# Kenya - Kenya Demographic and Health Survey 1998

Reference ID | KEN-KDHS-1998v01 |

Year | 1998 |

Country | Kenya |

Producer(s) | Kenya National Bureau of Statistics (KNBS) |

Sponsor(s) | Kenya National Bureau of Statistics - KNBS - National AIDS Control Council - NACC - National AIDS/STD Control Programme - NASCOP - Ministry of Public Health and Sanitation - - Kenya Medical Research Institute - KEMRI - National C |

Collection(s) |

Created on

Oct 23, 2012

Last modified

Dec 22, 2016

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1046754

Sampling

Sampling Procedure

The estimates from a sample survey are affected by two types of errors: (1) nonsampling errors, and (2) sampling errors. Nonsampling (measurement) errors are the results of shortcomings in the implemention of 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 1998 Kenya Demographic and Health Survey (KDHS) to minimize this type of error, nonsampling errors are impossible to entirely avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 1998 KDHS is only one of many samples that could have been selected from the same population, using the same 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 the 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 formulas for calculating sampling errors. However, the 1998 KDHS sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulae. The computer software used to calculate sampling errors for the 1998 Kenya Demographic and Health Survey (KDHS) is the ISSA Sampling Error Module. This module uses the Taylor linearization method of variance estimation for survey estimates that are means or proportions. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

The Taylor linearisation method treats any percentage or average as a ratio estimate, r = y/x, where y represents the total sample value for variable y, and x represents the total number of cases in the group or subgroup under consideration. The variance of r is computed using the formula given below, with the standard error being the square root of the variance: