The Suriname Multiple Indicator Cluster Survey (MICS) was carried out in 2010 by the Ministry of Social Affairs and Housing in collaboration with General Bureau of Statistics and the Institute for Social Research (IMWO) of the University of Suriname. Financial and technical support was provided by the United Nations Children’s Fund (UNICEF). The Suriname MICS was carried out as part of the fourth round of the global MICS household survey programme with the technical and financial support from UNICEF. MICS is a nationally representative sample survey of women aged 15‐49 and children under age five of 7,407 responding households out of a total of 9,356 sampled households. The main purpose of MICS 2010 is to support the government of Suriname to generate statistically sound and comparable data for monitoring the situation of children and women in the country. MICS 4 covers topics related to nutrition, child health, water and sanitation, reproductive health, child development, literary and education, child protection, HIV and AIDS, mass media and the use of information and communication technology and attitude towards domestic violence.
Kind of data
Sample survey data [ssd]
- v01: Edited, anonymous datasets for public distribution.
Unit of analysis
The survey covered all de jure household members (usual residents), all women aged between 15-49 years, and all children under 5 living in the household.
Producers and sponsors
United Nations Children’s Fund
Ministry of Social Affairs and Housing
General Bureau of Statistics
Institute for Social Research
University of Suriname
United Nations Children’s Fund
Financial and technical support
The primary objective of the sample design for the Suriname Multiple Indicator Cluster Survey was to produce statistically reliable estimates of most indicators, at the national level, for areas classified as urban, rural coastal and rural interior, and for the 10 districts of the country - Paramaribo, Wanica, Nickerie, Coronie, Marowijne, Commewijne, Sarramacca, Para, Brokopondo, and Sipaliwini.
A multi-stage, stratified cluster sampling approach was used for the selection of the survey sample.
The target sample size for the Suriname MICS was calculated as 9,000 households. For the calculation of the sample size, the key indicator used was the number of children younger than 5 years of age who had had diarrhoea in the past two weeks before the survey using the estimate of the last MICS3 survey.
Suriname is divided into 10 districts and 62 'ressorten' by law. The 'ressorten' are subdivisions at the district level. For purposes of conducting the fieldwork during the Seventh Population and Housing Census the General Bureau of Statistics subdivided each ressort in the coastal area (lowland and savannah) into 'telblokken'. A 'telblok' also called an enumeration block, was considered to be the manageable workload for a Census enumerator for the fieldwork period of two weeks and would ideally have between 100 and 150 objects. An object can be any kind of building or a construction work, like, churches, schools, stores, houses, dwellings etc. In order to clarify: not every object stands for a dwelling or living quarters of a household. In the interior (rainforest) a somewhat different fieldwork approach was used, whereby teams consisting of 5-7 fieldworkers canvassed clusters of villages. These clusters were called 'telgebieden' and were expected to have approximately 500 households, or the workload of 5 interviewers. "Telgebied" can also be called an enumeration area.
The 2004 census frame was used for the selection of clusters as the results of the 2004 Census provide a basis for provisional estimates on the number of households. Thus, the 'telblokken' and 'telgebieden' were considered the best currently available subdivisions by the General Bureau of Statistics and formed the basis for the MICS 2010 sample design.
Each of the 10 districts in the country is allocated to one of these strata, but with three towns (Nw. Nickerie in Nickerie district, and Meerzorg and Tamanredjo in Commewijne district) being counted as urban, even though they are located in what are otherwise rural districts.
In the case of MICS3, the total sample had been about 6,000 households. Survey results had been reported not only for the three strata, but also for a five-way breakdown of districts. This was achieved by grouping districts as follows: Paramaribo; Wanica and Para; Nickerie, Coronie and Saramacca; Commewijne and Marowijne; and Brokopondo and Sipaliwini. In the case of MICS4, the sample size was increased to 9,000 households. One of the main benefits of this increase was that it would permit the reporting of indicators at the district level.
The allocation within each stratum was done with probability proportional to size, where the population of each area (from the 2004 census) was used as the measure of size. It was only after the fieldwork was completed that it was realized that the samples allocated to several of the individual districts were insufficient to provide satisfactory estimates for many of the variables. A more equal allocation to each district would have provided more precise estimates for the smaller districts.
It should be noted that, according to sampling theory, it is the size of the sample, rather than the proportion of the population covered, that is the key factor in determining the precision of the estimate. Several districts have sample sizes that are around the 500 level, which is slightly on the low side. An allocation of about 700 households would have been more appropriate, which could have been achieved by reducing the allocation for Paramaribo. In the rural interior, the allocation for Brokopondo might usefully have been increased, with a corresponding reduction in Sipaliwini. Only 140 households were allocated to Cornie, reflecting its small population of less than 1,000 households, but it would have been necessary to cover a larger number of households there (say 300 or 400) in order to obtain reliable estimates.
The actual sample selection in the selected clusters was done as follows. In urban and rural coastal areas, where enumeration districts (EDs) usually contain about 150 households, one pointer address (PA) was selected at random within the ED. If it was not the address of a private household, the next address was taken as the starting point. Twenty adjacent addresses (1 to 20) were then selected around this PA, and a printed map provided to each team, showing the location of each address. In rural areas the enumeration areas might consist of either one village or several smaller villages combined. Where a village was very isolated, it was treated as one enumeration area, even though sometimes it did not contain many households.
The sampling procedures are more fully described in "Suriname Multiple Indicator Cluster Survey 2010 - Final Report" pp.178-181.
Of the 9,356 households selected for the sample, 8,532 were found to be occupied. Successful interviews were conducted in 7,407 of the 8,532 occupied households resulting in a household response rate of 86.8 percent. In the interviewed households, 7,237 women (age 15‐49) were identified. Of these, 6,290 were successfully interviewed, yielding a response rate of 86.9 percent. In addition, 3,462 children under age five were listed in the household questionnaire. Questionnaires were completed for 3,308 of these children, which corresponds to a response rate of 95.6 percent. Overall response rates of 75.5 and 83.0 are calculated for the women’s and under‐5’s interviews respectively.
The Suriname Multiple Indicator Cluster Survey sample is not self‐weighting. Essentially, by allocating sampling units disproportionately in each of the regions, different sampling fractions were used in each region. For this reason, sample weights were calculated and these were used in the subsequent analyses of the survey data.
The major component of the weight is the reciprocal of the sampling fraction employed in selecting the number of sample households in that particular sampling stratum (h) and PSU (i).
The sampling fractions for households in each enumeration area (cluster) included the first stage probability of selection of the enumeration area in that particular sampling stratum and the second stage probability of selection of a household in the sample enumeration area (cluster).
A second component in the calculation of sample weights takes into account the level of non‐response for the household and individual interviews. The adjustment for household non‐response is equal to the inverse value of:
RRh = Number of interviewed households in stratum h/ Number of occupied households listed in stratum h
The non‐response adjustment factors for women's and under‐5's questionnaires were applied to the adjusted household weights. Numbers of eligible women and under‐5 children were obtained from the roster of household members in the Household Questionnaire for households where interviews were completed.
The weights for the households were calculated by multiplying the above factors for each enumeration area. These weights were then standardized (or normalized), one purpose of which is to make the weighted sum of the interviewed sample units equal the total sample size at the national level. Normalization is achieved by dividing the full sample weights (adjusted for non‐response) by the average of these weights across all households at the national level. This is performed by multiplying the sample weights by a constant factor equal to the unweighted number of households at the national level divided by the weighted total number of households (using the full sample weights adjusted for non‐response). A similar standardization procedure was followed in obtaining standardized weights for the women’s and under‐5’s questionnaires. In the 483 sample enumeration areas (clusters), adjusted (normalized) weights varied between a minimum of 0.406618 in the case of women in clusters 392 to 450 and a maximum of 2.036209 in the case of children in clusters 126 to 169 and clusters 462 to 465.
Sample weights were appended to all data sets and analyses were performed by weighting each household, woman, and under‐5 with these sample weights.
Dates of collection
Mode of data collection
Data collection supervision
There is one supervisor for each of the 12 data collection teams in the field.
The questionnaires for the Generic MICS were structured questionnaires based on the MICS4 model questionnaire with some modifications and additions. Household questionnaires were administered in each household, which collected various information on household members including sex, age and relationship. The household questionnaire includes household listing form, education, water and sanitation, household characteristics, insecticide treated nets (in Brokopondo and Sipaliwini only), indoor residual spraying (in Brokopondo and Sipaliwini only), child labour, child discipline and hand washing.
In addition to a household questionnaire, questionnaires were administered in each household for women age 15-49 and children under age five. For children, the questionnaire was administered to the mother or primary caretaker of the child.
The women's questionnaire includes woman's background, access to mass media and use of information/communication technology, desire for last birth, illness symptoms, maternal and newborn health, contraception, unmet need, attitudes towards domestic violence, marriage/union, sexual behavior, HIV/AIDS.
The children's questionnaire includes child's age, birth registration, early childhood development, breastfeeding, care of illness, malaria (in Brokopondo and Sipaliwini only), Immunization (Yellow Fever in Brokopondo and Sipaliwini only) and anthropometry.
In addition to the administration of questionnaires, fieldwork teams observed the place for handwashing and measured the weights and heights of children age under 5 years. Details and findings of these measurements are provided in the respective sections of the report.
The questionnaires included very few non‐standard MICS questions, such as on women’s ownership and use of cell phones, as well as a further question to mothers of children under 5 whose child’s birth had not been registered.
It should be noted that the Malaria related modules and questions were only administered in Brokopondo and Sipaliwini. The same approach was used on vaccination against Yellow Fever.
General Bureau of Statistics
Data were entered using the CSPro software. The data were entered on 6 microcomputers and carried out by 15 data entry operators on a shift system basis and one data entry supervisor. In order to ensure quality control, all questionnaires were double entered and internal consistency checks were performed. Procedures and standard programs developed under the global MICS4 programme and adapted to the Suriname questionnaire were used throughout. Data processing began simultaneously with data collection in July 2010 and was completed early January 2011. Data were analysed using the Statistical Package for Social Sciences (SPSS) software program and the model tabulation syntax developed by UNICEF facilitated the generation of the estimates.
The sample of respondents selected in the Suriname Multiple Indicator Cluster Survey is only one of the samples that could have been selected from the same population, using the same design and 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 the estimates from all possible samples. The extent of variability is not known exactly, but can be estimated statistically from the survey data.
The following sampling error measures are presented in this appendix for each of the selected indicators:
- Standard error (se): Sampling errors are usually measured in terms of standard errors for particular indicators (means, proportions, etc.). Standard error is the square root of the variance of the estimate. The Taylor linearization method is used for the estimation of standard errors.
- Coefficient of variation (se/r) is the ratio of the standard error to the value of the indicator, and is a measure of the relative sampling error.
- Design effect (deff) is the ratio of the actual variance of an indicator, under the sampling method used in the survey, to the variance calculated under the assumption of simple random sampling. The square root of the design effect (deft) is used to show the efficiency of the sample design in relation to the precision. A deft value of 1.0 indicates that the sample design is as efficient as a simple random sample, while a deft value above 1.0 indicates the increase in the standard error due to the use of a more complex sample design.
- Confidence limits are calculated to show the interval within which the true value for the population can be reasonably assumed to fall, with a specified level of confidence. For any given statistic calculated from the survey, the value of that statistic will fall within a range of plus or minus two times the standard error (r + 2.se or r – 2.se) of the statistic in 95 percent of all possible samples of identical size and design.
For the calculation of sampling errors from MICS data, SPSS Version 18 Complex Samples module has been used. The results are shown in the tables that follow. In addition to the sampling error measures described above, the tables also include weighted and unweighted counts of denominators for each indicator.
Sampling errors are calculated for indicators of primary interest, for the national level, for the districts, and for urban, rural coastal, rural interior, and total rural areas. One of the selected indicators is based on households, 6 are based on household members, 19 are based on women, and 17 are based on children under 5. All indicators presented here are in the form of proportions.
Note that indicators related to malaria modules are only included in the tables for rural interior and for the two districts of Brokopondo and Sipaliwini.
Other forms of data appraisal
A series of data quality tables are available to review the quality of the data and include the following:
- Age distribution of the household population
- Age distribution of eligible and interviewed women
- Age distribution of under-5s in household and under 5 questionnaires
- Women’s completion rates by socio-economic characteristics of households
- Completion rates for under-5 questionnaires by socio-economic characteristics of households
- Completeness of reporting
- Completeness of information for anthropometric indicators
- Heaping in anthropometric measurements
- Observation of bednets and places for hand washing
- Observation of women's health cards
- Observation of under-5s birth certificates
- Observation of vaccination cards
- Presence of mother in the household and the person interviewed for the under-5 questionnaire
- Selection of children age 2–14 years for the child discipline module
- School attendance by single age
The results of each of these data quality tables are shown in appendix D in document "Suriname Multiple Indicator Cluster Survey 2010 - Final Report" pp.205-217.
Users of the data agree to keep confidential all data contained in these datasets and to make no attempt to identify, trace or contact any individual whose data is included in these datasets.
Survey datasets are distributed at no cost for legitimate research, with the condition that we receive a description of the objectives of any research project that will be using the data prior to authorizing their distribution.
Use of the dataset must be acknowledged using a citation which would include:
- the Identification of the Primary Investigator
- the title of the survey (including country, acronym and year of implementation)
- the survey reference number
- the source and date of download.
Ministry of Social Affairs and Housing, General Bureau of Statistics, Institute for Social Research of University of Suriname, and United Nations Children’s Fund. Suriname Multiple Indicator Cluster Survey (MICS) 2010, Ref. SUR_2010_MICS_v01_M. Dataset downloaded from [url] on [date].
Disclaimer and copyrights
The user of the data acknowledges that the original collector of the data, the authorized distributor of the data, and the relevant funding agency bear no responsibility for use of the data or for interpretations or inferences based upon such uses.