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Housing Vacancies and Homeownership (CPS/HVS)
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SOURCE AND ACCURACY OF ESTIMATES

The Current Population Survey (CPS) sample is selected from a frame based on the 1990 census and is spread over 754 sample areas, which represent 2,007 geographic areas in the United States.

Of the 72,000 housing units contained in the CPS sample, approximately 61,200 are eligible for interview each month; of this number, 3,900 occupied units, on the average, are visited but interviews are not obtained because occupants are not found at home after repeated calls or are unavailable for some other reason.

The CPS is sponsored by the Bureau of Labor Statistics to collect reliable employment data. In addition, the CPS provides important demographic information on characteristics of householders. The survey is conducted monthly during the week of the 19th. Interviews are collected in person or by phone (if certain criteria are met) on race, sex, age, citizenship, family status, and other topics. A sample unit is interviewed for four consecutive months, then temporarily dropped from the sample, then interviewed again for another four consecutive months. In this way, sample units are rotated in and out of the sample. The annual data in this report represent a 12-month average for the calendar year.

The CPS estimation procedure for occupied units involves the inflation of the weighted sample results to independent estimates of the total civilian noninstitutional population of the United States by age, race, sex, and Hispanic/non-Hispanic categories. These independent estimates are based on statistics from the decennial censuses of population; statistics on births, deaths, immigration, and emigration; and statistics on the strength of the Armed Forces.

In 1994, the CPS became a totally computerized survey with the implementation of the Computer Assisted Survey Information Collection (CASIC). The CASIC tools consist of state-of-the-art computer-assisted modules for data collection and processing.

Research has shown that the CPS and the 2000 census produced significant differences in homeownership rates. For example, the April 2000 census produced a homeownership rate of 66.2 percent, while for the first half of 2000, the CPS produced a rate of 67.2 percent. This illustrates that caution should be used when making comparisons between the 2000 census and CPS data.

Since the CPS estimates are based on a sample, they may differ somewhat from the figures that would have been obtained if a complete census had been taken using the same questionnaires, instructions, and enumerators. There are two types of errors possible in an estimate based on a sample survey: sampling and non-sampling. The accuracy of a survey result depends on both types of errors, but the full extent of the non-sampling error is unknown. Consequently, particular care should be exercised in the interpretation of figures based on a relatively small number of cases or on small differences between estimates. The standard errors provided for the CPS estimates primarily indicate the magnitude of the sampling error. They also partially measure the effect of some non-sampling errors in responses and enumeration; but do not measure any systematic biases in the data. (Bias is the difference averaged over all possible samples, between the estimate and the desired value.)

Nonsampling errors can be attributed to many sources, including:

• the inability to obtain information about all cases in the sample,
• definitional difficulties,
• differences in the interpretation of questions,
• inability or unwillingness on the part of respondents to provide correct information,
• inability to recall information,
• errors made in collection such as recording or coding the data,
• errors made in processing the data,
• errors made in estimating values for missing data,
• and failure to represent all units with the sample (undercoverage).

Undercoverage in the CPS results from missed housing units and misclassifying housing units. Ratio estimation to independent controls, as described previously, partially corrects for the bias due to survey undercoverage. However, biases exist in the estimates to the extent that missed households have different characteristics than interviewed households.

The standard errors shown in the tables are primarily measures of sampling variability, that is, of the variations that occurred by chance because a sample rather than the entire population was surveyed. The sample estimate and its standard error enable one to construct confidence intervals; ranges that would include the average results of all possible samples with a known probability. For example, if all possible samples were selected, each of these being surveyed under essentially the same general conditions and using the same sample design, and if an estimate and its standard error were calculated from each sample, then approximately 90- percent of the intervals from 1.6 standard errors below the estimate to 1.6 standard errors above the estimate would include the average result of all possible samples.

The average estimate derived from all possible samples is or is not contained in any particular computed interval. However, for a particular sample, one can say with specified confidence that the average estimate derived from all possible samples is included in the confidence interval.

To perform the most common test, let x and y be sample estimates for two characteristics of interest. Let the standard error on the difference x-y be SEDIFF. If the ratio R = (x-y)/SEDIFF is between -1.6 and +1.6, no conclusion about the difference between the characteristics is justified at the 0.10 level of significance. If, on the other hand, this ratio is smaller than -1.6 or larger than +1.6, the observed difference is significant at the 0.10 level. In this event, it is a commonly accepted practice to say that the characteristics are different. Of course, sometimes this conclusion will be wrong. When the characteristics are, in fact, the same, there is a 10 percent chance of concluding that they are different. All statements of comparison in the text have passed a hypothesis test at the 0.10 level of significance or better. This means that, for most differences cited in the text, the estimated difference between characteristics is greater than 1.6 times the standard error of the difference.

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