Overview of Census Evaluation Methods

1. Overview of Census EvaluationMethods

2. Why evaluate ? The census is a huge operation including many stages It is not perfect and errors can and do occur at all stages of the census operation Many countries have recognized the need to evaluate the overall quality of their census results and have employed various methods for evaluating census coverage as well as certain types of content error

3. Purpose of census evaluation • To provide users with some measures of quality of census data to help them interpret the results • To identify types and sources of error in order to assist the planning of future censuses • To serve as a basis for constructing the best estimate of census aggregates, such as total population, or to provide adjustment ofcensus results

4. Planning Census Evaluation Programme • Census evaluation programme should be developed as part of the overall census programme and integrated with other census activities • Census errors can happen at all phases of census operation such as questionnaire design, mapping, enumeration, data capture, coding, editing, etc. • Evaluation of data accuracy may have two parts: • Preliminary evaluation will enable the identification of any problem areas that have not been previously detected in its process • More extensive evaluation should be undertaken on data items where problems have been identified • The results of the evaluation should be made available to census data users

5. Scope and methods of evaluation • Scope of evaluation of census data should be decided during the planning phase • Scope might be determined for example as: • Estimate coverage error at national level, regional level or provincial level • Analyze evidence of age misreporting • Compare census data with independent data sources (surveys, registers) • Methods of census evaluation should be determined according to resources and objectives

6. Institutional organization • Census evaluation team should be established • Team should be trained for the evaluation methods including demographic methods, other sources of data, etc. • Team should consist of staff having experience on different census topics such as demography, education, housing, labor force, etc. • Team should have knowledge on historical events and transition in population structure in the country • Team should collaborate with related institutions

7. Institutional organization • Equipment needed for census data evaluation including hardware and software should be assessed in the initial stage of planning • Cost of evaluation should be included in overall census budget

8. What are census errors? Coverage errors: Errors in the count of persons or housing units resulting from cases having been “missed” or counted erroneously Content errors: Errors in the characteristics of persons or housing units resulting from the interview operation (enumerators/respondents), coding, editing, etc.

9. Coverage errors Omissions Missing housing units, households and/or persons during census enumeration If the whole housing unit is missed, all households and persons living in the housing unit will also be missed Major causes of omissions are: Failure to cover whole land area of a country in creating EAs Mistakes made by enumerators in canvassing assigned areas Ambiguous definitions of EAs, unclear boundaries of EAs, faulty maps or coverage error during the pre-census listing exercise

10. Coverage errors Omissions contd. In addition, omissions within EAs can result because all or some of the members of the household were not present at the time of enumeration Proxy respondents can inadvertently or deliberately omit some members of a household

11. Coverage errors Duplications Occur when persons, households or housing units are counted more than once Reasons for duplications include: Overlapping of enumerator’s assignments owing to errors done during pre-census listing and delineation Failure by enumerators to clearly identify boundaries In practice, the number of omissions usually exceeds the number of duplicates (net under-counts)

12. Coverage errors Erroneous Inclusions This includes: Housing units, households and persons enumerated while they should have not been enumerated (e.g. babies born after the census reference date) Housing units, households and persons enumerated in a wrong place

13. Coverage errors Gross error This is the sum of duplications, erroneous inclusions and omissions Net error This is the difference between over-counts and under-counts Net census under-count exists when number of omissions exceeds the number of duplicates and erroneous enumerations Net census over-count is the opposite

14. Content errors Every phase of census data collection and processing has the potential for introducing errors into the census results The interviewing operation in which enumerators and respondents can make errors Many other operations such as coding, editing where the personnel or the procedures cause errors which affect the census content Content errors add bias and nonsampling variance components to the total mean square error of a census statistic

15. Methods for evaluation of census errors Single Source of Data Demographic analysis of the census Interpenetration studies Multiple Sources of Data Non-matching studies Demographic analysis using previous censuses Comparison with administrative sources or existing surveys Matching studies Post Enumeration Surveys Record checks

16. Single Source of Data Demographic Analysis of the Census Average number of persons per household Sex- and age- ratios Tabulations... For an overall assessment of quality: an age pyramid is a standard method stable population analysis can be undertaken as long as assumptions pertaining to constant fertility and mortality and no migration are met, for countries with declining mortality a quasi-stable model may be appropriate

17. Single Source of Data Strengths and weaknesses Methods that depend on a single datasource provide less insight into the magnitude and types of errors in the census data The advantage is that the methods using such sources do not require additional data to be collected No need for sophisticated matching although this is also a limitation Itprovides a general impression of quality of the census data

18. Single Source of Data Interpenetration studies Method involves drawing subsamples, selected in an identical manner, from the census frame Each subsample should be capable of providing valid estimates of population parameters Assignment of personnel (i.e. enumerators, coders, data entry staff, etc.) is done randomly The method helps to provide an appraisal of the quality of census information and procedures

19. Single Source -Interpenetration studies Strengths and weaknesses Gives good idea of different contribution of component errors to total census error Helps to identify operational stages that contribute to census error, thus identifying procedural limitations in a census Disadvantages include: That it is an expensive operation demanding many field staff, intensive training and close supervision Relatively complex in designing and implementation

20. Multiple Sources of Data – Non matching studies Demographic Analysis Comparison with data from administrative registers such as vital registration system Demographic analysis based on successive censuses such as the cohort component method More discussion of demographic methods is the focus of this workshop

21. Multiple Sources of Data – Non matching studies Comparison with existing household surveys In theory any probability sample of households or persons can be used to evaluate coverage and content error in a census if: They have identical items with same concepts and definitions They are independent from the census They have been conducted close to the census date There is a sufficient identification information to facilitate accurate matching

22. Multiple sources - Non-matching studies Strengths and weaknesses Review census results at aggregate rather than unit level i.e. provides only estimates of net census error Evaluates very limited characteristics such as sex and age distributions Advantage They are relatively cheap compared to matching studies

23. Non matching methods - Demographic Analysis Strengths and weaknesses No additional data is needed to be collected to perform the analysis Less costly In statistical offices with sufficient numbers of demographers there is no need for additional staff to do the technical analysis On the negative side these methods provide less insight into the different contributions of component errors to total error in the census Quality of sources (Vital Statistics…)

24. Multiple Sources of Data – Matching studies Record checks Census records are matched with a sample of records from registration systems such as the vital registration system The relevant respondents to the census questionnaire are traced to the time synchronized with the census Sources include: Previous census Birth registrations School enrolment Citizen registration card Immigration registers etc.

25. Multiple Sources of Data – Matching studies Record checks contd. Both coverage and content errors could be measured through the above comparisons To evaluate coverage efficiently the following preconditions are essential: A large proportion of census population should be covered in registration system The census and registration system should be independent from each other There should be sufficient information in records

26. Multiple Sources of Data – Matching studies To evaluate content efficiently the following preconditions are essential: The registers system should contain some relevant items covered in the census such as age, sex, education, relationship, marital status etc. Definitions of items should be identical between the census and the registers Countries that have used record checks include: Denmark, Finland, Norway, Sweden and Canada

27. Multiple Sources of Data – Matching studies Strengths and weaknesses It provide separate estimates of coverage and content error Prospects of evaluating more characteristics compared to what can be done with non-matching studies Challenges Calls for high level technical skills including managerial Matching is expensive

28. Multiple Sources of Data – Matching studies Post Enumeration Survey (PES) Consists of two separate coverage studies : A survey conducted using a sample frame independent of the census. Persons from this survey are matched to the census to estimate the number of persons missed in the census A survey conducted using a sample drawn from persons enumerated in the census. This sample is re-enumerated to determine if the sample person or unit was erroneously enumerated (inc. erroneously located)

29. Multiple Sources of Data – Matching studies Post Enumeration Survey (PES) contd. Results can be used to evaluate the reliability of some characteristics such as sex, age, marital status, relationship to reference person or head of the household. For some countries the results of PES can be used to adjust some census results Facilitates better interpretation of census results

30. Post enumeration survey Advantages: Its results can be used to independently evaluate census coverage and content error, including reliability of selected characteristics collected in a census Incorporates matching of individuals or units between the census and PES Its results are generally more reliable than those of the census i.e. it is justification for evaluation

31. Post enumeration survey Challenges: Requires highly skilled field and professional staff Matching is complex As it is supposed to be carried out immediately after the census at times there is lack of adequate funds and staff to implement the PES exercise

32. Thank You!