the difference between a case study and a survey is

Case Study vs. Survey

What's the difference.

Case studies and surveys are both research methods used in various fields to gather information and insights. However, they differ in their approach and purpose. A case study involves an in-depth analysis of a specific individual, group, or situation, aiming to understand the complexities and unique aspects of the subject. It often involves collecting qualitative data through interviews, observations, and document analysis. On the other hand, a survey is a structured data collection method that involves gathering information from a larger sample size through standardized questionnaires. Surveys are typically used to collect quantitative data and provide a broader perspective on a particular topic or population. While case studies provide rich and detailed information, surveys offer a more generalizable and statistical overview.

Further Detail

Introduction.

When conducting research, there are various methods available to gather data and analyze it. Two commonly used methods are case study and survey. Both approaches have their own unique attributes and can be valuable in different research contexts. In this article, we will explore the characteristics of case study and survey, highlighting their strengths and limitations.

A case study is an in-depth investigation of a particular individual, group, or phenomenon. It involves collecting detailed information about the subject of study through various sources such as interviews, observations, and document analysis. Case studies are often used in social sciences, psychology, and business research to gain a deep understanding of complex issues.

One of the key attributes of a case study is its ability to provide rich and detailed data. Researchers can gather extensive information about the subject, including their background, experiences, and perspectives. This depth of data allows for a comprehensive analysis and interpretation of the case, providing valuable insights into the phenomenon under investigation.

Furthermore, case studies are particularly useful when studying rare or unique cases. Since case studies focus on specific individuals or groups, they can shed light on situations that are not easily replicated or observed in larger populations. This makes case studies valuable in exploring complex and nuanced phenomena that may not be easily captured through other research methods.

However, it is important to note that case studies have certain limitations. Due to their in-depth nature, case studies are often time-consuming and resource-intensive. Researchers need to invest significant effort in data collection, analysis, and interpretation. Additionally, the findings of a case study may not be easily generalized to larger populations, as the focus is on a specific case rather than a representative sample.

Despite these limitations, case studies offer a unique opportunity to explore complex issues in real-life contexts. They provide a detailed understanding of individual experiences and can generate hypotheses for further research.

A survey is a research method that involves collecting data from a sample of individuals through a structured questionnaire or interview. Surveys are widely used in social sciences, market research, and public opinion studies to gather information about a larger population. They aim to provide a snapshot of people's opinions, attitudes, behaviors, or characteristics.

One of the main advantages of surveys is their ability to collect data from a large number of respondents. By reaching out to a representative sample, researchers can generalize the findings to a larger population. Surveys also allow for efficient data collection, as questionnaires can be distributed electronically or in person, making it easier to gather a wide range of responses in a relatively short period.

Moreover, surveys offer a structured approach to data collection, ensuring consistency in the questions asked and the response options provided. This allows for easy comparison and analysis of the data, making surveys suitable for quantitative research. Surveys can also be conducted anonymously, which can encourage respondents to provide honest and unbiased answers, particularly when sensitive topics are being explored.

However, surveys also have their limitations. One of the challenges is the potential for response bias. Respondents may provide inaccurate or socially desirable answers, leading to biased results. Additionally, surveys often rely on self-reported data, which may be subject to memory recall errors or misinterpretation of questions. Researchers need to carefully design the survey instrument and consider potential biases to ensure the validity and reliability of the data collected.

Furthermore, surveys may not capture the complexity and depth of individual experiences. They provide a snapshot of people's opinions or behaviors at a specific point in time, but may not uncover the underlying reasons or motivations behind those responses. Surveys also rely on predetermined response options, limiting the range of possible answers and potentially overlooking important nuances.

Case studies and surveys are both valuable research methods, each with its own strengths and limitations. Case studies offer in-depth insights into specific cases, providing rich and detailed data. They are particularly useful for exploring complex and unique phenomena. On the other hand, surveys allow for efficient data collection from a large number of respondents, enabling generalization to larger populations. They provide structured and quantifiable data, making them suitable for statistical analysis.

Ultimately, the choice between case study and survey depends on the research objectives, the nature of the research question, and the available resources. Researchers need to carefully consider the attributes of each method and select the most appropriate approach to gather and analyze data effectively.

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Case Study vs. Survey: What's the Difference?

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Distinguishing Between Case Study & Survey Methods

Key Difference – Case Study vs Survey

When carrying out research, case studies and surveys are two methods used by researchers. Although both are used to collect information, there is a key difference between a case study and a survey. A case study involves researching an individual, group, or specific situation in-depth, usually over a long period of time. On the other hand, a survey involves gathering data from an entire population or a very large sample to understand opinions on a specific topic. The main difference between the two methods is that case studies produce rich, descriptive data, while surveys do not; instead, the data collected from surveys is more statistically significant.

Key Takeaways

• Case studies involve in-depth research of an individual, group, or specific situation, while surveys gather data from an entire population or a large sample.
• Case studies produce rich, descriptive data, while surveys produce data that is more statistically significant.
• Case studies are used in qualitative research, while surveys are mostly used in quantitative research.

What is a Case Study?

A case study refers to an in-depth study in which an individual, group, or a particular situation is studied. This is used in both natural and social sciences. In the natural sciences, a case study can be used to validate a theory or even a hypothesis. In the social sciences, case studies are used extensively to study human behavior and comprehend various social aspects. For example, in psychology, case studies are conducted to comprehend individual behavior. In such cases, the researcher records the entire history of the individual so that it enables him to identify various patterns of behavior. One of the classic examples of a case study is Sigmund Freud’s study of Anna O.

Case studies typically produce rich descriptive data. However, they cannot be used to provide generalizations on an entire population since the sample of a case study is usually limited to a single individual or a few individuals. Various research techniques, such as interviews, direct and participatory observation, and documents can be used for case studies.

What is a Survey?

A survey refers to research where data is gathered from an entire population or a very large sample to understand the opinions on a particular matter. In modern society, surveys are often used in politics and marketing. For example, imagine a situation where an organization wishes to understand the opinions of consumers on their latest product. Naturally, the organization would conduct a survey to comprehend the opinions of the consumer.

One of the most powerful research techniques used for surveys is the questionnaire. For this, the researcher creates a set of questions on the topic for which he will gather information from the participants. Unlike case studies, the data gathered from surveys is not very descriptive. Instead, they are statistically significant.

What is the difference between Case Study and Survey?

Definitions of Case Study and Survey: Case Study: A case study refers to an in-depth study in which an individual, group, or a particular situation is studied. Survey: A survey refers to research where data is gathered from an entire population or a very large sample to understand the opinions on a particular matter. Characteristics of Case Study and Survey: Research Type: Case Study: Case studies are used in qualitative research. Survey: Surveys are mostly used in quantitative research. Data: Case Study: Case studies produce rich in-depth data. Survey: Surveys produce numerical data. Sample: Case Study: For a case study, a relatively small population is chosen. This can vary from a few individuals to groups. Survey: For a survey, a large population can be used as the sample.

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Case Study vs. Survey — What's the Difference?

Difference Between Case Study and Survey

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Methodology

• Survey Research | Definition, Examples & Methods

Survey Research | Definition, Examples & Methods

Published on August 20, 2019 by Shona McCombes . Revised on June 22, 2023.

Survey research means collecting information about a group of people by asking them questions and analyzing the results. To conduct an effective survey, follow these six steps:

• Determine who will participate in the survey
• Decide the type of survey (mail, online, or in-person)
• Design the survey questions and layout
• Distribute the survey
• Analyze the responses
• Write up the results

Surveys are a flexible method of data collection that can be used in many different types of research .

Table of contents

What are surveys used for, step 1: define the population and sample, step 2: decide on the type of survey, step 3: design the survey questions, step 4: distribute the survey and collect responses, step 5: analyze the survey results, step 6: write up the survey results, other interesting articles, frequently asked questions about surveys.

Surveys are used as a method of gathering data in many different fields. They are a good choice when you want to find out about the characteristics, preferences, opinions, or beliefs of a group of people.

Common uses of survey research include:

• Social research : investigating the experiences and characteristics of different social groups
• Market research : finding out what customers think about products, services, and companies
• Health research : collecting data from patients about symptoms and treatments
• Politics : measuring public opinion about parties and policies
• Psychology : researching personality traits, preferences and behaviours

Surveys can be used in both cross-sectional studies , where you collect data just once, and in longitudinal studies , where you survey the same sample several times over an extended period.

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Before you start conducting survey research, you should already have a clear research question that defines what you want to find out. Based on this question, you need to determine exactly who you will target to participate in the survey.

Populations

The target population is the specific group of people that you want to find out about. This group can be very broad or relatively narrow. For example:

• The population of Brazil
• US college students
• Second-generation immigrants in the Netherlands
• Customers of a specific company aged 18-24
• British transgender women over the age of 50

Your survey should aim to produce results that can be generalized to the whole population. That means you need to carefully define exactly who you want to draw conclusions about.

Several common research biases can arise if your survey is not generalizable, particularly sampling bias and selection bias . The presence of these biases have serious repercussions for the validity of your results.

It’s rarely possible to survey the entire population of your research – it would be very difficult to get a response from every person in Brazil or every college student in the US. Instead, you will usually survey a sample from the population.

The sample size depends on how big the population is. You can use an online sample calculator to work out how many responses you need.

There are many sampling methods that allow you to generalize to broad populations. In general, though, the sample should aim to be representative of the population as a whole. The larger and more representative your sample, the more valid your conclusions. Again, beware of various types of sampling bias as you design your sample, particularly self-selection bias , nonresponse bias , undercoverage bias , and survivorship bias .

There are two main types of survey:

• A questionnaire , where a list of questions is distributed by mail, online or in person, and respondents fill it out themselves.
• An interview , where the researcher asks a set of questions by phone or in person and records the responses.

Which type you choose depends on the sample size and location, as well as the focus of the research.

Questionnaires

Sending out a paper survey by mail is a common method of gathering demographic information (for example, in a government census of the population).

• You can easily access a large sample.
• You have some control over who is included in the sample (e.g. residents of a specific region).
• The response rate is often low, and at risk for biases like self-selection bias .

Online surveys are a popular choice for students doing dissertation research , due to the low cost and flexibility of this method. There are many online tools available for constructing surveys, such as SurveyMonkey and Google Forms .

• You can quickly access a large sample without constraints on time or location.
• The data is easy to process and analyze.
• The anonymity and accessibility of online surveys mean you have less control over who responds, which can lead to biases like self-selection bias .

If your research focuses on a specific location, you can distribute a written questionnaire to be completed by respondents on the spot. For example, you could approach the customers of a shopping mall or ask all students to complete a questionnaire at the end of a class.

• You can screen respondents to make sure only people in the target population are included in the sample.
• You can collect time- and location-specific data (e.g. the opinions of a store’s weekday customers).
• The sample size will be smaller, so this method is less suitable for collecting data on broad populations and is at risk for sampling bias .

Oral interviews are a useful method for smaller sample sizes. They allow you to gather more in-depth information on people’s opinions and preferences. You can conduct interviews by phone or in person.

• You have personal contact with respondents, so you know exactly who will be included in the sample in advance.
• You can clarify questions and ask for follow-up information when necessary.
• The lack of anonymity may cause respondents to answer less honestly, and there is more risk of researcher bias.

Like questionnaires, interviews can be used to collect quantitative data: the researcher records each response as a category or rating and statistically analyzes the results. But they are more commonly used to collect qualitative data : the interviewees’ full responses are transcribed and analyzed individually to gain a richer understanding of their opinions and feelings.

Next, you need to decide which questions you will ask and how you will ask them. It’s important to consider:

• The type of questions
• The content of the questions
• The phrasing of the questions
• The ordering and layout of the survey

Open-ended vs closed-ended questions

There are two main forms of survey questions: open-ended and closed-ended. Many surveys use a combination of both.

Closed-ended questions give the respondent a predetermined set of answers to choose from. A closed-ended question can include:

• A binary answer (e.g. yes/no or agree/disagree )
• A scale (e.g. a Likert scale with five points ranging from strongly agree to strongly disagree )
• A list of options with a single answer possible (e.g. age categories)
• A list of options with multiple answers possible (e.g. leisure interests)

Closed-ended questions are best for quantitative research . They provide you with numerical data that can be statistically analyzed to find patterns, trends, and correlations .

Open-ended questions are best for qualitative research. This type of question has no predetermined answers to choose from. Instead, the respondent answers in their own words.

Open questions are most common in interviews, but you can also use them in questionnaires. They are often useful as follow-up questions to ask for more detailed explanations of responses to the closed questions.

The content of the survey questions

To ensure the validity and reliability of your results, you need to carefully consider each question in the survey. All questions should be narrowly focused with enough context for the respondent to answer accurately. Avoid questions that are not directly relevant to the survey’s purpose.

When constructing closed-ended questions, ensure that the options cover all possibilities. If you include a list of options that isn’t exhaustive, you can add an “other” field.

Phrasing the survey questions

In terms of language, the survey questions should be as clear and precise as possible. Tailor the questions to your target population, keeping in mind their level of knowledge of the topic. Avoid jargon or industry-specific terminology.

Survey questions are at risk for biases like social desirability bias , the Hawthorne effect , or demand characteristics . It’s critical to use language that respondents will easily understand, and avoid words with vague or ambiguous meanings. Make sure your questions are phrased neutrally, with no indication that you’d prefer a particular answer or emotion.

Ordering the survey questions

The questions should be arranged in a logical order. Start with easy, non-sensitive, closed-ended questions that will encourage the respondent to continue.

If the survey covers several different topics or themes, group together related questions. You can divide a questionnaire into sections to help respondents understand what is being asked in each part.

If a question refers back to or depends on the answer to a previous question, they should be placed directly next to one another.

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Before you start, create a clear plan for where, when, how, and with whom you will conduct the survey. Determine in advance how many responses you require and how you will gain access to the sample.

When you are satisfied that you have created a strong research design suitable for answering your research questions, you can conduct the survey through your method of choice – by mail, online, or in person.

There are many methods of analyzing the results of your survey. First you have to process the data, usually with the help of a computer program to sort all the responses. You should also clean the data by removing incomplete or incorrectly completed responses.

If you asked open-ended questions, you will have to code the responses by assigning labels to each response and organizing them into categories or themes. You can also use more qualitative methods, such as thematic analysis , which is especially suitable for analyzing interviews.

Statistical analysis is usually conducted using programs like SPSS or Stata. The same set of survey data can be subject to many analyses.

Finally, when you have collected and analyzed all the necessary data, you will write it up as part of your thesis, dissertation , or research paper .

In the methodology section, you describe exactly how you conducted the survey. You should explain the types of questions you used, the sampling method, when and where the survey took place, and the response rate. You can include the full questionnaire as an appendix and refer to it in the text if relevant.

Then introduce the analysis by describing how you prepared the data and the statistical methods you used to analyze it. In the results section, you summarize the key results from your analysis.

In the discussion and conclusion , you give your explanations and interpretations of these results, answer your research question, and reflect on the implications and limitations of the research.

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

A questionnaire is a data collection tool or instrument, while a survey is an overarching research method that involves collecting and analyzing data from people using questionnaires.

A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviors. It is made up of 4 or more questions that measure a single attitude or trait when response scores are combined.

To use a Likert scale in a survey , you present participants with Likert-type questions or statements, and a continuum of items, usually with 5 or 7 possible responses, to capture their degree of agreement.

Individual Likert-type questions are generally considered ordinal data , because the items have clear rank order, but don’t have an even distribution.

Overall Likert scale scores are sometimes treated as interval data. These scores are considered to have directionality and even spacing between them.

The type of data determines what statistical tests you should use to analyze your data.

The priorities of a research design can vary depending on the field, but you usually have to specify:

• Your research questions and/or hypotheses
• Your overall approach (e.g., qualitative or quantitative )
• The type of design you’re using (e.g., a survey , experiment , or case study )
• Your sampling methods or criteria for selecting subjects
• Your data collection methods (e.g., questionnaires , observations)
• Your data collection procedures (e.g., operationalization , timing and data management)
• Your data analysis methods (e.g., statistical tests  or thematic analysis )

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Research Writing and Analysis

• NVivo Group and Study Sessions
• SPSS This link opens in a new window
• Statistical Analysis Group sessions
• Using Qualtrics
• Dissertation and Data Analysis Group Sessions
• Research Process Flow Chart
• Research Alignment This link opens in a new window
• Step 1: Seek Out Evidence
• Step 2: Explain
• Step 3: The Big Picture
• Step 4: Own It
• Step 5: Illustrate
• Annotated Bibliography
• Literature Review This link opens in a new window
• Systematic Reviews & Meta-Analyses
• How to Synthesize and Analyze
• Synthesis and Analysis Practice
• Synthesis and Analysis Group Sessions
• Problem Statement
• Purpose Statement
• Quantitative Research Questions
• Qualitative Research Questions
• Trustworthiness of Qualitative Data
• Analysis and Coding Example- Qualitative Data
• Thematic Data Analysis in Qualitative Design
• Dissertation to Journal Article This link opens in a new window
• International Journal of Online Graduate Education (IJOGE) This link opens in a new window
• Journal of Research in Innovative Teaching & Learning (JRIT&L) This link opens in a new window

Writing a Case Study

What is a case study?

A Case study is: 

• An in-depth research design that primarily uses a qualitative methodology but sometimes​​ includes quantitative methodology.
• Used to examine an identifiable problem confirmed through research.
• Used to investigate an individual, group of people, organization, or event.
• Used to mostly answer "how" and "why" questions.

What are the different types of case studies?

Note: These are the primary case studies. As you continue to research and learn

about case studies you will begin to find a robust list of different types. 

Who are your case study participants?

What is triangulation ? 

Validity and credibility are an essential part of the case study. Therefore, the researcher should include triangulation to ensure trustworthiness while accurately reflecting what the researcher seeks to investigate.

How to write a Case Study?

When developing a case study, there are different ways you could present the information, but remember to include the five parts for your case study.

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• URL: https://resources.nu.edu/researchtools

2.2 Approaches to Research

Learning objectives.

By the end of this section, you will be able to:

• Describe the different research methods used by psychologists
• Discuss the strengths and weaknesses of case studies, naturalistic observation, surveys, and archival research
• Compare longitudinal and cross-sectional approaches to research
• Compare and contrast correlation and causation

There are many research methods available to psychologists in their efforts to understand, describe, and explain behavior and the cognitive and biological processes that underlie it. Some methods rely on observational techniques. Other approaches involve interactions between the researcher and the individuals who are being studied—ranging from a series of simple questions to extensive, in-depth interviews—to well-controlled experiments.

Each of these research methods has unique strengths and weaknesses, and each method may only be appropriate for certain types of research questions. For example, studies that rely primarily on observation produce incredible amounts of information, but the ability to apply this information to the larger population is somewhat limited because of small sample sizes. Survey research, on the other hand, allows researchers to easily collect data from relatively large samples. While this allows for results to be generalized to the larger population more easily, the information that can be collected on any given survey is somewhat limited and subject to problems associated with any type of self-reported data. Some researchers conduct archival research by using existing records. While this can be a fairly inexpensive way to collect data that can provide insight into a number of research questions, researchers using this approach have no control on how or what kind of data was collected. All of the methods described thus far are correlational in nature. This means that researchers can speak to important relationships that might exist between two or more variables of interest. However, correlational data cannot be used to make claims about cause-and-effect relationships.

Correlational research can find a relationship between two variables, but the only way a researcher can claim that the relationship between the variables is cause and effect is to perform an experiment. In experimental research, which will be discussed later in this chapter, there is a tremendous amount of control over variables of interest. While this is a powerful approach, experiments are often conducted in artificial settings. This calls into question the validity of experimental findings with regard to how they would apply in real-world settings. In addition, many of the questions that psychologists would like to answer cannot be pursued through experimental research because of ethical concerns.

Clinical or Case Studies

In 2011, the New York Times published a feature story on Krista and Tatiana Hogan, Canadian twin girls. These particular twins are unique because Krista and Tatiana are conjoined twins, connected at the head. There is evidence that the two girls are connected in a part of the brain called the thalamus, which is a major sensory relay center. Most incoming sensory information is sent through the thalamus before reaching higher regions of the cerebral cortex for processing.

Link to Learning

Watch this CBC video about Krista's and Tatiana's lives to learn more.

The implications of this potential connection mean that it might be possible for one twin to experience the sensations of the other twin. For instance, if Krista is watching a particularly funny television program, Tatiana might smile or laugh even if she is not watching the program. This particular possibility has piqued the interest of many neuroscientists who seek to understand how the brain uses sensory information.

These twins represent an enormous resource in the study of the brain, and since their condition is very rare, it is likely that as long as their family agrees, scientists will follow these girls very closely throughout their lives to gain as much information as possible (Dominus, 2011).

Over time, it has become clear that while Krista and Tatiana share some sensory experiences and motor control, they remain two distinct individuals, which provides invaluable insight for researchers interested in the mind and the brain (Egnor, 2017).

In observational research, scientists are conducting a clinical or case study when they focus on one person or just a few individuals. Indeed, some scientists spend their entire careers studying just 10–20 individuals. Why would they do this? Obviously, when they focus their attention on a very small number of people, they can gain a precious amount of insight into those cases. The richness of information that is collected in clinical or case studies is unmatched by any other single research method. This allows the researcher to have a very deep understanding of the individuals and the particular phenomenon being studied.

If clinical or case studies provide so much information, why are they not more frequent among researchers? As it turns out, the major benefit of this particular approach is also a weakness. As mentioned earlier, this approach is often used when studying individuals who are interesting to researchers because they have a rare characteristic. Therefore, the individuals who serve as the focus of case studies are not like most other people. If scientists ultimately want to explain all behavior, focusing attention on such a special group of people can make it difficult to generalize any observations to the larger population as a whole. Generalizing refers to the ability to apply the findings of a particular research project to larger segments of society. Again, case studies provide enormous amounts of information, but since the cases are so specific, the potential to apply what’s learned to the average person may be very limited.

Naturalistic Observation

If you want to understand how behavior occurs, one of the best ways to gain information is to simply observe the behavior in its natural context. However, people might change their behavior in unexpected ways if they know they are being observed. How do researchers obtain accurate information when people tend to hide their natural behavior? As an example, imagine that your professor asks everyone in your class to raise their hand if they always wash their hands after using the restroom. Chances are that almost everyone in the classroom will raise their hand, but do you think hand washing after every trip to the restroom is really that universal?

This is very similar to the phenomenon mentioned earlier in this chapter: many individuals do not feel comfortable answering a question honestly. But if we are committed to finding out the facts about hand washing, we have other options available to us.

Suppose we send a classmate into the restroom to actually watch whether everyone washes their hands after using the restroom. Will our observer blend into the restroom environment by wearing a white lab coat, sitting with a clipboard, and staring at the sinks? We want our researcher to be inconspicuous—perhaps standing at one of the sinks pretending to put in contact lenses while secretly recording the relevant information. This type of observational study is called naturalistic observation : observing behavior in its natural setting. To better understand peer exclusion, Suzanne Fanger collaborated with colleagues at the University of Texas to observe the behavior of preschool children on a playground. How did the observers remain inconspicuous over the duration of the study? They equipped a few of the children with wireless microphones (which the children quickly forgot about) and observed while taking notes from a distance. Also, the children in that particular preschool (a “laboratory preschool”) were accustomed to having observers on the playground (Fanger, Frankel, & Hazen, 2012).

It is critical that the observer be as unobtrusive and as inconspicuous as possible: when people know they are being watched, they are less likely to behave naturally. If you have any doubt about this, ask yourself how your driving behavior might differ in two situations: In the first situation, you are driving down a deserted highway during the middle of the day; in the second situation, you are being followed by a police car down the same deserted highway ( Figure 2.7 ).

It should be pointed out that naturalistic observation is not limited to research involving humans. Indeed, some of the best-known examples of naturalistic observation involve researchers going into the field to observe various kinds of animals in their own environments. As with human studies, the researchers maintain their distance and avoid interfering with the animal subjects so as not to influence their natural behaviors. Scientists have used this technique to study social hierarchies and interactions among animals ranging from ground squirrels to gorillas. The information provided by these studies is invaluable in understanding how those animals organize socially and communicate with one another. The anthropologist Jane Goodall , for example, spent nearly five decades observing the behavior of chimpanzees in Africa ( Figure 2.8 ). As an illustration of the types of concerns that a researcher might encounter in naturalistic observation, some scientists criticized Goodall for giving the chimps names instead of referring to them by numbers—using names was thought to undermine the emotional detachment required for the objectivity of the study (McKie, 2010).

The greatest benefit of naturalistic observation is the validity , or accuracy, of information collected unobtrusively in a natural setting. Having individuals behave as they normally would in a given situation means that we have a higher degree of ecological validity, or realism, than we might achieve with other research approaches. Therefore, our ability to generalize the findings of the research to real-world situations is enhanced. If done correctly, we need not worry about people or animals modifying their behavior simply because they are being observed. Sometimes, people may assume that reality programs give us a glimpse into authentic human behavior. However, the principle of inconspicuous observation is violated as reality stars are followed by camera crews and are interviewed on camera for personal confessionals. Given that environment, we must doubt how natural and realistic their behaviors are.

The major downside of naturalistic observation is that they are often difficult to set up and control. In our restroom study, what if you stood in the restroom all day prepared to record people’s hand washing behavior and no one came in? Or, what if you have been closely observing a troop of gorillas for weeks only to find that they migrated to a new place while you were sleeping in your tent? The benefit of realistic data comes at a cost. As a researcher you have no control of when (or if) you have behavior to observe. In addition, this type of observational research often requires significant investments of time, money, and a good dose of luck.

Sometimes studies involve structured observation. In these cases, people are observed while engaging in set, specific tasks. An excellent example of structured observation comes from Strange Situation by Mary Ainsworth (you will read more about this in the chapter on lifespan development). The Strange Situation is a procedure used to evaluate attachment styles that exist between an infant and caregiver. In this scenario, caregivers bring their infants into a room filled with toys. The Strange Situation involves a number of phases, including a stranger coming into the room, the caregiver leaving the room, and the caregiver’s return to the room. The infant’s behavior is closely monitored at each phase, but it is the behavior of the infant upon being reunited with the caregiver that is most telling in terms of characterizing the infant’s attachment style with the caregiver.

Another potential problem in observational research is observer bias . Generally, people who act as observers are closely involved in the research project and may unconsciously skew their observations to fit their research goals or expectations. To protect against this type of bias, researchers should have clear criteria established for the types of behaviors recorded and how those behaviors should be classified. In addition, researchers often compare observations of the same event by multiple observers, in order to test inter-rater reliability : a measure of reliability that assesses the consistency of observations by different observers.

Often, psychologists develop surveys as a means of gathering data. Surveys are lists of questions to be answered by research participants, and can be delivered as paper-and-pencil questionnaires, administered electronically, or conducted verbally ( Figure 2.9 ). Generally, the survey itself can be completed in a short time, and the ease of administering a survey makes it easy to collect data from a large number of people.

Surveys allow researchers to gather data from larger samples than may be afforded by other research methods . A sample is a subset of individuals selected from a population , which is the overall group of individuals that the researchers are interested in. Researchers study the sample and seek to generalize their findings to the population. Generally, researchers will begin this process by calculating various measures of central tendency from the data they have collected. These measures provide an overall summary of what a typical response looks like. There are three measures of central tendency: mode, median, and mean. The mode is the most frequently occurring response, the median lies at the middle of a given data set, and the mean is the arithmetic average of all data points. Means tend to be most useful in conducting additional analyses like those described below; however, means are very sensitive to the effects of outliers, and so one must be aware of those effects when making assessments of what measures of central tendency tell us about a data set in question.

There is both strength and weakness of the survey in comparison to case studies. By using surveys, we can collect information from a larger sample of people. A larger sample is better able to reflect the actual diversity of the population, thus allowing better generalizability. Therefore, if our sample is sufficiently large and diverse, we can assume that the data we collect from the survey can be generalized to the larger population with more certainty than the information collected through a case study. However, given the greater number of people involved, we are not able to collect the same depth of information on each person that would be collected in a case study.

Another potential weakness of surveys is something we touched on earlier in this chapter: People don't always give accurate responses. They may lie, misremember, or answer questions in a way that they think makes them look good. For example, people may report drinking less alcohol than is actually the case.

Any number of research questions can be answered through the use of surveys. One real-world example is the research conducted by Jenkins, Ruppel, Kizer, Yehl, and Griffin (2012) about the backlash against the US Arab-American community following the terrorist attacks of September 11, 2001. Jenkins and colleagues wanted to determine to what extent these negative attitudes toward Arab-Americans still existed nearly a decade after the attacks occurred. In one study, 140 research participants filled out a survey with 10 questions, including questions asking directly about the participant’s overt prejudicial attitudes toward people of various ethnicities. The survey also asked indirect questions about how likely the participant would be to interact with a person of a given ethnicity in a variety of settings (such as, “How likely do you think it is that you would introduce yourself to a person of Arab-American descent?”). The results of the research suggested that participants were unwilling to report prejudicial attitudes toward any ethnic group. However, there were significant differences between their pattern of responses to questions about social interaction with Arab-Americans compared to other ethnic groups: they indicated less willingness for social interaction with Arab-Americans compared to the other ethnic groups. This suggested that the participants harbored subtle forms of prejudice against Arab-Americans, despite their assertions that this was not the case (Jenkins et al., 2012).

Archival Research

Some researchers gain access to large amounts of data without interacting with a single research participant. Instead, they use existing records to answer various research questions. This type of research approach is known as archival research . Archival research relies on looking at past records or data sets to look for interesting patterns or relationships.

For example, a researcher might access the academic records of all individuals who enrolled in college within the past ten years and calculate how long it took them to complete their degrees, as well as course loads, grades, and extracurricular involvement. Archival research could provide important information about who is most likely to complete their education, and it could help identify important risk factors for struggling students ( Figure 2.10 ).

In comparing archival research to other research methods, there are several important distinctions. For one, the researcher employing archival research never directly interacts with research participants. Therefore, the investment of time and money to collect data is considerably less with archival research. Additionally, researchers have no control over what information was originally collected. Therefore, research questions have to be tailored so they can be answered within the structure of the existing data sets. There is also no guarantee of consistency between the records from one source to another, which might make comparing and contrasting different data sets problematic.

Longitudinal and Cross-Sectional Research

Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again at age 40.

Another approach is cross-sectional research. In cross-sectional research , a researcher compares multiple segments of the population at the same time. Using the dietary habits example above, the researcher might directly compare different groups of people by age. Instead of studying a group of people for 20 years to see how their dietary habits changed from decade to decade, the researcher would study a group of 20-year-old individuals and compare them to a group of 30-year-old individuals and a group of 40-year-old individuals. While cross-sectional research requires a shorter-term investment, it is also limited by differences that exist between the different generations (or cohorts) that have nothing to do with age per se, but rather reflect the social and cultural experiences of different generations of individuals that make them different from one another.

To illustrate this concept, consider the following survey findings. In recent years there has been significant growth in the popular support of same-sex marriage. Many studies on this topic break down survey participants into different age groups. In general, younger people are more supportive of same-sex marriage than are those who are older (Jones, 2013). Does this mean that as we age we become less open to the idea of same-sex marriage, or does this mean that older individuals have different perspectives because of the social climates in which they grew up? Longitudinal research is a powerful approach because the same individuals are involved in the research project over time, which means that the researchers need to be less concerned with differences among cohorts affecting the results of their study.

Often longitudinal studies are employed when researching various diseases in an effort to understand particular risk factors. Such studies often involve tens of thousands of individuals who are followed for several decades. Given the enormous number of people involved in these studies, researchers can feel confident that their findings can be generalized to the larger population. The Cancer Prevention Study-3 (CPS-3) is one of a series of longitudinal studies sponsored by the American Cancer Society aimed at determining predictive risk factors associated with cancer. When participants enter the study, they complete a survey about their lives and family histories, providing information on factors that might cause or prevent the development of cancer. Then every few years the participants receive additional surveys to complete. In the end, hundreds of thousands of participants will be tracked over 20 years to determine which of them develop cancer and which do not.

Clearly, this type of research is important and potentially very informative. For instance, earlier longitudinal studies sponsored by the American Cancer Society provided some of the first scientific demonstrations of the now well-established links between increased rates of cancer and smoking (American Cancer Society, n.d.) ( Figure 2.11 ).

As with any research strategy, longitudinal research is not without limitations. For one, these studies require an incredible time investment by the researcher and research participants. Given that some longitudinal studies take years, if not decades, to complete, the results will not be known for a considerable period of time. In addition to the time demands, these studies also require a substantial financial investment. Many researchers are unable to commit the resources necessary to see a longitudinal project through to the end.

Research participants must also be willing to continue their participation for an extended period of time, and this can be problematic. People move, get married and take new names, get ill, and eventually die. Even without significant life changes, some people may simply choose to discontinue their participation in the project. As a result, the attrition rates, or reduction in the number of research participants due to dropouts, in longitudinal studies are quite high and increase over the course of a project. For this reason, researchers using this approach typically recruit many participants fully expecting that a substantial number will drop out before the end. As the study progresses, they continually check whether the sample still represents the larger population, and make adjustments as necessary.

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How to use surveys to improve the impact of your case studies

• Written July 4, 2019
• by Nico Prins

Case studies are one of the most effective ways to turn hesitant leads into customers. Some people even go as far as saying that a good case study is as effective as a personal recommendation.

Yet the problem is that because case studies are a chance for you to shout about your results, most companies end up producing the same sort of content. This is often along the lines of ‘‘ Here’s How we Saved this Client a Million Dollars ”. 

The stats are one part of a case study that almost everyone gets. Most case studies struggle to get past the headline number. As a result, they miss out on the customer journey. In this guide, I’ll share with you how to write a long-form case study that tells a story and is relevant to potential clients. I’ll cover why you need to do this, and how, in the sections below.

What type of case studies you need

If you are running a company, you probably have a diverse client base. For example, the marketing agency that I consult for does  content marketing  and SEO consultancy work with a number of banks, Software as a Service companies, and enterprise level bricks and mortar companies. These clients operate in different verticals. While the overarching problem they face is often the same, their pain points are different.

The case studies you create for your business should reflect the range of clients you work with. To make them as relevant as possible they need to incorporate these different pain points within the copy. The closer you can align the case study with the problems a certain type of client is facing, the more impactful the case study will be.

Ultimately it is this relevancy that can be the difference between you winning a contract or losing it. For this reason, I strongly recommend creating a range of case studies that reflect the diversity of your clients.

You can see how Hubspot have taken this approach with the case studies they have on their site. The case studies cross eight different niches. The number of verticals reflects the diversity of their client base.

How to prepare your case study

Now, case studies obviously require a little bit of research on the client. The most powerful case studies mirror the customer journey of a client from consideration through to purchase. This means looking at the following:

• The problem they were facing when they contacted you
• What it was like to work together with you
• What was the impact of your work

Your aim is to get past the headline figures. For example, the end result of your work together might be a headline figure – $100,000 saved. But how that revenue is used, for example enabling a company to hire another employee, is a fact that could resonate with other potential clients. It is this combination of data and insights that provide a powerful case study.

The best way to get this information is a combination of surveys and interviews. A good survey will help you build a picture of the clients experience working together with you. The interview is where you ask pertinent questions that help fill in the finer details.

The kind of information you are looking to collect through your survey is a mixture of qualitative and quantitative data. Quantitative, which is all that numerical stuff. Things like money saved, money earned, or additional traffic brought to a website.

Qualitative data is wordier. This includes things like clients’  sentiments, personal experiences and lessons learned. To collect this information you need to ask the right questions.

You can loosely divide your survey questions into open or closed-ended. Closed-ended questions usually end up with ‘yes’ or ‘no’ answers. Asking someone to rate something on a scale of 1-5 is also a closed-ended question.

Open-ended questions are much more unpredictable. This looks like something along the lines of ‘How do you feel about …?’ The person on the other end could really come out with anything.

Ideally, you want less than 20 questions in your survey. They should be a mixture of open-ended questions and close-ended questions. The aim of these questions, as I mentioned previously, is to build the framework for your story. You want to collect the key facts and get insights you can use for the follow-up interview.

What questions to ask in your survey

When creating your survey questions you want to ask questions that follow the outline of the user journey. This essentially means covering those three points that I listed above and using a mixture of closed and open-ended questions.

The questions you pose in your survey should be specific. Break down big points into multiple questions. This is useful for customer satisfaction survey because each question provides insights into a different aspect of your business.

So, for example, you might break down the question, “ why did you choose to work with our company? ” to:

• What problem were you facing before you started working with us?
• What would have happened if you couldn’t solve the problem?
• What factor convinced you to choose our company over a competitor?

You can see how these three questions dig a little deeper. You could, of course, use the same approach with close-ended questions. Using this approach you can acquire information you can then use in the follow-up interview.

How to turn your case study into a story

It’s a generally agreed fact in copywriting circles that the amount of copy you need is proportional to the cost of the product or service you are selling. The more expensive the product or service the more you have to explain, show, educate and convince a prospect to make a purchase.

If you’re going to write a long piece of content you need to make it interesting. This is where storytelling comes into play.

Our lives are based on stories. We read stories in magazines, online, in the newspapers and watch made up stories on TV. Stories draw you in. Research by Jenifer Aaker , a professor of marketing at Stanford Business School, found that a story is significantly more memorable than facts alone.

Moreover, setting a case study within the framework of a story provides you with an opportunity to tap into emotional triggers. This is important given the fact that we make decisions based on both emotional and rational factors.

So you can think of it a bit like a glowing review from a customer, except you’re in control. That’s not to mention a whole range of extra benefits. Case studies are inherently good for natural keyword targeting. They’re also a great way to build client relationships.

Pulling it all together

Once you’ve got all of your data lined up, writing your case study is a breeze. Like all content, the first thing you need is a compelling headline. The example I gave at the start of this article were obviously jokes, but they’re also a pretty good indication of what you want to aim for.

The goal is to draw people in, so it’s good to try and squeeze in the kind of results they can expect from you. Even if it’s not a million dollars. Utilise the framework of a story to lead them towards your end goal. Finally, you hit them with a call to action.

You can see how I used this formula for a case study that I created to promote the software promos that my partner and I run.

• Start with a headline figure
• Place the case study within the context of a story
• Include insights from the person you worked with
• Provide proof in the form of screenshots
• Finish with a call to action

I link to this case study in the postscript of emails when I’m pitching my service. The majority of people I subsequently speak to read the piece. I know from discussing it with people I’ve worked with that the case study played an important role in their final decision.

Hubspot utilizes a similar approach. Their case study for  Rock & Roll Hall of Fame  is a good example. They have an above the fold synopsis of the case study, followed by the headline figures and an extended piece of copy. It’s a nice format that satisfies the needs of scan readers and people who like to investigate before they make a decision.

Remember that design can play an important part in your case study. You want to highlight the key facts for people who are just going to scan read your content. You can do this with a well laid out case study. Below is an example of a custom page design that I created for a previous client that highlights all of the key facts above the fold.

The core part of the case study is for people who want to have access to as much information as possible before they make a decision. Don’t be afraid to have 1,000 words or so of content. Just make sure that you apply copywriting principles, like using heading to break up the text, if you do so.

Wrapping up

We said earlier that surveys and case studies tie together nicely because one generates data and the other presents it. This a pretty straightforward relationship, but it seems like not everybody has gotten the memo.

I’m sure you’ve seen any number of boring case studies which say next to nothing. This happens when people know they’re supposed to write case studies, but they’re not really sure what to include. Or they might know what they want to say, but they don’t have any compelling quotes or statistics to back it up, so their case studies have very little real-world impact. This is why surveys and first-hand data is so crucial to creating a compelling case study.

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Understanding and Evaluating Survey Research

A variety of methodologic approaches exist for individuals interested in conducting research. Selection of a research approach depends on a number of factors, including the purpose of the research, the type of research questions to be answered, and the availability of resources. The purpose of this article is to describe survey research as one approach to the conduct of research so that the reader can critically evaluate the appropriateness of the conclusions from studies employing survey research.

SURVEY RESEARCH

Survey research is defined as "the collection of information from a sample of individuals through their responses to questions" ( Check & Schutt, 2012, p. 160 ). This type of research allows for a variety of methods to recruit participants, collect data, and utilize various methods of instrumentation. Survey research can use quantitative research strategies (e.g., using questionnaires with numerically rated items), qualitative research strategies (e.g., using open-ended questions), or both strategies (i.e., mixed methods). As it is often used to describe and explore human behavior, surveys are therefore frequently used in social and psychological research ( Singleton & Straits, 2009 ).

Information has been obtained from individuals and groups through the use of survey research for decades. It can range from asking a few targeted questions of individuals on a street corner to obtain information related to behaviors and preferences, to a more rigorous study using multiple valid and reliable instruments. Common examples of less rigorous surveys include marketing or political surveys of consumer patterns and public opinion polls.

Survey research has historically included large population-based data collection. The primary purpose of this type of survey research was to obtain information describing characteristics of a large sample of individuals of interest relatively quickly. Large census surveys obtaining information reflecting demographic and personal characteristics and consumer feedback surveys are prime examples. These surveys were often provided through the mail and were intended to describe demographic characteristics of individuals or obtain opinions on which to base programs or products for a population or group.

More recently, survey research has developed into a rigorous approach to research, with scientifically tested strategies detailing who to include (representative sample), what and how to distribute (survey method), and when to initiate the survey and follow up with nonresponders (reducing nonresponse error), in order to ensure a high-quality research process and outcome. Currently, the term "survey" can reflect a range of research aims, sampling and recruitment strategies, data collection instruments, and methods of survey administration.

Given this range of options in the conduct of survey research, it is imperative for the consumer/reader of survey research to understand the potential for bias in survey research as well as the tested techniques for reducing bias, in order to draw appropriate conclusions about the information reported in this manner. Common types of error in research, along with the sources of error and strategies for reducing error as described throughout this article, are summarized in the Table .

Sources of Error in Survey Research and Strategies to Reduce Error

The goal of sampling strategies in survey research is to obtain a sufficient sample that is representative of the population of interest. It is often not feasible to collect data from an entire population of interest (e.g., all individuals with lung cancer); therefore, a subset of the population or sample is used to estimate the population responses (e.g., individuals with lung cancer currently receiving treatment). A large random sample increases the likelihood that the responses from the sample will accurately reflect the entire population. In order to accurately draw conclusions about the population, the sample must include individuals with characteristics similar to the population.

It is therefore necessary to correctly identify the population of interest (e.g., individuals with lung cancer currently receiving treatment vs. all individuals with lung cancer). The sample will ideally include individuals who reflect the intended population in terms of all characteristics of the population (e.g., sex, socioeconomic characteristics, symptom experience) and contain a similar distribution of individuals with those characteristics. As discussed by Mady Stovall beginning on page 162, Fujimori et al. ( 2014 ), for example, were interested in the population of oncologists. The authors obtained a sample of oncologists from two hospitals in Japan. These participants may or may not have similar characteristics to all oncologists in Japan.

Participant recruitment strategies can affect the adequacy and representativeness of the sample obtained. Using diverse recruitment strategies can help improve the size of the sample and help ensure adequate coverage of the intended population. For example, if a survey researcher intends to obtain a sample of individuals with breast cancer representative of all individuals with breast cancer in the United States, the researcher would want to use recruitment strategies that would recruit both women and men, individuals from rural and urban settings, individuals receiving and not receiving active treatment, and so on. Because of the difficulty in obtaining samples representative of a large population, researchers may focus the population of interest to a subset of individuals (e.g., women with stage III or IV breast cancer). Large census surveys require extremely large samples to adequately represent the characteristics of the population because they are intended to represent the entire population.

DATA COLLECTION METHODS

Survey research may use a variety of data collection methods with the most common being questionnaires and interviews. Questionnaires may be self-administered or administered by a professional, may be administered individually or in a group, and typically include a series of items reflecting the research aims. Questionnaires may include demographic questions in addition to valid and reliable research instruments ( Costanzo, Stawski, Ryff, Coe, & Almeida, 2012 ; DuBenske et al., 2014 ; Ponto, Ellington, Mellon, & Beck, 2010 ). It is helpful to the reader when authors describe the contents of the survey questionnaire so that the reader can interpret and evaluate the potential for errors of validity (e.g., items or instruments that do not measure what they are intended to measure) and reliability (e.g., items or instruments that do not measure a construct consistently). Helpful examples of articles that describe the survey instruments exist in the literature ( Buerhaus et al., 2012 ).

Questionnaires may be in paper form and mailed to participants, delivered in an electronic format via email or an Internet-based program such as SurveyMonkey, or a combination of both, giving the participant the option to choose which method is preferred ( Ponto et al., 2010 ). Using a combination of methods of survey administration can help to ensure better sample coverage (i.e., all individuals in the population having a chance of inclusion in the sample) therefore reducing coverage error ( Dillman, Smyth, & Christian, 2014 ; Singleton & Straits, 2009 ). For example, if a researcher were to only use an Internet-delivered questionnaire, individuals without access to a computer would be excluded from participation. Self-administered mailed, group, or Internet-based questionnaires are relatively low cost and practical for a large sample ( Check & Schutt, 2012 ).

Dillman et al. ( 2014 ) have described and tested a tailored design method for survey research. Improving the visual appeal and graphics of surveys by using a font size appropriate for the respondents, ordering items logically without creating unintended response bias, and arranging items clearly on each page can increase the response rate to electronic questionnaires. Attending to these and other issues in electronic questionnaires can help reduce measurement error (i.e., lack of validity or reliability) and help ensure a better response rate.

Conducting interviews is another approach to data collection used in survey research. Interviews may be conducted by phone, computer, or in person and have the benefit of visually identifying the nonverbal response(s) of the interviewee and subsequently being able to clarify the intended question. An interviewer can use probing comments to obtain more information about a question or topic and can request clarification of an unclear response ( Singleton & Straits, 2009 ). Interviews can be costly and time intensive, and therefore are relatively impractical for large samples.

Some authors advocate for using mixed methods for survey research when no one method is adequate to address the planned research aims, to reduce the potential for measurement and non-response error, and to better tailor the study methods to the intended sample ( Dillman et al., 2014 ; Singleton & Straits, 2009 ). For example, a mixed methods survey research approach may begin with distributing a questionnaire and following up with telephone interviews to clarify unclear survey responses ( Singleton & Straits, 2009 ). Mixed methods might also be used when visual or auditory deficits preclude an individual from completing a questionnaire or participating in an interview.

FUJIMORI ET AL.: SURVEY RESEARCH

Fujimori et al. ( 2014 ) described the use of survey research in a study of the effect of communication skills training for oncologists on oncologist and patient outcomes (e.g., oncologist’s performance and confidence and patient’s distress, satisfaction, and trust). A sample of 30 oncologists from two hospitals was obtained and though the authors provided a power analysis concluding an adequate number of oncologist participants to detect differences between baseline and follow-up scores, the conclusions of the study may not be generalizable to a broader population of oncologists. Oncologists were randomized to either an intervention group (i.e., communication skills training) or a control group (i.e., no training).

Fujimori et al. ( 2014 ) chose a quantitative approach to collect data from oncologist and patient participants regarding the study outcome variables. Self-report numeric ratings were used to measure oncologist confidence and patient distress, satisfaction, and trust. Oncologist confidence was measured using two instruments each using 10-point Likert rating scales. The Hospital Anxiety and Depression Scale (HADS) was used to measure patient distress and has demonstrated validity and reliability in a number of populations including individuals with cancer ( Bjelland, Dahl, Haug, & Neckelmann, 2002 ). Patient satisfaction and trust were measured using 0 to 10 numeric rating scales. Numeric observer ratings were used to measure oncologist performance of communication skills based on a videotaped interaction with a standardized patient. Participants completed the same questionnaires at baseline and follow-up.

The authors clearly describe what data were collected from all participants. Providing additional information about the manner in which questionnaires were distributed (i.e., electronic, mail), the setting in which data were collected (e.g., home, clinic), and the design of the survey instruments (e.g., visual appeal, format, content, arrangement of items) would assist the reader in drawing conclusions about the potential for measurement and nonresponse error. The authors describe conducting a follow-up phone call or mail inquiry for nonresponders, using the Dillman et al. ( 2014 ) tailored design for survey research follow-up may have reduced nonresponse error.

CONCLUSIONS

Survey research is a useful and legitimate approach to research that has clear benefits in helping to describe and explore variables and constructs of interest. Survey research, like all research, has the potential for a variety of sources of error, but several strategies exist to reduce the potential for error. Advanced practitioners aware of the potential sources of error and strategies to improve survey research can better determine how and whether the conclusions from a survey research study apply to practice.

The author has no potential conflicts of interest to disclose.

• Key Differences

Know the Differences & Comparisons

Difference Between Survey and Experiment

While surveys collected data, provided by the informants, experiments test various premises by trial and error method. This article attempts to shed light on the difference between survey and experiment, have a look.

Content: Survey Vs Experiment

Comparison chart, definition of survey.

By the term survey, we mean a method of securing information relating to the variable under study from all or a specified number of respondents of the universe. It may be a sample survey or a census survey. This method relies on the questioning of the informants on a specific subject. Survey follows structured form of data collection, in which a formal questionnaire is prepared, and the questions are asked in a predefined order.

Informants are asked questions concerning their behaviour, attitude, motivation, demographic, lifestyle characteristics, etc. through observation, direct communication with them over telephone/mail or personal interview. Questions are asked verbally to the respondents, i.e. in writing or by way of computer. The answer of the respondents is obtained in the same form.

Definition of Experiment

The term experiment means a systematic and logical scientific procedure in which one or more independent variables under test are manipulated, and any change on one or more dependent variable is measured while controlling for the effect of the extraneous variable. Here extraneous variable is an independent variable which is not associated with the objective of study but may affect the response of test units.

In an experiment, the investigator attempts to observe the outcome of the experiment conducted by him intentionally, to test the hypothesis or to discover something or to demonstrate a known fact. An experiment aims at drawing conclusions concerning the factor on the study group and making inferences from sample to larger population of interest.

Key Differences Between Survey and Experiment

The differences between survey and experiment can be drawn clearly on the following grounds:

• A technique of gathering information regarding a variable under study, from the respondents of the population, is called survey. A scientific procedure wherein the factor under study is isolated to test hypothesis is called an experiment.
• Surveys are performed when the research is of descriptive nature, whereas in the case of experiments are conducted in experimental research.
• The survey samples are large as the response rate is low, especially when the survey is conducted through mailed questionnaire. On the other hand, samples required in the case of experiments is relatively small.
• Surveys are considered suitable for social and behavioural science. As against this, experiments are an important characteristic of physical and natural sciences.
• Field research refers to the research conducted outside the laboratory or workplace. Surveys are the best example of field research. On the contrary, Experiment is an example of laboratory research. A laboratory research is nothing but research carried on inside the room equipped with scientific tools and equipment.
• In surveys, the data collection methods employed can either be observation, interview, questionnaire, or case study. As opposed to experiment, the data is obtained through several readings of the experiment.

While survey studies the possible relationship between data and unknown variable, experiments determine the relationship. Further, Correlation analysis is vital in surveys, as in social and business surveys, the interest of the researcher rests in understanding and controlling relationships between variables. Unlike experiments, where casual analysis is significant.

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sanjay kumar yadav says

November 17, 2016 at 1:08 am

Ishika says

September 9, 2017 at 9:30 pm

The article was quite helpful… Thank you.

May 21, 2018 at 3:26 pm

Can you develop your Application for Android

Surbhi S says

May 21, 2018 at 4:21 pm

Yeah, we will develop android app soon.

October 31, 2018 at 12:32 am

If I was doing an experiment with Poverty and Education level, which do you think would be more appropriate for me?

Thanks, Chris

Ndaware M.M says

January 7, 2021 at 2:29 am

So interested,

Victoria Addington says

May 18, 2023 at 5:31 pm

Thank you for explaining the topic

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What the data says about abortion in the u.s..

Pew Research Center has conducted many surveys about abortion over the years, providing a lens into Americans’ views on whether the procedure should be legal, among a host of other questions.

In a  Center survey  conducted nearly a year after the Supreme Court’s June 2022 decision that  ended the constitutional right to abortion , 62% of U.S. adults said the practice should be legal in all or most cases, while 36% said it should be illegal in all or most cases. Another survey conducted a few months before the decision showed that relatively few Americans take an absolutist view on the issue .

Find answers to common questions about abortion in America, based on data from the Centers for Disease Control and Prevention (CDC) and the Guttmacher Institute, which have tracked these patterns for several decades:

How many abortions are there in the U.S. each year?

How has the number of abortions in the u.s. changed over time, what is the abortion rate among women in the u.s. how has it changed over time, what are the most common types of abortion, how many abortion providers are there in the u.s., and how has that number changed, what percentage of abortions are for women who live in a different state from the abortion provider, what are the demographics of women who have had abortions, when during pregnancy do most abortions occur, how often are there medical complications from abortion.

This compilation of data on abortion in the United States draws mainly from two sources: the Centers for Disease Control and Prevention (CDC) and the Guttmacher Institute, both of which have regularly compiled national abortion data for approximately half a century, and which collect their data in different ways.

The CDC data that is highlighted in this post comes from the agency’s “abortion surveillance” reports, which have been published annually since 1974 (and which have included data from 1969). Its figures from 1973 through 1996 include data from all 50 states, the District of Columbia and New York City – 52 “reporting areas” in all. Since 1997, the CDC’s totals have lacked data from some states (most notably California) for the years that those states did not report data to the agency. The four reporting areas that did not submit data to the CDC in 2021 – California, Maryland, New Hampshire and New Jersey – accounted for approximately 25% of all legal induced abortions in the U.S. in 2020, according to Guttmacher’s data. Most states, though,  do  have data in the reports, and the figures for the vast majority of them came from each state’s central health agency, while for some states, the figures came from hospitals and other medical facilities.

Discussion of CDC abortion data involving women’s state of residence, marital status, race, ethnicity, age, abortion history and the number of previous live births excludes the low share of abortions where that information was not supplied. Read the methodology for the CDC’s latest abortion surveillance report , which includes data from 2021, for more details. Previous reports can be found at  stacks.cdc.gov  by entering “abortion surveillance” into the search box.

For the numbers of deaths caused by induced abortions in 1963 and 1965, this analysis looks at reports by the then-U.S. Department of Health, Education and Welfare, a precursor to the Department of Health and Human Services. In computing those figures, we excluded abortions listed in the report under the categories “spontaneous or unspecified” or as “other.” (“Spontaneous abortion” is another way of referring to miscarriages.)

Guttmacher data in this post comes from national surveys of abortion providers that Guttmacher has conducted 19 times since 1973. Guttmacher compiles its figures after contacting every known provider of abortions – clinics, hospitals and physicians’ offices – in the country. It uses questionnaires and health department data, and it provides estimates for abortion providers that don’t respond to its inquiries. (In 2020, the last year for which it has released data on the number of abortions in the U.S., it used estimates for 12% of abortions.) For most of the 2000s, Guttmacher has conducted these national surveys every three years, each time getting abortion data for the prior two years. For each interim year, Guttmacher has calculated estimates based on trends from its own figures and from other data.

The latest full summary of Guttmacher data came in the institute’s report titled “Abortion Incidence and Service Availability in the United States, 2020.” It includes figures for 2020 and 2019 and estimates for 2018. The report includes a methods section.

In addition, this post uses data from StatPearls, an online health care resource, on complications from abortion.

An exact answer is hard to come by. The CDC and the Guttmacher Institute have each tried to measure this for around half a century, but they use different methods and publish different figures.

The last year for which the CDC reported a yearly national total for abortions is 2021. It found there were 625,978 abortions in the District of Columbia and the 46 states with available data that year, up from 597,355 in those states and D.C. in 2020. The corresponding figure for 2019 was 607,720.

The last year for which Guttmacher reported a yearly national total was 2020. It said there were 930,160 abortions that year in all 50 states and the District of Columbia, compared with 916,460 in 2019.

• How the CDC gets its data: It compiles figures that are voluntarily reported by states’ central health agencies, including separate figures for New York City and the District of Columbia. Its latest totals do not include figures from California, Maryland, New Hampshire or New Jersey, which did not report data to the CDC. ( Read the methodology from the latest CDC report .)
• How Guttmacher gets its data: It compiles its figures after contacting every known abortion provider – clinics, hospitals and physicians’ offices – in the country. It uses questionnaires and health department data, then provides estimates for abortion providers that don’t respond. Guttmacher’s figures are higher than the CDC’s in part because they include data (and in some instances, estimates) from all 50 states. ( Read the institute’s latest full report and methodology .)

While the Guttmacher Institute supports abortion rights, its empirical data on abortions in the U.S. has been widely cited by  groups  and  publications  across the political spectrum, including by a  number of those  that  disagree with its positions .

These estimates from Guttmacher and the CDC are results of multiyear efforts to collect data on abortion across the U.S. Last year, Guttmacher also began publishing less precise estimates every few months , based on a much smaller sample of providers.

The figures reported by these organizations include only legal induced abortions conducted by clinics, hospitals or physicians’ offices, or those that make use of abortion pills dispensed from certified facilities such as clinics or physicians’ offices. They do not account for the use of abortion pills that were obtained  outside of clinical settings .

(Back to top)

The annual number of U.S. abortions rose for years after Roe v. Wade legalized the procedure in 1973, reaching its highest levels around the late 1980s and early 1990s, according to both the CDC and Guttmacher. Since then, abortions have generally decreased at what a CDC analysis called  “a slow yet steady pace.”

Guttmacher says the number of abortions occurring in the U.S. in 2020 was 40% lower than it was in 1991. According to the CDC, the number was 36% lower in 2021 than in 1991, looking just at the District of Columbia and the 46 states that reported both of those years.

(The corresponding line graph shows the long-term trend in the number of legal abortions reported by both organizations. To allow for consistent comparisons over time, the CDC figures in the chart have been adjusted to ensure that the same states are counted from one year to the next. Using that approach, the CDC figure for 2021 is 622,108 legal abortions.)

There have been occasional breaks in this long-term pattern of decline – during the middle of the first decade of the 2000s, and then again in the late 2010s. The CDC reported modest 1% and 2% increases in abortions in 2018 and 2019, and then, after a 2% decrease in 2020, a 5% increase in 2021. Guttmacher reported an 8% increase over the three-year period from 2017 to 2020.

As noted above, these figures do not include abortions that use pills obtained outside of clinical settings.

Guttmacher says that in 2020 there were 14.4 abortions in the U.S. per 1,000 women ages 15 to 44. Its data shows that the rate of abortions among women has generally been declining in the U.S. since 1981, when it reported there were 29.3 abortions per 1,000 women in that age range.

The CDC says that in 2021, there were 11.6 abortions in the U.S. per 1,000 women ages 15 to 44. (That figure excludes data from California, the District of Columbia, Maryland, New Hampshire and New Jersey.) Like Guttmacher’s data, the CDC’s figures also suggest a general decline in the abortion rate over time. In 1980, when the CDC reported on all 50 states and D.C., it said there were 25 abortions per 1,000 women ages 15 to 44.

That said, both Guttmacher and the CDC say there were slight increases in the rate of abortions during the late 2010s and early 2020s. Guttmacher says the abortion rate per 1,000 women ages 15 to 44 rose from 13.5 in 2017 to 14.4 in 2020. The CDC says it rose from 11.2 per 1,000 in 2017 to 11.4 in 2019, before falling back to 11.1 in 2020 and then rising again to 11.6 in 2021. (The CDC’s figures for those years exclude data from California, D.C., Maryland, New Hampshire and New Jersey.)

The CDC broadly divides abortions into two categories: surgical abortions and medication abortions, which involve pills. Since the Food and Drug Administration first approved abortion pills in 2000, their use has increased over time as a share of abortions nationally, according to both the CDC and Guttmacher.

The majority of abortions in the U.S. now involve pills, according to both the CDC and Guttmacher. The CDC says 56% of U.S. abortions in 2021 involved pills, up from 53% in 2020 and 44% in 2019. Its figures for 2021 include the District of Columbia and 44 states that provided this data; its figures for 2020 include D.C. and 44 states (though not all of the same states as in 2021), and its figures for 2019 include D.C. and 45 states.

Guttmacher, which measures this every three years, says 53% of U.S. abortions involved pills in 2020, up from 39% in 2017.

Two pills commonly used together for medication abortions are mifepristone, which, taken first, blocks hormones that support a pregnancy, and misoprostol, which then causes the uterus to empty. According to the FDA, medication abortions are safe  until 10 weeks into pregnancy.

Surgical abortions conducted  during the first trimester  of pregnancy typically use a suction process, while the relatively few surgical abortions that occur  during the second trimester  of a pregnancy typically use a process called dilation and evacuation, according to the UCLA School of Medicine.

In 2020, there were 1,603 facilities in the U.S. that provided abortions,  according to Guttmacher . This included 807 clinics, 530 hospitals and 266 physicians’ offices.

While clinics make up half of the facilities that provide abortions, they are the sites where the vast majority (96%) of abortions are administered, either through procedures or the distribution of pills, according to Guttmacher’s 2020 data. (This includes 54% of abortions that are administered at specialized abortion clinics and 43% at nonspecialized clinics.) Hospitals made up 33% of the facilities that provided abortions in 2020 but accounted for only 3% of abortions that year, while just 1% of abortions were conducted by physicians’ offices.

Looking just at clinics – that is, the total number of specialized abortion clinics and nonspecialized clinics in the U.S. – Guttmacher found the total virtually unchanged between 2017 (808 clinics) and 2020 (807 clinics). However, there were regional differences. In the Midwest, the number of clinics that provide abortions increased by 11% during those years, and in the West by 6%. The number of clinics  decreased  during those years by 9% in the Northeast and 3% in the South.

The total number of abortion providers has declined dramatically since the 1980s. In 1982, according to Guttmacher, there were 2,908 facilities providing abortions in the U.S., including 789 clinics, 1,405 hospitals and 714 physicians’ offices.

The CDC does not track the number of abortion providers.

In the District of Columbia and the 46 states that provided abortion and residency information to the CDC in 2021, 10.9% of all abortions were performed on women known to live outside the state where the abortion occurred – slightly higher than the percentage in 2020 (9.7%). That year, D.C. and 46 states (though not the same ones as in 2021) reported abortion and residency data. (The total number of abortions used in these calculations included figures for women with both known and unknown residential status.)

The share of reported abortions performed on women outside their state of residence was much higher before the 1973 Roe decision that stopped states from banning abortion. In 1972, 41% of all abortions in D.C. and the 20 states that provided this information to the CDC that year were performed on women outside their state of residence. In 1973, the corresponding figure was 21% in the District of Columbia and the 41 states that provided this information, and in 1974 it was 11% in D.C. and the 43 states that provided data.

In the District of Columbia and the 46 states that reported age data to  the CDC in 2021, the majority of women who had abortions (57%) were in their 20s, while about three-in-ten (31%) were in their 30s. Teens ages 13 to 19 accounted for 8% of those who had abortions, while women ages 40 to 44 accounted for about 4%.

The vast majority of women who had abortions in 2021 were unmarried (87%), while married women accounted for 13%, according to  the CDC , which had data on this from 37 states.

In the District of Columbia, New York City (but not the rest of New York) and the 31 states that reported racial and ethnic data on abortion to  the CDC , 42% of all women who had abortions in 2021 were non-Hispanic Black, while 30% were non-Hispanic White, 22% were Hispanic and 6% were of other races.

Looking at abortion rates among those ages 15 to 44, there were 28.6 abortions per 1,000 non-Hispanic Black women in 2021; 12.3 abortions per 1,000 Hispanic women; 6.4 abortions per 1,000 non-Hispanic White women; and 9.2 abortions per 1,000 women of other races, the  CDC reported  from those same 31 states, D.C. and New York City.

For 57% of U.S. women who had induced abortions in 2021, it was the first time they had ever had one,  according to the CDC.  For nearly a quarter (24%), it was their second abortion. For 11% of women who had an abortion that year, it was their third, and for 8% it was their fourth or more. These CDC figures include data from 41 states and New York City, but not the rest of New York.

Nearly four-in-ten women who had abortions in 2021 (39%) had no previous live births at the time they had an abortion,  according to the CDC . Almost a quarter (24%) of women who had abortions in 2021 had one previous live birth, 20% had two previous live births, 10% had three, and 7% had four or more previous live births. These CDC figures include data from 41 states and New York City, but not the rest of New York.

The vast majority of abortions occur during the first trimester of a pregnancy. In 2021, 93% of abortions occurred during the first trimester – that is, at or before 13 weeks of gestation,  according to the CDC . An additional 6% occurred between 14 and 20 weeks of pregnancy, and about 1% were performed at 21 weeks or more of gestation. These CDC figures include data from 40 states and New York City, but not the rest of New York.

About 2% of all abortions in the U.S. involve some type of complication for the woman , according to an article in StatPearls, an online health care resource. “Most complications are considered minor such as pain, bleeding, infection and post-anesthesia complications,” according to the article.

The CDC calculates  case-fatality rates for women from induced abortions – that is, how many women die from abortion-related complications, for every 100,000 legal abortions that occur in the U.S .  The rate was lowest during the most recent period examined by the agency (2013 to 2020), when there were 0.45 deaths to women per 100,000 legal induced abortions. The case-fatality rate reported by the CDC was highest during the first period examined by the agency (1973 to 1977), when it was 2.09 deaths to women per 100,000 legal induced abortions. During the five-year periods in between, the figure ranged from 0.52 (from 1993 to 1997) to 0.78 (from 1978 to 1982).

The CDC calculates death rates by five-year and seven-year periods because of year-to-year fluctuation in the numbers and due to the relatively low number of women who die from legal induced abortions.

In 2020, the last year for which the CDC has information , six women in the U.S. died due to complications from induced abortions. Four women died in this way in 2019, two in 2018, and three in 2017. (These deaths all followed legal abortions.) Since 1990, the annual number of deaths among women due to legal induced abortion has ranged from two to 12.

The annual number of reported deaths from induced abortions (legal and illegal) tended to be higher in the 1980s, when it ranged from nine to 16, and from 1972 to 1979, when it ranged from 13 to 63. One driver of the decline was the drop in deaths from illegal abortions. There were 39 deaths from illegal abortions in 1972, the last full year before Roe v. Wade. The total fell to 19 in 1973 and to single digits or zero every year after that. (The number of deaths from legal abortions has also declined since then, though with some slight variation over time.)

The number of deaths from induced abortions was considerably higher in the 1960s than afterward. For instance, there were 119 deaths from induced abortions in  1963  and 99 in  1965 , according to reports by the then-U.S. Department of Health, Education and Welfare, a precursor to the Department of Health and Human Services. The CDC is a division of Health and Human Services.

Note: This is an update of a post originally published May 27, 2022, and first updated June 24, 2022.

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Key facts about the abortion debate in America

Public opinion on abortion, three-in-ten or more democrats and republicans don’t agree with their party on abortion, partisanship a bigger factor than geography in views of abortion access locally, do state laws on abortion reflect public opinion, most popular.

About Pew Research Center Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of The Pew Charitable Trusts .

• Open access
• Published: 27 March 2024

Evaluating the association between duration of breastfeeding and fine motor development among children aged 20 to 24 months in Butajira, Ethiopia: a case-control study

• Rediate Shiferaw 1 ,
• Robel Yirgu 2 &
• Yalemwork Getnet 2  

BMC Pediatrics volume  24 , Article number:  216 ( 2024 ) Cite this article

57 Accesses

Metrics details

A Suitable environment and proper child nutrition are paramount to a child’s physical and mental development. Different environmental factors contribute to proper child development. Breast milk is an important source of nutrition during the early years of life and contains essential nutrients that are the building blocks for growth and development.

To assess the association between the duration of breastfeeding and fine motor development among children aged 20 to 24 months living in Butajira, southern Ethiopia.

Community-based case-control study design was employed among mother-child dyads of children aged 20 to 24 months in Butajira Southern Ethiopia. Children were screened for fine motor delay using the Denver II developmental screening and identified as cases and controls. A repeated visit was done to gather the rest of the information and 332 samples, 83 cases, and 249 controls were available and assessed. Epi-data version 4.4.2.1 software was used to prepare a data entry template, which was later exported to and analyzed using STATA version 14 statistical software. Finally, a Multivariable logistic regression model was used to adjust for confounders and estimate the independent effect of breastfeeding duration on fine motor development.

We didn’t find a significant association between the duration of breastfeeding from 21 to 24 months and fine motor delay compared to children who were breastfed less than 18 months[AOR: 0.86, 95% CI: (0.36, 2.05)]. Children who have mothers > 35 years of age were 78% less likely than children who had mothers younger than 25 years, Children who had mothers in secondary school and above were 77% less likely than mothers who didn’t have formal education, Females were 1.86 times more likely than males, and Children who scored 20–29 on the Home score were 51% less likely than Children who scored < 20 to have fine motor delay.

Duration of breastfeeding was not significantly associated with fine motor delay for children aged 20 to 24 months old. The age of the mother, the educational status of the mother, being female, and Home score were identified to have a significant association with fine motor delay. Improving the educational status and empowerment of women is essential. Further work should be done on avoiding gender differences starting from a young age and creating a conducive environment for child development is crucial.

Peer Review reports

The first 2 years can predict the quality of life a child can have. Appropriate connections are formed and wired in this window if this stage is passed then it is hard to rewire the brain connections [ 1 ]. Child development is a dynamic process that is a result of the interaction between biological and environmental factors [ 2 ]. Motor development is also seen as an indicator of global child development [ 3 ]. Motor development is the development of the child’s bones, muscles, and ability to move around and manipulate their environment [ 4 ]. Motor development is a critical factor in child behavior, being associated with the foundation of cognitive and social-emotional development[ 3 ]. Fine motor development is very important for the development of gross motor skills and is connected to how a child performs later on other cognitive tasks, reading and solving mathematical problems [ 5 ]. Fine motor skill is the ability to control movement through activities and coordination of the nervous system, fibril, and muscles such as fingers and hands [ 6 ]. Fine motor skills are important to do certain activities such as eating and handwriting. The United Nations has set sustainable developmental Goals to improve early child development by 2030. Goal 4 Target 4.2 supplies all children to get access to quality early childhood development so they are ready for primary education [ 7 ].

Child development is a dynamic process that is a result of the interaction between biological and environmental factors. Although infant development is influenced by several factors, The centrality of good nutrition cannot be ignored by providing the important building blocks for development [ 2 ]. Breastfeeding is the main source of important nutrients for children at this age. Breastfeeding has been identified by the World Health Organization (WHO) as an ideal source that contains important nutrients that can help for the optimal growth and development of children. WHO recommends continuing to breastfeed for up to 2 years with additional complementary foods [ 8 ]. Especially fatty acids Docosahexaenoic acid and Arachidonic acid in breast milk are important for brain growth and development and the formation of important synapses or connections in the brain. When a child is adequately nourished with important nutrients in the foundational period it creates a base for lifetime proper brain function [ 9 ]. Motor skills are also affected by factors such as caregiving practice, and stimulating environments [ 1 ]. Nutritional supplementation and psychosocial stimulation together result in greater improvements in child development than either intervention alone [ 9 ]. Determining the solo influence of breastfeeding on child development is difficult because child development is interrelated and associated with different environmental and biological factors. The complexity of child development makes it difficult to evaluate these effects [ 10 ]. The effects of environmental factors are pronounced in areas with limited access to the requirements for development [ 11 ]. Especially in developing countries, the problem can be worse due to limited resources in the environment that can aggravate the problem [ 12 ]. In resource-limited environments, limited resources such as poor stimulating environments and poor nutrition can limit the developmental potential of the children. Therefore, we need to study the effect of multiple environmental factors and nutrition on child development especially in a developing country context. To our best knowledge there are limited studies regarding developmental delays and also the practice of assessing child development in Ethiopia is low. Therefore, knowing the current status and assessing the impact is helpful for early intervention to prevent different adverse outcomes.

Materials and methods

Study design, area, and period.

Community-based Case-control study was conducted from March to May 2019 among children aged 20 to 24 months living in Butajira Health and demographic surveillance site located in Southern Nations and Nationalities (SNNP), Ethiopia. The area is located 135 km south of Addis Ababa and 50 km to the west of Zeway town in the Rift Valley, 8.2 o north latitude and 38.5 o east longitude. The climate varies from arid dry lowland areas at altitudes around 1,500 m (tropical climate) to cool mountainous areas up to 3,500 m above mean sea level (temperate climate). The livelihood of the residents is based on mixed farming. Khat (Catha edulis Forsk) and chili-peppers are the main cash crops, while maize and “false banana” or Ensete (Ensete ventricosun) are the main staples [ 13 ].

Source population

All children within the age group of 20 to 24 months living in Butajira HDSS are the Source population.

Study population

Children within the age group of 20 to 24 months living in Butajira HDSS have been identified as cases and controls based on the Denver developmental screening test.

Case definition

Cases were children who were identified as being suspect for fine motor delay and controls were children without fine motor delay.

Case (Suspect ): Two or more cautions (Item on which the age line fails or between the 75th and 90th percentile). This means 75% of the children can pass the test below the child’s age, and /or One or more delays (a child fails to perform an activity that fails completely to the left of the age line) using the Denver developmental screening test.

It is considered that a child fails to perform an item that 90% of children can perform at an earlier age.

Control (normal)

No delays and a maximum of one caution using the Denver developmental screening test.

Study variables

The outcome variable was Fine motor delay. The exposure variables were Nutritional factors (Breastfeeding duration, Dietary habits of the infant), Child characteristics (sex of the child, birth order), Socio-demographic variables (age of mother, occupation of mother, education status of mother, socioeconomic status), and Caregiving practice: (Home environment.)

Sample size calculation

The required sample size was calculated using EPI INFO 7 software using an unmatched case-control study using Proportion of Controls among those who breastfeed < 6 months P = 89.97% and Proportion of Cases among those who breastfeed < 6 months P = 76.4% and OR = 0.36 A study done in Taiwan [ 14 ].

At precision level of 5%, 95% confidence interval, and 80% power and using r = 3(ratio of cases to controls) and 10% for non-response compensation the sample size becomes 360 with 271 controls and 90 cases.

Sampling method

A survey (screening) was conducted from March to May 2019 in Butajira HDSS by obtaining a sampling frame from the Butajira HDSS. Participants were all children from 20 to 24 months living in the Butajira HDSS. The total population of children in Butajira HDSS from 20 to 24 months was 453.

After going into each Keble and household 376 children that were available were assessed using the Denver developmental screening test and identified as cases and controls. We found 85 cases and 291 controls. Then after identifying the households with cases and controls a repeated visit on the same household and on the same child was done to gather the rest of the information. After visiting the households 332 samples 332 samples 83 cases and 249 controls were available and were assessed using interviewer-administered questioners.

Operational definition

Caregiver (caretaker).

The people who look after infants and young children [ 15 ].

Breastfeeding less than 18 months

mothers while in the data collection period report that they have breastfed their babies less than 18 months.

Continue to breastfeed 18 to 20 months

mothers while in the data collection period report that they have breastfed their babies from 18 to 20 months and stopped.

Continue to breastfeed 21 to 24 months

mothers while in the data collection period report that they have breastfed their babies from 21 to 24 months.

• Fine motor development

The fine motor section of Denver II contains 33 items. Each test item on Denver II is presented on a chart by a horizontal bar partitioned into 25, 50, 75 and 90 percentile ages of passing the items. After calculating the exact age draw the age line after drawing the age line the child was asked to perform an activity to the left of the age line, this was done until the child was able to pass three or more consecutive items. Also, the child was tested for items above the age line until the child failed three or more consecutive items.

A child can pass-fail or refuse an item on which the age line fails.

By then identifying the child’s outcome using all the scores that the child has and finding the results will be carried out.

The scoring has 4 items.

“P” for pass – the child successfully performs the item or the caregiver reports (when appropriate) that the child does the item.

“F” for fail- the child does not successfully perform the item, or the caregiver reports (when appropriate) that the child does not do the item.

“N.O” for no opportunity- the child has not had the opportunity to perform the item, due to restrictions from the caregiver or other reasons. This score may only be used on “report” items.

“R” for refusal- the child refuses to attempt the item. Refusal can be minimized by telling the child what to do rather than asking. If given instruction in proper administration, the caregiver may administer the item. Report items cannot be scored as refusals.

no delays and a maximum of one caution.

Caution items are interpreted when a child fails or refuses an item on which the age line fails or between the 75th and 90th percentile. This means 75% of the children can pass the test below the child’s age. Delays are considered when a child fails to perform an activity that fails completely to the left of the age line. (Not on the item that the age line passes) It is considered that a child fails to perform an item that 90% of children can perform at an earlier age. This means 75% of the children can pass the test below the child’s age. When a child passes, fails, or refuses an item that is between the 25th and 75th percentile it is considered normal.

Delays suspect

Two or more cautions and /or one or more delays.

Caution items are interpreted when a child fails or refuses an item on which the age line fails or between the 75th and 90th percentile. This means 75% of the children can pass the test below the child’s age [ 16 ].

Adequate dietary diversity

Children who receive foods from 4 or more food groups using 24-hour recall [ 8 ].

Inadequate dietary diversity

Children who received foods less than four groups using 24-hour recall [ 8 ].

Household wealth index —is households’ living status and was constructed by using household asset data on housing conditions like the type of floor, the material of the wall, the material of roof; ownership of assets like radio, TV, telephone, vehicle; the presence of functional latrine, source of drinking water, ownership of domestic animals, ownership of farmland and amount of grain harvested in the last production year among others. After running principal components analysis (PCA) in STATA, the households’ wealth index was grouped into quintiles (lowest quintile, second quintile, middle quintile, fourth quintile, and highest quintile).

Data collection instrument and procedure

Development was assessed by the Denver developmental screening test which is designed to test the development of the child. The data collection started by Screening for a suspect for fine motor development. The fine motor was assessed using the Denver II developmental screening test. The tool contains different materials that help to examine the development of the child and a test form that contains all the developmental domains in sections. The Denver II tool was adapted in Jimma into a developing country context and was validated in Butajira Ethiopia [ 17 , 18 ]. The Denver II was assessed by a BSC nurse trained and certified for assessing children using the Denver developmental screening test.

The test was done in a natural and comfortable environment where the child could play with minimal disturbance in the presence of the caretaker. The test was started by informing the mother that the child is not expected to pass all the items.

The test contains a total of 125 items in four developmental domains: personal-social, fine motor, language, and gross motor. The fine motor section of Denver II contains 33 items. Each test item on Denver II is presented on a chart by a horizontal bar partitioned into 25, 50, 75 and 90 percentile ages of passing the items.

Draw the exact age without rounding off days, weeks, or months. Age scales are placed at the top and bottom of the page. Spaces between the age marks represent 1 month until 24 months. After carefully identifying the child’s age draw the age line using the age scales draw an age line from the top to the bottom of the form. After drawing the age line the child was asked to perform an activity to the left of the age line, this was done until the child was able to pass three or more consecutive items. Also, the child was tested for items above the age line until the child failed three or more consecutive items.

For each item, there are 25th, 50th, 75th and 90th percentile.

The age line, pass through the following tasks.

16. Dump coffee bean demonstrated.

Show the child 2 or 3 times how to dump the coffee bean out of the bottle. Then ask the child to get it out. (Do not use the word “dump.”)

Pass if the child dumps the coffee bean out of the bottle or rakes the coffee bean close to the opening and then dumps it out. Do not pass if the child removes the coffee bean with a finger.

17. Tower of cubes – 2, 4, 6, 8.

With the child sitting high enough at the table so that elbows are level with table top and hands are on the table, place the blocks on the table in front of the child. Encourage the child to stack them by demonstration and words. It may be helpful to hand the blocks to the child, one at a time. Three trials may be given.

Pass Tower of 2 cubes if the child puts one block on top of another so that it does not fall when he/she removes his/her hand.

Pass Tower of 4, 6, 8 cubes , depending upon the greatest number of blocks the child stacks in three trials.

A pass of 4, 6, or 8 cubes also passes the lower tower items (for example, passing Tower of 6 cubes also passes Tower of 2 and 4 cubes ).

“N.O” for no opportunity- the child has not had the opportunity to perform the item, due to restrictions from the caregiver or other reasons. This score may only be used on “report” items. “R” for refusal- the child refuses to attempt the item. Refusal can be minimized by telling the child what to do rather than asking. If given instruction in proper administration, the caregiver may administer the item. Report items cannot be scored as refusals.

By then identifying the child’s outcome using all the scores that the child has and finding the results were carried out.

no delays (the child successfully performs the action) and a maximum of one caution (between the 75th or 90th percentile).

two or more cautions and/or one or more delays (the child fails to perform an activity that fails completely to the left of the age line.)

refusal scores on one or more items completely to the left of the age line or on more than one item intersected by the age line in the 75-90% area.

Praise the child even for items that are failed. This will build the confidence of the child to attempt more difficult items.

Data on socio-demographic, breastfeeding, and nutritional histories were collected using interviewer-administered questions.

Total Breastfeeding duration was assessed from a study that assessed breastfeeding duration since birth [ 19 ]. It was taken by asking the mother to recall the total duration she breastfed her child. Whether she is still breastfeeding or to recall the time she stopped breastfeeding her child.

Complimentary food was assessed using WHO dietary diversity [ 8 ]. Dietary diversity was collected using dietary diversity scores adapted from the WHO standardized questionnaire for infant and young child feeding (IYCF). Mothers or caregivers were asked to recall all the food items that the child consumed during the past 24 h [ 8 ]. The home environment was assessed using the Home inventory used in different studies [ 20 ]. The Home score was assessed by interview-administered questionnaires. It was done by giving the mother a picture book and the mother will show the picture book to the child. Observation will be made on the interaction and the response the mother has towards her child. The interview was conducted in a free and friendly environment. The observation was made on the maternal and child interaction and maternal responses towards the child while asking other questions from the Home inventory.

The training was given to data collectors and supervisors regarding the objective and method of data collection and discussions were made for unclear questions in the questionnaire.

Data processing and analysis

Data were checked manually for completeness and entered into Epi-data version 4.2.2.1 statistical software and exported into STATA version 14 for data cleaning and analysis. Frequencies and summary statistics (median, interquartile range, percentage, and range) were used to describe the study population in relation to relevant variables.

Nutrition-related variables such as duration of breastfeeding were categorized based on the duration of breastfeeding in months and were grouped as breastfed less than 18 months, 18 to 20, and 21 to 24 months. Dietary diversity was also assessed using a Minimum dietary diversity score comparing children who had consumed four or more food groups and children who scored less than four groups using 24-hour recall. Socioeconomic status was analyzed based on the wealth index by using Principal component analysis (PCA). Childcare practices, maternal-child interaction were checked using the Home score.

Binary logistic regression was used to check for the association between the dependent, fine motor delay, and independent variables. Variables with P- value < 0.2 and which had clinical importance or subject matter were included in the multiple logistic regression. In the multiple logistic regressions, Variables with 95% CI of AOR which did not include 1 were considered to have significant association with the outcome variables. The goodness of fit test indicated (P = 0.0518) that the model was good enough to fit the data well.

Ethical consideration

Before data collection ethical clearance was obtained from Addis Ababa University School of Public Health Institutional Review Board (AAU-IRB). Written Informed consent was obtained from parents (legal guardians) before participating in the study. All study participants were informed about the purpose of the study, their right to deny participation, anonymity, and confidentiality of the information. All methods were carried out in accordance with relevant guidelines and regulations. The Denver II developmental screening test used in this study to measure the developmental milestone was assessed by a well-trained and certified data collector to ensure the safety of the children. It was conducted in a free and friendly environment. It was explained to the parents that the scale determines the child’s current developmental status and that it’s not an IQ test and the child is not expected to pass all the tests administered. The beneficence of the participants was assured by providing education to the participants about the benefits of breastfeeding, growth, and development. For Children identified with developmental delay, further education was given on methods of improving the motor skills of the Children. The confidentiality of the information of the participants was not disclosed.

Socio-demographic characteristics of the study participants

Mothers in the age group from 25 to 29 years were 35(42.17%) in the cases while 101(40.56%) were in the controls. The median age of the mothers was 28, IQR (25 33%). About 48(57.83%) of mothers in the cases and 93(37.65%) of mothers in the controls didn’t have any formal education. About 58(69.88%) of the cases and 185(74.60%) of mothers from the controls were Housewives. About 39(46.99%) fathers in the cases and 67(26.91%) in the controls didn’t have any formal education. About 23(27.71%) of the cases and 52(20.88%) in the controls were from the lowest quintile. About 69(83.13%) of the cases and 179(71.89%) of the controls were Rural residents (Table  1 ).

Child-related characteristics

The study included 168 male and 164 female children from the age group of 20–24 months. About 36(43.37%) males were cases while 132(53.01%) were in the controls (Table  2 ).

Delivery and nutritional characteristics of the study participants

Health facility delivery among the cases was 67(80.72%) and 213(85.54%) among the controls. Breastfeeding at least once was 81(97.59%) among the cases and 248(99.60%) among the controls. About 49(59.04%) mothers in the cases and 139(55.82%) in the controls reported that they are currently breastfeeding.

About 66(79.52%) children in the cases and 177(71.08%) children in the controls continued to be breastfed from 21 to 24 months. There was no significant variation among cases and controls by the duration of breastfeeding 95% CI (p = 0.234) (Table  3 ).

Dietary practices and nutritional characteristics of the children

About 46(55.42%) of children in the cases and 179(71.89%) in the controls started solid or semi-solid food within 6 to 8 months. There was a difference among cases and controls on children at the time of starting solids and semisolid foods (Table  4 ).

Caregiving practice

About 41(49.40%) of children in the cases and 172(69.08%) in the controls had a score between 20 and 29 on the Home score. The Home score had a minimum score of 13 and a maximum score of 32 (Table  5 ).

Association of different characteristics of children with suspect of fine motor delay

In the binary logistic regression variables with p-value < 0.2 or factors that had clinical importance were identified (Table  6 ).

After adjusting for these variables age of the mother, the educational status of the mother, the sex of the child, and the Home score were identified to have a significant association with fine motor delay.

We didn’t find a significant association between duration of breastfeeding and fine motor delay for children who were breastfed from 18 to 20 months [AOR: 0.45, 95% CI: (0.13, 1.56)] and for children who were breastfed from 21 to 24 months [AOR: 0.86, 95% CI: (0.36, 2.05)] compared to children who were breastfed less than 18 months. Children who have mothers > 35 years of age were 78% less likely to have fine motor delay than mothers who were < 25 years old [AOR: 0.22, 95% CI: (0.05, 0.87)]. Children who had mothers in primary school were 66% less likely [AOR: 0.34, 95% CI: (0.14, 0.81)] and children who had mothers in secondary school and above were 77% less likely [AOR 0. 23, 95% CI: (0.06, 0.80)] to have fine motor delay than mothers who didn’t have any formal education. Females were 1.86 times more likely to have fine motor delay than males [AOR: 1.86, 95% CI: (1.05, 3.28)]. Children who scored 20–29 on the Home score were 51% less likely to have fine motor delay than Children who scored < 20 [AOR: 0.49, 95% CI: (0.27, 0.88)] (Table  6 ).

Child development is an important aspect of human life. Development can be affected by different factors. Environmental factors and nutritional factors together play a significant role in child development. Nutritional factors have a great role in development but due to the adverse environmental and social factors, the outcome could be influenced by different factors, especially in developing countries [ 1 ].

Breastfeeding is known to have a significant effect on child growth and development [ 11 ] but in our study, We didn’t find a significant association between the duration of breastfeeding and fine motor delay for children who were breastfed from 18 to 20 and for children who were breastfed from 21 to 24 months compared to children who were breastfed less than 18 months.

Our findings are consistent with some studies that didn’t find a significant association between duration of breastfeeding and fine motor development [ 21 , 22 , 23 ]. All the studies acknowledged that breastfeeding is important for development but they suggested that other factors also have a role in influencing fine motor development. Similar to our study, A study in Singapore didn’t find a significant association between breastfeeding and fine motor development at 24 months [ 21 ]. Another study done in rural Brazil didn’t find a significant association between breastfeeding and fine motor development at 12 months and suggested home stimulation, maternal education, and income were influencing the outcome [ 22 ].

The study in Singapore suggested they have used specific research tools and have controlled for a large number of potential confounders and they didn’t find any relationship between breastfeeding on fine motor development [ 21 ]. The study in Brazil investigates the association between breastfeeding and mental and motor development, controlling for comprehensive measures of the child’s socioeconomic maternal, and environmental background, and nutritional status. They didn’t find a significant association between breastfeeding and motor development. They explained that the reason most studies have found an association between breastfeeding and development is that the studies have been done in relatively affluent populations where, in general, mothers who succeed in breastfeeding have higher socio-economic status, better educated with higher educational attainment. While In their study mothers who were breastfeeding longer had lower socioeconomic status, poorer education, and provided less stimulating home environments. They explained the reason that most studies found the association was due to incomplete adjustment for covariates, differences in methodological robustness, and types of tests used are likely to be contributory, which will result in an apparent breastfeeding benefit. To prevent this bias they controlled for different covariates. They suggested that no subgroup is differentially protected by breastfeeding, but rather that all groups benefit. The benefit of breastfeeding was an important factor that benefited all the comparison groups, while it has a beneficial effect, breastfeeding didn’t have a protective effect on fine motor development. The difference in the outcome was appreciated by other potential determinants. They found home stimulation and family income to be more important factors [ 22 ].

This is similar to our study finding that mothers who were breastfeeding longer had lower socio-economic status and poorer education. We also have found other environmental factors to be significantly associated. Similar to these studies environmental factors were playing a significant role in fine motor development.

All the studies acknowledged that breastfeeding is important for development but they suggested that other factors were influencing fine motor delay and we need to take into consideration other factors that could also affect or contribute to child development.

A systematic review also suggested that development is influenced by different environmental and psychological factors. Different factors need to be put into consideration that can affect the developmental potential of the children. Their analysis reveals that there are studies that have shown an apparent decrease in effect after multivariate analysis. Given that tight control of confounders resulted in a greater likelihood of the disappearance of the breastfeeding effect. Studies completed in middle-income and low-income countries were nearly twice as likely to find no association compared with studies set in developed countries. The fact that this relationship is less apparent in developing countries suggests that much of the observed relationship may be due to parental social advantage, confounding the choice to breastfeed [ 23 ].

In conclusion, the systematic review suggests that much of the reported effect of breastfeeding on child developmental abilities is due to maternal and socioeconomic effects. They suggested additional, future studies in this field are needed to rigorously control for all important confounders [ 23 ]. Development is not the solo effect of breastfeeding alone but a combination of different factors working together.

All these studies have used different developmental screening tools so the comparison should be done cautiously.

Contrary to our study A study in Malawi among children who breastfeed from 9 to 10 months found a small but significant protective effect on fine motor development at 12 to 18 months [ 24 ]. Studies in Western countries, a study done in Taiwan and Greece assessed the effect of duration of breastfeeding more than 6 months and fine motor assessed at 18 months. They found that any increase in the duration of breastfeeding was associated in decreasing in the odds of fine delay which persisted after controlling for different factors [ 25 , 26 ]. The Taiwan study has shown that mothers who breastfeed longer were older, had a university education, and were from a better socioeconomic class and suggested that the positive result could be due to the presence of these factors [ 25 ]. These factors were different in our setting, the majority of the mothers in this study who breastfeed for longer durations were less educated. Studies have shown that mothers who are more educated create a more favorable and stimulating environment and when breastfeeding is added to these factors there could be better results that can be helpful for child development [ 27 , 28 ]. This might be one of the reasons why we couldn’t find a significant association.

We have also found the age of the mother to have a significant association with the development of the child. We have found older mothers had more favorable outcomes than young mothers. Similar findings have suggested that older mothers tend to create a more favorable environment for child development and would also breastfeed for longer durations [ 29 – 31 ].

Also, we have found the education level of the mother to be significantly associated with fine motor delay. Children who had mothers in primary and secondary school were less likely to have fine motor delay than mothers who didn’t have formal education. Studies have shown that a mother’s education is important because as the educational level of the mother increases the level of stimuli the mother gives to her child also increases [ 27 ]. In addition to that, as the education level of the parents increases the socioeconomic status also could increase and will create a more favorable environment for the children [ 32 ].

Another factor that we found significant was the sex of the child. We have found females have greater odds of being affected by fine motor delay than males. Contrary to our study different studies have suggested females have a better score on fine motor and boys have a higher risk of having developmental delay [ 33 , 34 ]. While we cannot give a general conclusion other factors in the environment could affect the development of females. A study in India has shown that Girls are breastfed for shorter durations than boys due to the gender preferences of the mothers. Mothers will start early weaning for girls than boys to have another pregnancy and not to delay another pregnancy [ 35 ]. The gender preferences of the mother could affect the duration of breastfeeding and the care the child will have [ 36 ]. This gender preference could lead to a developmental delay in the female population.

We have found the Home score to have a significant association with fine motor development. Similarly, studies found the Home environment to have a significant association with fine motor development [ 37 , 38 ]. Motor development can be regulated critically by the home environment and maternal and child interaction [ 39 ]. The role of the mother or the caregiver has a protective role even for children growing up in limited environments such as low socioeconomic status, low levels of education, chronic illness, conflict, and mental health problems of caregivers. Mothers’ sensitivity is important because it creates a conducive environment for the development of the child [ 40 ]. A study done in Iran did not find a significant positive association between home motor affordances and motor development in their sample. They suggested that this could be due to the tool that they used was not sensitive enough to detect differences [ 3 ]. Home environment is a very important factor for childhood development a study has shown in nutrition-related interventions certain amount of stimulation from the environment was necessary and nutritional intervention alone was insufficient to bring brain development [ 40 ].

The Strengths of this study are the study was a community-based case-control study which is helpful to asses multiple exposure or risk factors. We have also used new cases that were identified at the time of collection which could prevent misclassification bias. We have used tools that are validated in our setting which can measure the case of interest in a better way.

The following limitation needs to be taken into account when interpreting the results. Most of the mothers in our study group had the practice of long-term breastfeeding durations and conducting the study where different information or different study groups are available would help to further strengthen the study finding. Our study was conducted in a rural setting but including the figure of urban mothers would further enrich the information that can be found. Even though birth weight is an important factor for development we didn’t have information on the birth weight of the children.

Conclusions

This study still supports that breastfeeding is important for child development. However, in our study, we didn’t find a significant association between the duration of breastfeeding from 18 to 20 months and for duration of breastfeeding from 21 to 24 months compared to children who were breastfed less than 18 months on fine motor development. Children from older mothers were less likely to be affected than young mothers. Children who had mothers in primary and secondary school were less likely to have fine motor delay than mothers who didn’t have formal education. Females have higher odds of being suspect of fine motor delay than males. Children who had better maternal care practices or Home scores were less likely to be affected than Children who had lower maternal care practices or lower Home scores.

Based on our findings we forward the following recommendations: Health care providers should be the first-line source of information to provide appropriate information to the mothers and the community during delivery or during any visit the mother makes to the health facility. They should educate the mothers and the community about the importance of child feeding and childcare and creating a conducive environment for child development. Older mothers tend to create more conducive environments for child development. Delaying early pregnancies is helpful to have physically and psychologically mature mothers. Since mothers are the primary caretakers improving maternal education and empowerment to improve developmental outcomes is helpful for child development. Therefore policymakers should work on improving the educational status and empowerment of women and work on avoiding gender differences starting from a young age. Assessment of Developmental delay in children should also be done routinely by Health care providers to catch delays during the early years and to have early interventions. Further studies should be done in a different setup to appreciate the difference and the effect of other environmental factors. Further follow-up studies should be done to prevent recall bias in a better way. Thus, overall, child development can be influenced by different factors in the environment, and having a holistic approach is mandatory to tackle the problem.

Data Availability

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Abbreviations

Infant and Young Child Feeding

Arachidonic acid

Docosahexaenoic acid

Ethiopian Demographic and Health Survey

Health and Demographic Surveillance site

Nationalities and Peoples Regional State

World Health Organization

Adjusted Odds Ratio

Confidence Interval

Crude Odds Ratio

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Acknowledgements

We acknowledge the data collectors, study participants, and all those who were involved in the study. We would like to extend our gratitude to Professor Frances Abound for his comment, support, and advice. We would also like to thank Dr. Teklu Gemechu for his help and guidance and Miss. Mashresa Harisgo for her help and dedication during the data collection.

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Shiferaw, R., Yirgu, R. & Getnet, Y. Evaluating the association between duration of breastfeeding and fine motor development among children aged 20 to 24 months in Butajira, Ethiopia: a case-control study. BMC Pediatr 24 , 216 (2024). https://doi.org/10.1186/s12887-023-04391-6

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Received : 21 February 2023

Accepted : 27 October 2023

Published : 27 March 2024

DOI : https://doi.org/10.1186/s12887-023-04391-6

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• Breastfeeding duration
• Developmental delay

BMC Pediatrics

ISSN: 1471-2431

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ORIGINAL RESEARCH article

This article is part of the research topic.

Exploring the Interaction between Health-promoting and Health Risk Behaviours in Health

Association between life's essential 8 and periodontitis: A study based on NHANES 2009-2014□□□□ Provisionally Accepted

• 1 The Hospital of Shunyi District, China
• 2 Beijing Tiantan Hospital, Capital Medical University, China
• 3 Seventh Medical Center of PLA General Hospital, China
• 4 Shandong University of Traditional Chinese Medicine, China

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Background: This research aims to investigate the relationship between Life’s Essentials 8 (LE 8), the American Heart Association's latest indicator, and periodontitis. The purpose is to provide guidance on preventative measures. Methods: Data for our investigation were obtained from the National Health and Nutrition Examination Survey (NHANES) 2009 - 2014, with a total of 8784 participants eligible. LE8 scores were compiled from 8 index scores (the score for each component of diet, physical activity, nicotine exposure, sleep duration, body mass index, blood lipids, blood glucose, and blood pressure). Periodontitis was classified by the Centers for Disease Control and Prevention and American Academy of Periodontology (CDC/AAP). The study utilized multivariable logistic analyses to investigate the potential correlation. Results: After controlling for all covariates, LE8 was discovered to have a significant negative correlation with periodontitis prevalence [0.91 (0.88, 0.94)]. This trend continued to hold statistical significance even after converting LE8 into a categorical variable. Furthermore, a noteworthy adverse correlation was discovered across both genders, specifically males [0.35 (0.22, 0.55)] and females [0.39 (0.25, 0.60)], as well as for the majority of categorical classifications, namely ethnicity, age, education level, and marital status. However, only the age subgroups displayed some degree of significant difference from each other. Conclusion: Life's essential 8 was negatively associated with periodontitis, but more prospective trails are needed to confirm our findings.

Keywords: Periodontitis, Life's Essential 8, NHANES, Epidemiology, Risk factor(s)

Received: 16 Dec 2023; Accepted: 25 Mar 2024.

Copyright: © 2024 Hou, Zhang, Song, Li, Liu and Ma. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. KeGui Hou, The Hospital of Shunyi District, Beijing, China Mx. Zhaofeng Ma, The Hospital of Shunyi District, Beijing, China

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