Methodology and Quality Update

Latest Update on Methodology and Quality

08/12/2025

Statistical Presentation

Data description

In line with the General Authority for Statistics’ commitment to supporting national efforts to achieve the goals of Saudi Vision 2030 and to enhance the sustainability of environmental resources and quality of life, GASTAT issues the Household Environment Statistics publication, which is considered one of the main sources of environmental data at the household level in the Kingdom of Saudi Arabia. This publication aims to provide accurate and comprehensive information on the environmental conditions of households residing in the Kingdom, contributing to monitoring environmental changes and improving related policies and strategies.
The Household Environment Statistics cover the main characteristics as follows: 
•    The main sources of drinking water used by households at households.
•    Availability of water from the main source in households.
•    The extent to which water-saving devices or tools are used in households.
•    Methods of waste disposal in households. 
•    Environmental pollution experienced by households in their dwellings. 
•    Residents' awareness and knowledge of key environmental issues. 
•    Data on the main water source and the drinking water source in the household, in addition to data on the use of soap for handwashing, which is required for calculating one of the indicators of Sustainable Development Goal 6.
Data is also used to estimate:
•    Sustainable Development Goal indicators on drinking water in the household.
•    Sustainable Development Goal indicators on waste, sorting, and disposal methods in the household.

Classifications

The following classifications are applied in the Household Environment Statistics.
Saudi Classification of Specializations and Educational Levels:
An statistical classification based on the International Standard Classification of Education (ISCED_11) and (ISCED_13) for education and training, issued by the United Nations Educational, Scientific and Cultural Organization (UNESCO). It serves as the reference classification for organizing educational programs and qualifications according to their levels and fields of study. It comprehensively covers all educational programs, levels, and methods, spanning from early childhood education to higher education levels.
This classification is used to categorize individuals aged 15 years and older according to their specializations and educational levels.
National Code of Countries and Nationalities (3166 ISO – codes Country):
A statistical classification based on the international standard  (ISO 3166_Country codes), which is a standard issued by the International Organization for Standardization (ISO of the UN), and this classification gives numeric and literal codes for the world’s (248) countries, based on the classification of countries.  
The classification is used in the Household Environment Statistics publication to classify Saudi or non-Saudi individuals .
Metadata is collected through interviews, so that outputs can be produces in accordance with all relevant classifications.
The classifications are available on the GASTAT’s website      Classifications - General Authority for Statistics

Statistical concepts and definitions

Terms and concepts of the Household Environment Statistics:
•    Trees:
This statistical bulletin presents data on the most important types of trees used for this purpose. Trees are a form of plant life on Earth and differ from algae and grasses in their ability to survive for many years. Among the most important types of trees planted for home landscaping in the Kingdom.
•    Palm tree: 
A perennial tree that may be fruit-bearing or non-fruit-bearing. In the context of this bulletin, it refers to palm trees that produce dates. Its average height ranges from 2 to 15 meters.
•    Sidr tree (commonly known as Christ's thorn jujube):
It is a widely spread dense tree with deciduous leaves, a branched trunk, and light brown branches. Its height ranges from 5 to 12 meters.
•    Bougainvillea:
An evergreen climbing plant distinguished by its brightly colored flowers. It is widely used in landscaping and building facades due to its beauty and high tolerance to heat and drought. Its typical height ranges from 3 to 12 meters.
•    Conocarpus (commonly known as Damas): 
An evergreen, fast-growing tree widely used for afforestation and windbreaks. It is characterized by its high tolerance to heat and drought, as well as its dense foliage that provides good shade, making it suitable for gardens and roads. Its typical height ranges from 10 to 20 meters.
•    Nerium (Oleander): 
An evergreen shrub grown for ornamental purposes due to its beautiful and diverse flowers. It is tolerant of heat and drought; however, despite its attractiveness, it is a poisonous plant and should be handled with caution. Its typical height ranges from 2 to 6 meters, and it may reach up to 8 meters.
•    Acacia or Desert Thorn (commonly known as Salam or Samr): 
It is a tall, spiny desert shrub or tree, typically ranging in height from 2 to 6 meters.
•    Indian Jasmine: 
It is a shrub with white flowers, characterized by its strong growth and dark green leaves. The tree can reach a height of between 2 and 8 meters.
•    Moringa: 
"The Moringa tree is characterized by rapid growth, reaching a height of 10 to 12 meters. It is also considered a drought-tolerant species.
•    Acacia glauca:
It is a relatively small flowering tree with bright yellow flowers and compound green leaves. It is suitable for hot and semi-arid environments and reaches a height of 5 to 10 meters.
•    Grey Water:  
Greywater is water resulting from household uses, excluding toilet water. It includes water from sinks, bathrooms, and showers. It is considered a water resource that can be treated and reused for irrigation or non-potable purposes.
•    Piped water (networked water):
It is also called "household plumbing," and it refers to the pipes that deliver water to one or more faucets inside the house.
•    Automatic irrigation systems: 
They are systems that operate irrigation automatically—starting and stopping it without manual intervention—by using sensors and software that control the amount of water and the irrigation time. To conserve water, improve its use efficiency, and ensure that plants are irrigated regularly and in a manner that meets their needs.
•    Safety and security measures in the home:
-    Fire extinguishers.
-    Fire alarm systems.
    First aid box.
•    Drinking water source:
It refers to the point from which drinking water is obtained in the household.
•    Source of water used for handwashing and other non-drinking purposes in the household:
It refers to the point from which water used for handwashing and other non-drinking purposes is obtained in the household, and it is classified into:
-    Piped water (networked water):
It is also called "household plumbing," and it refers to the pipes that deliver water to one or more faucets inside the house.
-    Water distribution (tankers): 
They are trucks equipped with tanks that transport large quantities of water for distribution to households. 
-    Bottled water: 
It is water sold in stores in small or large bottles or refillable containers. This does not include water brought from other sources and stored in plastic containers.
-    Covered well: 
It is a well protected from surface runoff water by an internal lining or a cover above ground level, which acts as a barrier to prevent runoff water from mixing with the well water. The well is covered to prevent the entry of contaminants such as bird droppings and small animal waste. Water is extracted from it using pumps or manual lifting tools.
-    Open well: 
A well that lacks an inner lining layer or cover above ground level to protect it from running water and other sources of pollution (including bird and animal droppings).
-    Covered spring: 
Natural springs that are protected by a structure made of bricks, stones, or concrete, with water flowing directly from the spring into a pipe or reservoir without mixing with surface runoff water or sources of contamination.
-    Open spring: 
Natural springs that lack protective enclosures ("boxes") to shield them from runoff water and other sources of contamination (including bird droppings and animal waste).
-    Surface water: 
Open water sources above ground such as rivers, reservoirs, lakes, ponds, streams, canals and irrigation canals.
•    Water availability: 
Availability of water when needed in the household.  
•    Water interruption: 
The unavailability of water when needed in the household. 
•    Water waiting time: 
The duration between a water supply interruption and the time water reaches the household. 
•    Handwashing facility (sink): 
It is the place where hands are washed.
•    Water availability in the handwashing facility: 
Availability of water for handwashing at a designated handwashing facility. 
•    Soap or detergents: 
They are substances used with water to clean the hands from dirt such as dust, soil, and microorganisms that can cause various diseases, such as the common cold. 
•    Sharing the bathroom in the household:
Sharing a bathroom with others who are not household members. 
•    Automatic/Manual flush toilet (Western-style toilet): 
It is a type of sanitation facility that relies on water flushing, either automatically via a tank or manually by pouring water. It is classified among toilets that have a water seal to prevent the passage of insects and odors.
•    Pit toilet with a slab (Arabian toilet):
It is a type of traditional squat toilet, consisting of a pit with a concrete or stone slab, and it is used with a sanitation system.
•    Pit latrine without a slab / Open pit: 
It is a dry sanitation system that uses pits in the ground to collect waste and does not have a slab, platform, or seat. In other words, the toilet consists of a simple pit.
•    No toilet in the open field:
The absence of any sanitation facility or toilet at the place of residence or site, forcing residents to use open spaces or the outdoors for defecation.
•    Type of wastewater disposal: 
All installations and systems used to collect, transport, and deliver liquid sanitary waste to treatment or disposal sites. This includes pipeline networks, collection tanks, inspection chambers, pumping stations, manholes, ventilation valves, and other related components.
•    Public wastewater network
All installations and systems used to collect, transport, and deliver liquid sanitary waste to treatment or disposal sites. This includes pipeline networks, collection tanks, inspection chambers, pumping stations, manholes, ventilation valves, and other related components.
•    Sealed pit (concrete septic tank): 
It is a tank used to store the household’s wastewater, commonly referred to as a “bayarah.” It is used when no suitable sewage system is available. The tank is designed to hold wastewater for 10 to 30 days before it is emptied.
•    Soak pit (traditional bayarah):
An underground covered chamber with permeable walls that allows wastewater to slowly seep into the surrounding soil layers.
•    Emptying a cesspit / septic tank: 
Any suction and removal of wastewater when the tank becomes full, followed by its safe disposal.
•    Water-saving devices: 
Installation of high-efficiency flow devices in bathrooms, toilets, showers, and kitchen drains to reduce water consumption.
•    Water pump: 
A mechanical device used to transfer water from one place to another by increasing the water pressure, allowing it to flow through pipes to the desired location.
•    Automatic washing machine: 
A machine that washes clothes automatically without the need for manual intervention.
•    Automatic dishwasher: 
A machine that washes dishes automatically without the need for manual effort.
•    Drinking water desalination filter:
Filters designed to remove impurities and small contaminants, such as salts and heavy metals, by trapping harmful substances and producing purified drinking water.
•    Cleaning the water tank: 
Cleaning the ground or rooftop water tank during the year.
•    Waste disposal: 
It refers to the disposal of household waste that may include food scraps, paper, plastic, glass, or cardboard from the home. Waste is disposed of through:
•    Public containers outside the home (municipal containers): 
They are containers outside the home (municipal containers): These are containers used for temporarily storing waste and unwanted materials outside the household.
•    Waste sorting 
It is the process of separating waste, where organic waste (such as food scraps) is collected in separate bins for composting, and non-organic waste (such as paper) is placed in separate bins, along with metal and glass waste. This process helps in recycling. 
•    Food waste sorting: 
It is the process of separating food scraps from other types of waste.
•    Triple sorting process: 
It involves separating waste into three categories: food scraps, recyclable materials such as paper, glass, and metals, and other non-recyclable waste.
•    Public containers (municipal containers): 
Containers used to temporarily store waste and unwanted materials outside the household.
•    Visual pollution: 
It results from unwanted sounds that can cause disturbance and discomfort to living organisms in general, and to humans in particular. 
•    Noise pollution: 
It is caused by unwanted sounds that may lead to disturbance and discomfort for living beings in general, and particularly for humans.
•    Light pollution: 
It is the excessive use of artificial lights that alters the natural lighting of the environment, affecting human health and safety, wildlife, plant growth, increasing energy consumption, and disrupting ecosystems.
•    Air pollution: 
It is the presence of solid, liquid, or gaseous substances in the air in quantities that cause physiological, economic, and ecological harm to humans, animals, plants, machines, and equipment, or affect the nature of things. 
•    Organic products: 
They are products that are grown or produced without the use of genetically modified hormones or chemicals, such as preservatives and flavorings. Farmers rely on natural fertilizers to strengthen and enhance plant growth, so that the products can be classified as organic foods.
•    Environmental awareness: 
It refers to a general understanding or awareness of the relationship between humans and their natural environment, and the consequences of this relationship (such as: all types of pollution). It involves being conscious of environmental issues, acquiring knowledge, skills, and developing attitudes toward environmental matters.
•    Environmental issues: 
They refer to the harmful impacts of human activities on the environment. Environmental conservation practices aim to protect the natural environment at the individual, organizational, or governmental levels, for the benefit of both the environment and humans. Environmental concepts and issues are addressed through advocacy, education, and various activities.
•    Air pollution: 
It refers to the presence of harmful substances or elements in the air surrounding the home, which are detrimental to human health and the well-being of other living organisms. 
•    Increasing waste quantity: 
The increasing accumulation of waste in the street surrounding the home, which leads to the emission of unpleasant and disturbing odors. 
•    Water scarcity:
The shortage of access to clean, drinkable water or the lack of water supply.
•    Climate change: 
A significant and noticeable long-term change in weather conditions, including temperature, rainfall patterns, snowfall, and winds.
•    Desertification: 
It is the degradation of land in arid, semi-arid, and dry sub-humid areas. It results from human activities and climate change. Desertification does not refer to the expansion of existing deserts. It occurs because the ecosystems of drylands, which cover more than one-third of the Earth's land area, are highly vulnerable to overexploitation and improper land use.

Data sources

First source: Household Environment Statistics: 
It is a household survey in which information is collected by contacting a representative sample of households residing in occupied dwellings across all administrative regions in the Kingdom of Saudi Arabia, and completing an electronic questionnaire that contains a number of questions.  
The main published variables for Household Environment Statistics data are:
•    Source of drinking water used by households.
•    Availability of water from the main source in households.
•    The extent to which water-saving devices or tools are used in households
•    Methods of waste disposal in households. 
•    Environmental pollution experienced by households in their dwellings. 
•    Residents’ awareness and knowledge of major environmental issues.
Second source: Administrative record data from the following entities:
Ministry of Environment, Water and Agriculture – Percentage of population connected to a wastewater treatment system.

Designing the data collection tool

An electronic form (CAPI) was designed to ensure ease of use by field researchers, and the data was collected using a questionnaire prepared and designed by specialists at the General Authority for Statistics. During its design, international recommendations, standards, and definitions were taken into account, and it was also presented to relevant entities to gather their views and observations. The questions were formulated in a specific scientific manner to unify the format of question delivery by researchers.
The questionnaire was programmed, and the necessary tools for conducting computer-assisted personal and telephone interviews (CAPI & CATI) were developed.
Review and Correction Rules:
•     Audit and control rules have been established in the form to ensure that the data collected is consistent, accurate, and logical. These rules are designed to establish a logical relationship between answers and different questions and variables to help the researcher detect any errors directly when filling out the data with the household.
•    To ensure the quality of Household Energy Statistics Survey data, four types of review and correction rules were established, as follows:
•    Automated adjustment rules:

These rules are applied for the automatic calculation of certain fields or automatic adjustment of responses in specific fields to align with some questionnaires, totaling approximately 6 rules.
•    Navigation rules between sections and fields:
Special rules were programmed to regulate automatic navigation between sections and fields, based on the respondent’s input, totaling 37 rules.
•    Error rules:
These are rules that cannot be bypassed during the data entry process. The field researcher must correct the data by referring back to the respondent to verify its accuracy. The total number of these rules exceeds 222.
•    Alert rules (warnings):
These rules are designed to verify the correctness of the data entered by the researcher. The field researcher may override them if the data accuracy is confirmed, with a total of approximately 87 rules.
Percentage of population benefiting from at least basic drinking water services = ​  *100

Percentage of population with access to safely managed drinking water services = 

ercentage of the population using handwashing facilities with soap and water = ​  *100

Percentage of population benefiting from the safe management of basic waste collection services = ​​​​​

Percentage of population benefiting from safely managed limited waste collection services = 

Sections of the questionnaire: 

•    Geographic identification data.
•     Housing and household data.
•    Water and sanitation data.
•    Waste data.
•    General environmental data 
•    Head of household information.
Administrative Household Environment Statistics data:
A data collection tool was designed based on standardized data request tables sent to the relevant data providers, with the aim of obtaining periodic, coordinated, and verified data on household environment statistics indicators.
Household Environment Statistics questionnaire:  

Household Environment Statistics questionnaire:  

Questionnaire test (cognitive test)

Cognitive testing was conducted on a number of the questionnaire’s questions, based on the core pillars of cognitive testing. Several observations were recorded related to the following pillars: wording, comprehension, response options, and the measurement of disclosure feasibility. Accordingly, the final questionnaire was re-engineered.

Statistical population

The statistical population of the Household Environment Statistics Survey consists of all households residing in the Kingdom of Saudi Arabia that occupy conventional dwellings. The existing household frame serves as the primary sampling frame for this survey, as it includes the classification of households by administrative regions, housing characteristics, and other essential basic information required for implementing household surveys.

Sample Design

The sample was designed using a two-stage stratified cluster systematic random sampling method. In the first stage, a random sample of primary sampling units (counting areas) was selected for each stratum of the adopted sampling design. In the second stage, a systematic random sample of 16 households was selected from each chosen primary sampling unit.
Stratification

To increase the efficiency of the sample and enhance its representation of the target population, the primary sampling units in the sample frame were classified into homogeneous strata as follows: In order to obtain more accurate results compared to a simple random sample of the same size, and to ensure a sufficient number of households at publishable levels with acceptable precision, stratification was conducted as follows:
•    Governorates were used as actual strata due to the need to produce survey indicators separately for each governorate.
•    Implicit stratification was applied by ordering the primary sampling units within each governorate based on demographic indicators and housing condition indicators, which were combined into a single variable using factor analysis during the design of the Labor Force Survey sample.
Allocating the sample across strata:
Four approaches can be considered for allocating the total sample to the strata: (1) Equal allocation, (2) proportional allocation, (3) Neyman allocation, and (4) exponential allocation.
First approach – Equal allocation :
Under this approach, the required coefficients of variation for regional (governorate-level) estimates will be achieved. However, the variation between sample weights will be large, which would lead to substantial variances in the estimates at the administrative region level and at the national level.  The following is the equal allocation formula:

Where:
•    :Represents the total sample size in the study domain or administrative region
•    :: Represents the sample size allocated to the stratumh
•    : Represents the number of strata in the study domain 
Second approach – Proportional allocation :
In this approach, the sample is allocated to the regions based on the proportion of the population they represent. Although this approach would be optimal for administrative region estimates and national estimates, the estimates for smaller governorates (strata) would have large variances. And their sample sizes would be too small to publish any data for them. 

Where:
•     : Represents the total sample size in the study domain or administrative region
•     : Represents the sample size allocated to the stratum h
•     : Represents the size of stratumh in the frame 
Third approach – Optimal allocation (Neyman Allocation)
In this approach, the sample is allocated to the strata based on both the proportion of the population they represent and the amount of internal variability within them. Thus, a stratum with a larger population and lower variability receives a larger share of the sample, as shown in the formula below.  This approach was also not used for the same reason the second approach was not adopted, as it does not guarantee obtaining a sufficient sample size for publication in the smaller strata, especially when their variability is low.

Where:
•     : Represents the total sample size in the study domain or administrative region
•     : Represents the sample size allocated to the stratumh
•      : Represents the size of stratumh in the frame
•     : Represents the standard deviation of one of the study indicators for the stratum h  
Fourth approach – Power allocation
Power allocation aims to strike a balance between producing reliable regional estimates at the governorate level (first approach) and reliable national-level estimates (second approach). Several procedures are available to achieve this compromise. The actual procedure to be applied depends on the specific objectives of the survey. The simplest and most commonly used allocation method is the so-called “square root allocation.” Under this allocation, the sample is allocated to the regions in proportion to the square root of the population size (or any other measure of size) in the strata. Under the square root allocation, the sample is reallocated from the larger regions to the smaller ones compared to what would have occurred under proportional allocation. A more general form is the “power allocation,” discussed by Bankier (1988), under which the sample is allocated in proportion to represents the measure of size and the parameter 𝛼α can take values between zero and one  x^λ x   λ   The value 𝛼=0.5 α=0.5 corresponds to the square root allocation, while the extreme values of α yield equal allocation and proportional allocation. In other words, 𝛼=0
α=0 corresponds to the first approach, which is equal allocation, while 𝛼=1α=1 corresponds to the second approach, which is proportional allocation 
Since we were interested in both national-level estimates and estimates for each of the 150 governorates (strata), power allocation (𝜆=0.8λ=0.8) was used to allocate the sample across the governorates.  It was implemented using the number of housing units from the second stage of the census as the measure of size (MOS) (see Table M2 in the annex).  Table M1 in the annex shows the sample size of counting areas and households in each stratum (governorate) based on this allocation. The following is the power allocation formula:

Where:
•      :Represents the total sample size in the study domain or administrative region
 •    : Represents the sample size allocated to the stratumh
•   : Represents the size of stratumh in the frame
Design Effect and Determination of Cluster Size:  
The design effect is defined as the ratio of the variance of a survey estimate under the current sample design to the variance of the same estimate under a simple random sample design, as shown in the following formula:

There are two key factors that increase the value of the design effect:
•    Use of clustering
•    Increase in the variability of sampling weights
Clustering
Although the use of cluster sampling increases the design effect—and therefore increases the variance of survey estimates compared to a simple random sample of the same size—most household surveys rely on cluster sampling for the following reasons:

•    Cluster sampling reduces the cost of conducting personal interviews.
•    The geographic grouping of clusters enables better quality control of field operations, which improves data quality
The design effect due to clustering is calculated using the following formula:

Where:
•   Ρ : It is the intracluster correlation coefficient (or homogeneity rate) for the specified variable.
•  وm : It is the average number of households to be selected in each primary sampling unit (i.e., the cluster size).
In other words, the design effect is the factor by which the variance increases due to clustering. It should be noted that there are two levels of clustering:
•    Clustering resulting from grouping households within the counting area.
•    The clusters are the result of people gathering within the household.
In the Household Environment Survey, only the first type of clustering is present, resulting from grouping households within counting areas, since the household is the reporting unit. Therefore, the intracluster correlation coefficient (or homogeneity rate), 𝜌, is defined as follows:

Where:
BSS: It is the between–primary sampling unit (PSU) variance (BSS), and the within–primary sampling unit (PSU) variance (WSS) represents the variance among households within a PSU. From equation (7), it can be observed that the homogeneity rate will be high when households within a primary sampling unit are similar, that is, when the WSS is small compared to the BSS.
It can be observed from the above equation that the design effect (deff) is positively associated with the cluster size (m), and it is also influenced by the homogeneity coefficient (ρ).  The following is a comparison of the design effect when the homogeneity coefficient is low (0.05) and when it is higher (0.20). It should be noted that a homogeneity coefficient value of 0.20 is common for socioeconomic characteristics, such as household income level.  A response rate of 50% will be assumed, and three options for cluster size will be compared: 12، 16، 20

Table 2: Distribution of the sample at the level of administrative regions:

Number of households selected in the cluster	Homogeneity coefficient	Number of households selected in the cluster	Design effect
12	0.05	6	1.25
16	0.05	8	1.35
20	0.05	10	1.45
12	0.20	6	2
16	0.20	8	2.4
20	0.20	10	2.8

Based on the inputs in Table 2 above, the cluster size was set at 16 households, with an expected average of 8 responding households. This results in a design effect due to clustering ranging between 1.35 and 2.4.  Depending on the value of the homogeneity coefficient 
𝜌.  It should be noted that the design effect values presented here are used only to compare the two candidate cluster sizes and do not represent the final design effect values. This is because other factors—such as the variability of sampling weights and the homogeneity between strata—also contribute to the final design effect. The final value can be calculated after the survey data are collected.  This value was calculated at the administrative region level using data from the previous survey cycle (see Table 3 below).
Variability in weights
The second key factor influencing the design effect is the variability in weights. Therefore, efforts were made to minimize the variability in weights as much as possible by allocating the sample to the strata in a manner that approximates proportional allocation to size, which ensures the lowest possible variability in weights.  However, proportional allocation would result in a low and insufficient sample size in the smaller strata. Therefore, power allocation was used with a value of  This allocation produced the weights shown in Table M3 in the annex. The coefficient of variation for these weights was 0.81, resulting in a design effect related to weight variability equal to 1.65, as illustrated in the following formula: 

Calculation of sample size
The estimated sample size for each administrative region (study domain) was calculated using equation (9) below. The estimated sample size for each administrative region was then distributed across the strata (governorates) within that region using augmented power allocation with a power parameter λ. The calculated sample size for small governorates was increased so that it was not less than five counting areas, corresponding to 80 households, with the exception of one governorate (Al-‘Udayd, code 511), where all counting areas, totaling three, were selected.
Parameters used in estimating the sample size:
The sample size was calculated using the following parameters and specifications:
•    Estimates derived from the sample have a specified level of precision and a specified coefficient of variation (CV). The allowable coefficient of variation used in calculating the sample size was set to less than 1% at the national level, 2% at the administrative region level, and 10% at the governorate level. 
•    The design effect used at the administrative region level ranged between 0.5 and 3.05.
•    The expected response rate at the administrative region level ranged between 42% and 48%.
•    A confidence level of 𝛼
α was used in estimating the mean(1-α)=0.95.
•    The design effect, response rate, means, and standard deviations for the variables “percentage of households experiencing visual pollution” and “percentage of households using water-saving devices” were calculated using data from the previous survey cycle (2022).
Sample size estimation formula
 The sample size for each stratum ℎ
 (study domain) was calculated as follows:

Whereas:
•     :  It is the sample size for each stratum  (study domain).  
•     : Estimated design effect for each stratum   (study domain).
•     : Estimated response rate for each stratum  (study domain).
•     : : Allowed relative error in estimating the indicator for each stratum  (study domain). It should be noted that the allowable error is calculated by multiplying the relative error 
RE by the value of the proportion to be estimated from the survey 𝑃.

•    : Percentage of households experiencing visual pollution in stratum ℎ (study domain)
•    : It is the confidence level coefficient for the mentioned proportion in stratum ℎ (study domain).   ​​​​ 
The following are the parameter values used in calculating the sample size:
Table 3: Parameter values used in calculating the sample size at the administrative region level:

Statistical unit (sampling unit)

The statistical unit in the Household Environment Statistics Survey is the household.

Data collection

Data collection from the survey:
Data for the Household Environment Statistics Survey are collected through computer-assisted telephone interviews (CATI) and computer-assisted personal interviews (CAPI).
As well as administrative data from the Ministry of Environment, Water and Agriculture, which include:
Percentage of population connected to a wastewater treatment system. 

Data collection frequency 

The data collection process for Household Environment Statistics is carried out annually.

Reference area

Household Environment Statistics cover 13 administrative regions in the Kingdom of Saudi Arabia.

Reference period (time reference)

References period to the variables or dataset as following:
•    The identifying household data, housing characteristics, and household information are referenced to the date of the household visit.  
•    The data related to water, sanitation, waste, and environmental data are assigned to the year 2024.

Base period

Not applicable.

Measurement unit

All results are reported as percentage (such as: The relative distribution of the main source of drinking water in households at the level of the Kingdom of Saudi Arabia.

Time coverage

The data is available from the year 2018 to 2024.

Publication frequency

 The results of the Household Environment Statistics are published annually according to the approved statistical plan.

Statistical processing

Error detection

Meticulous processes were implemented to detect errors in the data collected, using automated and manual methods aimed at ensuring quality and accuracy.
These included the following:
•    Identifying illogical, out-of-range, or contradictory data.
•    Detecting missing or incomplete data and handling them according to established policies.
•    Reviewing internal consistency among questionnaire responses.
•    Reviewing and matching data to ensure their accuracy and precision in a manner suitable to their nature, to enhance the quality and accuracy of the statistics presented.
•    Comparing the current publication’s data with the previous year’s data to ensure their integrity and consistency in preparation for data processing, result extraction, and review.
•    Data processing and tabulation to verify accuracy.
All outputs are stored and uploaded to the database after being calculated by GASTAT, to be reviewed and processed by specialists in the Environment and Natural Resources Statistics Department using modern technologies and software designed for this purpose.

Data integration and matching from multiple sources 

Household Environment Statistics rely on two main sources:
•    Administrative record data from relevant entities.
•    Statistical survey data.
The process of data matching and consistency verification is carried out through several steps:
•    Checking for duplication or variation in values.
•    Comparing the common variables.
•    Resolving discrepancies by giving priority to the most accurate and comprehensive data.
This procedure aims to ensure the reliability and accuracy of the final data used in preparing the statistical publication, providing a clear and unified picture of the extracted environmental data.

Imputation and calibration

Sample weights
Sampling weights are factors used in analyzing data collected from a sample rather than from the entire population. Their purpose is to correct for biases resulting from differences in selection probabilities among households in the sample. This helps ensure that the analysis results are more accurately representative of the population.
Main uses of sampling weights:
Bias correction: Adjusting for biases resulting from unequal selection probabilities among members of the population.
Population representation: Ensuring that the results derived from the sample accurately reflect the true characteristics of the population.

How to calculate sampling weights:
Design weight: Reflects the probability of selecting each household in the sample. It is calculated as the inverse of the selection probability as follows:
 iIf the selection probability for a household from stratum   is  :
, then the weight of the sampled household is calculated as follows: :