research articles on sampling techniques

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings
• My Bibliography
• Collections
• Citation manager

Save citation to file

Email citation, add to collections.

• Create a new collection
• Add to an existing collection

Add to My Bibliography

Your saved search, create a file for external citation management software, your rss feed.

• Search in PubMed
• Search in NLM Catalog
• Add to Search

Qualitative Sampling Methods

Affiliation.

• 1 14742 School of Nursing, University of Texas Health Science Center, San Antonio, TX, USA.
• PMID: 32813616
• DOI: 10.1177/0890334420949218

Qualitative sampling methods differ from quantitative sampling methods. It is important that one understands those differences, as well as, appropriate qualitative sampling techniques. Appropriate sampling choices enhance the rigor of qualitative research studies. These types of sampling strategies are presented, along with the pros and cons of each. Sample size and data saturation are discussed.

Keywords: breastfeeding; qualitative methods; sampling; sampling methods.

PubMed Disclaimer

Similar articles

• [Saturation sampling in qualitative health research: theoretical contributions]. Fontanella BJ, Ricas J, Turato ER. Fontanella BJ, et al. Cad Saude Publica. 2008 Jan;24(1):17-27. doi: 10.1590/s0102-311x2008000100003. Cad Saude Publica. 2008. PMID: 18209831 Portuguese.
• (I Can't Get No) Saturation: A simulation and guidelines for sample sizes in qualitative research. van Rijnsoever FJ. van Rijnsoever FJ. PLoS One. 2017 Jul 26;12(7):e0181689. doi: 10.1371/journal.pone.0181689. eCollection 2017. PLoS One. 2017. PMID: 28746358 Free PMC article.
• Enhancing rigor in qualitative description: a case study. Milne J, Oberle K. Milne J, et al. J Wound Ostomy Continence Nurs. 2005 Nov-Dec;32(6):413-20. doi: 10.1097/00152192-200511000-00014. J Wound Ostomy Continence Nurs. 2005. PMID: 16301909 Review.
• Sampling Methods. Berndt AE. Berndt AE. J Hum Lact. 2020 May;36(2):224-226. doi: 10.1177/0890334420906850. Epub 2020 Mar 10. J Hum Lact. 2020. PMID: 32155099
• Sampling issues in qualitative research. Higginbottom GM. Higginbottom GM. Nurse Res. 2004;12(1):7-19. doi: 10.7748/nr2004.07.12.1.7.c5927. Nurse Res. 2004. PMID: 15493211 Review.
• Actioning atrial fibrillation identified by ambulance services in England: a qualitative exploration. Wilkinson C, Moloney S, McClelland G, Todd A, Doran T, Price C. Wilkinson C, et al. BMJ Open. 2024 Sep 5;14(9):e078777. doi: 10.1136/bmjopen-2023-078777. BMJ Open. 2024. PMID: 39237274 Free PMC article.
• Human errors in emergency medical services: a qualitative analysis of contributing factors. Poranen A, Kouvonen A, Nordquist H. Poranen A, et al. Scand J Trauma Resusc Emerg Med. 2024 Aug 30;32(1):78. doi: 10.1186/s13049-024-01253-7. Scand J Trauma Resusc Emerg Med. 2024. PMID: 39215372 Free PMC article.
• Exploring illness perceptions of multimorbidity among community-dwelling older adults: a mixed methods study. Okanlawon Bankole A, Jiwani RB, Avorgbedor F, Wang J, Osokpo OH, Gill SL, Jo Braden C. Okanlawon Bankole A, et al. Aging Health Res. 2023 Dec;3(4):100158. doi: 10.1016/j.ahr.2023.100158. Epub 2023 Sep 16. Aging Health Res. 2023. PMID: 38779434 Free PMC article.
• Virtual reality as a method of cognitive training of processing speed, working memory, and sustained attention in persons with acquired brain injury: a protocol for a randomized controlled trial. Johansen T, Matre M, Løvstad M, Lund A, Martinsen AC, Olsen A, Becker F, Brunborg C, Ponsford J, Spikman J, Neumann D, Tornås S. Johansen T, et al. Trials. 2024 May 22;25(1):340. doi: 10.1186/s13063-024-08178-7. Trials. 2024. PMID: 38778411 Free PMC article.
• Participatory Health Cadre Model to Improve Exclusive Breastfeeding Coverage with King's Conceptual System. Sukmawati E, Wijaya M, Hilmanto D. Sukmawati E, et al. J Multidiscip Healthc. 2024 Apr 24;17:1857-1875. doi: 10.2147/JMDH.S450634. eCollection 2024. J Multidiscip Healthc. 2024. PMID: 38699558 Free PMC article.
• Search in MeSH

LinkOut - more resources

Full text sources.

• Ovid Technologies, Inc.

Miscellaneous

• NCI CPTAC Assay Portal

• Citation Manager

NCBI Literature Resources

MeSH PMC Bookshelf Disclaimer

The PubMed wordmark and PubMed logo are registered trademarks of the U.S. Department of Health and Human Services (HHS). Unauthorized use of these marks is strictly prohibited.

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

• Knowledge Base

Methodology

• Sampling Methods | Types, Techniques & Examples

Sampling Methods | Types, Techniques & Examples

Published on September 19, 2019 by Shona McCombes . Revised on June 22, 2023.

When you conduct research about a group of people, it’s rarely possible to collect data from every person in that group. Instead, you select a sample . The sample is the group of individuals who will actually participate in the research.

To draw valid conclusions from your results, you have to carefully decide how you will select a sample that is representative of the group as a whole. This is called a sampling method . There are two primary types of sampling methods that you can use in your research:

• Probability sampling involves random selection, allowing you to make strong statistical inferences about the whole group.
• Non-probability sampling involves non-random selection based on convenience or other criteria, allowing you to easily collect data.

You should clearly explain how you selected your sample in the methodology section of your paper or thesis, as well as how you approached minimizing research bias in your work.

Table of contents

Population vs. sample, probability sampling methods, non-probability sampling methods, other interesting articles, frequently asked questions about sampling.

First, you need to understand the difference between a population and a sample , and identify the target population of your research.

• The population is the entire group that you want to draw conclusions about.
• The sample is the specific group of individuals that you will collect data from.

The population can be defined in terms of geographical location, age, income, or many other characteristics.

It is important to carefully define your target population according to the purpose and practicalities of your project.

If the population is very large, demographically mixed, and geographically dispersed, it might be difficult to gain access to a representative sample. A lack of a representative sample affects the validity of your results, and can lead to several research biases , particularly sampling bias .

Sampling frame

The sampling frame is the actual list of individuals that the sample will be drawn from. Ideally, it should include the entire target population (and nobody who is not part of that population).

Sample size

The number of individuals you should include in your sample depends on various factors, including the size and variability of the population and your research design. There are different sample size calculators and formulas depending on what you want to achieve with statistical analysis .

Receive feedback on language, structure, and formatting

Professional editors proofread and edit your paper by focusing on:

• Academic style
• Vague sentences
• Style consistency

See an example

Probability sampling means that every member of the population has a chance of being selected. It is mainly used in quantitative research . If you want to produce results that are representative of the whole population, probability sampling techniques are the most valid choice.

There are four main types of probability sample.

1. Simple random sampling

In a simple random sample, every member of the population has an equal chance of being selected. Your sampling frame should include the whole population.

To conduct this type of sampling, you can use tools like random number generators or other techniques that are based entirely on chance.

2. Systematic sampling

Systematic sampling is similar to simple random sampling, but it is usually slightly easier to conduct. Every member of the population is listed with a number, but instead of randomly generating numbers, individuals are chosen at regular intervals.

If you use this technique, it is important to make sure that there is no hidden pattern in the list that might skew the sample. For example, if the HR database groups employees by team, and team members are listed in order of seniority, there is a risk that your interval might skip over people in junior roles, resulting in a sample that is skewed towards senior employees.

3. Stratified sampling

Stratified sampling involves dividing the population into subpopulations that may differ in important ways. It allows you draw more precise conclusions by ensuring that every subgroup is properly represented in the sample.

To use this sampling method, you divide the population into subgroups (called strata) based on the relevant characteristic (e.g., gender identity, age range, income bracket, job role).

Based on the overall proportions of the population, you calculate how many people should be sampled from each subgroup. Then you use random or systematic sampling to select a sample from each subgroup.

4. Cluster sampling

Cluster sampling also involves dividing the population into subgroups, but each subgroup should have similar characteristics to the whole sample. Instead of sampling individuals from each subgroup, you randomly select entire subgroups.

If it is practically possible, you might include every individual from each sampled cluster. If the clusters themselves are large, you can also sample individuals from within each cluster using one of the techniques above. This is called multistage sampling .

This method is good for dealing with large and dispersed populations, but there is more risk of error in the sample, as there could be substantial differences between clusters. It’s difficult to guarantee that the sampled clusters are really representative of the whole population.

In a non-probability sample, individuals are selected based on non-random criteria, and not every individual has a chance of being included.

This type of sample is easier and cheaper to access, but it has a higher risk of sampling bias . That means the inferences you can make about the population are weaker than with probability samples, and your conclusions may be more limited. If you use a non-probability sample, you should still aim to make it as representative of the population as possible.

Non-probability sampling techniques are often used in exploratory and qualitative research . In these types of research, the aim is not to test a hypothesis about a broad population, but to develop an initial understanding of a small or under-researched population.

1. Convenience sampling

A convenience sample simply includes the individuals who happen to be most accessible to the researcher.

This is an easy and inexpensive way to gather initial data, but there is no way to tell if the sample is representative of the population, so it can’t produce generalizable results. Convenience samples are at risk for both sampling bias and selection bias .

2. Voluntary response sampling

Similar to a convenience sample, a voluntary response sample is mainly based on ease of access. Instead of the researcher choosing participants and directly contacting them, people volunteer themselves (e.g. by responding to a public online survey).

Voluntary response samples are always at least somewhat biased , as some people will inherently be more likely to volunteer than others, leading to self-selection bias .

3. Purposive sampling

This type of sampling, also known as judgement sampling, involves the researcher using their expertise to select a sample that is most useful to the purposes of the research.

It is often used in qualitative research , where the researcher wants to gain detailed knowledge about a specific phenomenon rather than make statistical inferences, or where the population is very small and specific. An effective purposive sample must have clear criteria and rationale for inclusion. Always make sure to describe your inclusion and exclusion criteria and beware of observer bias affecting your arguments.

4. Snowball sampling

If the population is hard to access, snowball sampling can be used to recruit participants via other participants. The number of people you have access to “snowballs” as you get in contact with more people. The downside here is also representativeness, as you have no way of knowing how representative your sample is due to the reliance on participants recruiting others. This can lead to sampling bias .

5. Quota sampling

Quota sampling relies on the non-random selection of a predetermined number or proportion of units. This is called a quota.

You first divide the population into mutually exclusive subgroups (called strata) and then recruit sample units until you reach your quota. These units share specific characteristics, determined by you prior to forming your strata. The aim of quota sampling is to control what or who makes up your sample.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Quartiles & Quantiles
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Prospective cohort study

Research bias

• Implicit bias
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic
• Social desirability bias

Here's why students love Scribbr's proofreading services

Discover proofreading & editing

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

Samples are used to make inferences about populations . Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.

Probability sampling means that every member of the target population has a known chance of being included in the sample.

Probability sampling methods include simple random sampling , systematic sampling , stratified sampling , and cluster sampling .

In non-probability sampling , the sample is selected based on non-random criteria, and not every member of the population has a chance of being included.

Common non-probability sampling methods include convenience sampling , voluntary response sampling, purposive sampling , snowball sampling, and quota sampling .

In multistage sampling , or multistage cluster sampling, you draw a sample from a population using smaller and smaller groups at each stage.

This method is often used to collect data from a large, geographically spread group of people in national surveys, for example. You take advantage of hierarchical groupings (e.g., from state to city to neighborhood) to create a sample that’s less expensive and time-consuming to collect data from.

Sampling bias occurs when some members of a population are systematically more likely to be selected in a sample than others.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

McCombes, S. (2023, June 22). Sampling Methods | Types, Techniques & Examples. Scribbr. Retrieved September 19, 2024, from https://www.scribbr.com/methodology/sampling-methods/

Is this article helpful?

Shona McCombes

Other students also liked, population vs. sample | definitions, differences & examples, simple random sampling | definition, steps & examples, sampling bias and how to avoid it | types & examples, get unlimited documents corrected.

✔ Free APA citation check included ✔ Unlimited document corrections ✔ Specialized in correcting academic texts

Critical Review of Sampling Techniques in the Research Process in the World

37 Pages Posted: 13 Apr 2020

Naushad Khan

University of Agriculture, Peshawar - Institute of Development Studies; Institute of Development Studies

Date Written: April 9, 2020

Sampling techniques are the component of research which play great role in validity of the research result. Without good sampling good research conduction is impossible. It has two major types namely probability and non probability sampling. The probability sampling consists of simple random sampling, systematic sampling, stratified sampling, and multi stage sampling while in non probability sampling quota sampling, cluster sampling, purpose sampling, judgment sampling, snow ball sampling, expert sampling and convenience samplings are included Seeing to its importance the present study was arranged since, 8th April, 2020. The major objective was that to critically review the sampling techniques in the research process in the world. Total 14 articles were studied and analyzed the situation what methodology is better for conducting research. Hundred percent respondents told that the probability sampling is better than the non probability sampling for conducting research but this methodology is more expensive and time consuming which further delay the result of the study while the non probability sampling is not more expensive and time consuming in the world but its result is doubtful and does not mostly valid for implementation of generalization for population from which the sample has been selected. The result further explore that both methodologies have different advantages on their places but it is necessary for researchers to use proper methodology for their research and analyze the situation for the solution of problems. Research is a systematic and objective attempt for the solution of the problems which play great role for the development of a country and without good research the development of the country is impossible. Now a day the weighted economies of the world are China, America, India and Japan. They all conduct research for the development of their economy enhancement. The developed countries keep large funds for their research and they mostly use the probability sampling for their research because they have more funds, so their sampling representation is better than the poor countries of the world. They solve their problems of the economy very well and they arrange good projects for enhancing their economy. So the study shows that quality sampling play great role in the development of the countries. On the basis of problems the study recommends that to arrange more funds for their research; Mostly use probability sampling for their research because only quality research enhance the economy; Quality sampling is required for generalization of the problem. Proper sampling only gives valid result for the problem of population. The author also recommends that to use good and best sampling in the educational institutions for conducting their student research; Good funds by HEC is required for conducting quality research in the educational institution of the world; Research ethics in the institution is also a good tool for conducting quality research; Fake and plagiarized sample should be avoided in the institution for conducting research; Fake research never develop the world. Good expert team should be appointed by University for sampling their student research. Without good research the development of a world is impossible while for quality research proper sampling is the need of the world.

Keywords: Critical Review, Sampling Techniques, Research Process, World

Suggested Citation: Suggested Citation

Naushad Khan (Contact Author)

University of agriculture, peshawar - institute of development studies ( email ).

Institute of Development Studies Professor Colony Agriculture Peshawar Peshawar, khyber Pakhtunkhwa 25000 Pakistan

Institute of Development Studies ( email )

Do you have a job opening that you would like to promote on ssrn, paper statistics, related ejournals, sustainability at work ejournal.

Subscribe to this fee journal for more curated articles on this topic

Archaeology eJournal

Cultural anthropology: history, theory, methods & applications ejournal, biology & anthropology ejournal, data science, data analytics & informatics ejournal, information theory & research ejournal, education research negative results ejournal.

Educational resources and simple solutions for your research journey

What are Sampling Methods? Techniques, Types, and Examples

Every type of research includes samples from which inferences are drawn. The sample could be biological specimens or a subset of a specific group or population selected for analysis. The goal is often to conclude the entire population based on the characteristics observed in the sample. Now, the question comes to mind: how does one collect the samples? Answer: Using sampling methods. Various sampling strategies are available to researchers to define and collect samples that will form the basis of their research study.

In a study focusing on individuals experiencing anxiety, gathering data from the entire population is practically impossible due to the widespread prevalence of anxiety. Consequently, a sample is carefully selected—a subset of individuals meant to represent (or not in some cases accurately) the demographics of those experiencing anxiety. The study’s outcomes hinge significantly on the chosen sample, emphasizing the critical importance of a thoughtful and precise selection process. The conclusions drawn about the broader population rely heavily on the selected sample’s characteristics and diversity.

Table of Contents

What is sampling?

Sampling involves the strategic selection of individuals or a subset from a population, aiming to derive statistical inferences and predict the characteristics of the entire population. It offers a pragmatic and practical approach to examining the features of the whole population, which would otherwise be difficult to achieve because studying the total population is expensive, time-consuming, and often impossible. Market researchers use various sampling methods to collect samples from a large population to acquire relevant insights. The best sampling strategy for research is determined by criteria such as the purpose of the study, available resources (time and money), and research hypothesis.

For example, if a pet food manufacturer wants to investigate the positive impact of a new cat food on feline growth, studying all the cats in the country is impractical. In such cases, employing an appropriate sampling technique from the extensive dataset allows the researcher to focus on a manageable subset. This enables the researcher to study the growth-promoting effects of the new pet food. This article will delve into the standard sampling methods and explore the situations in which each is most appropriately applied.

What are sampling methods or sampling techniques?

Sampling methods or sampling techniques in research are statistical methods for selecting a sample representative of the whole population to study the population’s characteristics. Sampling methods serve as invaluable tools for researchers, enabling the collection of meaningful data and facilitating analysis to identify distinctive features of the people. Different sampling strategies can be used based on the characteristics of the population, the study purpose, and the available resources. Now that we understand why sampling methods are essential in research, we review the various sample methods in the following sections.

Types of sampling methods  

Before we go into the specifics of each sampling method, it’s vital to understand terms like sample, sample frame, and sample space. In probability theory, the sample space comprises all possible outcomes of a random experiment, while the sample frame is the list or source guiding sample selection in statistical research. The  sample  represents the group of individuals participating in the study, forming the basis for the research findings. Selecting the correct sample is critical to ensuring the validity and reliability of any research; the sample should be representative of the population. 

There are two most common sampling methods: 

• Probability sampling: A sampling method in which each unit or element in the population has an equal chance of being selected in the final sample. This is called random sampling, emphasizing the random and non-zero probability nature of selecting samples. Such a sampling technique ensures a more representative and unbiased sample, enabling robust inferences about the entire population. 
• Non-probability sampling:  Another sampling method is non-probability sampling, which involves collecting data conveniently through a non-random selection based on predefined criteria. This offers a straightforward way to gather data, although the resulting sample may or may not accurately represent the entire population. 

  Irrespective of the research method you opt for, it is essential to explicitly state the chosen sampling technique in the methodology section of your research article. Now, we will explore the different characteristics of both sampling methods, along with various subtypes falling under these categories. 

What is probability sampling?  

The probability sampling method is based on the probability theory, which means that the sample selection criteria involve some random selection. The probability sampling method provides an equal opportunity for all elements or units within the entire sample space to be chosen. While it can be labor-intensive and expensive, the advantage lies in its ability to offer a more accurate representation of the population, thereby enhancing confidence in the inferences drawn in the research.   

Types of probability sampling  

Various probability sampling methods exist, such as simple random sampling, systematic sampling, stratified sampling, and clustered sampling. Here, we provide detailed discussions and illustrative examples for each of these sampling methods: 

• Simple random sampling:  In simple random sampling, each individual has an equal probability of being chosen, and each selection is independent of the others. Because the choice is entirely based on chance, this is also known as the method of chance selection. In the simple random sampling method, the sample frame comprises the entire population. 

For example,  A fitness sports brand is launching a new protein drink and aims to select 20 individuals from a 200-person fitness center to try it. Employing a simple random sampling approach, each of the 200 people is assigned a unique identifier. Of these, 20 individuals are then chosen by generating random numbers between 1 and 200, either manually or through a computer program. Matching these numbers with the individuals creates a randomly selected group of 20 people. This method minimizes sampling bias and ensures a representative subset of the entire population under study. 

• Systematic sampling:  The systematic sampling approach involves selecting units or elements at regular intervals from an ordered list of the population. Because the starting point of this sampling method is chosen at random, it is more convenient than essential random sampling. For a better understanding, consider the following example.  

For example, considering the previous model, individuals at the fitness facility are arranged alphabetically. The manufacturer then initiates the process by randomly selecting a starting point from the first ten positions, let’s say 8. Starting from the 8th position, every tenth person on the list is then chosen (e.g., 8, 18, 28, 38, and so forth) until a sample of 20 individuals is obtained.  

• Stratified sampling: Stratified sampling divides the population into subgroups (strata), and random samples are drawn from each stratum in proportion to its size in the population. Stratified sampling provides improved representation because each subgroup that differs in significant ways is included in the final sample. 

For example, Expanding on the previous simple random sampling example, suppose the manufacturer aims for a more comprehensive representation of genders in a sample of 200 people, consisting of 90 males, 80 females, and 30 others. The manufacturer categorizes the population into three gender strata (Male, Female, and Others). Within each group, random sampling is employed to select nine males, eight females, and three individuals from the others category, resulting in a well-rounded and representative sample of 200 individuals. 

• Clustered sampling: In this sampling method, the population is divided into clusters, and then a random sample of clusters is included in the final sample. Clustered sampling, distinct from stratified sampling, involves subgroups (clusters) that exhibit characteristics similar to the whole sample. In the case of small clusters, all members can be included in the final sample, whereas for larger clusters, individuals within each cluster may be sampled using the sampling above methods. This approach is referred to as multistage sampling. This sampling method is well-suited for large and widely distributed populations; however, there is a potential risk of sample error because ensuring that the sampled clusters truly represent the entire population can be challenging. 

For example, Researchers conducting a nationwide health study can select specific geographic clusters, like cities or regions, instead of trying to survey the entire population individually. Within each chosen cluster, they sample individuals, providing a representative subset without the logistical challenges of attempting a nationwide survey. 

Use s of probability sampling  

Probability sampling methods find widespread use across diverse research disciplines because of their ability to yield representative and unbiased samples. The advantages of employing probability sampling include the following: 

• Representativeness  

Probability sampling assures that every element in the population has a non-zero chance of being included in the sample, ensuring representativeness of the entire population and decreasing research bias to minimal to non-existent levels. The researcher can acquire higher-quality data via probability sampling, increasing confidence in the conclusions. 

• Statistical inference  

Statistical methods, like confidence intervals and hypothesis testing, depend on probability sampling to generalize findings from a sample to the broader population. Probability sampling methods ensure unbiased representation, allowing inferences about the population based on the characteristics of the sample. 

• Precision and reliability  

The use of probability sampling improves the precision and reliability of study results. Because the probability of selecting any single element/individual is known, the chance variations that may occur in non-probability sampling methods are reduced, resulting in more dependable and precise estimations. 

• Generalizability  

Probability sampling enables the researcher to generalize study findings to the entire population from which they were derived. The results produced through probability sampling methods are more likely to be applicable to the larger population, laying the foundation for making broad predictions or recommendations. 

• Minimization of Selection Bias  

By ensuring that each member of the population has an equal chance of being selected in the sample, probability sampling lowers the possibility of selection bias. This reduces the impact of systematic errors that may occur in non-probability sampling methods, where data may be skewed toward a specific demographic due to inadequate representation of each segment of the population. 

What is non-probability sampling?  

Non-probability sampling methods involve selecting individuals based on non-random criteria, often relying on the researcher’s judgment or predefined criteria. While it is easier and more economical, it tends to introduce sampling bias, resulting in weaker inferences compared to probability sampling techniques in research. 

Types of Non-probability Sampling   

Non-probability sampling methods are further classified as convenience sampling, consecutive sampling, quota sampling, purposive or judgmental sampling, and snowball sampling. Let’s explore these types of sampling methods in detail. 

• Convenience sampling:  In convenience sampling, individuals are recruited directly from the population based on the accessibility and proximity to the researcher. It is a simple, inexpensive, and practical method of sample selection, yet convenience sampling suffers from both sampling and selection bias due to a lack of appropriate population representation. 

For example, imagine you’re a researcher investigating smartphone usage patterns in your city. The most convenient way to select participants is by approaching people in a shopping mall on a weekday afternoon. However, this convenience sampling method may not be an accurate representation of the city’s overall smartphone usage patterns as the sample is limited to individuals present at the mall during weekdays, excluding those who visit on other days or never visit the mall.

• Consecutive sampling: Participants in consecutive sampling (or sequential sampling) are chosen based on their availability and desire to participate in the study as they become available. This strategy entails sequentially recruiting individuals who fulfill the researcher’s requirements. 

For example, In researching the prevalence of stroke in a hospital, instead of randomly selecting patients from the entire population, the researcher can opt to include all eligible patients admitted over three months. Participants are then consecutively recruited upon admission during that timeframe, forming the study sample. 

• Quota sampling:  The selection of individuals in quota sampling is based on non-random selection criteria in which only participants with certain traits or proportions that are representative of the population are included. Quota sampling involves setting predetermined quotas for specific subgroups based on key demographics or other relevant characteristics. This sampling method employs dividing the population into mutually exclusive subgroups and then selecting sample units until the set quota is reached.  

For example, In a survey on a college campus to assess student interest in a new policy, the researcher should establish quotas aligned with the distribution of student majors, ensuring representation from various academic disciplines. If the campus has 20% biology majors, 30% engineering majors, 20% business majors, and 30% liberal arts majors, participants should be recruited to mirror these proportions. 

• Purposive or judgmental sampling: In purposive sampling, the researcher leverages expertise to select a sample relevant to the study’s specific questions. This sampling method is commonly applied in qualitative research, mainly when aiming to understand a particular phenomenon, and is suitable for smaller population sizes. 

For example, imagine a researcher who wants to study public policy issues for a focus group. The researcher might purposely select participants with expertise in economics, law, and public administration to take advantage of their knowledge and ensure a depth of understanding.  

• Snowball sampling:  This sampling method is used when accessing the population is challenging. It involves collecting the sample through a chain-referral process, where each recruited candidate aids in finding others. These candidates share common traits, representing the targeted population. This method is often used in qualitative research, particularly when studying phenomena related to stigmatized or hidden populations. 

For example, In a study focusing on understanding the experiences and challenges of individuals in hidden or stigmatized communities (e.g., LGBTQ+ individuals in specific cultural contexts), the snowball sampling technique can be employed. The researcher initiates contact with one community member, who then assists in identifying additional candidates until the desired sample size is achieved.

Uses of non-probability sampling  

Non-probability sampling approaches are employed in qualitative or exploratory research where the goal is to investigate underlying population traits rather than generalizability. Non-probability sampling methods are also helpful for the following purposes: 

• Generating a hypothesis  

In the initial stages of exploratory research, non-probability methods such as purposive or convenience allow researchers to quickly gather information and generate hypothesis that helps build a future research plan.  

• Qualitative research  

Qualitative research is usually focused on understanding the depth and complexity of human experiences, behaviors, and perspectives. Non-probability methods like purposive or snowball sampling are commonly used to select participants with specific traits that are relevant to the research question.  

• Convenience and pragmatism  

Non-probability sampling methods are valuable when resource and time are limited or when preliminary data is required to test the pilot study. For example, conducting a survey at a local shopping mall to gather opinions on a consumer product due to the ease of access to potential participants.  

Probability vs Non-probability Sampling Methods  

Frequently asked questions  

• What is multistage sampling ? Multistage sampling is a form of probability sampling approach that involves the progressive selection of samples in stages, going from larger clusters to a small number of participants, making it suited for large-scale research with enormous population lists.  
• What are the methods of probability sampling? Probability sampling methods are simple random sampling, stratified random sampling, systematic sampling, cluster sampling, and multistage sampling.
• How to decide which type of sampling method to use? Choose a sampling method based on the goals, population, and resources. Probability for statistics and non-probability for efficiency or qualitative insights can be considered . Also, consider the population characteristics, size, and alignment with study objectives.
• What are the methods of non-probability sampling? Non-probability sampling methods are convenience sampling, consecutive sampling, purposive sampling, snowball sampling, and quota sampling.
• Why are sampling methods used in research? Sampling methods in research are employed to efficiently gather representative data from a subset of a larger population, enabling valid conclusions and generalizations while minimizing costs and time.  

R Discovery is a literature search and research reading platform that accelerates your research discovery journey by keeping you updated on the latest, most relevant scholarly content. With 250M+ research articles sourced from trusted aggregators like CrossRef, Unpaywall, PubMed, PubMed Central, Open Alex and top publishing houses like Springer Nature, JAMA, IOP, Taylor & Francis, NEJM, BMJ, Karger, SAGE, Emerald Publishing and more, R Discovery puts a world of research at your fingertips.  

Try R Discovery Prime FREE for 1 week or upgrade at just US$72 a year to access premium features that let you listen to research on the go, read in your language, collaborate with peers, auto sync with reference managers, and much more. Choose a simpler, smarter way to find and read research – Download the app and start your free 7-day trial today !  

Related Posts

What is Research Impact: Types and Tips for Academics

Research in Shorts: R Discovery’s New Feature Helps Academics Assess Relevant Papers in 2mins 

• Privacy Policy

Home » Sampling Methods – Types, Techniques and Examples

Sampling Methods – Types, Techniques and Examples

Table of Contents

Sampling refers to the process of selecting a subset of data from a larger population or dataset in order to analyze or make inferences about the whole population.

In other words, sampling involves taking a representative sample of data from a larger group or dataset in order to gain insights or draw conclusions about the entire group.

Sampling Methods

Sampling methods refer to the techniques used to select a subset of individuals or units from a larger population for the purpose of conducting statistical analysis or research.

Sampling is an essential part of the Research because it allows researchers to draw conclusions about a population without having to collect data from every member of that population, which can be time-consuming, expensive, or even impossible.

Types of Sampling Methods

Sampling can be broadly categorized into two main categories:

Probability Sampling

This type of sampling is based on the principles of random selection, and it involves selecting samples in a way that every member of the population has an equal chance of being included in the sample.. Probability sampling is commonly used in scientific research and statistical analysis, as it provides a representative sample that can be generalized to the larger population.

Type of Probability Sampling :

• Simple Random Sampling: In this method, every member of the population has an equal chance of being selected for the sample. This can be done using a random number generator or by drawing names out of a hat, for example.
• Systematic Sampling: In this method, the population is first divided into a list or sequence, and then every nth member is selected for the sample. For example, if every 10th person is selected from a list of 100 people, the sample would include 10 people.
• Stratified Sampling: In this method, the population is divided into subgroups or strata based on certain characteristics, and then a random sample is taken from each stratum. This is often used to ensure that the sample is representative of the population as a whole.
• Cluster Sampling: In this method, the population is divided into clusters or groups, and then a random sample of clusters is selected. Then, all members of the selected clusters are included in the sample.
• Multi-Stage Sampling : This method combines two or more sampling techniques. For example, a researcher may use stratified sampling to select clusters, and then use simple random sampling to select members within each cluster.

Non-probability Sampling

This type of sampling does not rely on random selection, and it involves selecting samples in a way that does not give every member of the population an equal chance of being included in the sample. Non-probability sampling is often used in qualitative research, where the aim is not to generalize findings to a larger population, but to gain an in-depth understanding of a particular phenomenon or group. Non-probability sampling methods can be quicker and more cost-effective than probability sampling methods, but they may also be subject to bias and may not be representative of the larger population.

Types of Non-probability Sampling :

• Convenience Sampling: In this method, participants are chosen based on their availability or willingness to participate. This method is easy and convenient but may not be representative of the population.
• Purposive Sampling: In this method, participants are selected based on specific criteria, such as their expertise or knowledge on a particular topic. This method is often used in qualitative research, but may not be representative of the population.
• Snowball Sampling: In this method, participants are recruited through referrals from other participants. This method is often used when the population is hard to reach, but may not be representative of the population.
• Quota Sampling: In this method, a predetermined number of participants are selected based on specific criteria, such as age or gender. This method is often used in market research, but may not be representative of the population.
• Volunteer Sampling: In this method, participants volunteer to participate in the study. This method is often used in research where participants are motivated by personal interest or altruism, but may not be representative of the population.

Applications of Sampling Methods

Applications of Sampling Methods from different fields:

• Psychology : Sampling methods are used in psychology research to study various aspects of human behavior and mental processes. For example, researchers may use stratified sampling to select a sample of participants that is representative of the population based on factors such as age, gender, and ethnicity. Random sampling may also be used to select participants for experimental studies.
• Sociology : Sampling methods are commonly used in sociological research to study social phenomena and relationships between individuals and groups. For example, researchers may use cluster sampling to select a sample of neighborhoods to study the effects of economic inequality on health outcomes. Stratified sampling may also be used to select a sample of participants that is representative of the population based on factors such as income, education, and occupation.
• Social sciences: Sampling methods are commonly used in social sciences to study human behavior and attitudes. For example, researchers may use stratified sampling to select a sample of participants that is representative of the population based on factors such as age, gender, and income.
• Marketing : Sampling methods are used in marketing research to collect data on consumer preferences, behavior, and attitudes. For example, researchers may use random sampling to select a sample of consumers to participate in a survey about a new product.
• Healthcare : Sampling methods are used in healthcare research to study the prevalence of diseases and risk factors, and to evaluate interventions. For example, researchers may use cluster sampling to select a sample of health clinics to participate in a study of the effectiveness of a new treatment.
• Environmental science: Sampling methods are used in environmental science to collect data on environmental variables such as water quality, air pollution, and soil composition. For example, researchers may use systematic sampling to collect soil samples at regular intervals across a field.
• Education : Sampling methods are used in education research to study student learning and achievement. For example, researchers may use stratified sampling to select a sample of schools that is representative of the population based on factors such as demographics and academic performance.

Examples of Sampling Methods

Probability Sampling Methods Examples:

• Simple random sampling Example : A researcher randomly selects participants from the population using a random number generator or drawing names from a hat.
• Stratified random sampling Example : A researcher divides the population into subgroups (strata) based on a characteristic of interest (e.g. age or income) and then randomly selects participants from each subgroup.
• Systematic sampling Example : A researcher selects participants at regular intervals from a list of the population.

Non-probability Sampling Methods Examples:

• Convenience sampling Example: A researcher selects participants who are conveniently available, such as students in a particular class or visitors to a shopping mall.
• Purposive sampling Example : A researcher selects participants who meet specific criteria, such as individuals who have been diagnosed with a particular medical condition.
• Snowball sampling Example : A researcher selects participants who are referred to them by other participants, such as friends or acquaintances.

How to Conduct Sampling Methods

some general steps to conduct sampling methods:

• Define the population: Identify the population of interest and clearly define its boundaries.
• Choose the sampling method: Select an appropriate sampling method based on the research question, characteristics of the population, and available resources.
• Determine the sample size: Determine the desired sample size based on statistical considerations such as margin of error, confidence level, or power analysis.
• Create a sampling frame: Develop a list of all individuals or elements in the population from which the sample will be drawn. The sampling frame should be comprehensive, accurate, and up-to-date.
• Select the sample: Use the chosen sampling method to select the sample from the sampling frame. The sample should be selected randomly, or if using a non-random method, every effort should be made to minimize bias and ensure that the sample is representative of the population.
• Collect data: Once the sample has been selected, collect data from each member of the sample using appropriate research methods (e.g., surveys, interviews, observations).
• Analyze the data: Analyze the data collected from the sample to draw conclusions about the population of interest.

When to use Sampling Methods

Sampling methods are used in research when it is not feasible or practical to study the entire population of interest. Sampling allows researchers to study a smaller group of individuals, known as a sample, and use the findings from the sample to make inferences about the larger population.

Sampling methods are particularly useful when:

• The population of interest is too large to study in its entirety.
• The cost and time required to study the entire population are prohibitive.
• The population is geographically dispersed or difficult to access.
• The research question requires specialized or hard-to-find individuals.
• The data collected is quantitative and statistical analyses are used to draw conclusions.

Purpose of Sampling Methods

The main purpose of sampling methods in research is to obtain a representative sample of individuals or elements from a larger population of interest, in order to make inferences about the population as a whole. By studying a smaller group of individuals, known as a sample, researchers can gather information about the population that would be difficult or impossible to obtain from studying the entire population.

Sampling methods allow researchers to:

• Study a smaller, more manageable group of individuals, which is typically less time-consuming and less expensive than studying the entire population.
• Reduce the potential for data collection errors and improve the accuracy of the results by minimizing sampling bias.
• Make inferences about the larger population with a certain degree of confidence, using statistical analyses of the data collected from the sample.
• Improve the generalizability and external validity of the findings by ensuring that the sample is representative of the population of interest.

Characteristics of Sampling Methods

Here are some characteristics of sampling methods:

• Randomness : Probability sampling methods are based on random selection, meaning that every member of the population has an equal chance of being selected. This helps to minimize bias and ensure that the sample is representative of the population.
• Representativeness : The goal of sampling is to obtain a sample that is representative of the larger population of interest. This means that the sample should reflect the characteristics of the population in terms of key demographic, behavioral, or other relevant variables.
• Size : The size of the sample should be large enough to provide sufficient statistical power for the research question at hand. The sample size should also be appropriate for the chosen sampling method and the level of precision desired.
• Efficiency : Sampling methods should be efficient in terms of time, cost, and resources required. The method chosen should be feasible given the available resources and time constraints.
• Bias : Sampling methods should aim to minimize bias and ensure that the sample is representative of the population of interest. Bias can be introduced through non-random selection or non-response, and can affect the validity and generalizability of the findings.
• Precision : Sampling methods should be precise in terms of providing estimates of the population parameters of interest. Precision is influenced by sample size, sampling method, and level of variability in the population.
• Validity : The validity of the sampling method is important for ensuring that the results obtained from the sample are accurate and can be generalized to the population of interest. Validity can be affected by sampling method, sample size, and the representativeness of the sample.

Advantages of Sampling Methods

Sampling methods have several advantages, including:

• Cost-Effective : Sampling methods are often much cheaper and less time-consuming than studying an entire population. By studying only a small subset of the population, researchers can gather valuable data without incurring the costs associated with studying the entire population.
• Convenience : Sampling methods are often more convenient than studying an entire population. For example, if a researcher wants to study the eating habits of people in a city, it would be very difficult and time-consuming to study every single person in the city. By using sampling methods, the researcher can obtain data from a smaller subset of people, making the study more feasible.
• Accuracy: When done correctly, sampling methods can be very accurate. By using appropriate sampling techniques, researchers can obtain a sample that is representative of the entire population. This allows them to make accurate generalizations about the population as a whole based on the data collected from the sample.
• Time-Saving: Sampling methods can save a lot of time compared to studying the entire population. By studying a smaller sample, researchers can collect data much more quickly than they could if they studied every single person in the population.
• Less Bias : Sampling methods can reduce bias in a study. If a researcher were to study the entire population, it would be very difficult to eliminate all sources of bias. However, by using appropriate sampling techniques, researchers can reduce bias and obtain a sample that is more representative of the entire population.

Limitations of Sampling Methods

• Sampling Error : Sampling error is the difference between the sample statistic and the population parameter. It is the result of selecting a sample rather than the entire population. The larger the sample, the lower the sampling error. However, no matter how large the sample size, there will always be some degree of sampling error.
• Selection Bias: Selection bias occurs when the sample is not representative of the population. This can happen if the sample is not selected randomly or if some groups are underrepresented in the sample. Selection bias can lead to inaccurate conclusions about the population.
• Non-response Bias : Non-response bias occurs when some members of the sample do not respond to the survey or study. This can result in a biased sample if the non-respondents differ from the respondents in important ways.
• Time and Cost : While sampling can be cost-effective, it can still be expensive and time-consuming to select a sample that is representative of the population. Depending on the sampling method used, it may take a long time to obtain a sample that is large enough and representative enough to be useful.
• Limited Information : Sampling can only provide information about the variables that are measured. It may not provide information about other variables that are relevant to the research question but were not measured.
• Generalization : The extent to which the findings from a sample can be generalized to the population depends on the representativeness of the sample. If the sample is not representative of the population, it may not be possible to generalize the findings to the population as a whole.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

Cluster Sampling – Types, Method and Examples

Stratified Random Sampling – Definition, Method...

Quota Sampling – Types, Methods and Examples

Probability Sampling – Methods, Types and...

Systematic Sampling – Types, Method and Examples

Simple Random Sampling – Types, Method and...

• Open access
• Published: 17 September 2024

Possibility of the optimum monitoring and evaluation (M&E) production frontier for risk-informed health governance in disaster-prone districts of West Bengal, India

• Moumita Mukherjee 1 , 2 &
• Anuj Batta 3 , 4  

Journal of Health, Population and Nutrition volume  43 , Article number:  148 ( 2024 ) Cite this article

Metrics details

An efficient M&E system in public healthcare is crucial for achieving universal health coverage in low- and middle-income countries, especially when the need for service remains unmet due to the exposure of the population to disaster risks and uncertainties. Current research has conducted exploratory and predictive analyses to estimate the determinants of sustainable M&E solutions for ensuring uninterrupted access during and after disasters. The aim was to estimate the efficiency of reaching a higher M&E production frontier via the Cobb‒Douglas model and stochastic frontier model as the basic theoretical and empirical frameworks. The research followed a deductive approach and used a stratified purposive sampling method to collect data from different layers of health and disaster governance in a flood-prone rural setting in the Malda, South 24 Parganas and Purulia districts in West Bengal, India. The present mixed-method study revealed multiple challenges in healthcare seeking during disasters and how a well-structured M&E system can increase system readiness to combat these challenges. The stochastic frontier model estimated the highest M&E frontier producing the most attainable M&E effectiveness through horizontal convergence between departments, enhanced coordination, the availability of frontline health workers at health centers, the adoption of learned innovation and the outsourcing of the evaluation component to external evaluators to improve M&E process quality. Although the study has several limitations, it shows the potential to increase technical and allocative efficiency through building skills in innovative techniques and applying them in process implementation. In the future, research on strategy improvement followed by real-world evidence-based policy advocacy is needed to increase the impact of M&E on access to healthcare services.

Introduction

The normative approach for designing health policy directives evolved gradually in the history of healthcare economics, shifting the focus from free market solutions to altruistic solutions to correct market failures [ 1 ]. Despite the availability of effective equitable health schemes, covariate shocks such as natural disasters interrupt equitable access to healthcare, impacting population health outcomes, with greater impacts on the child population [ 2 , 3 , 4 , 5 ]. Additionally, the impact is manifold in a less-equipped, less resilient supply-side environment, aggravating poor health outcomes [ 6 , 7 , 8 ]. One major component of the strengthening health system is focusing on enhancing different elements of any select implementation, such as SMART goal setting and adopting an efficient innovative M&E toolkit [ 9 ]. Given this context, this study aims to investigate the determinants of an efficient and effective M&E system to increase the health system’s resilience and render uninterrupted services during disasters. It attempts to explore the role of resource availability, local governance and moderating catalysts to strengthen it to push the existing M&E frontier to a markedly higher but attainable level.

The impact of natural disasters on population and health is evident from UNISDR [ 7 ] and World Bank [ 10 ] reports. Over a period of two decades, natural disasters affected 4.4 billion people and killed 1.3 billion people, and the world economy incurred a $2 trillion loss. Between 1970 and 2020, natural hazards affected 6.9 billion people and killed more than 2 million people in the Asia–Pacific region, indicating that the impact intensified over time [ 11 ]. Among the different influencing factors, systemic gaps in the M&E component of social programmes eventually contribute to increasing the gaps in social achievements, leading to increased economic costs [ 12 ]. The direct economic cost of disasters is increasing to $170 billion, indicating significant concerns for policymakers [ 8 ].

Among the different types of natural disasters, floods, cyclones or heatwaves affect the health status of children living in coastal areas, riverbank regions or arid regions [ 4 ]. These events influence the population directly by increasing morbidity and mortality due to a lack of access to safe water and sanitation and indirectly by prohibiting access to health centers due to inundation or extreme heat [ 13 , 14 , 15 ]. An increase in the incidence of common ailments creates pressure on the health system with less readiness even for routine health services, contributing to disaster-led social inequity, which is a greater global health concern [ 16 , 17 , 18 , 19 , 20 ]. Under the changing climate scenario, in 2050, South Asia is expected to have 59.1 million undernourished children under the age of five, which is greater than that in any other developing region [ 15 ]. The current study explored ways to improve system readiness through sustainable M&E solutions to reduce the impact of climatic shock-induced health shocks on poor children in the exposed pockets of the three districts in West Bengal, India, and identified the determinants of an effective M&E model for strengthening governance.

Interrupted service availability during disasters increases child-specific health deprivations—mortality and morbidity—and increases vulnerability further [ 21 , 22 , 23 , 24 , 25 ]. Gaps in M&E system effectiveness through improvements in operational efficiency are considered impactful interventions, which are missing in many health system responses to disasters [ 13 , 26 ]. As reported in the literature, M&E enhances the volume and veracity of program outcomes by identifying the best resource allocation and process improvement methods, which, if replicated in other similar contexts, can contribute to greater social benefits [ 27 , 28 ]. Monitoring is an M&E component that assesses the process and outputs throughout the implementation period, whereas evaluation measures the degree of outcome achievement at different time points [ 29 ]. A lack of understanding about ex ante, ex post and intermittent analyses is responsible for the limited awareness of the importance of the effectiveness of the M&E system as a success accelerator in healthcare programmes. With respect to the efficiency gaps in the M&E component of governance, the World Bank [ 10 ] study has shown that ontological gaps in the logical framework, unskilled resource use in program implementation, and irregular periodic evaluation of performance and process are contributors to the failure of the M&E component to ensure effectiveness. Zedtwitz and Gassmann [ 30 ] inferred from their research that internationalization of the M&E component can increase the operational efficiency of the M&E system through adoption of innovation, which ultimately increases comparative advantage [ 31 ]. One study assessing the process effectiveness of a nutrition programme in Gujarat, India, revealed that 89% of frontline workers lacked orientation training, whereas another study showed that capacity building for Anganwadi workers improved performance quality significantly and contributed to programme coverage [ 32 , 33 ]. Moreover, M&E performance depends on several internal governance attributes, such as the culture of decision-making, efficiency in resource allocation, availability of skilled health human resources, and social leadership capabilities [ 34 , 35 ]. One recent study demonstrated the application of novel analytics to predict the progress and trends of certain diseases to control their spread [ 36 ]. They proposed a hybrid model combining logistic and susceptible-exposed-infectious-recovered (SEIR) models so that forecasting based on existing trends can be made with the highest accuracy [ 36 ]. If such novel and innovative concepts are adopted and analytic-driven M&E is designed, it might exponentially increase M&E effectiveness. Second, in terms of innovative monitoring systems, another study on air quality monitoring revealed that monitoring air quality during lockdowns during pandemics helped to assess how to maintain good air quality even when situations became normal. Furthermore, a study evaluating public health disaster response in North America revealed that learning from previous M&E implementation challenges is a major barrier to improving M&E effectiveness [ 37 ]. According to a set of studies, the lack of initiatives in learning from the past lessons of health response programmes during and after a disaster retards the exploration of factors that may improve process efficiency and enhance M&E effectiveness [ 26 , 37 , 38 , 39 , 40 , 41 , 42 ]. Therefore, a smart monitoring system built on lessons learned from past implementations during a disaster can help create effective policy decisions and maintain efficient service delivery learning from structured innovative monitoring systems adopted during a disaster [ 26 , 37 , 42 , 43 , 44 ].

The stochastic frontier model has been applied in the literature to determine the degree and magnitude of influence of the technical efficiency and quality dimensions of M&E systems with higher reliability and robustness [ 45 ]. Additionally, other contributors visible in different studies are the lack of initiatives and related resource allocation aimed at capacity-strengthening initiatives for community health workers [ 46 , 47 , 48 ]. Furthermore, studies adopting stochastic frontier analysis or data envelopment analysis have identified significant catalysts responsible for successfully amplifying the impact of enabling factors [ 49 ]. Among them, system strategies, along with convergent service delivery linking different line departments, are found to be significant, on the basis of which the M&E theoretical framework under exposure to covariate shocks is sometimes constructed [ 50 ]. Given the context of the present research, the stochastic frontier model is applied, and different layers of models are tested to obtain the best model with a higher level of technical efficiency where system factors are considered independent variables.

Notably, different initiatives have started to test how far an integrated M&E system can increase the efficiency of programs to enhance child health outcomes. In this process, different components include capacity building by mid-level practitioners and implementers responsible for delivering child-centric services; designing a risk and impact M&E framework for health governance; and covering preparedness, response and mitigation components in health disaster management plans. This comprehensive plan aims to ensure child-specific healthcare services during disasters and reduce the defaulter rate. In this process, an integrated M&E system must be tested with periodic spatial and temporal analyses of child-specific indicators to inform the system of risk to fill the service delivery gaps. Therefore, a comprehensive situation analysis becomes a prerequisite before developing a context-specific M&E model for designing specific indicators and testing its feasibility, and the current study makes little effort in this direction.

According to the Intergovernmental Panel on Climate Change [ 51 ], to reduce the severity, interconnectedness, and irreversibility of the impacts of climate change, risk-informed institutions are necessary to help in the adaptation process, reducing exposure to risk and vulnerability to climate events. Enhanced M&E output adds to the adaptation process, further increasing coping ability and system resilience to combat shocks. Climatic and nonclimatic drivers influence the severity of child-centric health service delivery, affecting supply-side performance and the community's responsiveness to policies and programs [ 52 ]. The evidence suggests that community inclusiveness increases in an efficient supply-side environment, ensuring return on investment in child-centric services [ 52 ]. Effective management through the production of an ideal quantity of M&E output with quality has become a threshold standard for ensuring the improvement of development indicators in vulnerable geographical pockets [ 16 , 35 , 53 ].

Given this context, the current research objective is to conduct a situation analysis to estimate the risk-informed M&E production frontier with the least inefficiency after identifying the supply-side vulnerabilities and risks in accessing child health services. This objective is to satisfy the goal of increasing the elasticity of service effectiveness by reaching a higher M&E frontier in three of the disaster-prone districts in West Bengal, India.

Therefore, reducing the social and economic costs of targeting implementation gaps during disasters is crucial [ 8 ]. In such circumstances, the potential of strengthening M&E is less realized with suboptimal capabilities, as is the case for other developing counterparts worldwide [ 9 , 60 , 61 , 62 ]. Exploring the determinants of M&E effectiveness is the step in formulating the pathway of periodic risk and impact analysis to strengthen the M&E system. The three districts in West Bengal State of India considered in this study—Malda, South 24 Parganas and Purulia—are in such a vulnerable pocket; thus, periodic climate risk and impact analyses are needed to ensure service delivery during and after the occurrence of natural hazards. Therefore, exploring the factors affecting M&E effectiveness when designing a framework for periodic climate risk and impact analysis is needed to increase health system readiness.

The Smart Art of Literature Summary.

Methodology

Smart art methodology chart

Study area and population

West Bengal is situated on the east and stretches from the mountains to the sea. West Bengal is divided into five administrative divisions, Burdwan, Jalpaiguri, Presidency, Medinipur and Malda, which are further divided into 23 districts. The region is very distinct from hills to the riverine delta.

The state has a total area of 88,752 square kilometers, is the 14th largest in terms of area, is home to nearly 92 million people and is the 4th most populous state in India. It is the second most densely populated area, with a population density of 1028 people per square kilometer. The population of West Bengal increased from 80.2 million in 2001 to 91.3 million in 2011, accounting for 7.5% of India’s total population. The child population (0–17 years) constitutes 33 percent of the total population (29.9 million), and adolescents (10–19 years) constitute 20 percent (18.2 million). It is predominantly rural in nature, with almost 32% of the urban population being in the 4th highest urbanized state in India. The Schedule Caste (23.5%) and Schedule Tribe (5.8%) are the major socially marginalized groups in the state. The sex ratio in West Bengal between 2001 and 2011 increased from 934 females per 1000 males to 950 females per 1000 males. Although the literacy rate improved from 69.0% in 2001 to 76.3% in 2011, the female literacy rate was lower than the male literacy rate by 11% points [ 63 ] (Table  1 ).

The demographic profiles of the population in the study districts are presented along with the state- and country-level figures. The percentage of the female population is almost identical and less than 50% of that of the male population across the districts. A little heterogeneity is evident with respect to the child population; however, the sex ratio depicts a similar pattern of male‒female distribution as that reflected in the adult population. The percentage of the socially marginalized population is much greater in South 24 Parganas and Purulia and is greater than the state average. The female literacy rate is higher for South 24 Parganas. The unemployment rate is very high across districts.

Data collection

The study involved a desk review of the literature on the current health status of children and their access to healthcare during normal times vis-à-vis disaster time in the district of concern to design the instruments under a mixed-method approach following positivism and critical realism philosophy. A review of the literature was conducted as background research for the main primary study. The research databases searched included ProQuest and Google Scholar with filters for the last five years; public healthcare; journal articles; and the English language. The search terms used were as follows: monitoring and evaluation in healthcare, sustainable healthcare, healthcare for disaster management, healthcare efficiency, healthcare effectiveness, disaster management healthcare, monitoring and evaluation, flood areas, stochastic frontier model, social and economic cost for disaster management, Cobb‒Douglas model, child health, and public healthcare for disaster management. The results indicated that there were slightly more than 400 conference and journal articles in the first stage. However, to maintain the quality of the articles, the search was limited to journal articles, which resulted in 296 journal articles. Among those 296 journal articles, 46 were included because of their relevance to the current research.

The study followed a stratified purposive sampling procedure to create a sample of respondents. The community development blocks are stratified by the degree of vulnerability according to the exposure to disaster, namely, more vulnerable, moderately vulnerable and less vulnerable, by collecting information about the impact on sector-specific indicators. Service providers are purposively selected from the block-level governance of select blocks. The quantitative data are collected via a structured questionnaire, and at the block level, officials who are interviewed face-to-face individually are selected:

Block Medical Officers, Health (BMOH) from the Department of Health and Family Welfare (DH&FW);

Child development project officers (CDPOs) and block welfare officers (BWOs) from the Department for Women and Child Development (DWD&CD);

Block Disaster Management Officers (BDMO) from the Department of Disaster Management and Civil Defense (DM&CD);

School inspectors (SIs) from the School Education Department (SED) and assistant engineers (AEs) from the Public Health Engineering Department (PHED).

The participants were interviewed to identify programme implementation-related challenges due to the occurrence of hazards to assess the barriers to service delivery at all levels of governance due to floods. In each of the districts, 60 to 75 officials from the selected departments are engaged through departmental convergence to ensure uninterrupted delivery of healthcare services to the children during the disaster. Among them, 10 CDPOs, 10 BDMOs and 10 BMOHs, 10 SIs, and 5 other officials, such as AE, BWO and SBCC workers, were interviewed from each of the 3 districts to assess the research issue, and a total of 112 interviews were successful.

Smart art: conceptual and analytical framework

The collected data are preprocessed via Stata 14.0. The data wrangling steps include normalization and missing value handling. The internal consistency between the items used to create the variables is tested via Cronbach’s alpha test on the items constituting the governance variables. The Cronbach’s alpha values revealed that almost all the variables, except Diversity in Connectivity, exhibited greater consistency among the items composing the variable (Table  2 ). The estimated stochastic frontier models have undergone different postestimation techniques, namely, the likelihood ratio (LR) test, efficiency test (AIC) and consistency test (BIC). STATA 14.0 was used for the econometric analysis of the quantitative data. Bivariate and multivariate analyses were performed to explore the research objectives.

The study contributes to the strategic management of implementation programmes at the block level of governance in 3 select disaster-prone districts in West Bengal, India. The findings will help to build and maintain an integrated M&E system to ensure child-centric health and nutrition services during disasters. It helps block-level governance identify how far vertical and horizontal departmental integration, learning and adaptation of innovation and, to some extent, timely outsourcing of select M&E components are the determinants of a successful and effective M&E system to run uninterrupted services. The goal is to reduce child mortality, malnutrition and morbidities in vulnerable populations.

Context of child health outcomes—associated risks due to gaps in healthcare access in West Bengal and in the study districts

According to the findings of the National Family Health Survey 5 (IIPS, [ 2 ]), the percentage of respondents who had four or more ANC visits was 75.8%, which is higher than the state average in Malda and South 24 Parganas and profoundly low in Purulia. Moreover, most of them visit from the first trimester (72.6%), indicating improvement in coverage of full antenatal care services in the state, where similar patterns, such as full ANC check-ups, are visible across the study districts (higher than the state average in Malda and South 24 Parganas and lower in Purulia). In contrast, PNC check-ups are lower in Malda, such as in the Purulia district, than in the state average. The percentage of women who had a live birth in the five years preceding the survey and who received a postnatal check within two days of birth for their most recent birth was 68.0%. A total of 91.7 percent of deliveries in a health facility cover both private and public facilities, and approximately 3 out of 4 institutional deliveries have taken place in public facilities. Although the rate of institutional delivery was greater at the state level than at the district level, the opposite scenario is evident with respect to delivery in public facilities, which was surprisingly greater in Purulia. The percentage delivered with assistance from health personnel was 94.1%, which was lower in Malda than in the state and other district averages under study, revealing similar patterns, such as PNC uptake—reflecting a shortage of skilled health workers leading to less access to their service in terms of availability, which demands further research. Among women who had a live birth in the 5 years preceding the survey for the most recent birth that was delivered in a public health facility, the average out-of-pocket expenditure per delivery in a public health facility was Rs. 2683 and is comparatively lower in Malda district. The percentage of children (12–23 months) who were fully vaccinated before the survey (according to a vaccination card or the mother's recall) was 87.8%. The percentage who received the most vaccinations in a public health facility was 96.3% in the state. Immunization uptake was greater in South 24 Parganas than in the state average (Table  3 ).

Findings from the primary study

The distribution of respondents in the three study districts revealed the highest degree of participation in Purulia, a comparatively moderate degree of participation in Malda and the lowest degree of participation in South 24 Parganas.

This study gathered information on the existing M&E structure at the governance level, the frequency of data collection and analysis for monitoring, and the nature of the evaluation conducted. Evidently, M&E activities are mostly the responsibility of block-level officials appointed as M&E personnel. According to the interviewed block representatives of governance, monthly monitoring is practiced by most of them in Purulia (83.7%), 65.7% of them in South 24 Parganas, whereas only 33.3% of the block representatives interviewed in Malda reported the practice of monthly monitoring activity. Among the representatives of Malda, more than 50% reported that they practice annual monitoring of routine services. In contrast, evaluation reflects the joint participation of gram panchayat and block-level officials, with district-specific variation in the degree of responsibility, as reflected in the study (Figs.  1 and 2 ).

Distribution of the respondents in the three study districts

District-level monitoring and evaluation system prevailing at the time of the study

Results from the stochastic frontier models (Table  4 )

The LR test results assume that Model 1 is nested in Model 2, Model 2 is nested in Model 3 and Model 3 is nested in Model 4. The P values revealed that Model 2 was better than Model 1, Model 3 was better than Model 2, and Model 4 was the best model among the four estimated models (Table  5 ). AIC tests the efficiency and has shown that Model 4 is the best finite-dimensional model with the assumption that the true (unknown) model has infinite dimensions. On the other hand, according to the results of the BIC tests of consistency, where the true model is finite, the best correct model is Model 4, which satisfies the condition that the probability of achieving technical efficiency toward the value ‘1’ increases with increasing population size.

Therefore, all the tests show that Model 4 is the best model and produces the highest level of technical efficiency. Next, the firm's degree of returns to scale in the production process is tested. Therefore, deviation from the use of constant returns to scale is not significantly different from zero (Table  6 ).

Figure  1 A shows the distributional pattern of the predicted technical efficiency of Model 1 and Model 2, and Fig.  1 B shows that for Model 3 and Model 4. It is evident that the distributions of predicted technical efficiency are right skewed in Model 1 and Model 2, which are corrected after the implementation of learning and innovation (Model 3) and partial outsourcing in the M&E process (Model 4).

In Model 1, four inputs are incorporated—health human resources, availability of medical stock and equipment, and degree of horizontal and vertical integration within the healthcare department and other line departments—to determine whether they have any significant influence on M&E performance effectiveness during or after a disaster. Model 1 shows that the availability of ANM and ASHA in health centers, the condition of the health centers due to flood exposure, and the degree of convergence between departments affect the effectiveness of the M&E system in generating smooth health care access during a disaster. As reflected in the model, the availability of ASHA and ANM in the center during a disaster and constrained working conditions in the center increase the need for integrated M&E coordination to ensure proper preparedness analysis and planning, depicting significantly greater M&E effectiveness at the 95% and 99% levels of significance, respectively. The model also reflects that ‘excellent to satisfactory’ levels of horizontal convergence in M&E activities with a team of health workers and integrated coordination between health and disaster management to prepare a joint health action plan for disaster-prone GPs (to implement in pre, during- and postdisaster situations adopting scientific risk and impact analysis techniques) significantly increase the effectiveness of the M&E system—at the 99% level of significance, with a moderate magnitude of influence on the average effectiveness. However, the influence of diversity and connectivity was not significant. The combined technical and allocative efficiency estimated from this model is 0.136 .

In Model 2 , variables representing the moderating factor set ‘efficiency of M&E strategies’ comprising ‘Learning and Innovation Development’, ‘Quality of Implementation Process', and 'Learning and Adaptation’ are included to test the second hypothesis. The capacity development of frontline workers with innovative approaches in data collection, maintenance, analysis and planning helps to sustain the contribution of the labor component significantly at the 95% level of significance. Moreover, learning and adopting the innovative M&E approach significantly increases M&E effectiveness at the 99% level. Model 2 shows that both governance factors positively but not significantly affect M&E effectiveness; the direct influence of horizontal convergence and vertical integration becomes weaker. Therefore, departmental convergence and integrated coordination affect M&E performance effectiveness through efficient M&E strategies. This is reflected through integrated learning and adaptation and shows that the combined efficiency increases from 0.136 to 0.160 .

The results improve further in Model 3, where the interaction between the adoption of learned innovation and process quality improvement is included. In other words, technical improvement in the process of risk and impact analysis, e.g., with the learning and application of analytics, is likely to strengthen the usability of the M&E system. This model shows that the significance levels of all the other model factors are confounded by the inclusion of innovative risk and impact assessment processes—more specifically, they are based on real-time evidence. It can be inferred that the impact of disasters on operational efficiency gradually weakens. This model reflects a sharp increase in efficiency from 0.160 to 0.944 .

To increase efficiency further ( Model 4), this model is tested by incorporating a new variable, outsourcing of some of the M&E components, for example, ex ante and ex post evaluations, to remove M&E operational bias and improve effectiveness. Therefore, if the implementation of periodic health risk and impact assessment is conducted with quality improvement in the M&E process and outsourcing the evaluation component to external evaluators significantly influences M&E effectiveness at the 95% level of significance, the predicted efficiency level increases from 0.944 to 0.946 ( marginally ) (Fig.  3 ).

Technical efficiency charts

In summary, the present research explored how to improve the M&E system of health service delivery in disaster-prone vulnerable pockets by adopting a convergence mode with disaster management and different line departments to ensure child-centric services. This study revealed that governance-related factors influence the achievement of the highest M&E production possibility frontier. The adoption of innovation is key to success, along with the outsourcing of certain components. It also tested ways to improve the technical and allocative efficiency of the comprehensive M&E system to push it to the highest effectiveness frontier, identifying the best possible combination of inputs. This study contributes to the theory of the M&E effectiveness strategy by applying the foundational theory of the neoclassical production function to combat system inefficiencies during any disaster. The conceptualization of a present-day implementation research problem from a neoclassical theoretical lens strengthened its root. This helps to compute robust results when a stochastic model is applied for empirical analysis. This could be termed a novel contribution of the present study, although it is small and indicative given the limited geographical focus. Future research should test this strategic model in different geographic settings as well as in different disaster contexts, such as M&E, for surveillance and pandemic management.

The current study tested how far systemic factors affect the comprehensive M&E production possibility frontier and how the levels of combined technical and allocative efficiency of an integrated M&E system can be enhanced so that the health system can reach maximum effectiveness in terms of the frontier. The stochastic frontier model thus estimated the best possible input combinations to achieve the maximum possible effectiveness (output) in controlling the impact of the disaster on health service delivery. The geographical setting selected for the primary study was three disaster-prone areas in West Bengal on the basis of the relatively high degree of exposure to risk and vulnerabilities.

To ensure health service delivery during a disaster, the Disaster Management and Civil Defense departments, which are equipped with technical assistance from an international organization, have initiated the process of developing an integrated M&E system where the selected districts are chosen as the implementation settings [ 65 , 66 , 67 ]. It comprises components to assess the risks and vulnerabilities in access, the status of service delivery gaps under exposure to risk, and how it impacts child health outcomes. The initiative aims to reduce child-specific health vulnerabilities, adjusting system risk through increasing system resilience. The current study examined how system resilience can be ensured through increasing technical efficiency in the allocation and utilization of system resources with efficient M&E strategies (integrated departmental coordination, action, capacity building and implementation). The study tested how much the initiatives help increase M&E effectiveness, measured in terms of reaching the highest M&E production possibility frontier (when technical efficiency → 1, i.e., the maximum), assuming that higher M&E performance (production) increases the resilience of the system and adjusts it to disaster risk to ensure child-specific services. The present study is a valuable addition to this initiative from the academic side, as it focuses only on the health sector.

The results of the stochastic frontier model have led to different policy dimensions. First, Model 1 shows that the implementation of integrated M&E in convergence mode has greater efficiency in terms of performing effectively. This study is in line with studies that investigated how far a nutrition programme can be successful at achieving horizontal integration with health and education [ 5 , 68 , 69 , 70 ]. Hawkes et al. [ 71 ] mentioned that these opportunities are not yet optimally utilized; therefore, the current contribution adds further. Another study exploring the impact of horizontal and vertical system coordination on the efficiency of the health system in Kenya has shown that challenges in integrated coordination increase transaction cost-reducing efficiency, which decreases the effectiveness of the health system [ 72 ]. Another study has shown that inefficient management strategies during the COVID-19 pandemic resulted in delayed health system responses, affecting health services in terms of delayed care, e.g., orthopedic and neurological surgeries in government hospitals in the West Bank of the Palestinian territories [ 73 ]. Therefore, if health system resilience is not built by correcting the weaker components of the system, service delivery can be jeopardized during any disaster.

Second, Model 2 shows that components of M&E strategic efficiency significantly facilitate sustaining the impact of the enabling input of the M&E effectiveness production function through reducing diversity elements and enhancing the connectivity elements in vertical and horizontal integration. The significance of horizontal convergence, connectivity and vertical coordination influencing M&E performance via innovation in M&E capacity building for frontline workers may be due to integrated preparedness planning at the GP level in disaster-prone areas. If it can be added to the district-level platform involving other concerned-line departments connected to child-centric healthcare services, the effectiveness of the intervention will increase, as found in other programmes [ 23 , 65 , 66 , 67 , 74 ].

The empirical model also reflects that vertical integration in governance is highly important for increasing M&E effectiveness, which is in line with the findings of a study in which the monitoring of frontline workers in Gujarat, India, was not successful at achieving results due to a lack of vertical integration in governance, as it created a less efficient frontline system [ 32 , 75 ]. However, the current study has found a solution to create effectiveness through the adoption of innovations, such as the application of analytics, to improve database management by integrating all the vertical levels, which is visible in two other studies. One study conducted in Uganda showed that improving the health system's capacity innovatively by altering and strengthening resources at integrated and connected local service delivery points increased health system performance in a sustainable manner, which is evident from the findings of the current research [ 62 ]. Another study in Nigeria exploring the impact of capacity building of health workers on program effectiveness has shown that contextually customized training materials, guided supervision, innovation in data collection and validation methods using comparable monitoring indicators improved the performance of state malaria programs under vertical integration [ 76 ].

Another study in Uganda showed that one of the most underutilized components in health systems management is the proper use of health records and that a mismatch between frontline workers’ and policy-level willingness to build technical capacities is a significant determinant of less utilization; however, such mismatch was not significantly evident in our study [ 61 ]. This indicates that the presence of system- and policy-level willingness is the primary criterion for increasing data utilization. In the absence of such information, the initiative for adopting innovation in building efficient M&E strategies will be difficult to initiate. A study in the KSA on health system transformation showed that knowledge building at the implementation level should be combined with research plans and efforts to build strong research governance, which was not tested in the current study and should be focused on the next level of research after the initiation of implementation [ 77 ].

However, studies exploring the usability of a strong M&E system as an innovative governance tool show that an integrated M&E system comprising the collection and analysis of bottom-up data and good coordination among policymakers, stakeholders and service providers foster need-driven decisions and policies that ultimately reduce the likelihood of market failure—as evident from a study that explored the capability of a monitoring system to monitor the health workforce in the German Federal State of Rhineland-Palatinate and matches the inference of the current study [ 78 ]. Therefore, it can be inferred that the success of an M&E system depends not only on the willingness of the service provider or policy maker but also on the need to integrate crucial stakeholders in the whole process, starting from the planning phase to the implementation phase, to increase its effectiveness. The current study has shown similar findings—M&E effectiveness increases with inter- and intradepartmental integration—and when innovation is adopted, health governance factors use innovation as an instrument to increase M&E effectiveness.

It is apparent that in Model 3, the incorporation of one interaction term has improved the predictability and explanatory power of the stochastic frontier model between learning and adaptation and process quality innovation. We examined whether the integrated health work force was trained with innovative technology to improve the quality of the data collection, analysis and use of data integrating the coordination between the health department and disaster management department. The results showed that the quality of the periodic child risk and impact assessment could be improved. In line with the current research, a systematic literature review exploring the barriers to and facilitators of implementing trauma-informed healthcare has shown that the perceived significance of the initiative for policymakers and implementers, flexible policy and training merged with the process of aligning changes, and user feedback analysis are the main enablers [ 34 ]. Several studies have identified the importance of the lessons learned to create efficient M&E systems and achieve effective public health responses during disasters [ 26 , 37 , 41 ]. In line with these studies, the current research has shown that improving the effectiveness is possible when learning is deployed in the process. Although the current study included implementers at the ground level, state-level policymakers are involved in the next stage of exploration.

Furthermore, according to Model 3, if the health workforce is trained on innovative concepts during capacity-building activities, the quality of implementation improves through learning, adaptation and application. Concerning the use of innovation, one mixed methods study in England assessing the fidelity of a digital health service programme to the structure specification has shown that variation in the delivery of the digital diabetes prevention programme by four different providers may influence the effectiveness of the process, which was continuously improved on the basis of user experience feedback [ 79 ]. Two recent studies have shown the relevance of adopting innovative monitoring systems and applying big data analytics [ 36 , 43 , 44 ]. They have shown how such innovation contributes to more effective informed policy decisions with higher accuracy and minimum error [ 36 , 43 , 44 ]. Therefore, further research is needed to assess how training on innovation will be procured and how fidelity to the requirements of health governance will be created to ensure an appropriate threshold of effectiveness. Thus, periodic risk and impact analysis becomes highly effective at ensuring access to services during disasters embedded in horizontal and vertical integration between and within departments.

Model 4 incorporates one variable representing the views of different stakeholders on M&E outsourcing to increase M&E effectiveness. According to the respondents, outsourcing M&E activities to external evaluators or agencies significantly increases the effectiveness of the intervention. The literature reflects differing views of outsourcing when a sector faces any disruption—whether technological, social, or natural [ 10 ]. In such circumstances, that sector designs short-term plans to resume and long-term plans for recovery to maintain service continuity sustainably [ 10 , 62 , 80 , 81 , 82 ]. Studies by Aragão and Fontana [ 80 ] explored the policy inclination to disfavour outsourcing during disruptions such as natural disasters and inferred through their study that the efficient use of outsourcing increases the likelihood of service continuity during such disruptions. However, legal and procedural factors should be investigated further to understand the comprehensive set of enablers and barriers to outsourcing affecting service continuity.

M&E skill building significantly increases operational efficiency and process quality, influencing M&E effectiveness under departmental convergence. The integrated M&E with the disaster management department jumps to a significantly higher frontier with the training and adoption of innovations. Finally, outsourcing the evaluation component can further enhance the efficiency level to reach a higher level of M&E frontiers given contextual adversities.

Study limitations

This study has several limitations. The study was limited by its very small sample size due to time, cost and mobility constraints. Second, the study followed purposive sampling to select the interviewees. However, when the study was conducted, the concept of integrated M&E was piloted in select blocks, which is one of the reasons for purposive selection. In the future, a large-scale longitudinal study should be designed to conduct a large survey covering multiple districts following a stratified random sampling procedure. Second, the analysis uses only classical approaches. No analysis has been conducted to propose a comprehensive decision support system using machine learning/deep learning algorithms. Therefore, it might lack higher predictive accuracy and precision. Future research is required to consider this dimension.

The present work adds value to the literature in many ways. These findings provide a direction for strengthening the decision support system (DSS) of integrated local governance and identifying the contextual determinants. This study fills such a knowledge gap for any social programme as visible in the literature [ 32 , 45 , 75 ]. Furthermore, to make the system more effective and accessible, each program can design an M&E DSS automating the whole process, starting from data acquisition to analytics and evaluation while considering the determinants related to the data and performing real-time analysis. This topic is under consideration for future research.

The proposed integrated process can be utilized to form a workforce team at the gram panchayat, block and district levels, and the comprehensive fully proofing M&E system can eventually be realized as a pilot. Furthermore, the collection of real-time data for developing preparedness plans can assure health service delivery during a disaster. Two connected real-time databases need to be trained via machine learning and deep learning techniques to modify the action plan regularly and guide the health workforce in disaster-prone areas. Lessons from the implementation in the form of impact evaluation are then documented and applied for further modifications and changes if needed. In the next phase, the tested model can be replicated in other similar vulnerable locations with continuous process improvements based on the user experience, ultimately reducing the impact of disasters on the health outcomes of vulnerable children.

Moreover, the development and maintenance of electronic risk and impact analysis of healthcare services in disaster-prone districts are planned after the development of technical skills among ground-level workers while setting an M&E technical hierarchy at each level of governance on the basis of learning. Continuous research at the implementation level is required to establish, test, implement and run the process cyclically so that the predicted level of M&E output can be achieved after ensuring the predicted level of technical efficiency. Further exploration is recommended to test how to minimize technical inefficiencies via the use of digital health tools to increase social benefits by reducing the cost of intervention.

Availability of data and materials

This information has been added as supplementary material.

Wagstaff, A. Inequalities in health in developing countries—swimming against the tide?’ Policy Research Working Paper Series 2795, The World Bank; 2002.

International Institute of Population Sciences and Macro International. National Family Health Survey, 2015–16. Mumbai; 2021.

United Nations Children’s Fund. UNICEF annual report 2017. New York, NY: UNICEF Division of Communication; 2018.

Google Scholar  

United Nations Children’s Fund. The state of the world's children 2012: children in an urban world. eSocialSciences, 2012.

Kim SS, Avula R, Ved R, Kohli N, Singh K, van den Bold M, Kadiyala S, Menon P. Understanding the role of intersectoral convergence in the delivery of essential maternal and child nutrition interventions in Odisha, India: a qualitative study. BMC Public Health. 2017;17(1):161–73.

Article   PubMed   PubMed Central   Google Scholar  

United Nations Children’s Fund. The state of the World’s Children 2019: children, food and nutrition-growing well in a changing world. New York, NY: UNICEF Division of Communication; 2019.

Book   Google Scholar  

United Nations Office for Disaster Risk Reduction. Annual Report https://www.unisdr.org/files/37302_annualreport2013.pdf (2013)

United Nations Office for Disaster Risk Reduction. Global assessment report on disaster risk reduction, Switzerland. https://www.undrr.org/media/79594 (2022).

Sahebjamnia N, Torabi SA, Mansouri SA. Building organizational resilience in the face of multiple disruptions. Int J Prod Econ. 2018;197:63–83.

Article   Google Scholar  

The World Bank. Assessing the Monitoring and Evaluation Systems of IFC and MIGA, Biennial Report on Operations Evaluation. Washington DC: The World Bank Group; 2013.

United Nations Office for Disaster Risk Reduction. Scoping study on compound, cascading and systemic risks in the Asia Pacific, Switzerland. 2021.

Topp L, Mair D, Smillie L, Cairney P. Knowledge management for policy impact: the case of the European commission’s joint research centre. Palgrave Commun. 2018. https://doi.org/10.1057/s41599-018-0143-3 .

Collier P. The bottom billion. Econ Rev Deddington. 2007;25(1):17.

Fewtrell L, Kay D. An attempt to quantify the health impacts of flooding in the UK using an urban case study. Public Health. 2008;122(5):446–51.

Article   PubMed   Google Scholar  

Pickett KE, Vafai Y, Mathai M, Small N. The social determinants of child health and inequalities in child health. Paediatr Child Health. 2022;32(3):88–94. https://doi.org/10.1016/j.paed.2021.12.003 .

Mengiste SA, Antypas K, Johannessen MR, Klein J, Kazemi G. eHealth policy framework in low and lower middle-income countries; a PRISMA systematic review and analysis. BMC Health Serv Res. 2023;23(1):1–15.

Phalkey RK, Aranda-Jan C, Marx S, Höfle B, Sauerborn R. Systematic review of current efforts to quantify the impacts of climate change on undernutrition. Proc Natl Acad Sci. 2015;112(33):E4522–9.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Campbell-Lendrum DH, Woodruff R, Prüss-Üstün A, Corvalán CF, World Health Organization. Climate change: quantifying the health impact at national and local levels. Geneva: World Health Organization; 2007.

Confalonieri U, Menne B, Akhtar R, Ebi KL, Hauengue M, Kovats RS, Revich B, Woodward A. Human health. In: Parry ML, Canziani OF, Palutikoff JP, van der Linden PJ, editors. Climate change 2007: impacts, adaptation and vulnerability: contribution of Working Group II to the fourth assessment report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press; 2007.

Woodward A, Smith KR, Campbell-Lendrum D, Chadee DD, Honda Y, Liu Q, Confalonieri U. Climate change and health: on the latest IPCC report. Lancet. 2014;383(9924):1185–9.

Attanasio O, Cattan S, Meghir C. Early childhood development, human capital, and poverty. Ann Rev Econ. 2022;14:853–92.

Dasgupta P, Ray D. Inequality as a determinant of malnutrition and unemployment: Theory. Econ J. 1986;96(384):1011–34.

Mukherjee M, Fountis A. Ensuring higher efficiency in the M&E operational process to improve the nutrition service delivery effectiveness during disaster. IOSR J Hum Soc Sci. 2020;25(12):47–55.

Ramadan MA, Borgonovi E. ’Performance measurement and management in nongovernmental organizations. IOSR J Bus Manag. 2015;17(2):70–765.

Tacoli C, Bukhari B, Fisher S. Urban poverty, food security and climate change. Human Settlements Group, International Institute for Environment and Development (IIED). (2013).

Gossip K, Gouda H, Lee YY, et al. Monitoring and evaluation of disaster response efforts undertaken by local health departments: a rapid realist review. BMC Health Serv Res. 2017;17:450. https://doi.org/10.1186/s12913-017-2396-8 .

Mukherjee M. Ex ante inequality and under-nutrition vulnerability dynamics: case study of the sundarbans delta region, West Bengal, India. Food Nutr Sci. 2014;5(20):19051.

Patz JA, Olson SH, Uejio CK, Gibbs HK. Disease emergence from global climate and land use change. Med Clin North Am. 2008;92(6):1473–91.

Freeman HE, Rossi PH, Lipsey MW. Evaluation: a systematic approach. California: Sage Publications; 1993.

Zedtwitz M, Gassmann O. Market versus Technology Drive in R&D Internationalization: Four different patterns of managing research and development. Res Policy 2002;31(4):569–588.

de Meyer A, Mizushima A. Global R&D management. R&D Manag. 1989;19(2):135–46.

Chudasama RK, Patel UV, Kadri AM, Mitra A, Thakkar D, Oza J. Evaluation of integrated child development services program in Gujarat, India for the years 2012 to 2015. Indian J Public Health. 2016;60(2):124–30.

Datta SS, Boratne D, Cherian J, Joice YS, Vignesh JT, Singh Z. Performance of Anganwadi centres in urban and rural area: a facility survey in coastal south India. Indian J Matern Child Health. 2010;12:1–9.

Duke II, Edet GH. Organizational culture as a determinant of nongovernmental organization performance: primer evidence from Nigeria. Int Bus Manag. 2012;4(1):66–75.

Ranabhat CL, Atkinson J, Park MB, Kim CB, Jakovljevic M. The influence of universal health coverage on life expectancy at birth (LEAB) and healthy life expectancy (HALE): a multicountry cross-sectional study. Front Pharmacol. 2018. https://doi.org/10.3389/fphar.2018.00960 .

Nawaz SA, Li J, Bhatti UA, Bazai SU, Zafar A, Bhatti MA, Mehmood A, Ain QU, Shoukat MU. A hybrid approach to forecast the COVID-19 epidemic trend. PLoS ONE. 2021;16(10): e0256971.

Savoia E, Agboola F, Biddinger PD. Use of after action reports (AARs) to promote organizational and systems learning in emergency preparedness. Int J Environ Res Public Health. 2012;9(8):2949–63.

Birnbaum ML, Daily EK, O’Rourke AP, Kushner J. Research and evaluations of the health aspects of disasters, Part VI: interventional research and the disaster logic model. Prehosp Disaster Med. 2016;31(2):181–94.

Auf der Heide E. The importance of evidence-based disaster planning. Ann Emerg Med. 2006;47(1):34–49.

Donohue AK, Tuohy RV. Lessons we don’t learn: a study of the lessons of disasters, why we repeat them, and how we can learn them. 2006.

The World Bank. Building resilient communities: risk management and response to natural disasters through social funds and community-driven development operations. 2009.

Wang W, Li H, Huang M. A literature review on the impact of disasters on healthcare systems, the role of nursing in disaster management, and strategies for cancer care delivery in disaster-affected populations. Front Oncol. 2023;2023(13):1178092. https://doi.org/10.3389/fonc.1178092 .

Aamir M, Li Z, Bazai S, Wagan RA, Bhatti UA, Nizamani MM, Akram S. Spatiotemporal change of air-quality patterns in Hubei province—a preto post-COVID-19 analysis using path analysis and regression. Atmosphere. 2021;12(10):1338.

Article   CAS   Google Scholar  

Bhatti UA, Wu G, Bazai SU, Nawaz SA, Baryalai M, Bhatti MA, Hasnain A, Nizamani MM. A preto post-COVID-19 change of air quality patterns in anhui province using path analysis and regression. Polish J Environ Stud. 2022;31(5):4029–42.

Chakrabarti S, Raghunathan K, Alderman H, Menon P, Nguyen P. India’s Integrated Child Development Services programme; equity and extent of coverage in 2006 and 2016. Bull World Health Organ. 2019;97(4):270–82.

Desai S, Dubey A. Caste in 21st century India: competing narratives. Econ Pol Wkly. 2012;46(11):40.

Majhi B, Dey K, Ghosh M, Biswas R. Strategies in flood disaster management: west bengal, in combating disaster: perspective in the new millennium. In: Banerjee A, Mallick B, Sarkar D, Datta H, editors. Ensuring higher efficiency in the M&E operational process. Kolkata: ACB Publication; 2005. p. 139–44.

Nyawira L, Njuguna RG, Tsofa B, Musiega A, Munywoki J, Hanson K, Mulwa A, Molyneux S, Maina I, Normand C, Jemutai J. Examining the influence of health sector coordination on the efficiency of county health systems in Kenya. BMC Health Serv Res. 2023;23(1):1–16.

Vitezić N, Cankar SS, Linšak Ž. Effectiveness measurement using DEA & BSC methods in public health services. NISPAcee J Public Adm Policy. 2019;12(1):199–216.

Bourne M, Franco-Santos M, Micheli P, Pavlov A. Performance measurement and management: a system of systems perspective. Int J Prod Res. 2018;56(8):2788–99.

Intergovernmental Panel on Climate Change. Climate change 2022 impacts, adaptation and vulnerability working group II contribution to the sixth assessment report of the intergovernmental panel on climate change, summary for policymakers. IPCC: Switzerland; 2022.

Maity B. Interstate differences in the performance of Anganwadi centres under integrated child development services scheme. Econ Pol Wkly. 2016;51(51):59–66.

Kanjilal B, Mazumdar PG, Mukherjee M, RahamanHafijur M. Nutritional status of children in India: household socioeconomic condition as the contextual determinant. Int J Equity Health. 2010;9(19):1–13.

United Nations Children’s Fund. Technical note: emergency risk informed situation analysis. http://www.unicefinemergencies.com/downloads/eresource/docs/KRR/Guidance%20Risk%20Informed%20SitAn%20FINAL.pdf (2016).

Masten AS. Global perspectives on resilience in children and youth. Child Dev. 2014;85(1):6–20.

Evans GW, Kim P. Childhood poverty, chronic stress, self-regulation, and coping. Child Dev Perspect. 2013;7(1):43–8.

Serafica FC, Vargas LA. Cultural diversity in the development of child psychopathology. In: Cicchetti D, Cohen DJ, editors. Developmental psychopathology: Theory and method, vol. 1. 2nd ed. Hoboken, NJ: Wiley; 2006. p. 588–626.

Das T, Lohar D, Dutta Ray I. Disasters in West Bengal: An Interdisciplinary Study. Kolkata: ACB Publications. Jadavpur University; 2011.

Chhotray V, Few R. Postdisaster recovery and ongoing vulnerability: ten years after the supercyclone of 1999 in Orissa, India. Global Environ Change. 2012;22(3):695–702.

Granja C, Janssen W, Johansen MA. Factors determining the success and failure of e-health interventions: systematic review of the literature. J Med Int Res. 2018;20(5):e10235.

Miiro C, Ndawula JC, Musudo E, Nabuuma OP, Mpaata CN, Nabukenya S, Akaka A, Bebembeire O, Sanya D. Achieving optimal health data impact in rural African healthcare settings: measures to barriers in Bukomansimbi District, Central Uganda. Int J Equity Health. 2022;21(1):187.

Tracey S, O’Sullivan TL, Lane DE, Guy E, Courtemanche J. Promoting resilience using an asset-based approach to business continuity planning. SAGE Open. 2017. https://doi.org/10.1177/2158244017706712 .

Census of India. Primary census abstract. New Delhi: Government of India; 2011.

StataCorp. STATA treatment-effects reference manual: potential outcomes/counterfactual outcomes release 13. College Station, Texas: StataCorp; 2013.

District Disaster Management Plan Malda (2018) Child risk and impact analysis.

District Disaster Management Plan South 24 Parganas (2018) Child risk and impact analysis.

District Disaster Management Plan Purulia (2018) Child risk and impact analysis .

Food and Agricultural Organization and World Food Programme. In: The state of food insecurity in the world 2015: meeting the 2015 international hunger targets: taking stock of uneven progress. Rome; 2013.

Drake L, Meena F, Aurina E. School feeding programs in middle childhood and adolescence. In: Bundy D, de Silva N, Horton SE, Jamison D, Patton G, editors. Child and adolescent health and development. Washington, DC: World Bank; 2017. p. 79–98.

Kristjansson EA, Gelli A, Welch V, Greenhalgh T, Liberato S, Francis D, Espejo F. Costs, and cost-outcome of school feeding programmes and feeding programmes for young children. Evidence and recommendations. Int J Educ Dev. 2016;48(C):79–83.

Hawkes C, Ruel MT, Salm L, Sinclair B, Branca F. Double-duty actions: seizing programme and policy opportunities to address malnutrition in all its forms. The Lancet. 2019;395(10218):142–55.

Patton MQ. Utilization-focused evaluation. In: Kellaghan T, Stufflebeam DL, editors. International Handbook of educational evaluation. Dordrecht: Springer; 2003. p. 223–42.

Chapter   Google Scholar  

Atary M, Abu-Rmeileh NM. Deferral of elective surgeries during the COVID-19 pandemic and its impact on Palestinian patients: a cross-sectional study. Confl Heal. 2023;17(1):13.

United Nations Children’s Fund. Guidance for risk—informed programming. New York, NY: Humanitarian Support and Transition Support; 2018.

Chudasama RK, Patel UV, Verma PB, Vala M, Rangoonwala M, Sheth A, Viramgami A. Evaluation of Anganwadi centres performance under Integrated Child Development Services (ICDS) program in Gujarat state, India during the year 2012–13. J Mahatma Gandhi Inst Med Sci. 2015;20(1):60–5.

Adewole A, Ajumobi O, Waziri N, Umar AA, Bala U, Gidado S, Ugbenyo G, Simple E, Igbaver I, Attahiru A, Michael CA. Malaria Frontline Project: strategic approaches to improve malaria control program leveraging experiences from Kano and Zamfara States, Nigeria, 2016–2019. BMC Health Serv Res. 2023;23(1):147.

Alfawaz AA, Salman KA, Alotaibi FH, Almogbel FS, Al-Jaroudi D, Alrowily MJ, Derkaoui AB, Alqahtani AS, Fadlallah R, Jamal D, El-Jardali F. Baseline assessment of health research systems in Saudi Arabia: harnessing efforts and mobilizing actions. J Epidemiol Glob Health. 2022;12(4):400–12.

Kuhlmann E, Lauxen O, Larsen C. Regional health workforce monitoring as governance innovation: a German model to coordinate sectoral demand, skill mix and mobility. Hum Resour Health. 2016;14(1):1–9.

Miles LM, Hawkes RE, French DP. Description of the nationally implemented National Health Service digital diabetes prevention programme and rationale for its development: mixed methods study. BMC Health Serv Res. 2023;23(1):1–18.

Aragão JPS, Fontana ME. Outsourcing strategies in public services under budgetary constraints: analysing perceptions of public managers. Public Org Rev. 2022;22(1):61–77.

Jafar E, Taneja U. Business continuity planning—a survey of hospitals in Delhi. J Public Health. 2017;25:699–709.

Papagiannidis S, Harris J, Morton D. WHO led the digital transformation of your company? A reflection of IT-related challenges during the pandemic. Int J Inf Manage. 2020;55: 102166.

Download references

Acknowledgements

The authors are grateful for the support received from international intergovernmental organizations and nongovernmental organization officials to strengthen the methodology of the study given the contextual realities. Considerable support has been received from the Department for Disaster Management and Civil Defense in the districts of Malda, South 24 Parganas and Purulia of West Bengal from assistance to arrange meetings and face-to-face interviews with general logistics and effective coordination, without which data collection would not have been possible. The authors are extremely grateful to Einfach Business Analytics Pvt. Ltd., Kolkata, India, to take the initiative to conduct the research.

Open Access funding enabled and organized by Projekt DEAL. The authors received no funding to conduct the research.

Author information

Authors and affiliations.

Institute of International Health, Charité - Universitätsmedizin, Berlin, Germany

Moumita Mukherjee

Einfach Business Analytics Pvt. Ltd., Kolkata, India

Indian Institute of Technology, New Delhi, Delhi, India

Quanolytics, New Delhi, Delhi, India

You can also search for this author in PubMed   Google Scholar

Contributions

Dr. Moumita Mukherjee has conceptualised the research problem, conducted review of existing literature, collected, analysed and interpreted the analysis. Mr. Batta has contributed in the methodology—conceptualising, identifying the modelling technique and helping in the interpretation. Dr. Mukherjee wrote the paper. Mr. Batta contributed in writing a part of the methodology, and in formatting the paper.

Corresponding author

Correspondence to Moumita Mukherjee .

Ethics declarations

Ethics approval and consent to participate.

The ethical approval is attached. The survey tool and method are designed while keeping in mind the gender and sociocultural sensitivities and rights of the respondents, not in violation of any of them. Informed consent was obtained from all the departments and the respondents before the interviews began. If any of the respondents denied replying, the interviewer stopped there and moved to the next respondent. The interviewers explained the purpose of the interviews and ensured that all the information remained confidential and was used only for the study. The personal details of the respondents are not revealed anywhere.

Consent for publication

Competing interests.

The authors declare that they have no competing interests.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file1, variable description.

The variables used for testing the hypotheses via the stochastic frontier model are the availability of ASHA and ANM in the center during a disaster; the running conditions of the center during a disaster, which are based on the impact on the availability and condition of the medicine stock; the equipment, water supply, and sanitation in the center during and after a disaster; the degree of departmental convergence in decentralized governance to create a risk-informed M&E plan; the vertical integration adopted in building M&E strategies within a department for the poor and vulnerable population; and the strategic efficiency (moderating factor), which includes the technical efficiency of the strategy used—Learning and Innovation Development, Learning and Adaptation and Quality of Implementation process. The dependent variable was M&E production in the healthcare system.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Mukherjee, M., Batta, A. Possibility of the optimum monitoring and evaluation (M&E) production frontier for risk-informed health governance in disaster-prone districts of West Bengal, India. J Health Popul Nutr 43 , 148 (2024). https://doi.org/10.1186/s41043-024-00632-1

Download citation

Received : 14 May 2024

Accepted : 24 August 2024

Published : 17 September 2024

DOI : https://doi.org/10.1186/s41043-024-00632-1

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Monitoring and evaluation
• Effectiveness
• Disaster risk reduction

Journal of Health, Population and Nutrition

ISSN: 2072-1315

• General enquiries: [email protected]

share this!

September 17, 2024

This article has been reviewed according to Science X's editorial process and policies . Editors have highlighted the following attributes while ensuring the content's credibility:

fact-checked

peer-reviewed publication

trusted source

Surface water sampling reveals large numbers of juvenile krill undetected by conventional monitoring methods

by Wageningen University

In 2018–2019, researchers of Wageningen Marine Research joined the Japanese research vessel Kaiyo-maru (Fisheries Agency Japan; FAJ) on an Antarctic expedition to sample the upper surface waters with the Surface and Under Ice Trawl. Results showed that a large part of the Antarctic krill population resided in the upper two meters of the water column.

The upper surface layer is usually missed by standard survey nets or acoustics that are used in monitoring. The individuals that remained close to the surface were almost all juvenile krill, in contrast to deeper water layers in which adult individuals were also found. During the second half of the expedition, the krill disappeared from the upper surface and were only found in deeper waters and closer to the continental shelf. This shift in distribution is likely caused by a combination of environmental factors and the progression of time.

Together with Japanese and US colleagues, the researchers published the results of their work in the journals Progress in Oceanography and Frontiers in Marine Science .

Zooplankton

There was a special interest in studying the distribution patterns of Antarctic krill, because this is a commercially harvested species. Therefore, the results are important for fisheries management in the Southern Ocean carried out by CCAMLR (Commission on the Conservation of Antarctic Marine Living Resources).

But apart from Antarctic krill, there were many other zooplankton species that inhabited the upper surface waters. An amphipod with the name Themisto gaudichaudi was also very abundant. In contrast to the krill, relatively large numbers of this animal remained in the upper surface area throughout the expedition.

Such information can be useful to understand variations in the food web structure in different areas of the Southern Ocean. It may, for example, explain shifts in distribution patterns of predators such as birds or whales, or reveal the importance of Themisto gaudichaudi as a food source for surface-feeding predators in the absence of the assumed main food source (krill). Results of the studies were presented as a meeting document at the CCAMLR working group that recently met in Leeuwarden.

Fokje L. Schaafsma et al, Demography of Antarctic krill (Euphausia superba) from the KY1804 austral summer survey in the eastern Indian sector of the Southern Ocean (80 to 150˚E), including specific investigations of the upper surface waters, Frontiers in Marine Science (2024). DOI: 10.3389/fmars.2024.1411130

Journal information: Frontiers in Marine Science

Provided by Wageningen University

Explore further

Feedback to editors

New material with wavy layers of atoms exhibits unusual superconducting properties

49 minutes ago

Researchers build AI model database to find new alloys for nuclear fusion facilities

Greylag geese with similar personalities have higher hatching success, study suggests

Can captive tigers be part of the effort to save wild populations?

Proteins in tooth enamel offer window into ancient and modern human wellness

2 hours ago

Mysteries of the bizarre 'pseudogap' in quantum physics finally untangled

Are cows pickier than goats? Answers from innovative large-scale feeding experiments from 275 years ago

3 hours ago

Research predicts rise in tropical hydraulic failure

Human genome stored on 'everlasting' memory crystal

Scientists say there is enough evidence to agree to global action on microplastics

Relevant physicsforums posts, why does a series of pulses generate a pitch.

9 hours ago

And Now, here comes COVID-19 version BA.2, BA.4, BA.5,...

Sep 18, 2024

Therapeutic Interfering Particle

Epothilone b study connected to 'hard problem of consciousness' model.

Sep 9, 2024

Any stereo audio learning resources for other languages?

Sep 8, 2024

Too much fluoride might lower IQ in kids?

Sep 6, 2024

More from Biology and Medical

Related Stories

Whales are recovering from near extinction, but industrial fishing around Antarctica competes for their sole food source

Sep 10, 2024

Krill provide insights into how marine species can adapt to warmer waters

Aug 1, 2024

Microplastic found in Antarctic krill and salps

Mar 29, 2023

Antarctic krill head south: Scientists fear the movement will have a negative impact on predators

Dec 19, 2023

Antarctic krill can lock away similar levels of carbon as seagrass and mangroves, finds study

Sep 17, 2024

Antarctic krill use 'hotspots' for their young

Jul 25, 2019

Recommended for you

Scientists unearth key clues to cuisine of resident killer whale populations

4 hours ago

Fossil site in Massachusetts reveals 320-million-year-old ecosystem

5 hours ago

Let us know if there is a problem with our content

Use this form if you have come across a typo, inaccuracy or would like to send an edit request for the content on this page. For general inquiries, please use our contact form . For general feedback, use the public comments section below (please adhere to guidelines ).

Please select the most appropriate category to facilitate processing of your request

Thank you for taking time to provide your feedback to the editors.

Your feedback is important to us. However, we do not guarantee individual replies due to the high volume of messages.

E-mail the story

Your email address is used only to let the recipient know who sent the email. Neither your address nor the recipient's address will be used for any other purpose. The information you enter will appear in your e-mail message and is not retained by Phys.org in any form.

Newsletter sign up

Get weekly and/or daily updates delivered to your inbox. You can unsubscribe at any time and we'll never share your details to third parties.

More information Privacy policy

Donate and enjoy an ad-free experience

We keep our content available to everyone. Consider supporting Science X's mission by getting a premium account.

E-mail newsletter

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

The PMC website is updating on October 15, 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• HHS Author Manuscripts

Purposeful sampling for qualitative data collection and analysis in mixed method implementation research

Lawrence a. palinkas.

1 School of Social Work, University of Southern California, Los Angeles, CA 90089-0411

Sarah M. Horwitz

2 Department of Child and Adolescent Psychiatry, New York University, New York, NY

Carla A. Green

3 Center for Health Research, Kaiser Permanente Northwest, Portland, OR

Jennifer P. Wisdom

4 George Washington University, Washington DC

Naihua Duan

5 New York State Neuropsychiatric Institute and Department of Psychiatry, Columbia University, New York, NY

Kimberly Hoagwood

Purposeful sampling is widely used in qualitative research for the identification and selection of information-rich cases related to the phenomenon of interest. Although there are several different purposeful sampling strategies, criterion sampling appears to be used most commonly in implementation research. However, combining sampling strategies may be more appropriate to the aims of implementation research and more consistent with recent developments in quantitative methods. This paper reviews the principles and practice of purposeful sampling in implementation research, summarizes types and categories of purposeful sampling strategies and provides a set of recommendations for use of single strategy or multistage strategy designs, particularly for state implementation research.

Recently there have been several calls for the use of mixed method designs in implementation research ( Proctor et al., 2009 ; Landsverk et al., 2012 ; Palinkas et al. 2011 ; Aarons et al., 2012). This has been precipitated by the realization that the challenges of implementing evidence-based and other innovative practices, treatments, interventions and programs are sufficiently complex that a single methodological approach is often inadequate. This is particularly true of efforts to implement evidence-based practices (EBPs) in statewide systems where relationships among key stakeholders extend both vertically (from state to local organizations) and horizontally (between organizations located in different parts of a state). As in other areas of research, mixed method designs are viewed as preferable in implementation research because they provide a better understanding of research issues than either qualitative or quantitative approaches alone ( Palinkas et al., 2011 ). In such designs, qualitative methods are used to explore and obtain depth of understanding as to the reasons for success or failure to implement evidence-based practice or to identify strategies for facilitating implementation while quantitative methods are used to test and confirm hypotheses based on an existing conceptual model and obtain breadth of understanding of predictors of successful implementation ( Teddlie & Tashakkori, 2003 ).

Sampling strategies for quantitative methods used in mixed methods designs in implementation research are generally well-established and based on probability theory. In contrast, sampling strategies for qualitative methods in implementation studies are less explicit and often less evident. Although the samples for qualitative inquiry are generally assumed to be selected purposefully to yield cases that are “information rich” (Patton, 2001), there are no clear guidelines for conducting purposeful sampling in mixed methods implementation studies, particularly when studies have more than one specific objective. Moreover, it is not entirely clear what forms of purposeful sampling are most appropriate for the challenges of using both quantitative and qualitative methods in the mixed methods designs used in implementation research. Such a consideration requires a determination of the objectives of each methodology and the potential impact of selecting one strategy to achieve one objective on the selection of other strategies to achieve additional objectives.

In this paper, we present different approaches to the use of purposeful sampling strategies in implementation research. We begin with a review of the principles and practice of purposeful sampling in implementation research, a summary of the types and categories of purposeful sampling strategies, and a set of recommendations for matching the appropriate single strategy or multistage strategy to study aims and quantitative method designs.

Principles of Purposeful Sampling

Purposeful sampling is a technique widely used in qualitative research for the identification and selection of information-rich cases for the most effective use of limited resources ( Patton, 2002 ). This involves identifying and selecting individuals or groups of individuals that are especially knowledgeable about or experienced with a phenomenon of interest ( Cresswell & Plano Clark, 2011 ). In addition to knowledge and experience, Bernard (2002) and Spradley (1979) note the importance of availability and willingness to participate, and the ability to communicate experiences and opinions in an articulate, expressive, and reflective manner. In contrast, probabilistic or random sampling is used to ensure the generalizability of findings by minimizing the potential for bias in selection and to control for the potential influence of known and unknown confounders.

As Morse and Niehaus (2009) observe, whether the methodology employed is quantitative or qualitative, sampling methods are intended to maximize efficiency and validity. Nevertheless, sampling must be consistent with the aims and assumptions inherent in the use of either method. Qualitative methods are, for the most part, intended to achieve depth of understanding while quantitative methods are intended to achieve breadth of understanding ( Patton, 2002 ). Qualitative methods place primary emphasis on saturation (i.e., obtaining a comprehensive understanding by continuing to sample until no new substantive information is acquired) ( Miles & Huberman, 1994 ). Quantitative methods place primary emphasis on generalizability (i.e., ensuring that the knowledge gained is representative of the population from which the sample was drawn). Each methodology, in turn, has different expectations and standards for determining the number of participants required to achieve its aims. Quantitative methods rely on established formulae for avoiding Type I and Type II errors, while qualitative methods often rely on precedents for determining number of participants based on type of analysis proposed (e.g., 3-6 participants interviewed multiple times in a phenomenological study versus 20-30 participants interviewed once or twice in a grounded theory study), level of detail required, and emphasis of homogeneity (requiring smaller samples) versus heterogeneity (requiring larger samples) ( Guest, Bunce & Johnson., 2006 ; Morse & Niehaus, 2009 ; Padgett, 2008 ).

Types of purposeful sampling designs

There exist numerous purposeful sampling designs. Examples include the selection of extreme or deviant (outlier) cases for the purpose of learning from an unusual manifestations of phenomena of interest; the selection of cases with maximum variation for the purpose of documenting unique or diverse variations that have emerged in adapting to different conditions, and to identify important common patterns that cut across variations; and the selection of homogeneous cases for the purpose of reducing variation, simplifying analysis, and facilitating group interviewing. A list of some of these strategies and examples of their use in implementation research is provided in Table 1 .

Purposeful sampling strategies in implementation research

Embedded in each strategy is the ability to compare and contrast, to identify similarities and differences in the phenomenon of interest. Nevertheless, some of these strategies (e.g., maximum variation sampling, extreme case sampling, intensity sampling, and purposeful random sampling) are used to identify and expand the range of variation or differences, similar to the use of quantitative measures to describe the variability or dispersion of values for a particular variable or variables, while other strategies (e.g., homogeneous sampling, typical case sampling, criterion sampling, and snowball sampling) are used to narrow the range of variation and focus on similarities. The latter are similar to the use of quantitative central tendency measures (e.g., mean, median, and mode). Moreover, certain strategies, like stratified purposeful sampling or opportunistic or emergent sampling, are designed to achieve both goals. As Patton (2002 , p. 240) explains, “the purpose of a stratified purposeful sample is to capture major variations rather than to identify a common core, although the latter may also emerge in the analysis. Each of the strata would constitute a fairly homogeneous sample.”

Challenges to use of purposeful sampling

Despite its wide use, there are numerous challenges in identifying and applying the appropriate purposeful sampling strategy in any study. For instance, the range of variation in a sample from which purposive sample is to be taken is often not really known at the outset of a study. To set as the goal the sampling of information-rich informants that cover the range of variation assumes one knows that range of variation. Consequently, an iterative approach of sampling and re-sampling to draw an appropriate sample is usually recommended to make certain the theoretical saturation occurs ( Miles & Huberman, 1994 ). However, that saturation may be determined a-priori on the basis of an existing theory or conceptual framework, or it may emerge from the data themselves, as in a grounded theory approach ( Glaser & Strauss, 1967 ). Second, there are a not insignificant number in the qualitative methods field who resist or refuse systematic sampling of any kind and reject the limiting nature of such realist, systematic, or positivist approaches. This includes critics of interventions and “bottom up” case studies and critiques. However, even those who equate purposeful sampling with systematic sampling must offer a rationale for selecting study participants that is linked with the aims of the investigation (i.e., why recruit these individuals for this particular study? What qualifies them to address the aims of the study?). While systematic sampling may be associated with a post-positivist tradition of qualitative data collection and analysis, such sampling is not inherently limited to such analyses and the need for such sampling is not inherently limited to post-positivist qualitative approaches ( Patton, 2002 ).

Purposeful Sampling in Implementation Research

Characteristics of implementation research.

In implementation research, quantitative and qualitative methods often play important roles, either simultaneously or sequentially, for the purpose of answering the same question through convergence of results from different sources, answering related questions in a complementary fashion, using one set of methods to expand or explain the results obtained from use of the other set of methods, using one set of methods to develop questionnaires or conceptual models that inform the use of the other set, and using one set of methods to identify the sample for analysis using the other set of methods ( Palinkas et al., 2011 ). A review of mixed method designs in implementation research conducted by Palinkas and colleagues (2011) revealed seven different sequential and simultaneous structural arrangements, five different functions of mixed methods, and three different ways of linking quantitative and qualitative data together. However, this review did not consider the sampling strategies involved in the types of quantitative and qualitative methods common to implementation research, nor did it consider the consequences of the sampling strategy selected for one method or set of methods on the choice of sampling strategy for the other method or set of methods. For instance, one of the most significant challenges to sampling in sequential mixed method designs lies in the limitations the initial method may place on sampling for the subsequent method. As Morse and Neihaus (2009) observe, when the initial method is qualitative, the sample selected may be too small and lack randomization necessary to fulfill the assumptions for a subsequent quantitative analysis. On the other hand, when the initial method is quantitative, the sample selected may be too large for each individual to be included in qualitative inquiry and lack purposeful selection to reduce the sample size to one more appropriate for qualitative research. The fact that potential participants were recruited and selected at random does not necessarily make them information rich.

A re-examination of the 22 studies and an additional 6 studies published since 2009 revealed that only 5 studies ( Aarons & Palinkas, 2007 ; Bachman et al., 2009 ; Palinkas et al., 2011 ; Palinkas et al., 2012 ; Slade et al., 2003) made a specific reference to purposeful sampling. An additional three studies ( Henke et al., 2008 ; Proctor et al., 2007 ; Swain et al., 2010 ) did not make explicit reference to purposeful sampling but did provide a rationale for sample selection. The remaining 20 studies provided no description of the sampling strategy used to identify participants for qualitative data collection and analysis; however, a rationale could be inferred based on a description of who were recruited and selected for participation. Of the 28 studies, 3 used more than one sampling strategy. Twenty-one of the 28 studies (75%) used some form of criterion sampling. In most instances, the criterion used is related to the individual’s role, either in the research project (i.e., trainer, team leader), or the agency (program director, clinical supervisor, clinician); in other words, criterion of inclusion in a certain category (criterion-i), in contrast to cases that are external to a specific criterion (criterion-e). For instance, in a series of studies based on the National Implementing Evidence-Based Practices Project, participants included semi-structured interviews with consultant trainers and program leaders at each study site ( Brunette et al., 2008 ; Marshall et al., 2008 ; Marty et al., 2007; Rapp et al., 2010 ; Woltmann et al., 2008 ). Six studies used some form of maximum variation sampling to ensure representativeness and diversity of organizations and individual practitioners. Two studies used intensity sampling to make contrasts. Aarons and Palinkas (2007) , for example, purposefully selected 15 child welfare case managers representing those having the most positive and those having the most negative views of SafeCare, an evidence-based prevention intervention, based on results of a web-based quantitative survey asking about the perceived value and usefulness of SafeCare. Kramer and Burns (2008) recruited and interviewed clinicians providing usual care and clinicians who dropped out of a study prior to consent to contrast with clinicians who provided the intervention under investigation. One study ( Hoagwood et al., 2007 ), used a typical case approach to identify participants for a qualitative assessment of the challenges faced in implementing a trauma-focused intervention for youth. One study ( Green & Aarons, 2011 ) used a combined snowball sampling/criterion-i strategy by asking recruited program managers to identify clinicians, administrative support staff, and consumers for project recruitment. County mental directors, agency directors, and program managers were recruited to represent the policy interests of implementation while clinicians, administrative support staff and consumers were recruited to represent the direct practice perspectives of EBP implementation.

Table 2 below provides a description of the use of different purposeful sampling strategies in mixed methods implementation studies. Criterion-i sampling was most frequently used in mixed methods implementation studies that employed a simultaneous design where the qualitative method was secondary to the quantitative method or studies that employed a simultaneous structure where the qualitative and quantitative methods were assigned equal priority. These mixed method designs were used to complement the depth of understanding afforded by the qualitative methods with the breadth of understanding afforded by the quantitative methods (n = 13), to explain or elaborate upon the findings of one set of methods (usually quantitative) with the findings from the other set of methods (n = 10), or to seek convergence through triangulation of results or quantifying qualitative data (n = 8). The process of mixing methods in the large majority (n = 18) of these studies involved embedding the qualitative study within the larger quantitative study. In one study (Goia & Dziadosz, 2008), criterion sampling was used in a simultaneous design where quantitative and qualitative data were merged together in a complementary fashion, and in two studies (Aarons et al., 2012; Zazelli et al., 2008 ), quantitative and qualitative data were connected together, one in sequential design for the purpose of developing a conceptual model ( Zazelli et al., 2008 ), and one in a simultaneous design for the purpose of complementing one another (Aarons et al., 2012). Three of the six studies that used maximum variation sampling used a simultaneous structure with quantitative methods taking priority over qualitative methods and a process of embedding the qualitative methods in a larger quantitative study ( Henke et al., 2008 ; Palinkas et al., 2010; Slade et al., 2008 ). Two of the six studies used maximum variation sampling in a sequential design ( Aarons et al., 2009 ; Zazelli et al., 2008 ) and one in a simultaneous design (Henke et al., 2010) for the purpose of development, and three used it in a simultaneous design for complementarity ( Bachman et al., 2009 ; Henke et al., 2008; Palinkas, Ell, Hansen, Cabassa, & Wells, 2011 ). The two studies relying upon intensity sampling used a simultaneous structure for the purpose of either convergence or expansion, and both studies involved a qualitative study embedded in a larger quantitative study ( Aarons & Palinkas, 2007 ; Kramer & Burns, 2008 ). The single typical case study involved a simultaneous design where the qualitative study was embedded in a larger quantitative study for the purpose of complementarity ( Hoagwood et al., 2007 ). The snowball/maximum variation study involved a sequential design where the qualitative study was merged into the quantitative data for the purpose of convergence and conceptual model development ( Green & Aarons, 2011 ). Although not used in any of the 28 implementation studies examined here, another common sequential sampling strategy is using criteria sampling of the larger quantitative sample to produce a second-stage qualitative sample in a manner similar to maximum variation sampling, except that the former narrows the range of variation while the latter expands the range.

Purposeful sampling strategies and mixed method designs in implementation research

Criterion-i sampling as a purposeful sampling strategy shares many characteristics with random probability sampling, despite having different aims and different procedures for identifying and selecting potential participants. In both instances, study participants are drawn from agencies, organizations or systems involved in the implementation process. Individuals are selected based on the assumption that they possess knowledge and experience with the phenomenon of interest (i.e., the implementation of an EBP) and thus will be able to provide information that is both detailed (depth) and generalizable (breadth). Participants for a qualitative study, usually service providers, consumers, agency directors, or state policy-makers, are drawn from the larger sample of participants in the quantitative study. They are selected from the larger sample because they meet the same criteria, in this case, playing a specific role in the organization and/or implementation process. To some extent, they are assumed to be “representative” of that role, although implementation studies rarely explain the rationale for selecting only some and not all of the available role representatives (i.e., recruiting 15 providers from an agency for semi-structured interviews out of an available sample of 25 providers). From the perspective of qualitative methodology, participants who meet or exceed a specific criterion or criteria possess intimate (or, at the very least, greater) knowledge of the phenomenon of interest by virtue of their experience, making them information-rich cases.

However, criterion sampling may not be the most appropriate strategy for implementation research because by attempting to capture both breadth and depth of understanding, it may actually be inadequate to the task of accomplishing either. Although qualitative methods are often contrasted with quantitative methods on the basis of depth versus breadth, they actually require elements of both in order to provide a comprehensive understanding of the phenomenon of interest. Ideally, the goal of achieving theoretical saturation by providing as much detail as possible involves selection of individuals or cases that can ensure all aspects of that phenomenon are included in the examination and that any one aspect is thoroughly examined. This goal, therefore, requires an approach that sequentially or simultaneously expands and narrows the field of view, respectively. By selecting only individuals who meet a specific criterion defined on the basis of their role in the implementation process or who have a specific experience (e.g., engaged only in an implementation defined as successful or only in one defined as unsuccessful), one may fail to capture the experiences or activities of other groups playing other roles in the process. For instance, a focus only on practitioners may fail to capture the insights, experiences, and activities of consumers, family members, agency directors, administrative staff, or state policy leaders in the implementation process, thus limiting the breadth of understanding of that process. On the other hand, selecting participants on the basis of whether they were a practitioner, consumer, director, staff, or any of the above, may fail to identify those with the greatest experience or most knowledgeable or most able to communicate what they know and/or have experienced, thus limiting the depth of understanding of the implementation process.

To address the potential limitations of criterion sampling, other purposeful sampling strategies should be considered and possibly adopted in implementation research ( Figure 1 ). For instance, strategies placing greater emphasis on breadth and variation such as maximum variation, extreme case, confirming and disconfirming case sampling are better suited for an examination of differences, while strategies placing greater emphasis on depth and similarity such as homogeneous, snowball, and typical case sampling are better suited for an examination of commonalities or similarities, even though both types of sampling strategies include a focus on both differences and similarities. Alternatives to criterion sampling may be more appropriate to the specific functions of mixed methods, however. For instance, using qualitative methods for the purpose of complementarity may require that a sampling strategy emphasize similarity if it is to achieve depth of understanding or explore and develop hypotheses that complement a quantitative probability sampling strategy achieving breadth of understanding and testing hypotheses ( Kemper et al., 2003 ). Similarly, mixed methods that address related questions for the purpose of expanding or explaining results or developing new measures or conceptual models may require a purposeful sampling strategy aiming for similarity that complements probability sampling aiming for variation or dispersion. A narrowly focused purposeful sampling strategy for qualitative analysis that “complements” a broader focused probability sample for quantitative analysis may help to achieve a balance between increasing inference quality/trustworthiness (internal validity) and generalizability/transferability (external validity). A single method that focuses only on a broad view may decrease internal validity at the expense of external validity ( Kemper et al., 2003 ). On the other hand, the aim of convergence (answering the same question with either method) may suggest use of a purposeful sampling strategy that aims for breadth that parallels the quantitative probability sampling strategy.

Purposeful and Random Sampling Strategies for Mixed Method Implementation Studies

• (1) Priority and sequencing of Qualitative (QUAL) and Quantitative (QUAN) can be reversed.
• (2) Refers to emphasis of sampling strategy.

Furthermore, the specific nature of implementation research suggests that a multistage purposeful sampling strategy be used. Three different multistage sampling strategies are illustrated in Figure 1 below. Several qualitative methodologists recommend sampling for variation (breadth) before sampling for commonalities (depth) ( Glaser, 1978 ; Bernard, 2002 ) (Multistage I). Also known as a “funnel approach”, this strategy is often recommended when conducting semi-structured interviews ( Spradley, 1979 ) or focus groups ( Morgan, 1997 ). This approach begins with a broad view of the topic and then proceeds to narrow down the conversation to very specific components of the topic. However, as noted earlier, the lack of a clear understanding of the nature of the range may require an iterative approach where each stage of data analysis helps to determine subsequent means of data collection and analysis ( Denzen, 1978 ; Patton, 2001) (Multistage II). Similarly, multistage purposeful sampling designs like opportunistic or emergent sampling, allow the option of adding to a sample to take advantage of unforeseen opportunities after data collection has been initiated (Patton, 2001, p. 240) (Multistage III). Multistage I models generally involve two stages, while a Multistage II model requires a minimum of 3 stages, alternating from sampling for variation to sampling for similarity. A Multistage III model begins with sampling for variation and ends with sampling for similarity, but may involve one or more intervening stages of sampling for variation or similarity as the need or opportunity arises.

Multistage purposeful sampling is also consistent with the use of hybrid designs to simultaneously examine intervention effectiveness and implementation. An extension of the concept of “practical clinical trials” ( Tunis, Stryer & Clancey, 2003 ), effectiveness-implementation hybrid designs provide benefits such as more rapid translational gains in clinical intervention uptake, more effective implementation strategies, and more useful information for researchers and decision makers ( Curran et al., 2012 ). Such designs may give equal priority to the testing of clinical treatments and implementation strategies (Hybrid Type 2) or give priority to the testing of treatment effectiveness (Hybrid Type 1) or implementation strategy (Hybrid Type 3). Curran and colleagues (2012) suggest that evaluation of the intervention’s effectiveness will require or involve use of quantitative measures while evaluation of the implementation process will require or involve use of mixed methods. When conducting a Hybrid Type 1 design (conducting a process evaluation of implementation in the context of a clinical effectiveness trial), the qualitative data could be used to inform the findings of the effectiveness trial. Thus, an effectiveness trial that finds substantial variation might purposefully select participants using a broader strategy like sampling for disconfirming cases to account for the variation. For instance, group randomized trials require knowledge of the contexts and circumstances similar and different across sites to account for inevitable site differences in interventions and assist local implementations of an intervention ( Bloom & Michalopoulos, 2013 ; Raudenbush & Liu, 2000 ). Alternatively, a narrow strategy may be used to account for the lack of variation. In either instance, the choice of a purposeful sampling strategy is determined by the outcomes of the quantitative analysis that is based on a probability sampling strategy. In Hybrid Type 2 and Type 3 designs where the implementation process is given equal or greater priority than the effectiveness trial, the purposeful sampling strategy must be first and foremost consistent with the aims of the implementation study, which may be to understand variation, central tendencies, or both. In all three instances, the sampling strategy employed for the implementation study may vary based on the priority assigned to that study relative to the effectiveness trial. For instance, purposeful sampling for a Hybrid Type 1 design may give higher priority to variation and comparison to understand the parameters of implementation processes or context as a contribution to an understanding of effectiveness outcomes (i.e., using qualitative data to expand upon or explain the results of the effectiveness trial), In effect, these process measures could be seen as modifiers of innovation/EBP outcome. In contrast, purposeful sampling for a Hybrid Type 3 design may give higher priority to similarity and depth to understand the core features of successful outcomes only.

Finally, multistage sampling strategies may be more consistent with innovations in experimental designs representing alternatives to the classic randomized controlled trial in community-based settings that have greater feasibility, acceptability, and external validity. While RCT designs provide the highest level of evidence, “in many clinical and community settings, and especially in studies with underserved populations and low resource settings, randomization may not be feasible or acceptable” ( Glasgow, et al., 2005 , p. 554). Randomized trials are also “relatively poor in assessing the benefit from complex public health or medical interventions that account for individual preferences for or against certain interventions, differential adherence or attrition, or varying dosage or tailoring of an intervention to individual needs” ( Brown et al., 2009 , p. 2). Several alternatives to the randomized design have been proposed, such as “interrupted time series,” “multiple baseline across settings” or “regression-discontinuity” designs. Optimal designs represent one such alternative to the classic RCT and are addressed in detail by Duan and colleagues (this issue) . Like purposeful sampling, optimal designs are intended to capture information-rich cases, usually identified as individuals most likely to benefit from the experimental intervention. The goal here is not to identify the typical or average patient, but patients who represent one end of the variation in an extreme case, intensity sampling, or criterion sampling strategy. Hence, a sampling strategy that begins by sampling for variation at the first stage and then sampling for homogeneity within a specific parameter of that variation (i.e., one end or the other of the distribution) at the second stage would seem the best approach for identifying an “optimal” sample for the clinical trial.

Another alternative to the classic RCT are the adaptive designs proposed by Brown and colleagues ( Brown et al, 2006 ; Brown et al., 2008 ; Brown et al., 2009 ). Adaptive designs are a sequence of trials that draw on the results of existing studies to determine the next stage of evaluation research. They use cumulative knowledge of current treatment successes or failures to change qualities of the ongoing trial. An adaptive intervention modifies what an individual subject (or community for a group-based trial) receives in response to his or her preferences or initial responses to an intervention. Consistent with multistage sampling in qualitative research, the design is somewhat iterative in nature in the sense that information gained from analysis of data collected at the first stage influences the nature of the data collected, and the way they are collected, at subsequent stages ( Denzen, 1978 ). Furthermore, many of these adaptive designs may benefit from a multistage purposeful sampling strategy at early phases of the clinical trial to identify the range of variation and core characteristics of study participants. This information can then be used for the purposes of identifying optimal dose of treatment, limiting sample size, randomizing participants into different enrollment procedures, determining who should be eligible for random assignment (as in the optimal design) to maximize treatment adherence and minimize dropout, or identifying incentives and motives that may be used to encourage participation in the trial itself.

Alternatives to the classic RCT design may also be desirable in studies that adopt a community-based participatory research framework ( Minkler & Wallerstein, 2003 ), considered to be an important tool on conducting implementation research ( Palinkas & Soydan, 2012 ). Such frameworks suggest that identification and recruitment of potential study participants will place greater emphasis on the priorities and “local knowledge” of community partners than on the need to sample for variation or uniformity. In this instance, the first stage of sampling may approximate the strategy of sampling politically important cases ( Patton, 2002 ) at the first stage, followed by other sampling strategies intended to maximize variations in stakeholder opinions or experience.

On the basis of this review, the following recommendations are offered for the use of purposeful sampling in mixed method implementation research. First, many mixed methods studies in health services research and implementation science do not clearly identify or provide a rationale for the sampling procedure for either quantitative or qualitative components of the study ( Wisdom et al., 2011 ), so a primary recommendation is for researchers to clearly describe their sampling strategies and provide the rationale for the strategy.

Second, use of a single stage strategy for purposeful sampling for qualitative portions of a mixed methods implementation study should adhere to the same general principles that govern all forms of sampling, qualitative or quantitative. Kemper and colleagues (2003) identify seven such principles: 1) the sampling strategy should stem logically from the conceptual framework as well as the research questions being addressed by the study; 2) the sample should be able to generate a thorough database on the type of phenomenon under study; 3) the sample should at least allow the possibility of drawing clear inferences and credible explanations from the data; 4) the sampling strategy must be ethical; 5) the sampling plan should be feasible; 6) the sampling plan should allow the researcher to transfer/generalize the conclusions of the study to other settings or populations; and 7) the sampling scheme should be as efficient as practical.

Third, the field of implementation research is at a stage itself where qualitative methods are intended primarily to explore the barriers and facilitators of EBP implementation and to develop new conceptual models of implementation process and outcomes. This is especially important in state implementation research, where fiscal necessities are driving policy reforms for which knowledge about EBP implementation barriers and facilitators are urgently needed. Thus a multistage strategy for purposeful sampling should begin first with a broader view with an emphasis on variation or dispersion and move to a narrow view with an emphasis on similarity or central tendencies. Such a strategy is necessary for the task of finding the optimal balance between internal and external validity.

Fourth, if we assume that probability sampling will be the preferred strategy for the quantitative components of most implementation research, the selection of a single or multistage purposeful sampling strategy should be based, in part, on how it relates to the probability sample, either for the purpose of answering the same question (in which case a strategy emphasizing variation and dispersion is preferred) or the for answering related questions (in which case, a strategy emphasizing similarity and central tendencies is preferred).

Fifth, it should be kept in mind that all sampling procedures, whether purposeful or probability, are designed to capture elements of both similarity and differences, of both centrality and dispersion, because both elements are essential to the task of generating new knowledge through the processes of comparison and contrast. Selecting a strategy that gives emphasis to one does not mean that it cannot be used for the other. Having said that, our analysis has assumed at least some degree of concordance between breadth of understanding associated with quantitative probability sampling and purposeful sampling strategies that emphasize variation on the one hand, and between the depth of understanding and purposeful sampling strategies that emphasize similarity on the other hand. While there may be some merit to that assumption, depth of understanding requires both an understanding of variation and common elements.

Finally, it should also be kept in mind that quantitative data can be generated from a purposeful sampling strategy and qualitative data can be generated from a probability sampling strategy. Each set of data is suited to a specific objective and each must adhere to a specific set of assumptions and requirements. Nevertheless, the promise of mixed methods, like the promise of implementation science, lies in its ability to move beyond the confines of existing methodological approaches and develop innovative solutions to important and complex problems. For states engaged in EBP implementation, the need for these solutions is urgent.

Multistage Purposeful Sampling Strategies

Acknowledgments

This study was funded through a grant from the National Institute of Mental Health (P30-MH090322: K. Hoagwood, PI).

Bats (Mammalia: Chiroptera) of Ubajara National Park, Ceará, Brazil: a diversity assessment using complementary sampling methods

• ORIGINAL PAPER
• Published: 16 September 2024

Cite this article

• Ana C. Pavan   ORCID: orcid.org/0000-0002-0653-6186 1   nAff8 ,
• Gustavo L. Urbieta   ORCID: orcid.org/0000-0002-1373-4498 2 ,
• Werther P. Ramalho   ORCID: orcid.org/0000-0002-1049-2307 3 , 4 ,
• Gabryella S. Mesquita   ORCID: orcid.org/0000-0001-8933-0425 3 ,
• Jeanneson Sales   ORCID: orcid.org/0000-0001-7768-4146 5 ,
• Fábio Falcão   ORCID: orcid.org/0000-0002-2748-7117 6 &
• Tarcilla Valtuille   ORCID: orcid.org/0000-0003-2053-0802 7  

Bats are unique among mammals in their capacity for powered flight and present high species diversity and feeding habits in the Neotropical region. Despite the remarkable increase in knowledge on the distribution of neotropical bats in recent decades, information on the species’ occurrence throughout Brazil is still widely heterogeneous, with significant knowledge gaps in many biomes. The Ubajara National Park (PNU), northwestern Ceará, is an area of extreme biodiversity in the Caatinga biome, characterized by several natural caves associated with a noticeable bat community. Herein, we carried out a complementary inventory of bat diversity in the PNU, focusing on six caves and their surrounding foraging sites. Two surveys totaling 36 sampling nights were conducted using complementary methods such as mist nets, harp trap, roosting searches, and acoustic monitoring. Thirty species of bats belonging to eight families were recorded. We found significant complementarity between the sampling methods resulting in the stabilization of the rarefaction curve. Eight species were found in roosting colonies in at least one of the sampled cavities. A total of 965 individuals from 18 species, with the majority belonging to the family Phyllostomidae, were recorded using active sampling techniques. Passive acoustic monitoring yielded 14 different sonotypes of species from the Emballonuridae, Mormoopidae, Molossidae, Vespertilionidae, and Noctilionidae families. The acoustic activity of bats from distinct families was higher in the dry season and varied throughout the night. Two species registered with passive acoustic monitoring were among the captured ones, thus reinforcing the importance of diversifying methodologies to obtain more complete bat inventories.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save.

• Get 10 units per month
• Download Article/Chapter or eBook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Similar content being viewed by others

Changes in activity along the year in a community of insectivorous bats inhabiting a montane ecosystem of central Mexico

Bats (Chiroptera, Vespertilionidae) in Orenburg Oblast

Empty in summer, crowded during migration structure of assemblage, distribution pattern and habitat use by bats (chiroptera: vespertilionidae) in a narrow, marine peninsula, data availability.

The datasets generated during the current study are available from the corresponding author on reasonable request.

Aguiar LMS, Taddei VA (1995) Workshop sobre a conservação dos morcegos brasileiros. Chiropt Neotrop 1:24–29

Google Scholar  

Aguiar LMS, Pereira MJR, Zortéa M, Machado RB (2020) Where are the bats? An environmental complementarity analysis in a megadiverse country. Divers Distrib 26:1510–1522. https://doi.org/10.1111/ddi.13137

Article   Google Scholar  

Alencar DM, De, Falcão FDC, Tinoco MS (2020) Use of visible Implant Elastomer (VIE) as an efficient method for marking bats. Bol Da Soc Bras Mastozool 89:107–111

Appel G, Capaverde UD, de Oliveira LQ et al (2022) Use of complementary methods to Sample bats in the Amazon. Acta Chiropterologica 23:499–511. https://doi.org/10.3161/15081109acc2021.23.2.017

Arias-Aguilar A, Hintze F, Ludmilla et al (2018) Who’s calling? Acoustic identification of Brazilian bats. Mammal Res 63:231–253. https://doi.org/10.1007/s13364-018-0367-z

Arita H (1993) Conservation biology of the cave bats of Mexico. J Mammal 74:693–702. https://doi.org/10.2307/1382291

Avila-Flores R, Medellín RA (2004) Ecological, taxonomic, and physiological correlates of cave use by Mexican bats. J Mammal 85:675–687. https://doi.org/10.1644/BOS-127

Barataud M, Giosa S, Leblanc F et al (2013) Identification et écologie acoustique des chiroptères de guyane française. Le Rhinolophe 19:103–145

Barros JDS, Bernard E (2023) Species richness, occurrence and rarity of bats in Brazilian caves. Austral Ecol 48:2144–2170. https://doi.org/10.1111/aec.13453

Barros JDS, Bernard E, Ferreira RL (2020) Ecological preferences of neotropical cave bats in roost site selection and their implications for conservation. Basic Appl Ecol 45:31–41. https://doi.org/10.1016/j.baae.2020.03.007

Bergallo HG, Esberard CEL, Mello MAR et al (2003) Bat species richness in Atlantic forest: what is the minimum sampling effort? Biotropica 35:278–288. https://doi.org/10.1111/j.1744-7429.2003.tb00286.x

Bernard E, Aguiar LMS, Machado RB (2011) Discovering the Brazilian bat fauna: a task for two centuries? Mamm Rev 41:23–39. https://doi.org/10.1111/j.1365-2907.2010.00164.x

Burgin CJ, Colella JP, Kahn PL, Upham NS (2018) How many species of mammals are there? J Mammal 99:1–14. https://doi.org/10.1093/jmammal/gyx147

Carvalho WD, Miguel JD, da Silva Xavier B et al (2023) Complementarity between mist-netting and low-cost acoustic recorders to sample bats in amazonian rainforests and savannahs. Community Ecol 24:47–60. https://doi.org/10.1007/s42974-022-00131-5

CECAV (2022) Cadastro Nacional de Informações Espeleológicas (CANIE), Instituto Chico Mendes de Conservação da Biodiversidade, Ministério do Meio Ambiente. https://www.icmbio.gov.br/cecav/canie.html

Chao A, Gotelli NJ, Hsieh TC et al (2014) Rarefaction and extrapolation with Hill numbers: a framework for sampling and estimation in species diversity studies. Ecol Monogr 84:45–67. https://doi.org/10.1890/13-0133.1

R Core Team (2024) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/

Costa LM, Lourenço EC, de Damasceno Júnior D A, et al (2021) Ilha Grande, one of the locations with the most records of bat species (Mammalia, Chiroptera) in Rio De Janeiro state: results of a long-term ecological study. Pap Avulsos Zool 61:e20216122. https://doi.org/10.11606/1807-0205/2021.61.22

Crawley MJ (2012) The R Book. Wiley-Blackwell, Chichester, England

Book   Google Scholar  

Díaz MM, Solari S, Gregorin R et al (2021) Clave De Identificación De Los murciélagos Neotropicales. PCMA (Programa de Conservación de los Murciélagos de Argentina), Tucumán

Falcão F, Pavan AC (2023) Mormoopid bats from Brazil: updates on the geographic distribution of three species and their echolocation calls. Mammalia 87:228–237. https://doi.org/10.1515/mammalia-2022-0102

Falcão F, Dodonov P, Caselli CB et al (2021) Landscape structure shapes activity levels and composition of aerial insectivorous bats at different spatial scales. Biodivers Conserv 30:2545–2564. https://doi.org/10.1007/s10531-021-02210-x

Feijó A, Rocha PA (2017) Morcegos Da Estação Ecológica Aiuaba, Ceará, nordeste do Brasil: Uma unidade de proteção integral na Caatinga. Mastozoología Neotrop 24:333–346

Fenton MB, Simmons NB (2014) Bats: a world of science and mystery. University of Chicago Press

Fonseca GAB, Herrmann G, Leite YLR et al (1996) Lista Anotada dos mamíferos do Brasil. Occas Pap Conserv Biol 4:1–38

Garbino GST, Lim BK, Tavares VC (2020) Systematics of big-eyed bats, genus Chiroderma Peters, 1860 (Chiroptera: Phyllostomidae). https://doi.org/10.11646/zootaxa.4846.1.1 . Zootaxa 4846:

Garbino GST, Gregorin R, Lima IP et al (2022) Updated checklist of Brazilian bats: versão 2020. Comitê da Lista de Morcegos do Brasil—CLMB. Sociedade Brasileira para o Estudo de Quirópteros (SBEQ). https://www.sbeq.net/lista-de-especies . Accessed 03 October 2022

Garcia ACL, Leal ESB, Rohde C et al (2014) The bats of northeastern Brazil: a panorama. Anim Biol 64:141–150. https://doi.org/10.1163/15707563-00002440

Gardner AL (2007) Mammals of South America. Volume 1: marsupials, xenarthrans, shrews, and bats. University of Chicago Press, Chicago and London

Gregorin R, Pavan AC (2022) Comitê de Coleções Científicas. SBEQ 2022. http://www.sbeq.net/colec . Accessed 03 October 2022

Gregorin R, Tahara A, Mancini M et al (2022) Mixed sampling methods reveal elevated Bat richness in a Semideciduous Atlantic Forest Remnant. Acta Chiropterologica 24:139–150. https://doi.org/10.3161/15081109ACC2022.24.1.011

Guimarães MM, Ferreira RL (2014) Morcegos cavernícolas do Brasil: novos registros e desafios para conservação. Rev Bras Espeleol 2:1–33

Gurgel-Filho NM, Feijó A, Langguth A (2015) Pequenos mamíferos do Ceará (Marsupiais, Morcegos E Roedores sigmodontíneos) com discussão taxonômica de algumas espécies. Rev Nord Biol 23:3–150

Hintze F, Barbier E, Bernard E (2016) Emballonuridae Gervais, 1855 (Chiroptera) of Reserva Biológica De Saltinho (Atlantic Forest), in Brazil, revealed by echolocation. Check List 12:1–9. https://doi.org/10.15560/12.4.1925

Hintze F, Bernard E, Aguiar LM, Pereira MJ, Bobrowiec PED, Falcão F (2019) Comitê de Bioacústica. SBEQ 2019. v2. http://www.sbeq.net/bioacustic . Accessed 30 August 2022

Hsieh TC, Ma KH, Chao A (2016) iNEXT: an R package for rarefaction and extrapolation of species diversity (Hill numbers). Methods Ecol Evol 7:1451–1456. https://doi.org/10.1111/2041-210X.12613

Parque Nacional de Ubajara. Instituto Chico Mendes de Conservação da Biodiversidade (ICMBio), Em (2021) https://www.icmbio.gov.br/parnaubajara/quem-somos.html , acessado em 29 de Setembro de 2021.

Jung K, Kalko EKV, Von Helversen O (2007) Echolocation calls in central American emballonurid bats: signal design and call frequency alternation. J Zool 272:125–137. https://doi.org/10.1111/j.1469-7998.2006.00250.x

Kotila M, Suominen KM, Vasko VV et al (2023) Large-scale long-term passive-acoustic monitoring reveals spatio-temporal activity patterns of boreal bats. Ecography e06617. https://doi.org/10.1111/ecog.06617

Kunz TH (1982) Ecology of bats. Springer US, Boston, MA

Kwiecinski GG (2006) Phyllostomus discolor. Mamm Species 801:1–11. https://doi.org/10.1644/801.1

Lenth RV (2024) emmeans: Estimated Marginal Means, aka Least-Squares Means. R package version 1.10.1. https://CRAN.R-project.org/package=emmeans

Mares MA, Willig MR, Streilein KE, Lacher T (1981) The mammals of northeastern Brazil: a preliminary assessment. Ann Carnegie Museum 50:81–136. https://doi.org/10.5962/p.214487

Marsh CJ, Sica YV, Burgin CJ et al (2022) Expert range maps of global mammal distributions harmonised to three taxonomic authorities. J Biogeogr 49:979–992. https://doi.org/10.1111/jbi.14330

Article   PubMed   PubMed Central   Google Scholar  

Ministério do Meio Ambiente, MMA (2018) Mapa das áreas prioritárias para a conservação, utilização sustentável e repartição dos benefícios da biodiversidade brasileira, 2a. atualização. Portaria n. 463, Governo Federal do Brasil. https://www.gov.br/mma/pt-br/assuntos/ecossistemas-1/conservacao-1/areas-prioritarias/caatinga.jpg

Novaes RLM, Laurindo RS (2014) Morcegos Da Chapada do Araripe, Nordeste do Brasil. Pap Avulsos Zool 54:315–328. https://doi.org/10.1590/0031-1049.2014.54.22

Oliveira H, Oprea M, Dias R (2018) Distributional patterns and ecological determinants of bat occurrence inside caves: a broad scale meta-analysis. Diversity 10:49. https://doi.org/10.3390/d10030049

Otálora-Ardila A, Torres JM, Barbier E et al (2020) Thermally-assisted monitoring of bat abundance in an exceptional cave in Brazil’s caatinga drylands. Acta Chiropterologica 21:411–423. https://doi.org/10.3161/15081109ACC2019.21.2.016

Pilatti P, Moratelli R, Aguiar LMS, Astúa D (2021) Distribution and morphological variation of Xeronycteris vieirai Gregorin and Ditchfield, 2005 (Chiroptera, Phyllostomidae). Acta Chiropterologica 23:475–497. https://doi.org/10.3161/15081109ACC2021.23.2.016

Rocha PA, Pedroso MA, Feijó A et al (2014) Update on the distribution of Diphylla ecaudata Spix, 1823 (Mammalia, Chiroptera): new records from the Brazilian northeast. Check List 10:1541–1545. https://doi.org/10.15560/10.6.1541

Santos BM, Aguirre LF, Vazquez LB (2003) Phyllostomus hastatus. Mamm Species 722:1–6. https://doi.org/10.1644/0.722.1

Schnitzler H-U, Kalko EKV (2001) Echolocation by insect-eating bats. Bioscience 51:557–569. https://doi.org/10.1641/0006-3568(2001)051 [0557:EBIEB]2.0.CO;2

Sikes RS (2016) 2016 guidelines of the American Society of mammalogists for the use of wild mammals in research and education. J Mammal 97:663–688. https://doi.org/10.1093/jmammal/gyw078

Silva SSP, Guedes PG, Peracchi AL (2001) Levantamento preliminar dos morcegos do Parque Nacional De Ubajara (Mammalia, Chiroptera), Ceará, Brasil. Rev Bras Zool 18:139–144. https://doi.org/10.1590/s0101-81752001000100015

Silva MS, Ferreira RL (2009) Caracterização ecológica de algumas cavernas do Parque Nacional de Ubajara (Ceará) com considerações sobre o turismo nestas cavidades. Revista de Biologia e Ciências Da Terra 9(1):59–71

Silva SSP, Dias D, Martins MA, Guedes PG, de Almeida JC, da Cruz AP, Serra-Freire NM, Damascena JDS, Peracchi AL (2015) Bats (Mammalia: Chiroptera) from the caatinga scrublands of the crateus region, northeastern Brazil, with new records for the state of Ceará. Mastozoologia Neotropical 22(2)

Silva CR, Bernard E (2017) Bioacoustics as an important complementary tool in bat inventories in the caatinga drylands of Brazil. Acta Chiropterologica 19:409–418. https://doi.org/10.3161/15081109ACC2017.19.2.017

Silva da JMC, Leal IR, Tabarelli M (2018a) Caatinga: the largest tropical dry forest region in South America. Springer International Publishing, Cham

Silva UBT, Delgado-Jaramillo M, de Souza Aguiar LM, Bernard E (2018b) Species richness, geographic distribution, pressures, and threats to bats in the Caatinga drylands of Brazil. Biol Conserv 221:312–322. https://doi.org/10.1016/j.biocon.2018.03.028

Straube F, Bianconi G (2002) Sobre a grandeza e a unidade utilizada para estimar esforço de captura com utilização de redes-de-neblina. Chiropt Neotrop 8:150–152

Vargas-Mena JC, Alves-Pereira K, Barros MAS et al (2018) The bats of Rio Grande do Norte state, northeastern Brazil. Biota Neotrop 18:e20170417. https://doi.org/10.1590/1676-0611-bn-2017-0417

Verde RS, Silva RC, Calouro AM (2018) Activity patterns of frugivorous phyllostomid bats in an urban fragment in southwest Amazonia, Brazil. Iheringia Série Zool 108. https://doi.org/10.1590/1678-4766e2018016

Wickham H (2007) Reshaping Data with the reshape Package. J Stat Softw 21(12):1–20. https://doi.org/10.18637/jss.v021.i12

Wickham H (2016) ggplot2. Springer International Publishing, New York

Willig MR (1983) Composition, microgeographic variation, and sexual dimorphism in Caatingas and Cerrado bat communities from northeast Brazil. Bulletim Carnegie Museum Nat Hist 23:1–131

Wilson DE, Mittermeier RA (2019) Handbook of the Mammals of the World, vol 9. Bats. Lynx Edicions, Barcelona, ES

Zeppelini CG, Jerônimo I, Rego KMC et al (2017) Description of Whole-Night Activity Patterns for Neotropical Bat species. Acta Chiropterologica 19:155–160. https://doi.org/10.3161/15081109ACC2017.19.1.012

Zuur AF, Ieno EN, Elphick CS (2010) A protocol for data exploration to avoid common statistical problems. Methods Ecol Evol 1:3–14. https://doi.org/10.1111/j.2041-210X.2009.00001.x

Download references

Acknowledgements

We thank Elecnor Brasil and Dossel Ambiental, particularly Leonardo Gomes, for the supervision and coordination of the project, and Tullio dos Santos and Helbert Barbosa, from Juma Consultoria Ambiental, for their support in the field and in the development of the project. WPR is a regional development researcher supported by Fundação de Amparo à Pesquisa do Estado de Goiás and Conselho Nacional de Desenvolvimento Científico e Tecnológico (process nº 317724/2021-5).

The study was funded by Serra da Ibiapaba Transmissora de Energia S. A. as a requirement of the environmental licensing process for installation of a power transmission line system.

Author information

Ana C. Pavan

Present address: Instituto Tecnológico Vale (ITV), Belém, PA, Brazil

Authors and Affiliations

Museu de Zoologia da Universidade de São Paulo, São Paulo, SP, Brazil

Universidade Federal do Amapá (Programa de Pós-Graduação em Biodiversidade Tropical), Macapá, AP, Brazil

Gustavo L. Urbieta

Instituto Boitatá de Etnobiologia e Conservação da Fauna, Goiânia, GO, Brazil

Werther P. Ramalho & Gabryella S. Mesquita

Laboratório de Ecologia, Evolução e Sistemática de Vertebrados, Instituto Federal Goiano, Rio Verde, GO, Brazil

Werther P. Ramalho

Departamento de Sistemática e Ecologia (Programa de Pós-graduação em Ciências Biológicas), Universidade Federal da Paraíba, Campus I, João Pessoa, PB, Brazil

Jeanneson Sales

Tetrapoda Consultoria Ambiental, Ilhéus, BA, Brazil

Fábio Falcão

Juma Pesquisa e Consultoria Ambiental, Brasília, DF, Brazil

Tarcilla Valtuille

You can also search for this author in PubMed   Google Scholar

Contributions

ACP and TV designed the study; GLU, GSM, WPR and TV collected the data; ACP, GLU, JS, FF, and WPR analyzed the data and wrote the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Ana C. Pavan .

Ethics declarations

Ethical approval.

All procedures involving bat capture and handling were authorized by the Brazilian Institute of the Environment and Renewable Natural Resources (IBAMA) and followed the recommendations of the American Society of Mammalogists (ASM).

Conflict of interest

The authors have no competing interests to declare that are relevant to the content of this article.

Additional information

Communicated by Karol Zub.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Supplementary material 2, supplementary material 3, supplementary material 4, supplementary material 5, supplementary material 6, supplementary material 7, supplementary material 8, supplementary material 9, supplementary material 10, supplementary material 11, supplementary material 12, supplementary material 13, supplementary material 14, supplementary material 15.

Supplementary Material 16

Supplementary Material 17

Supplementary Material 18

Supplementary Material 19

Supplementary Material 20

Supplementary Material 21

Supplementary Material 22

Supplementary Material 23

Supplementary Material 24

Supplementary Material 25

Supplementary Material 26

Supplementary Material 27

Supplementary Material 28

Supplementary Material 29

Appendix 1: descriptive data of the caves surveyed in this study.

All caves are formed in limestone rocks. This type of rock outcrop is evident in some of them (e.g., Gruta do Pendurado and Urso Fóssil). Temperature and relative humidity data were taken at least in one area inside the cavities in both campaigns (dry and rainy seasons). The caves at higher altitudes are Gruta do Urso Fóssil, Gruta do Pendurado, and Gruta do Macaco Fóssil. The predominant vegetation around the park caves is similar to Moist Forest Enclaves (e.g., Chapada do Araripe). However, in several places, there are signs of domestication of the flora represented by fruit species not common in this type of vegetation, for example, the mangroves. As the terrain gains altitude, the floristic composition becomes heterogeneous; initially, it is possible to see a shrubby caatinga, then an open arboreal caatinga, and finally, typical Atlantic Forest formations. The cave Furnas de Araticum, located in the park’s buffer zone, is surrounded predominantly by shrubby caatinga formations.

Gruta de Ubajara

A cave with approximately 1,120 m mapped, it is the park’s main tourist attraction and the only cave in the tourist route. According to the National Center for Research and Conservation of Caves (CECAV/ICMBio), Gruta de Ubajara may be more than 2 km long. There are four entrances, but only one allows easy access to its interior. It has 15 halls, an underground river (including a waterfall), and fossilized remains of living species. The cave has undergone various anthropogenic modifications, including the establishment of a wedding altar with constructed steps and statues (during the period of 1940–1956), as well as the implementation of artificial illumination since 1992 (though some form of lighting had been present for approximately four decades). Out of the total cave length of 1120 m, the initial 420 m encompass areas with strategically positioned artificial lighting fixtures and dedicated tourist infrastructures, signifying the intensive utilization zone. Conversely, the remaining 700 m are devoid of illumination and restricted for access solely with IBAMA authorization, exclusively intended for research or technical visits. The temperature measurements were obtained at five distinct locations within the cave, revealing a range from 23.3 °C to 26.1 °C. Simultaneously, the relative humidity levels varied between 53% and 99% across the same sampling sites.

Gruta do Morcego Branco

Gruta do Morcego Branco is approximately 300 m from the Gruta de Ubajara, precisely on the limestone outcrop that stands directly across the latter’s entrance. It develops in a network of narrow, low-height galleries that have elliptical cross-sections, typically formed in a dynamic siphon regime. The cave has a single entrance and is approximately 274 m long (Silva and Ferreira, 2009). The cave may have received this name due to the presence of hematophagous bats with silver-gray coloration. The cave is not part of the park’s tourist route. Although there is no internal watercourse, a minor rivulet courses directly beneath its entrance, with an approximate vertical drop of 3 m. The temperature and relative humidity of the air inside the cave were 23.5 °C and 79–91%, respectively.

Gruta do Macaco Fóssil

Gruta do Macaco Fóssil is located in “Morro da Bandeira” mountain and is not listed in the Management Plan of the PNU (ICMBIO, 2001). Located near the hill’s summit, access to this cave is the most challenging due to its rugged topography and dense vegetation. While comparatively smaller in size when compared to the others, this cave comprises an extension surpassing 50 m. Its entrance is circular and narrow (about 0.5 m in diameter) with a slope of approximately 3 m. Due to an abyss in its interior, it is possible to explore its first chamber only. The temperature measurements were recorded at three distinct locations within the cave, exhibiting a range of 22.3 °C to 23.6 °C, and relative humidity levels varied between 81% and 99%.

Gruta do Urso Fóssil

Gruta do Urso Fóssil is on the hill known as “Morro do Pendurado,” a substantial metacalcarenite outcrop with a formation dating back over 540 million years. The cave spans approximately 195 m in length and features a primary entrance, along with two secondary entrances consisting of two large windows to which access is difficult. This vast cave features at least four chambers, an underground watercourse during the rainy season, and an abyss. The temperature observed at four cave locations ranged between 22.8 °C and 26.8 °C, with relative humidity indices between 71 and 99%.

Gruta do Pendurado

Gruta do Pendurado was discovered in 1978 and is registered in the National Register of Caves of the Brazilian Society of Speleology (SBE) under the acronym CNC/SBE CE-05. It has an approximate extension of 154 m and a single entrance of difficult access. The cave’s interior is characterized by a complex system of branched galleries, exhibiting a gradual reduction in height as one moves away from the entrance. Moreover, the presence of an abyss further adds up to the cave’s distinct features. The temperature measured at three different cave areas ranged from 23.4 °C to 25.7 °C, with humidity indices between 65 and 99%.

Furnas de Araticum

The cave is approximately 200 m long and has at least four wide entrances (more than 5 m wide), a watercourse in the rainy season, and an abyss. During the rainy season, puddles of water formed on the external cave ceiling, from which water dripped into the cave’s interior. This cave is on the park’s limits, surrounded by a small community of pig farmers and traditional families of the region. Therefore, domestic animals such as pigs (despite the existence of swine housing) and cats were also inhabiting this cave, promoting a dangerous interaction from an epidemiological perspective (bats + pigs + domestic cats). The temperature in Furna de Araticum measured in four cave locations ranged between 25.1 °C and 27.9 °C and humidity between 61 and 81%.

Gruta de Santa Bárbara

Located in the park’s buffer zone, Gruta de Santa Bárbara is the most geographically isolated cave, approximately 926 m from Furna de Araticum. Located at the top of the hill, the cave is exclusively accessible through a steep and spiraling ascent with loose and rugged rock fragments. The grotto has two slit-shaped entrances in opposite positions. The surrounding vegetation is composed almost entirely of shrubby Caatinga. This cave was not included in the wildlife sampling permit. For this reason, we did not sample this area with capture traps and active search. However, during the rainy season campaign, one night of passive acoustic recording was carried out.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Pavan, A.C., Urbieta, G.L., Ramalho, W.P. et al. Bats (Mammalia: Chiroptera) of Ubajara National Park, Ceará, Brazil: a diversity assessment using complementary sampling methods. Mamm Res (2024). https://doi.org/10.1007/s13364-024-00761-2

Download citation

Received : 24 August 2023

Accepted : 27 August 2024

Published : 16 September 2024

DOI : https://doi.org/10.1007/s13364-024-00761-2

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Conservation unit
• Find a journal
• Publish with us
• Track your research