The Benefits and Challenges of Model Assisted Survey Sampling for Data Quality and Efficiency
What is Model Assisted Survey Sampling and Why You Need It
If you are involved in any kind of research or data collection that requires surveying a population, you might have heard of the term "model assisted survey sampling". But what does it mean and why should you care?
model assisted survey sampling pdf 14
Model assisted survey sampling is a method of using statistical models to improve the quality and efficiency of survey sampling. It involves combining information from sample data and auxiliary data (such as census data, administrative data, satellite data, etc.) to estimate population parameters (such as means, totals, proportions, etc.) with higher accuracy and lower costs than traditional survey methods.
Model assisted survey sampling has many benefits for researchers and data users. Some of them are:
It can reduce the sample size needed to achieve a given level of precision, which can save time and money.
It can increase the coverage and representativeness of the sample, which can reduce bias and improve validity.
It can handle complex survey designs and nonresponse issues, which can enhance reliability and comparability.
It can provide more detailed and disaggregated estimates for subpopulations or small areas, which can support policy making and decision making.
In this article, we will explain how model assisted survey sampling works, what types of models are used in survey sampling, what are the advantages and challenges of model assisted survey sampling, and how you can learn more about it. By the end of this article, you will have a better understanding of this powerful technique and how it can help you with your research or data needs.
How Model Assisted Survey Sampling Works
The basic idea behind model assisted survey sampling is to use a statistical model to relate the variable of interest (such as income, health status, education level, etc.) to some auxiliary variables (such as age, gender, location, etc.) that are available for both the sample and the population. The model can then be used to predict the variable of interest for the population units that are not in the sample, and to adjust the sample estimates to account for sampling errors, nonresponse, or other sources of uncertainty.
The basic steps of model assisted survey sampling are:
Select a sample from the population using a probability-based sampling design, such as simple random sampling, stratified sampling, cluster sampling, etc.
Collect data on the variable of interest and the auxiliary variables for the sample units, using a survey questionnaire, an interview, an observation, etc.
Obtain data on the auxiliary variables for the population units, using a census, an administrative database, a satellite image, etc.
Fit a statistical model to the sample data, using a regression method, a calibration method, an imputation method, etc.
Use the model to estimate the variable of interest for the population units that are not in the sample, and to adjust the sample estimates to match the population totals or other constraints.
Calculate the standard errors or confidence intervals for the estimates, using a variance estimation method, such as linearization, bootstrap, jackknife, etc.
The choice of the sampling design, the auxiliary variables, the statistical model, and the variance estimation method depends on the research question, the data availability, and the assumptions and objectives of the analysis. In the next section, we will discuss some of the common types of models used in survey sampling and how they differ from each other.
Types of Models Used in Survey Sampling
There are many types of models that can be used in survey sampling, depending on the purpose and nature of the estimation problem. Some of the most widely used types are:
Regression models
Calibration models
Imputation models
We will briefly describe each of these types and give some examples of how they can be applied in survey sampling.
Regression Models
Regression models are models that express the relationship between a dependent variable (the variable of interest) and one or more independent variables (the auxiliary variables) using a mathematical function. For example, a linear regression model assumes that the dependent variable is a linear function of the independent variables plus an error term. A logistic regression model assumes that the dependent variable is a binary outcome (such as yes/no) that follows a logistic function of the independent variables plus an error term.
Regression models can be used to estimate population parameters from sample data by fitting the model to the sample data and then applying it to the population data. For example, if we want to estimate the average income of a population based on a sample survey, we can fit a linear regression model that relates income to age, gender, education level, and other auxiliary variables that are available for both the sample and the population. We can then use this model to predict income for each unit in the population and calculate the average income as well as its standard error.
Calibration Models
Calibration models are models that adjust the sample weights to make them consistent with some known population totals or other constraints. For example, if we know that 50% of the population is female and 20% is urban, we can adjust the sample weights so that they also add up to 50% for females and 20% for urban units. This can reduce bias and improve precision by making sure that the sample is representative of the population.
Calibration models can be used to estimate population parameters from sample data by applying a calibration function to the original sample weights and then using these calibrated weights to calculate weighted estimates. For example, if we want to estimate the proportion of people who are satisfied with their health care based on a sample survey, we can apply a calibration function that adjusts the sample weights according to age group, gender group, and region group totals that are available from a census or another source. We can then use these calibrated weights to calculate the proportion of satisfied people as well as its standard error.
Imputation Models
Imputation models are models that fill in missing data or correct errors in data using information from other sources. For example, if some respondents did not answer a question in a survey or gave an invalid answer, we can impute their missing or erroneous values using information from other respondents who have similar characteristics or from other variables that are related to the question.
Imputation models can be used to estimate population parameters from incomplete or inaccurate data by applying an imputation method to fill in or correct the data and then using these imputed data to calculate estimates. For example, estimate the average number of children per household based on a sample survey, we can apply an imputation method that fills in the missing or erroneous values of the number of children variable using information from other variables, such as household size, marital status, age group, etc. We can then use these imputed data to calculate the average number of children as well as its standard error.
Advantages and Challenges of Model Assisted Survey Sampling
Model assisted survey sampling has many advantages over traditional survey methods that do not use models. Some of the main advantages are:
It can improve the accuracy and precision of the estimates by reducing sampling errors and nonsampling errors.
It can reduce the cost and burden of data collection by allowing smaller sample sizes or simpler survey designs.
It can increase the scope and detail of the estimates by providing more information for subpopulations or small areas.
It can enhance the comparability and consistency of the estimates by using standardized and harmonized methods and data sources.
However, model assisted survey sampling also has some challenges and limitations that need to be considered. Some of the main challenges are:
It requires more technical skills and expertise to design, implement, and evaluate model assisted survey sampling methods.
It depends on the availability and quality of auxiliary data sources that are compatible and reliable for the estimation problem.
It involves more assumptions and uncertainties that need to be checked and validated for the model to be appropriate and robust.
It may raise some ethical and legal issues regarding data privacy and confidentiality when using external data sources or sharing data outputs.
Therefore, model assisted survey sampling should be used with caution and care, taking into account the specific context and objectives of each estimation problem. It is not a one-size-fits-all solution, but rather a flexible and powerful tool that can be adapted and customized to different situations and needs.
Examples of Model Assisted Survey Sampling Applications
To illustrate how model assisted survey sampling can be applied in practice, we will provide some examples of how it has been used in different domains and fields. These examples are not exhaustive, but rather indicative of the diversity and potential of model assisted survey sampling methods.
Agriculture
One of the domains where model assisted survey sampling has been widely used is agriculture. Estimating crop yields or land use is a common challenge for agricultural statistics, as it requires collecting data from large and heterogeneous areas that are often difficult to access or measure. Model assisted survey sampling can help to overcome this challenge by using auxiliary data from satellite images, remote sensing, geographic information systems (GIS), or other sources to improve the sampling design, the estimation method, or both.
For example, one study used model assisted survey sampling to estimate rice yield in Thailand using a combination of ground survey data and satellite imagery data. The study used a stratified two-stage cluster sampling design to select a sample of rice fields from each province. The ground survey data collected information on rice area, yield, and other variables for each sampled field. The satellite imagery data provided information on normalized difference vegetation index (NDVI), which is a measure of greenness and biomass, for each field. The study fitted a linear regression model that related rice yield to NDVI and other auxiliary variables, such as soil type, irrigation type, etc. The study then used this model to predict rice yield for each field in the population and calculate the provincial and national estimates as well as their standard errors. The study found that using model assisted survey sampling reduced the relative standard error of the national estimate from 9.8% to 6.4%, compared to using traditional survey methods without models.
Health
Another domain where model assisted survey sampling has been widely used is health. Estimating disease prevalence or mortality rates is a common challenge for health statistics, as it requires collecting data from large and diverse populations that are often hard to reach or identify. Model assisted survey sampling can help to overcome this challenge by using auxiliary data from administrative records, health registers, census data, or other sources to improve the sampling design, the estimation method, or both.
For example, one study used model assisted survey sampling to estimate HIV prevalence in South Africa using a combination of household survey data and antenatal clinic data. The study used a stratified multistage cluster sampling design to select a sample of households from each province. The household survey data collected information on HIV status, demographic characteristics, and other variables for each sampled individual. The antenatal clinic data provided information on HIV status and other variables for pregnant women who attended public health facilities. The study fitted a logistic regression model that related HIV status to age, sex, province, and other auxiliary variables, using both the household survey data and the antenatal clinic data. The study then used this model to estimate HIV prevalence for each age-sex-province group in the population and calculate the national estimate as well as its standard error. The study found that using model assisted survey sampling increased the accuracy and precision of the national estimate, compared to using traditional survey methods without models.
Education
A third domain where model assisted survey sampling has been widely used is education. Measuring student achievement or school quality is a common challenge for education statistics, as it requires collecting data from large and complex populations that are often subject to nonresponse or measurement errors. Model assisted survey sampling can help to overcome this challenge by using auxiliary data from school records, administrative data, census data, or other sources to improve the sampling design, the estimation method, or both.
For example, one study used model assisted survey sampling to measure mathematics achievement in Chile using a combination of student test data and school census data. The study used a stratified two-stage cluster sampling design to select a sample of schools and students from each region. The student test data collected information on mathematics scores and other variables for each sampled student. The school census data provided information on school characteristics and other variables for each school in the population. The study fitted a multilevel regression model that related mathematics scores to student characteristics, school characteristics, and other auxiliary variables, using both the student test data and the school census data. The study then used this model to estimate mathematics scores for each school in the population and calculate the regional and national estimates as well as their standard errors. The study found that using model assisted survey sampling reduced the sampling variance and improved the comparability of the estimates, compared to using traditional survey methods without models.
How to Learn More About Model Assisted Survey Sampling
If you are interested in learning more about model assisted survey sampling, there are many resources and references that you can use to deepen your knowledge and skills. Some of them are:
Books and Journals
There are many books and journals that cover model assisted survey sampling in depth, from theoretical foundations to practical applications. Some of them are:
Model Assisted Survey Sampling by Carl-Erik Särndal, Bengt Swensson, and Jan Wretman. This is a classic book that provides a comprehensive and rigorous introduction to model assisted survey sampling methods and theory.
Survey Sampling Theory and Applications by Raghunath Arnab. This is a modern book that provides a concise and accessible overview of survey sampling methods and applications, including model assisted survey sampling.
Survey Methodology by Robert M. Groves et al. This is a popular book that provides a practical and comprehensive guide to designing, conducting, and analyzing surveys, including model assisted survey sampling.
Journal of Official Statistics. This is a peer-reviewed journal that publishes articles on all aspects of official statistics, including model assisted survey sampling.
Survey Methodology. This is a peer-reviewed journal that publishes articles on all aspects of survey methodology, including model assisted survey sampling.
Journal of Survey Statistics and Methodology. This is a peer-reviewed journal that publishes articles on all aspects of survey statistics and methodology, including model assisted survey sampling.
Online Courses and Tutorials
There are also many online courses and tutorials that teach model assisted survey sampling in a practical way, using examples and exercises. Some of them are:
Survey Methodology. This is an online course offered by the University of Michigan that covers the basics of survey design, implementation, analysis, and reporting, including model assisted survey sampling.
Statistical Analysis of Survey Data. This is an online course offered by the University of Maryland that covers the basics of statistical analysis of survey data, including model assisted survey sampling.
YouTube videos that explain model assisted survey sampling methods and applications, using R and SAS software.
Model Assisted Survey Sampling with R. This is a blog post that shows how to use R to perform model assisted survey sampling analysis and estimation.
Model Assisted Survey Estimation with SAS. This is a white paper that shows how to use SAS to perform model assisted survey sampling analysis and estimation.
Software and Tools
Finally, there are also many software and tools that can help with model assisted survey sampling analysis and implementation. Some of them are:
R. This is a free and open source software for statistical computing and graphics that has many packages and functions for model assisted survey sampling, such as sampling, survey, VIM, etc.
SAS. This is a commercial software for data analysis and business intelligence that has many procedures and macros for model assisted survey sampling, such as SURVEYSELECT, SURVEYREG, SURVEYMEANS, etc.
Stata. This is a commercial software for data analysis and statistics that has many commands and features for model assisted survey sampling, such as svy, mimix, mipolate, etc.
SPSS. This is a commercial software for data analysis and social sciences that has some options and functions for model assisted survey sampling, such as weighting, imputation,