Data QualityIn the preparatory stage for the FALCOT 92 and during its implementation, every effort was made to ensure strict adherence to the methodology developed and the field protocol designed. However, classic problems may arise in any survey regardless of the control procedures used. The quality of data collected is usually determined by the extent of control on measurement errors (non-sampling errors) as long as the proper methodology is used to insure minimal amount of sampling errors which are uncontrollable. Having made every effort to control non-sampling errors and assuming that the questionnaire is carefully designed and pretested, it is only natural to expect to get a data set of fairly good quality. However, response and field worker bias may result in low quality data. As for the FALCOT 92, the general assessment of data quality is not the subject of this section. However, due to the sensitivity of the parameters we are trying to estimate and the procedures used, we present here a brief investigation of the quality of those variables entering estimates of childhood mortality and fertility. The overall quality of these variables will be assessed by evaluating age structures, average parities, sex ratios of CEB and proportion of dead children.
Table 2.4 shows the age structure of qualified females for analysis. The breakdown of the age structure of all females of child bearing age using 5-year categories is almost similar to the overall age structure of females in the same age interval which is compiled from the listing of ages of all household members. The only exception is the share of females in the age groups 20-24 and 25-29. While the first age group is under- represented, the second is over-represented. Assuming that the overall age structure is accurate, this slight misrepresentation may be viewed as an indicator of selection bias during randomization or during field work. The over- (under-) representation in these two age groups will have direct impact on the estimates of IMR and U5MR due to the fact that the average parities of these two age groups will be extensively used in calculations.
Table 2.4 Age structure of qualified females for child mortality estimation
There is also a clear case of age misreporting at age 16, with more than twice as many respondents (98) at age 16 as in ages 15 (44) and 16 (45). Over-sampling at other ages seems to have happened to a lesser extent (ages 32, 42 and 48).
Age misreporting may result in a slight bias in parity calculations since
the heaping usually occurs at the start of age groups. However, since age
heaping seems to have occurred in all categories, we expect the net effect
to be negligible. Had field workers been instructed to verify ages from
official documents, the effect of this phenomenon could have been reduced.
Sex Ratios and Proportion Dead
The sex ratios of the table may indicate the existence of under- reporting of female births, but could also stem from sampling error. This would probably be due to ommissions on the part of mothers giving female births who later (in a matter of days or weeks) have died. On the other hand, the sex ratio of dead children could indicate the existence of a severe case of under-reporting of dead males in three age groups. The abnormality of the sex ratios for both births and deaths casts some doubt on the quality of the data set. These abnormalities will directly affect our estimates of childhood mortality, and pave the way for an over- estimation of female infant mortality and under-estimation of male infant mortality. As for the proportion of dead children, we notice a drop in this proportion for the 25-29 age group. Moreover, this indicator does not increase normally for the first three age groups, as opposed to the last three age groups. Both the drop in the proportion of dead children and the slow increase for the three categories are indicators of problems with the data quality.
Table 2.5 Sex ratios of children ever born, dead children and proportion of dead children by age of mother at the time of interview
Conclusion Every measure we have used for checking the data quality has showed us a problem of some sort, the most serious being related to sex ratios. Our conclusion is that the available data - although suitable for many other analytical purposes - maybe are not good enough to produce reliable estimates of parameters of childhood mortality and fertility. Therefore, all estimates and analyses should be treated with caution.