# Botswana - Botswana AIDS Impact Survey 2001

Reference ID | BWA_2001_AIS-I_v01_M |

Year | 2001 |

Country | Botswana |

Producer(s) | Central Statistics Office (CSO) - Ministry of Finance and Development Planning |

Metadata | Documentation in PDF Download DDI Download RDF |

Created on | Mar 10, 2015 |

Last modified | Sep 28, 2016 |

Page views | 10017 |

Downloads | 2824 |

Data Appraisal

Estimates of Sampling Error SAMPLING ERRORS Two types of errors affect the estimates from a sample survey: (1) non-sampling error, and (2) sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the BIAS to minimise these type of errors, non-sampling errors are impossible to avoid and difficult to evaluate statistically. Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the BAIS is only one of many samples that could have been selected from the same population, using the same sample design and expected size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results. A sampling error is usually measured in terms of standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95 percent of all possible samples of identical size and design. If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulae for calculating sampling errors. However, the BAIS sample is the results of a stratified two stage design, and, consequently, it was necessary to use more complex formulae. Sampling errors for selected variables for the country as a whole, are presented in the table given below. In addition to the value (R) of type of statistic (mean, proportion) and standard error (SE) for each variable, the tables includes the weighted number (WN) of cases on which the statistic is based, the relative standard error (the standard error divided by the value of the statistic) and the 95 percent confidence limits (R. ± 2SE). The confidence limits may be interpreted by using the following example: the overall estimate of the proportion who ever heard HIV/AIDS (R) is 0.935 and its standard error is 0.054. To obtain the 95 percent confidence interval, twice the standard error is added to and subtracted from the estimate of R, 0.935± 2* 0.054. Thus, there is a 95 percent probability that the true value of R lies between 0.827 and 1.043. Note: See detailed sampling error calculation which is presented in 2001 BAIS-I final report. |