Statistical Methods, Standards and Guidelines x
planning, collection, processing, analysis, dissemination and preservation of Tanzania statistical
information.
The framework for the methods, standards and guidelines includes:
(i)
Methods for statistical production
(ii)
Main stages of statistical production
(iii)
Standards and guidelines for statistical production
(iv)
Standard classifications in statistical production
(v)
Statistical products and services
The framework outlines 21 standards and their related guidelines for Tanzania Statistical activities that ensure high quality statistical census and survey results. The Standards and guidelines provided, enhances harmonization, comparability and consistency of quality and integrity of statistical information and products disseminated by MDAs, LGAs and other stakeholders in Tanzania.
In conducting Census or Survey, the MDAs, LGAs and other stakeholders should engage personnel with knowledge, well experienced and who is familiar with census or survey methodologies and related techniques to effectively achieve the goals of the statistical standards.
The main objective of this document, therefore, is to ensure that data of high quality are being produced through effective use of statistical methods, concepts and definitions as well as the application of the quality control procedures in all stages of data production.
Statistical Methods, Standards and Guidelines 1
PART I:
STATISTICAL PRODUCTION
A:
METHODS AND STAGES OF STATISTICAL PRODUCTION
1.0
Introduction
Statistical production depends on various methods and stages that have an impact on the quality of
data, information and indicators produced and used. Therefore, it is imperative that, this process
addresses user needs and concerns in the early stages. In some cases, users demand disaggregated
data and statistics at the ultimate stages whereas the initial stages did not take into account these
needs. Thus, it is likewise essential that various stakeholders including subject matter specialists
be involved at initial stages of statistical production process to address the user needs. As a matter
of principle, quality measures have to be instituted in all stages of data production.
1.1 Methods of Statistical Production Statistical Production refers to the activity that is carried out within statistical information system and aimed at coming up with statistics as a final product. In producing Statistics there are various methods used depending on the source as elaborated below;
1.1.1 Administrative Records and Routine Data Systems
(i)
The methods are primarily established to manage operational processes. These
includes; vital events registration, agriculture, education, health, water, tax, trade
social security, etc;
(ii)
The data generated can also be used for strategical purposes;
(iii)
Have built-in mechanisms in service delivery to collect data for measuring early
results; and
(iv)
Assist in the production of input, process, output and outcome indicators.
1.1.2 Censuses and Surveys (a) Census (i) Collects data from every member of a given population within specified boundaries; (ii) It aims at providing total coverage of the population; (iii) Conducted after every specified interval (after every ten years is common for population census in most countries);
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(iv)
Costly and time consuming;
(v)
Basis (frame) for inter-censal sample surveys; and
(vi)
Fewer questions are asked and normally produce indicators that measure medium
and long term results (outcome and impact) of policies and programmes.
(b) Survey (i) Involves identifying and collecting data from a randomly selected portion (a sample) of a given population; (ii) Conducted periodically for different subjects during the inter-censal period; (iii) Many questions asked that produce indicators for measuring early as well as long term results (outcome and impact) of service delivery; and (iv) Supplements census and administrative data.
(c)
Surveillance and Longitudinal Studies
(i)
These are on-going, systematic collection, analysis, interpretation and
dissemination of data from a specific area or population; and
(ii)
Collects data for vital events (births, deaths and migration), health, education and
other demographic, social and economic variables.
1.1.3 Experimental and Case studies
An experimental study involves taking measurements of the system under study, manipulating the
system and then taking additional measurements using the same procedure to determine if the
manipulation has modified the values of the measurements.
A case study is based on an in-depth investigation of a person, a small group, a single situation, or a specific "case,”. It involves extensive research, including documented evidence of a particular issue or situation; symptoms, reactions, effects of certain stimuli, and the conclusion reached following the study. A case study may show a correlation between two factors, whether or not a causal relationship can also be proven. Case studies may be descriptive or explanatory.
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1.2 Stages in Statistical Production In producing statistics, a number of stages have to be considered. These are elaborated below.
1.2.1 Users Demand for Statistical Data (i) Internal and external users, approach the national statistics offices and statistics units in MDAs requesting data for planning and decision making purposes; (ii) The statistics experts have to discuss with data users and other stakeholders to identify data needs to be addressed; (iii) The statistics experts have to translate the data needs into objectives of the statistics production process; and (iv) There is a need to determine what method to use to generate the required statistical data.
1.2.2 Establishing Technical Committees (i) Determine the composition of technical committee based on type of data required; (ii) Involve expertise from different socio-economic fields and disciplines; (iii) Carry out critical analysis of the subject matter in question during the technical committee meetings; and (iv) Planning processes for the statistical production.
1.2.3 Formulation of Statistical Problem (i) Data needs are normally presented in non-statistical language; (ii) Need to come up with statistical formulations in order to produce the desired data and indicators; and (iii) Determine appropriate study design and the type of data needed.
1.2.4 Information needs (i) The technical committee has to determine what type of information has to be collected that will meet objectives and user needs; and (ii) An overall statement of information needed for socio-economic planning and decision making has to be pointed out.
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1.2.5 Tabulation and Analysis Plan (i) It is a planned way of summarizing and presenting the collected data; (ii) It includes frequency tables, cross tabulation and graphs; (iii) It involves computation of indicators and measures of association (correlations) and determining cause-effect relationships (regressions); and (iv) It needs analysis of variables (sex, age, locality, education, income levels, etc) in cross tabulations and regressions to extract disparities.
1.2.6 Formulation of Statistical Questions (i) Converting information required and indicators to questions; (ii) Mock interviews among experts to test the questions; (iii) Pre-testing which assist in checking how respondents understand the questions, what responses to expect, sensitivity and neutrality of questions, how to improve them, etc; and (iv) General or specific questions for different respondent or groups.
1.2.7 Data collection instruments/questionnaire design (i) Combining all questions into a form, questionnaire or checklist; (ii) Logical flow of questions to be considered; (iii) Separate or single instrument for different respondent categories to be reflected; (iv) Develop instruction manuals; and (v) Develop publicity and advocacy materials.
1.2.8 Sample design
(i)
Resources can determine whether to collect data from the whole or part of total
population. However, level of accuracy can be determined by the sample size;
(ii)
Identifying and selecting respondents to represent others including stratification of
sub-groups;
(iii)
Adequate sample size from different respondent categories; and
(iv)
Sampling weights for estimation of population parameters.
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1.2.9 Recruitment and training (i) Determine number and type of personnel (e.g. supervisors and enumerators) who will be involved in data collection; (ii) Criteria for recruiting and selecting data collection personnel; (iii) Training to build the capacity of the personnel for the data collection; and (iv) Time used for imparting general and specific skills to data collection personnel should be considered depending on the type and nature of the survey.
1.2.10 Pilot testing (i) Test the feasibility of survey instruments (Questionnaires, Manuals, Forms etc); (ii) Provide cost and time estimates for the whole survey; (iii) Tests all logistical procedures before main fieldwork; (iv) Determine areas of strengths and weaknesses of the data collection procedures; and (v) Enhance improvement of data production and logistical procedures.
1.2.11 Main fieldwork
(i)
Conduct advocacy and publicity campaigns before and during data collection to
attain the desired response rates;
(ii)
Supply of survey instruments to and from the field;
(iii)
Collect data from the earmarked respondents using appropriate questionnaire;
(iv)
Strengthen field supervision mechanisms and teamwork to improve data quality;
and
(v)
Conduct post-enumeration survey (evaluation) immediately after main fieldwork to
determine coverage, content and quality aspects of the data collected.
1.2.12 Data processing Due to emerging of modern technology, the use of electronic divces such as tablets/min laptops (Computer-Aided Personal Interview – CAPI) for the data collection has been adopted. When applying this modern technology, the use of an algorithm during data cleaning procedures to identify the completeness of an interview based on a set of key variables is inevitable.
However, with the use of paper questionnaires, the following should be considered:- (i) Manual editing of the filled-in questionnaire by editors to ensure all important fields of the questionnaire are completed accordingly;
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(ii) Data cleaning of printouts to ensure that all answers were entered as reported in the questionnaires and dairies; (iii) Institute field procedures for checking quality of data by supervisors and manual editors; (iv) Transfer data from data collection instruments into computer files (data entry); (v) Institute office procedures for checking quality of data before, during and after data entry; and (vi) Build capacity of data entry operators in terms of speed and accuracy.
1.2.13 Tabulation / Analysis (i) Implementing/produce tables as per tabulation plan/ analysis plan; (ii) Summarizing the collected data into tables and statistics / indicators; (iii) Disaggregation of data - presenting data such that socio-economic differentials are clearly seen; (iv) Analyzing within and among socio-economic categories – column or row totals, sex and geographical location as major analysis variables – analyzing important population characteristics by sex and location such that gender and urban/rural differences are clearly reflected; and (v) Make statistical inference from sample data to total population.
1.2.14 Interpretation and report writing (i) This involves extracting main messages from the tabulated / analyzed data; (ii) Composition of different experts among the authors. An expert eye / lens is very crucial at this stage to pick the critical issues; and (iii) Write separate chapters or reports on related findings.
1.2.15 Dissemination and Statistical Literacy (i) Informing users and stakeholders on the results using various means such as reports, media and website; (ii) Provide general and specific packages for various users; (iii) Promoting the policy agenda of the produced data; and (iv) Conducting Statistical literacy to users to understand the data.
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1.2.16 Documentation and Archiving (i) Preparing basic information datasheets describing the data; (ii) Archiving the raw data and reports; and (iii) Institute procedures for accessing the raw data including removing identification (anonymization) of census/survey respondents.
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B:
STANDARDS AND GUIDELINES FOR STATISTICAL PRODUCTION
2.0
Introduction
There are various methods of data production as outlined earlier in this document. This part dwells
in detail about these methods by providing relevant standards and guidelines.
2.1
SURVEY METHODOLOGY
2.1.1 Survey Planning
Standard 2.1.1: When starting a completely new survey or a new round of an existing survey;
MDAs, LGAs and other stakeholders must develop a written proposal (concept note) that sets
forth a justification, including: goals and objectives, potential users, related and previous surveys,
key survey estimates, the precision required of the estimates, the tabulation and analytic results
that will inform decisions and other uses, steps taken to prevent unnecessary duplication with
other sources of information, confidentiality of individual data, when and how frequently users
need the data and public access and use of the data.
The guidelines for this standard are:
Guideline 2.1.1.1: Surveys (and related activities such as focus groups, pretesting, pilot studies,
field tests, etc.) are collections of information subject to the requirements of an existing Statistics
Act. An initial step in planning a new survey or a revision of an ongoing survey should be to
contact the financing agency MDA, LGA, Development Partner (DP), and a stakeholder’s most
senior designated official to ensure the survey work is done in compliance with the law and
regulations. NBS approval will be required before the MDAs, LGAs and other stakeholders
embark on a data collection exercise from households and establishments.
Guideline 2.1.1.2: Planning is an important prerequisite when designing a new survey or implementing an amendment of an ongoing survey. Key planning activities include the following:
(a)
A justification for the survey
(i)
The rationale for the survey;
(ii)
Relationship to previous surveys;
(iii)
Survey goals and objectives;
(iv)
Hypotheses to be tested;
(v)
Definitions of key variables; and