied a post-stratification adjustment. We used the Statistics of the U.S. Census Bureau as the basis for the stratification and the post-stratification adjustment for firms in the private sector, and we used the Census of U.S. Governments as the basis for post stratification for public sector firms. This year we updated our data to reflect the 2002 Census of Governments. We also removed federal government employee counts from our post-stratification. Although these updates had only a small impact on the number of government firms, they reduced the number of government workers in the weights by approximately 7 million. This may have a small effect on our coverage and enrollment estimates.
The data are analyzed with SUDAAN, which computes appropriate standard error estimates by controlling for the complex design of the survey. All statistical tests are performed at the .05 level unless otherwise noted. For figures with multiple years, statistical tests are conducted for each year against the previous year shown. No statistical tests are conducted for years prior to 1999, meaning that the year prior to 1999 shown on the exhibits is the last year for which standard errors are available. Two types of significance tests performed are the t-Test and the Chi-square test.
Historical Data
Data in this report focus primarily on findings from surveys jointly authored by the Kaiser Family Foundation and the Health Research and Educational Trust, which have been conducted since 1999. Prior to 1999, the survey was conducted by the Health Insurance Association of America (HIAA) and KPMG using the same survey instrument, but data is not available for all the intervening years. Following the survey’s introduction in 1988, HIAA conducted the survey through 1990, but some data are not available to us. KPMG also conducted the survey from 1991-1998. However, in 1991, 1992, 1994, and 1997, only larger firms were sampled. In 1993, 1995, 1996, and 1998, KPMG interviewed both large and small firms.
This report uses data from the 1993, 1996, and 1998 KPMG Surveys of Employer-Sponsored Health Benefits and the 1999-2004 Kaiser/HRET Survey of Employer-Sponsored Health Benefits. For a longer-term perspective, we also use the 1988 survey of the nation’s employers conducted by the HIAA, on which the KPMG and Kaiser/ HRET surveys are based. Many questions in the HIAA, the KPMG, and Kaiser/HRET surveys are identical. The survey designs among the three surveys are also similar.
