Demographic and Health Survey 2019-2020
Liberia, 2019 - 2020
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Reference ID
LBR_2019_DHS_v01_M
Producer(s)
Liberia Institute of Statistics and Geo-Information Services (LISGIS)
Collection(s)
Metadata
Created on
Apr 15, 2021
Last modified
Apr 15, 2021
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Identification
Survey ID Number
LBR_2019_DHS_v01_M
Title
Demographic and Health Survey 2019-2020
Country/Economy
| Name | Country code |
|---|---|
| Liberia | LBR |
Study type
Demographic and Health Survey [hh/dhs]
Series Information
The 2019-20 Liberia Demographic and Health Survey (LDHS) is the fifth Demographic and Health Survey to be conducted in Liberia. Previous surveys were conducted in 1986, 1999/2000, 2007, and 2013.
Abstract
The 2019-20 Liberia Demographic and Health Survey (2019-20 LDHS) is a nationwide survey with a nationally representative sample of residential households. All women age 15-49 who are usual members of the selected households or who spent the night before the survey in the selected households were eligible for individual interviews. The primary objective of the 2019-20 LDHS is to provide up-to-date estimates of key demographic and health indicators necessary for program managers, policymakers, and implementers to monitor and evaluate the impact of existing policies and programs and to design new initiatives for health policies in Liberia. This survey is considered a key resource for the new sixth National Health Strategic Plan (NHSP) 2017-2021.
Specifically, the main objectives of the survey are:
- To collect high-quality data on fertility levels and preferences; contraceptive use; maternal and child health; neonatal, infant, and child mortality levels; maternal mortality; and other health issues relevant to the achievement of the Sustainable Development Goals (SDGs) (e.g., gender, nutrition, awareness regarding HIV/AIDS)
- To provide information on availability of, access to, and use of mosquito nets as part of national malaria control programs
- To assess protection of children from violence and exploitation
- To provide information on other health issues, such as tobacco use, tuberculosis, and health insurance
- To obtain data on women’s empowerment, domestic violence, and female genital cutting
- To test household salt for the presence of iodine
- To obtain data on child feeding practices, including breastfeeding, and collect anthropometric measures to assess the nutritional status of children under age 5 and women age 15-49
- To conduct anemia testing of women age 15-49 and children age 6-59 months
- To measure HIV prevalence levels among men age 15-59 and women age 15-49
- To measure hepatitis B and C prevalence levels among men age 15-59 and women age 15-49
- To measure the seroprevalence of Ebola virus disease (EVD) antibodies among men age 15-59 and women age 15-49 and collect data on risk factors related to Ebola
Specifically, the main objectives of the survey are:
- To collect high-quality data on fertility levels and preferences; contraceptive use; maternal and child health; neonatal, infant, and child mortality levels; maternal mortality; and other health issues relevant to the achievement of the Sustainable Development Goals (SDGs) (e.g., gender, nutrition, awareness regarding HIV/AIDS)
- To provide information on availability of, access to, and use of mosquito nets as part of national malaria control programs
- To assess protection of children from violence and exploitation
- To provide information on other health issues, such as tobacco use, tuberculosis, and health insurance
- To obtain data on women’s empowerment, domestic violence, and female genital cutting
- To test household salt for the presence of iodine
- To obtain data on child feeding practices, including breastfeeding, and collect anthropometric measures to assess the nutritional status of children under age 5 and women age 15-49
- To conduct anemia testing of women age 15-49 and children age 6-59 months
- To measure HIV prevalence levels among men age 15-59 and women age 15-49
- To measure hepatitis B and C prevalence levels among men age 15-59 and women age 15-49
- To measure the seroprevalence of Ebola virus disease (EVD) antibodies among men age 15-59 and women age 15-49 and collect data on risk factors related to Ebola
Kind of Data
Sample survey data [ssd]
Unit of Analysis
- Household
- Individual
- Children age 0-5
- Woman age 15-49
- Man age 15-59
- Individual
- Children age 0-5
- Woman age 15-49
- Man age 15-59
Version
Version Notes
The data dictionary was generated from hierarchical data that was downloaded from the The DHS Program website (http://dhsprogram.com).
Scope
Notes
The 2019-20 Liberia Demographic and Health Survey covered the following topics:
HOUSEHOLD
• Identification
• Usual members and visitors in the selected households
• Background information on each person listed, such as relationship to head of the household, age, sex, marital status, educational attainment, birth registration, and survivorship and residence of biological parents.
• Child labor, child discipline, and domestic violence
• Characteristics of the household's dwelling unit, such as the source of water, type of toilet facilities, type of fuel used for cooking, number of rooms, ownsership of livestock, possessions of durable goods, mosquito nets, and main material for the floor, roof and walls of the dwelling.
INDIVIDUAL WOMAN
• Identification
• Background characteristics (including age, education, and media exposure)
• Reproduction and child mortality
• Contraception
• Prenatal, delivery, and postnatal care
• Vaccinations and childhood illnesses
• Maternal and child health and nutrition
• Marriage and sexual activity
• Fertility preferences
• Women’s work and husbands’ background characteristics
• Knowledge, awareness, and behavior regarding HIV/AIDS and other sexually transmitted infections (STIs)
• Knowledge, attitudes, and behavior related to other health issues (e.g., injections, smoking, tuberculosis, childhood illnesses, and pregnancy and childbirth)
• Female genital cutting/mutilation
• Experiences during the Ebola outbreak in Liberia
• Adult and maternal mortality
• Domestic violence
INDIVIDUAL MAN
• Identification
• Background characteristics
• Reproduction
• Introduction and Survey Methodology
• Contraception
• Marriage and sexual activity
• Fertility preferences
• Employment and gender roles
• HIV/AIDS
• Experiences during the Ebola outbreak in Liberia
• Other health issues (e.g., injections, smoking, tuberculosis, and health insurance)
BIOMARKER
• Identification
• Weight, height, and hemoglobin measurement for children age 0-5
• Weight, height, hemoglobin measurement and HIV testing for women age 15-49
• HIV testing for men age 15-59
FIELDWORKER
• Background information on each fieldworker
HOUSEHOLD
• Identification
• Usual members and visitors in the selected households
• Background information on each person listed, such as relationship to head of the household, age, sex, marital status, educational attainment, birth registration, and survivorship and residence of biological parents.
• Child labor, child discipline, and domestic violence
• Characteristics of the household's dwelling unit, such as the source of water, type of toilet facilities, type of fuel used for cooking, number of rooms, ownsership of livestock, possessions of durable goods, mosquito nets, and main material for the floor, roof and walls of the dwelling.
INDIVIDUAL WOMAN
• Identification
• Background characteristics (including age, education, and media exposure)
• Reproduction and child mortality
• Contraception
• Prenatal, delivery, and postnatal care
• Vaccinations and childhood illnesses
• Maternal and child health and nutrition
• Marriage and sexual activity
• Fertility preferences
• Women’s work and husbands’ background characteristics
• Knowledge, awareness, and behavior regarding HIV/AIDS and other sexually transmitted infections (STIs)
• Knowledge, attitudes, and behavior related to other health issues (e.g., injections, smoking, tuberculosis, childhood illnesses, and pregnancy and childbirth)
• Female genital cutting/mutilation
• Experiences during the Ebola outbreak in Liberia
• Adult and maternal mortality
• Domestic violence
INDIVIDUAL MAN
• Identification
• Background characteristics
• Reproduction
• Introduction and Survey Methodology
• Contraception
• Marriage and sexual activity
• Fertility preferences
• Employment and gender roles
• HIV/AIDS
• Experiences during the Ebola outbreak in Liberia
• Other health issues (e.g., injections, smoking, tuberculosis, and health insurance)
BIOMARKER
• Identification
• Weight, height, and hemoglobin measurement for children age 0-5
• Weight, height, hemoglobin measurement and HIV testing for women age 15-49
• HIV testing for men age 15-59
FIELDWORKER
• Background information on each fieldworker
Coverage
Geographic Coverage
National coverage
Universe
The survey covered all de jure household members (usual residents), all women aged 15-49, all men age 15-59, and all children aged 0-5 resident in the household.
Producers and sponsors
Primary investigators
| Name | Affiliation |
|---|---|
| Liberia Institute of Statistics and Geo-Information Services (LISGIS) | Government of Liberia |
Producers
| Name | Affiliation | Role |
|---|---|---|
| ICF | The DHS Program | Provided technical assistance |
| Ministry of Health | Government of Liberia | Technical support |
Funding Agency/Sponsor
| Name | Abbreviation | Role |
|---|---|---|
| Government of Liberia | GovLBR | Financial support |
| United States Agency for International Development | USAID | Financial support |
| United States Centers for Disease Control and Prevention | CDC | Financial support |
| United Nations Population Fund | UNFPA | Financial support |
| United Nations Development Programme | UNDP | Financial support |
| United Nations Children’s Fund | UNICEF | Financial support |
| World Health Organization | WHO | Financial support |
| Global Alliance for Vaccine and Immunization | GAVI | Financial support |
| UN Women | Financial support |
Sampling
Sampling Procedure
The sampling frame used for the 2019-20 LDHS is based on the 2008 National Population and Housing Census (NPHC), conducted by the LISGIS. Liberia is divided into 15 counties grouped to form five geographical regions, with each region consisting of three counties. Each county is divided into districts and each district into clans. In the 2008 NPHC, each clan was subdivided into enumeration areas (EAs). An enumeration area is a geographical area assigned to an enumerator for the purpose of conducting a census count; according to the Liberian census frame, each EA consists of an average of 100 households.
The 2019-20 LDHS followed a stratified two-stage cluster design. The first stage involved selecting sample points (clusters) consisting of EAs. EAs were drawn with a probability proportional to their size within each sampling stratum. A total of 325 clusters were selected.
The second stage involved systematic sampling of households. A household listing operation was undertaken in all of the selected clusters. During the listing, an average of 129 households were found in each cluster, from which a fixed number of 30 households were selected with an equal probability systematic selection process; the total sample size was 9,745 households. Results from this sample will be representative at the national, urban (Greater Monrovia and all other urban areas), and rural levels, including each of the five regions. The survey will also produce separate representative results for most key indicators of the 15 counties.
For further details on sample selection, see Appendix A of the final report.
The 2019-20 LDHS followed a stratified two-stage cluster design. The first stage involved selecting sample points (clusters) consisting of EAs. EAs were drawn with a probability proportional to their size within each sampling stratum. A total of 325 clusters were selected.
The second stage involved systematic sampling of households. A household listing operation was undertaken in all of the selected clusters. During the listing, an average of 129 households were found in each cluster, from which a fixed number of 30 households were selected with an equal probability systematic selection process; the total sample size was 9,745 households. Results from this sample will be representative at the national, urban (Greater Monrovia and all other urban areas), and rural levels, including each of the five regions. The survey will also produce separate representative results for most key indicators of the 15 counties.
For further details on sample selection, see Appendix A of the final report.
Response Rate
All 9,745 households in the selected housing units were eligible for the survey, and 9,207 of these households were occupied. Of the occupied households, 9,068 were successfully interviewed, yielding a response rate of 99%. Of the successful household interviews, 5,192 were completed in 2019 and 3,876 in 2020.
In the interviewed households, 8,364 women age 15-49 were identified for individual interviews; 8,065 women were interviewed, yielding a response rate of 96%. A total of 4,527 men were eligible for individual interviews; 4,249 of these men were interviewed, producing a response rate of 94%.
In the interviewed households, 8,364 women age 15-49 were identified for individual interviews; 8,065 women were interviewed, yielding a response rate of 96%. A total of 4,527 men were eligible for individual interviews; 4,249 of these men were interviewed, producing a response rate of 94%.
Weighting
Due to the non-proportional allocation of the sample to different counties and their urban and rural areas and the possible differences in response rates, sampling weights will be required for any analysis using the 2019-20 LDHS data to ensure the actual representativeness of the survey results at the national level as well as the domain level. Since the 2019-20 LDHS sample is a two-stage stratified cluster sample, sampling weights were calculated based on sampling probabilities separately for each sampling stage and for each cluster.
For further details on sampling weights, see Appendix A.4 of the final report.
For further details on sampling weights, see Appendix A.4 of the final report.
Data Collection
Dates of Data Collection
| Start | End |
|---|---|
| 2019-09-16 | 2020-02-12 |
Data Collection Mode
Computer Assisted Personal Interview [capi]
Data Collection Notes
Data collection, which ran from October 16, 2019, to February 12, 2020, was carried out by 17 teams, with each team consisting of six members typically featuring the following composition: one supervisor, three female interviewers, one male interviewer, and two biomarker technicians.
All 17 teams were scheduled to deploy to the field on October 2; after an unanticipated delay, fieldwork began on October 16. The nine follow-up survey teams began fieldwork 2 weeks later. To ensure that all aspects of the survey were still well understood among fieldworkers, a series of refresher training sessions were held on October 7, 13, and 14.
Fieldwork monitoring was an integral part of the LDHS. Coordinators from LISGIS, monitoring assistants (previously training assistants) hired by LISGIS, and USAID Liberia senior staff visited teams regularly to review their work and monitor data quality. LISGIS organized coordinators and two biomarker monitoring assistants to visit teams, resolve any issues that arose in teams accessing clusters, monitor data and biomarker collection and quality, distribute supplies, and collect DBS cards from teams and drop them off at the NRL. Fieldwork monitoring assistants, on the other hand, moved from team to team in the field and closely monitored data collection and data quality, as well as supporting technological and technical aspects of fieldwork. LISGIS IT staff were deployed to teams on an as-needed basis to resolve complex CAPI-related issues, and two biomarker monitors observed biomarker collection over the course of the fieldwork. The DHS Program resident advisor monitored data collection and biomarker collection for the first half of data collection.
Two additional fieldwork monitoring visits by staff from The DHS Program were made from December 11-21 and January 16-31. During field visits, monitors provided the teams they visited with critical feedback to improve their performance. All monitors used the LDHS field-check tables as well as data quality and fieldwork status reports, based on data from completed clusters, to illustrate problems specific to each team visited.
All 17 teams were scheduled to deploy to the field on October 2; after an unanticipated delay, fieldwork began on October 16. The nine follow-up survey teams began fieldwork 2 weeks later. To ensure that all aspects of the survey were still well understood among fieldworkers, a series of refresher training sessions were held on October 7, 13, and 14.
Fieldwork monitoring was an integral part of the LDHS. Coordinators from LISGIS, monitoring assistants (previously training assistants) hired by LISGIS, and USAID Liberia senior staff visited teams regularly to review their work and monitor data quality. LISGIS organized coordinators and two biomarker monitoring assistants to visit teams, resolve any issues that arose in teams accessing clusters, monitor data and biomarker collection and quality, distribute supplies, and collect DBS cards from teams and drop them off at the NRL. Fieldwork monitoring assistants, on the other hand, moved from team to team in the field and closely monitored data collection and data quality, as well as supporting technological and technical aspects of fieldwork. LISGIS IT staff were deployed to teams on an as-needed basis to resolve complex CAPI-related issues, and two biomarker monitors observed biomarker collection over the course of the fieldwork. The DHS Program resident advisor monitored data collection and biomarker collection for the first half of data collection.
Two additional fieldwork monitoring visits by staff from The DHS Program were made from December 11-21 and January 16-31. During field visits, monitors provided the teams they visited with critical feedback to improve their performance. All monitors used the LDHS field-check tables as well as data quality and fieldwork status reports, based on data from completed clusters, to illustrate problems specific to each team visited.
Data Collectors
| Name | Abbreviation | Affiliation |
|---|---|---|
| Liberia Institute of Statistics and Geo-Information Services | LISGIS | Government of Liberia |
Questionnaires
Questionnaires
Seven questionnaires were used for the 2019-20 LDHS: the Household Questionnaire, the Woman’s Questionnaire, the Man’s Questionnaire, the Biomarker Questionnaire Part A, the Biomarker Questionnaire Part B, the Biomarker Revisit Questionnaire, and the Fieldworker Questionnaire. These questionnaires, based on The DHS Program’s standard questionnaires, were adapted to reflect the population and health issues relevant to Liberia. Suggestions were solicited from various stakeholders representing government ministries and agencies, nongovernmental organizations, and international donors. After all questionnaires were finalized in English, they were translated into a form of simple English commonly understood in Liberia.
Data Processing
Data Editing
Data processing for the 2019-20 LDHS began a few days after fieldwork started. As data collection was completed for each cluster, team supervisors transferred all electronic data files to the LISGIS central office in Monrovia via the Internet File Streaming System (IFSS), where they were stored on a password-protected computer. IFSS automatically encrypts the data and sends the data to a server, which in turn downloads the data to the data processing supervisor’s password-protected computer in the central office. These data files were registered and checked for inconsistencies, incompleteness, and outliers. Field supervisors were alerted of and resolved any errors any issues found.
The LISGIS data processing operation also included secondary editing, which required resolution of computeridentified inconsistencies and coding of open-ended questions. The data were processed by the LISGIS data processing manager and two secondary editors who took part in the pretest and main fieldwork training; they were supervised remotely by staff from The DHS Program. Data editing was accomplished using Censuses and Survey Processing (CSPro) software.
Biomarker paper questionnaires were compared with electronic data files to check for any inconsistencies in data entry. Daily generation of check reports in addition to weekly generation of field-check tables allowed for effective monitoring. Specific feedback was given to the teams to improve their performance. Secondary editing and data processing were initiated in October 2019 and completed in March 2020.
The LISGIS data processing operation also included secondary editing, which required resolution of computeridentified inconsistencies and coding of open-ended questions. The data were processed by the LISGIS data processing manager and two secondary editors who took part in the pretest and main fieldwork training; they were supervised remotely by staff from The DHS Program. Data editing was accomplished using Censuses and Survey Processing (CSPro) software.
Biomarker paper questionnaires were compared with electronic data files to check for any inconsistencies in data entry. Daily generation of check reports in addition to weekly generation of field-check tables allowed for effective monitoring. Specific feedback was given to the teams to improve their performance. Secondary editing and data processing were initiated in October 2019 and completed in March 2020.
Data Appraisal
Estimates of Sampling Error
The estimates from a sample survey are affected by two types of errors: nonsampling errors and sampling errors. Nonsampling errors are the results of mistakes made in implementing 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 2019-20 Liberia Demographic and Health Survey (LDHS) to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.
Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2019-20 LDHS 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 among all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.
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% 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 2019-20 LDHS sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed in SAS, using programs developed by ICF. These programs use the Taylor linearization method to estimate variances for survey estimates that are means, proportions, or ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.
Note: A more detailed description of estimates of sampling errors are presented in APPENDIX B of the survey report.
Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2019-20 LDHS 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 among all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.
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% 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 2019-20 LDHS sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed in SAS, using programs developed by ICF. These programs use the Taylor linearization method to estimate variances for survey estimates that are means, proportions, or ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.
Note: A more detailed description of estimates of sampling errors are presented in APPENDIX B of the survey report.
Data Appraisal
Data Quality Tables
- Household age distribution
- Age distribution of eligible and interviewed women
- Age distribution of eligible and interviewed men
- Completeness of reporting
- Births by calendar years
- Reporting of age at death in days
- Reporting of age at death in months
- Standardization exercise results from anthropometry training
- Height and weight data completeness and quality for children
- Height measurements from random subsample of measured children
See details of the data quality tables in Appendix C of the final report.
- Household age distribution
- Age distribution of eligible and interviewed women
- Age distribution of eligible and interviewed men
- Completeness of reporting
- Births by calendar years
- Reporting of age at death in days
- Reporting of age at death in months
- Standardization exercise results from anthropometry training
- Height and weight data completeness and quality for children
- Height measurements from random subsample of measured children
See details of the data quality tables in Appendix C of the final report.
Access policy
Contacts
| Name | Affiliation | |
|---|---|---|
| Information about The DHS Program | The DHS Program | reports@DHSprogram.com |
| General Inquiries | The DHS Program | info@dhsprogram.com |
| Data and Data Related Resources | The DHS Program | archive@dhsprogram.com |
Access conditions
Request Dataset Access
The following applies to DHS, MIS, AIS and SPA survey datasets (Surveys, GPS, and HIV).
To request dataset access, you must first be a registered user of the website. You must then create a new research project request. The request must include a project title and a description of the analysis you propose to perform with the data.
The requested data should only be used for the purpose of the research or study. To request the same or different data for another purpose, a new research project request should be submitted. The DHS Program will normally review all data requests within 24 hours (Monday - Friday) and provide notification if access has been granted or additional project information is needed before access can be granted.
DATASET ACCESS APPROVAL PROCESS
Access to DHS, MIS, AIS and SPA survey datasets (Surveys, HIV, and GPS) is requested and granted by country. This means that when approved, full access is granted to all unrestricted survey datasets for that country. Access to HIV and GIS datasets requires an online acknowledgment of the conditions of use.
Required Information
A dataset request must include contact information, a research project title, and a description of the analysis you propose to perform with the data.
Restricted Datasets
A few datasets are restricted and these are noted. Access to restricted datasets is requested online as with other datasets. An additional consent form is required for some datasets, and the form will be emailed to you upon authorization of your account. For other restricted surveys, permission must be granted by the appropriate implementing organizations, before The DHS Program can grant access. You will be emailed the information for contacting the implementing organizations. A few restricted surveys are authorized directly within The DHS Program, upon receipt of an email request.
When The DHS Program receives authorization from the appropriate organizations, the user will be contacted, and the datasets made available by secure FTP.
GPS/HIV Datasets/Other Biomarkers
Because of the sensitive nature of GPS, HIV and other biomarkers datasets, permission to access these datasets requires that you accept a Terms of Use Statement. After selecting GPS/HIV/Other Biomarkers datasets, the user is presented with a consent form which should be signed electronically by entering the password for the user's account.
Dataset Terms of Use
Once downloaded, the datasets must not be passed on to other researchers without the written consent of The DHS Program. All reports and publications based on the requested data must be sent to The DHS Program Data Archive in a Portable Document Format (pdf) or a printed hard copy.
Download Datasets
Datasets are made available for download by survey. You will be presented with a list of surveys for which you have been granted dataset access. After selecting a survey, a list of all available datasets for that survey will be displayed, including all survey, GPS, and HIV data files. However, only data types for which you have been granted access will be accessible. To download, simply click on the files that you wish to download and a "File Download" prompt will guide you through the remaining steps.
The following applies to DHS, MIS, AIS and SPA survey datasets (Surveys, GPS, and HIV).
To request dataset access, you must first be a registered user of the website. You must then create a new research project request. The request must include a project title and a description of the analysis you propose to perform with the data.
The requested data should only be used for the purpose of the research or study. To request the same or different data for another purpose, a new research project request should be submitted. The DHS Program will normally review all data requests within 24 hours (Monday - Friday) and provide notification if access has been granted or additional project information is needed before access can be granted.
DATASET ACCESS APPROVAL PROCESS
Access to DHS, MIS, AIS and SPA survey datasets (Surveys, HIV, and GPS) is requested and granted by country. This means that when approved, full access is granted to all unrestricted survey datasets for that country. Access to HIV and GIS datasets requires an online acknowledgment of the conditions of use.
Required Information
A dataset request must include contact information, a research project title, and a description of the analysis you propose to perform with the data.
Restricted Datasets
A few datasets are restricted and these are noted. Access to restricted datasets is requested online as with other datasets. An additional consent form is required for some datasets, and the form will be emailed to you upon authorization of your account. For other restricted surveys, permission must be granted by the appropriate implementing organizations, before The DHS Program can grant access. You will be emailed the information for contacting the implementing organizations. A few restricted surveys are authorized directly within The DHS Program, upon receipt of an email request.
When The DHS Program receives authorization from the appropriate organizations, the user will be contacted, and the datasets made available by secure FTP.
GPS/HIV Datasets/Other Biomarkers
Because of the sensitive nature of GPS, HIV and other biomarkers datasets, permission to access these datasets requires that you accept a Terms of Use Statement. After selecting GPS/HIV/Other Biomarkers datasets, the user is presented with a consent form which should be signed electronically by entering the password for the user's account.
Dataset Terms of Use
Once downloaded, the datasets must not be passed on to other researchers without the written consent of The DHS Program. All reports and publications based on the requested data must be sent to The DHS Program Data Archive in a Portable Document Format (pdf) or a printed hard copy.
Download Datasets
Datasets are made available for download by survey. You will be presented with a list of surveys for which you have been granted dataset access. After selecting a survey, a list of all available datasets for that survey will be displayed, including all survey, GPS, and HIV data files. However, only data types for which you have been granted access will be accessible. To download, simply click on the files that you wish to download and a "File Download" prompt will guide you through the remaining steps.
Citation requirements
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
- 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
Access authority
| Name | URL | |
|---|---|---|
| The DHS Program | archive@dhsprogram.com | Link |
Disclaimer and copyrights
Disclaimer
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.
Metadata production
DDI Document ID
DDI_LBR_2019_DHS_v01_M
Producers
| Name | Abbreviation | Affiliation | Role |
|---|---|---|---|
| Development Economics Data Group | DECDG | The World Bank | Documentation of the DDI |
Date of Metadata Production
2021-04-14
DDI Document version
Version 01 (April 2021). Metadata is excerpted from "Liberia Demographic and Health Survey 2019-20" Report.