survey based research design

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Survey Research: Definition, Examples and Methods

Survey Research is a quantitative research method used for collecting data from a set of respondents. It has been perhaps one of the most used methodologies in the industry for several years due to the multiple benefits and advantages that it has when collecting and analyzing data.

LEARN ABOUT: Behavioral Research

In this article, you will learn everything about survey research, such as types, methods, and examples.

Survey Research Definition

Survey Research is defined as the process of conducting research using surveys that researchers send to survey respondents. The data collected from surveys is then statistically analyzed to draw meaningful research conclusions. In the 21st century, every organization’s eager to understand what their customers think about their products or services and make better business decisions. Researchers can conduct research in multiple ways, but surveys are proven to be one of the most effective and trustworthy research methods. An online survey is a method for extracting information about a significant business matter from an individual or a group of individuals. It consists of structured survey questions that motivate the participants to respond. Creditable survey research can give these businesses access to a vast information bank. Organizations in media, other companies, and even governments rely on survey research to obtain accurate data.

The traditional definition of survey research is a quantitative method for collecting information from a pool of respondents by asking multiple survey questions. This research type includes the recruitment of individuals collection, and analysis of data. It’s useful for researchers who aim to communicate new features or trends to their respondents.

LEARN ABOUT: Level of Analysis Generally, it’s the primary step towards obtaining quick information about mainstream topics and conducting more rigorous and detailed quantitative research methods like surveys/polls or qualitative research methods like focus groups/on-call interviews can follow. There are many situations where researchers can conduct research using a blend of both qualitative and quantitative strategies.

LEARN ABOUT: Survey Sampling

Survey Research Methods

Survey research methods can be derived based on two critical factors: Survey research tool and time involved in conducting research. There are three main survey research methods, divided based on the medium of conducting survey research:

• Online/ Email:   Online survey research is one of the most popular survey research methods today. The survey cost involved in online survey research is extremely minimal, and the responses gathered are highly accurate.
• Phone:  Survey research conducted over the telephone ( CATI survey ) can be useful in collecting data from a more extensive section of the target population. There are chances that the money invested in phone surveys will be higher than other mediums, and the time required will be higher.
• Face-to-face:  Researchers conduct face-to-face in-depth interviews in situations where there is a complicated problem to solve. The response rate for this method is the highest, but it can be costly.

Further, based on the time taken, survey research can be classified into two methods:

• Longitudinal survey research:  Longitudinal survey research involves conducting survey research over a continuum of time and spread across years and decades. The data collected using this survey research method from one time period to another is qualitative or quantitative. Respondent behavior, preferences, and attitudes are continuously observed over time to analyze reasons for a change in behavior or preferences. For example, suppose a researcher intends to learn about the eating habits of teenagers. In that case, he/she will follow a sample of teenagers over a considerable period to ensure that the collected information is reliable. Often, cross-sectional survey research follows a longitudinal study .
• Cross-sectional survey research:  Researchers conduct a cross-sectional survey to collect insights from a target audience at a particular time interval. This survey research method is implemented in various sectors such as retail, education, healthcare, SME businesses, etc. Cross-sectional studies can either be descriptive or analytical. It is quick and helps researchers collect information in a brief period. Researchers rely on the cross-sectional survey research method in situations where descriptive analysis of a subject is required.

Survey research also is bifurcated according to the sampling methods used to form samples for research: Probability and Non-probability sampling. Every individual in a population should be considered equally to be a part of the survey research sample. Probability sampling is a sampling method in which the researcher chooses the elements based on probability theory. The are various probability research methods, such as simple random sampling , systematic sampling, cluster sampling, stratified random sampling, etc. Non-probability sampling is a sampling method where the researcher uses his/her knowledge and experience to form samples.

LEARN ABOUT: Survey Sample Sizes

The various non-probability sampling techniques are :

• Convenience sampling
• Snowball sampling
• Consecutive sampling
• Judgemental sampling
• Quota sampling

Process of implementing survey research methods:

• Decide survey questions:  Brainstorm and put together valid survey questions that are grammatically and logically appropriate. Understanding the objective and expected outcomes of the survey helps a lot. There are many surveys where details of responses are not as important as gaining insights about what customers prefer from the provided options. In such situations, a researcher can include multiple-choice questions or closed-ended questions . Whereas, if researchers need to obtain details about specific issues, they can consist of open-ended questions in the questionnaire. Ideally, the surveys should include a smart balance of open-ended and closed-ended questions. Use survey questions like Likert Scale , Semantic Scale, Net Promoter Score question, etc., to avoid fence-sitting.

LEARN ABOUT: System Usability Scale

• Finalize a target audience:  Send out relevant surveys as per the target audience and filter out irrelevant questions as per the requirement. The survey research will be instrumental in case the target population decides on a sample. This way, results can be according to the desired market and be generalized to the entire population.

LEARN ABOUT:  Testimonial Questions

• Send out surveys via decided mediums:  Distribute the surveys to the target audience and patiently wait for the feedback and comments- this is the most crucial step of the survey research. The survey needs to be scheduled, keeping in mind the nature of the target audience and its regions. Surveys can be conducted via email, embedded in a website, shared via social media, etc., to gain maximum responses.
• Analyze survey results:  Analyze the feedback in real-time and identify patterns in the responses which might lead to a much-needed breakthrough for your organization. GAP, TURF Analysis , Conjoint analysis, Cross tabulation, and many such survey feedback analysis methods can be used to spot and shed light on respondent behavior. Researchers can use the results to implement corrective measures to improve customer/employee satisfaction.

Reasons to conduct survey research

The most crucial and integral reason for conducting market research using surveys is that you can collect answers regarding specific, essential questions. You can ask these questions in multiple survey formats as per the target audience and the intent of the survey. Before designing a study, every organization must figure out the objective of carrying this out so that the study can be structured, planned, and executed to perfection.

LEARN ABOUT: Research Process Steps

Questions that need to be on your mind while designing a survey are:

• What is the primary aim of conducting the survey?
• How do you plan to utilize the collected survey data?
• What type of decisions do you plan to take based on the points mentioned above?

There are three critical reasons why an organization must conduct survey research.

• Understand respondent behavior to get solutions to your queries:  If you’ve carefully curated a survey, the respondents will provide insights about what they like about your organization as well as suggestions for improvement. To motivate them to respond, you must be very vocal about how secure their responses will be and how you will utilize the answers. This will push them to be 100% honest about their feedback, opinions, and comments. Online surveys or mobile surveys have proved their privacy, and due to this, more and more respondents feel free to put forth their feedback through these mediums.
• Present a medium for discussion:  A survey can be the perfect platform for respondents to provide criticism or applause for an organization. Important topics like product quality or quality of customer service etc., can be put on the table for discussion. A way you can do it is by including open-ended questions where the respondents can write their thoughts. This will make it easy for you to correlate your survey to what you intend to do with your product or service.
• Strategy for never-ending improvements:  An organization can establish the target audience’s attributes from the pilot phase of survey research . Researchers can use the criticism and feedback received from this survey to improve the product/services. Once the company successfully makes the improvements, it can send out another survey to measure the change in feedback keeping the pilot phase the benchmark. By doing this activity, the organization can track what was effectively improved and what still needs improvement.

Survey Research Scales

There are four main scales for the measurement of variables:

• Nominal Scale:  A nominal scale associates numbers with variables for mere naming or labeling, and the numbers usually have no other relevance. It is the most basic of the four levels of measurement.
• Ordinal Scale:  The ordinal scale has an innate order within the variables along with labels. It establishes the rank between the variables of a scale but not the difference value between the variables.
• Interval Scale:  The interval scale is a step ahead in comparison to the other two scales. Along with establishing a rank and name of variables, the scale also makes known the difference between the two variables. The only drawback is that there is no fixed start point of the scale, i.e., the actual zero value is absent.
• Ratio Scale:  The ratio scale is the most advanced measurement scale, which has variables that are labeled in order and have a calculated difference between variables. In addition to what interval scale orders, this scale has a fixed starting point, i.e., the actual zero value is present.

Benefits of survey research

In case survey research is used for all the right purposes and is implemented properly, marketers can benefit by gaining useful, trustworthy data that they can use to better the ROI of the organization.

Other benefits of survey research are:

• Minimum investment:  Mobile surveys and online surveys have minimal finance invested per respondent. Even with the gifts and other incentives provided to the people who participate in the study, online surveys are extremely economical compared to paper-based surveys.
• Versatile sources for response collection:  You can conduct surveys via various mediums like online and mobile surveys. You can further classify them into qualitative mediums like focus groups , and interviews and quantitative mediums like customer-centric surveys. Due to the offline survey response collection option, researchers can conduct surveys in remote areas with limited internet connectivity. This can make data collection and analysis more convenient and extensive.
• Reliable for respondents:  Surveys are extremely secure as the respondent details and responses are kept safeguarded. This anonymity makes respondents answer the survey questions candidly and with absolute honesty. An organization seeking to receive explicit responses for its survey research must mention that it will be confidential.

Survey research design

Researchers implement a survey research design in cases where there is a limited cost involved and there is a need to access details easily. This method is often used by small and large organizations to understand and analyze new trends, market demands, and opinions. Collecting information through tactfully designed survey research can be much more effective and productive than a casually conducted survey.

There are five stages of survey research design:

• Decide an aim of the research:  There can be multiple reasons for a researcher to conduct a survey, but they need to decide a purpose for the research. This is the primary stage of survey research as it can mold the entire path of a survey, impacting its results.
• Filter the sample from target population:  Who to target? is an essential question that a researcher should answer and keep in mind while conducting research. The precision of the results is driven by who the members of a sample are and how useful their opinions are. The quality of respondents in a sample is essential for the results received for research and not the quantity. If a researcher seeks to understand whether a product feature will work well with their target market, he/she can conduct survey research with a group of market experts for that product or technology.
• Zero-in on a survey method:  Many qualitative and quantitative research methods can be discussed and decided. Focus groups, online interviews, surveys, polls, questionnaires, etc. can be carried out with a pre-decided sample of individuals.
• Design the questionnaire:  What will the content of the survey be? A researcher is required to answer this question to be able to design it effectively. What will the content of the cover letter be? Or what are the survey questions of this questionnaire? Understand the target market thoroughly to create a questionnaire that targets a sample to gain insights about a survey research topic.
• Send out surveys and analyze results:  Once the researcher decides on which questions to include in a study, they can send it across to the selected sample . Answers obtained from this survey can be analyzed to make product-related or marketing-related decisions.

Survey examples: 10 tips to design the perfect research survey

Picking the right survey design can be the key to gaining the information you need to make crucial decisions for all your research. It is essential to choose the right topic, choose the right question types, and pick a corresponding design. If this is your first time creating a survey, it can seem like an intimidating task. But with QuestionPro, each step of the process is made simple and easy.

Below are 10 Tips To Design The Perfect Research Survey:

• Set your SMART goals:  Before conducting any market research or creating a particular plan, set your SMART Goals . What is that you want to achieve with the survey? How will you measure it promptly, and what are the results you are expecting?
• Choose the right questions:  Designing a survey can be a tricky task. Asking the right questions may help you get the answers you are looking for and ease the task of analyzing. So, always choose those specific questions – relevant to your research.
• Begin your survey with a generalized question:  Preferably, start your survey with a general question to understand whether the respondent uses the product or not. That also provides an excellent base and intro for your survey.
• Enhance your survey:  Choose the best, most relevant, 15-20 questions. Frame each question as a different question type based on the kind of answer you would like to gather from each. Create a survey using different types of questions such as multiple-choice, rating scale, open-ended, etc. Look at more survey examples and four measurement scales every researcher should remember.
• Prepare yes/no questions:  You may also want to use yes/no questions to separate people or branch them into groups of those who “have purchased” and those who “have not yet purchased” your products or services. Once you separate them, you can ask them different questions.
• Test all electronic devices:  It becomes effortless to distribute your surveys if respondents can answer them on different electronic devices like mobiles, tablets, etc. Once you have created your survey, it’s time to TEST. You can also make any corrections if needed at this stage.
• Distribute your survey:  Once your survey is ready, it is time to share and distribute it to the right audience. You can share handouts and share them via email, social media, and other industry-related offline/online communities.
• Collect and analyze responses:  After distributing your survey, it is time to gather all responses. Make sure you store your results in a particular document or an Excel sheet with all the necessary categories mentioned so that you don’t lose your data. Remember, this is the most crucial stage. Segregate your responses based on demographics, psychographics, and behavior. This is because, as a researcher, you must know where your responses are coming from. It will help you to analyze, predict decisions, and help write the summary report.
• Prepare your summary report:  Now is the time to share your analysis. At this stage, you should mention all the responses gathered from a survey in a fixed format. Also, the reader/customer must get clarity about your goal, which you were trying to gain from the study. Questions such as – whether the product or service has been used/preferred or not. Do respondents prefer some other product to another? Any recommendations?

Having a tool that helps you carry out all the necessary steps to carry out this type of study is a vital part of any project. At QuestionPro, we have helped more than 10,000 clients around the world to carry out data collection in a simple and effective way, in addition to offering a wide range of solutions to take advantage of this data in the best possible way.

From dashboards, advanced analysis tools, automation, and dedicated functions, in QuestionPro, you will find everything you need to execute your research projects effectively. Uncover insights that matter the most!

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What is survey research.

15 min read Find out everything you need to know about survey research, from what it is and how it works to the different methods and tools you can use to ensure you’re successful.

Survey research is the process of collecting data from a predefined group (e.g. customers or potential customers) with the ultimate goal of uncovering insights about your products, services, or brand overall .

As a quantitative data collection method, survey research can provide you with a goldmine of information that can inform crucial business and product decisions. But survey research needs careful planning and execution to get the results you want.

So if you’re thinking about using surveys to carry out research, read on.

Get started with our free survey maker tool

Types of survey research

Calling these methods ‘survey research’ slightly underplays the complexity of this type of information gathering. From the expertise required to carry out each activity to the analysis of the data and its eventual application, a considerable amount of effort is required.

As for how you can carry out your research, there are several options to choose from — face-to-face interviews, telephone surveys, focus groups (though more interviews than surveys), online surveys , and panel surveys.

Typically, the survey method you choose will largely be guided by who you want to survey, the size of your sample , your budget, and the type of information you’re hoping to gather.

Here are a few of the most-used survey types:

Face-to-face interviews

Before technology made it possible to conduct research using online surveys, telephone, and mail were the most popular methods for survey research. However face-to-face interviews were considered the gold standard — the only reason they weren’t as popular was due to their highly prohibitive costs.

When it came to face-to-face interviews, organizations would use highly trained researchers who knew when to probe or follow up on vague or problematic answers. They also knew when to offer assistance to respondents when they seemed to be struggling. The result was that these interviewers could get sample members to participate and engage in surveys in the most effective way possible, leading to higher response rates and better quality data.

Telephone surveys

While phone surveys have been popular in the past, particularly for measuring general consumer behavior or beliefs, response rates have been declining since the 1990s .

Phone surveys are usually conducted using a random dialing system and software that a researcher can use to record responses.

This method is beneficial when you want to survey a large population but don’t have the resources to conduct face-to-face research surveys or run focus groups, or want to ask multiple-choice and open-ended questions .

The downsides are they can: take a long time to complete depending on the response rate, and you may have to do a lot of cold-calling to get the information you need.

You also run the risk of respondents not being completely honest . Instead, they’ll answer your survey questions quickly just to get off the phone.

Focus groups (interviews — not surveys)

Focus groups are a separate qualitative methodology rather than surveys — even though they’re often bunched together. They’re normally used for survey pretesting and designing , but they’re also a great way to generate opinions and data from a diverse range of people.

Focus groups involve putting a cohort of demographically or socially diverse people in a room with a moderator and engaging them in a discussion on a particular topic, such as your product, brand, or service.

They remain a highly popular method for market research , but they’re expensive and require a lot of administration to conduct and analyze the data properly.

You also run the risk of more dominant members of the group taking over the discussion and swaying the opinions of other people — potentially providing you with unreliable data.

Online surveys

Online surveys have become one of the most popular survey methods due to being cost-effective, enabling researchers to accurately survey a large population quickly.

Online surveys can essentially be used by anyone for any research purpose – we’ve all seen the increasing popularity of polls on social media (although these are not scientific).

Using an online survey allows you to ask a series of different question types and collect data instantly that’s easy to analyze with the right software.

There are also several methods for running and distributing online surveys that allow you to get your questionnaire in front of a large population at a fraction of the cost of face-to-face interviews or focus groups.

This is particularly true when it comes to mobile surveys as most people with a smartphone can access them online.

However, you have to be aware of the potential dangers of using online surveys, particularly when it comes to the survey respondents. The biggest risk is because online surveys require access to a computer or mobile device to complete, they could exclude elderly members of the population who don’t have access to the technology — or don’t know how to use it.

It could also exclude those from poorer socio-economic backgrounds who can’t afford a computer or consistent internet access. This could mean the data collected is more biased towards a certain group and can lead to less accurate data when you’re looking for a representative population sample.

When it comes to surveys, every voice matters.

Find out how to create more inclusive and representative surveys for your research.

Panel surveys

A panel survey involves recruiting respondents who have specifically signed up to answer questionnaires and who are put on a list by a research company. This could be a workforce of a small company or a major subset of a national population. Usually, these groups are carefully selected so that they represent a sample of your target population — giving you balance across criteria such as age, gender, background, and so on.

Panel surveys give you access to the respondents you need and are usually provided by the research company in question. As a result, it’s much easier to get access to the right audiences as you just need to tell the research company your criteria. They’ll then determine the right panels to use to answer your questionnaire.

However, there are downsides. The main one being that if the research company offers its panels incentives, e.g. discounts, coupons, money — respondents may answer a lot of questionnaires just for the benefits.

This might mean they rush through your survey without providing considered and truthful answers. As a consequence, this can damage the credibility of your data and potentially ruin your analyses.

What are the benefits of using survey research?

Depending on the research method you use, there are lots of benefits to conducting survey research for data collection. Here, we cover a few:

1.   They’re relatively easy to do

Most research surveys are easy to set up, administer and analyze. As long as the planning and survey design is thorough and you target the right audience , the data collection is usually straightforward regardless of which survey type you use.

2.   They can be cost effective

Survey research can be relatively cheap depending on the type of survey you use.

Generally, qualitative research methods that require access to people in person or over the phone are more expensive and require more administration.

Online surveys or mobile surveys are often more cost-effective for market research and can give you access to the global population for a fraction of the cost.

3.   You can collect data from a large sample

Again, depending on the type of survey, you can obtain survey results from an entire population at a relatively low price. You can also administer a large variety of survey types to fit the project you’re running.

4.   You can use survey software to analyze results immediately

Using survey software, you can use advanced statistical analysis techniques to gain insights into your responses immediately.

Analysis can be conducted using a variety of parameters to determine the validity and reliability of your survey data at scale.

5.   Surveys can collect any type of data

While most people view surveys as a quantitative research method, they can just as easily be adapted to gain qualitative information by simply including open-ended questions or conducting interviews face to face.

How to measure concepts with survey questions

While surveys are a great way to obtain data, that data on its own is useless unless it can be analyzed and developed into actionable insights.

The easiest, and most effective way to measure survey results, is to use a dedicated research tool that puts all of your survey results into one place.

When it comes to survey measurement, there are four measurement types to be aware of that will determine how you treat your different survey results:

Nominal scale

With a nominal scale , you can only keep track of how many respondents chose each option from a question, and which response generated the most selections.

An example of this would be simply asking a responder to choose a product or brand from a list.

You could find out which brand was chosen the most but have no insight as to why.

Ordinal scale

Ordinal scales are used to judge an order of preference. They do provide some level of quantitative value because you’re asking responders to choose a preference of one option over another.

Ratio scale

Ratio scales can be used to judge the order and difference between responses. For example, asking respondents how much they spend on their weekly shopping on average.

Interval scale

In an interval scale, values are lined up in order with a meaningful difference between the two values — for example, measuring temperature or measuring a credit score between one value and another.

Step by step: How to conduct surveys and collect data

Conducting a survey and collecting data is relatively straightforward, but it does require some careful planning and design to ensure it results in reliable data.

Step 1 – Define your objectives

What do you want to learn from the survey? How is the data going to help you? Having a hypothesis or series of assumptions about survey responses will allow you to create the right questions to test them.

Step 2 – Create your survey questions

Once you’ve got your hypotheses or assumptions, write out the questions you need answering to test your theories or beliefs. Be wary about framing questions that could lead respondents or inadvertently create biased responses .

Step 3 – Choose your question types

Your survey should include a variety of question types and should aim to obtain quantitative data with some qualitative responses from open-ended questions. Using a mix of questions (simple Yes/ No, multiple-choice, rank in order, etc) not only increases the reliability of your data but also reduces survey fatigue and respondents simply answering questions quickly without thinking.

Find out how to create a survey that’s easy to engage with

Step 4 – Test your questions

Before sending your questionnaire out, you should test it (e.g. have a random internal group do the survey) and carry out A/B tests to ensure you’ll gain accurate responses.

Step 5 – Choose your target and send out the survey

Depending on your objectives, you might want to target the general population with your survey or a specific segment of the population. Once you’ve narrowed down who you want to target, it’s time to send out the survey.

After you’ve deployed the survey, keep an eye on the response rate to ensure you’re getting the number you expected. If your response rate is low, you might need to send the survey out to a second group to obtain a large enough sample — or do some troubleshooting to work out why your response rates are so low. This could be down to your questions, delivery method, selected sample, or otherwise.

Step 6 – Analyze results and draw conclusions

Once you’ve got your results back, it’s time for the fun part.

Break down your survey responses using the parameters you’ve set in your objectives and analyze the data to compare to your original assumptions. At this stage, a research tool or software can make the analysis a lot easier — and that’s somewhere Qualtrics can help.

Get reliable insights with survey software from Qualtrics

Gaining feedback from customers and leads is critical for any business, data gathered from surveys can prove invaluable for understanding your products and your market position, and with survey software from Qualtrics, it couldn’t be easier.

Used by more than 13,000 brands and supporting more than 1 billion surveys a year, Qualtrics empowers everyone in your organization to gather insights and take action. No coding required — and your data is housed in one system.

Get feedback from more than 125 sources on a single platform and view and measure your data in one place to create actionable insights and gain a deeper understanding of your target customers .

Automatically run complex text and statistical analysis to uncover exactly what your survey data is telling you, so you can react in real-time and make smarter decisions.

We can help you with survey management, too. From designing your survey and finding your target respondents to getting your survey in the field and reporting back on the results, we can help you every step of the way.

And for expert market researchers and survey designers, Qualtrics features custom programming to give you total flexibility over question types, survey design, embedded data, and other variables.

No matter what type of survey you want to run, what target audience you want to reach, or what assumptions you want to test or answers you want to uncover, we’ll help you design, deploy and analyze your survey with our team of experts.

Ready to find out more about Qualtrics CoreXM?

Get started with our free survey maker tool today

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• Doing Survey Research | A Step-by-Step Guide & Examples

Doing Survey Research | A Step-by-Step Guide & Examples

Published on 6 May 2022 by Shona McCombes . Revised on 10 October 2022.

Survey research means collecting information about a group of people by asking them questions and analysing 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
• Analyse 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: analyse the survey results, step 6: write up the survey results, 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 longitudinal studies , where you survey the same sample several times over an extended period.

Prevent plagiarism, run a free check.

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
• University students in the UK
• Second-generation immigrants in the Netherlands
• Customers of a specific company aged 18 to 24
• British transgender women over the age of 50

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

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 university student in the UK. 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 generalise 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.

There are two main types of survey:

• A questionnaire , where a list of questions is distributed by post, 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 post 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.

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 analyse.
• The anonymity and accessibility of online surveys mean you have less control over who responds.

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 centre 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 shop’s weekday customers).
• The sample size will be smaller, so this method is less suitable for collecting data on broad populations.

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 analyses the results. But they are more commonly used to collect qualitative data : the interviewees’ full responses are transcribed and analysed 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 analysed 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.

Use language that respondents will easily understand, and avoid words with vague or ambiguous meanings. Make sure your questions are phrased neutrally, with no bias towards one answer or another.

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.

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 post, online, or in person.

There are many methods of analysing 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 cleanse 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 organising them into categories or themes. You can also use more qualitative methods, such as thematic analysis , which is especially suitable for analysing 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 analysed 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 analyse it. In the results section, you summarise the key results from your analysis.

A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviours. It is made up of four 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 five or seven 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 analyse your data.

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

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A Comprehensive Guide to Survey Research Methodologies

For decades, researchers and businesses have used survey research to produce statistical data and explore ideas. The survey process is simple, ask questions and analyze the responses to make decisions. Data is what makes the difference between a valid and invalid statement and as the American statistician, W. Edwards Deming said:

“Without data, you’re just another person with an opinion.” - W. Edwards Deming

In this article, we will discuss what survey research is, its brief history, types, common uses, benefits, and the step-by-step process of designing a survey.

What is Survey Research

A survey is a research method that is used to collect data from a group of respondents in order to gain insights and information regarding a particular subject. It’s an excellent method to gather opinions and understand how and why people feel a certain way about different situations and contexts.

Brief History of Survey Research

Survey research may have its roots in the American and English “social surveys” conducted around the turn of the 20th century. The surveys were mainly conducted by researchers and reformers to document the extent of social issues such as poverty. ( 1 ) Despite being a relatively young field to many scientific domains, survey research has experienced three stages of development ( 2 ):

-       First Era (1930-1960)

-       Second Era (1960-1990)

-       Third Era (1990 onwards)

Over the years, survey research adapted to the changing times and technologies. By exploiting the latest technologies, researchers can gain access to the right population from anywhere in the world, analyze the data like never before, and extract useful information.

Survey Research Methods & Types

Survey research can be classified into seven categories based on objective, data sources, methodology, deployment method, and frequency of deployment.

Surveys based on Objective

Exploratory survey research.

Exploratory survey research is aimed at diving deeper into research subjects and finding out more about their context. It’s important for marketing or business strategy and the focus is to discover ideas and insights instead of gathering statistical data.

Generally, exploratory survey research is composed of open-ended questions that allow respondents to express their thoughts and perspectives. The final responses present information from various sources that can lead to fresh initiatives.

Predictive Survey Research

Predictive survey research is also called causal survey research. It’s preplanned, structured, and quantitative in nature. It’s often referred to as conclusive research as it tries to explain the cause-and-effect relationship between different variables. The objective is to understand which variables are causes and which are effects and the nature of the relationship between both variables.

Descriptive Survey Research

Descriptive survey research is largely observational and is ideal for gathering numeric data. Due to its quantitative nature, it’s often compared to exploratory survey research. The difference between the two is that descriptive research is structured and pre-planned.

 The idea behind descriptive research is to describe the mindset and opinion of a particular group of people on a given subject. The questions are every day multiple choices and users must choose from predefined categories. With predefined choices, you don’t get unique insights, rather, statistically inferable data.

Survey Research Types based on Concept Testing

Monadic concept testing.

Monadic testing is a survey research methodology in which the respondents are split into multiple groups and ask each group questions about a separate concept in isolation. Generally, monadic surveys are hyper-focused on a particular concept and shorter in duration. The important thing in monadic surveys is to avoid getting off-topic or exhausting the respondents with too many questions.

Sequential Monadic Concept Testing

Another approach to monadic testing is sequential monadic testing. In sequential monadic surveys, groups of respondents are surveyed in isolation. However, instead of surveying three groups on three different concepts, the researchers survey the same groups of people on three distinct concepts one after another. In a sequential monadic survey, at least two topics are included (in random order), and the same questions are asked for each concept to eliminate bias.

Based on Data Source

Primary data.

Data obtained directly from the source or target population is referred to as primary survey data. When it comes to primary data collection, researchers usually devise a set of questions and invite people with knowledge of the subject to respond. The main sources of primary data are interviews, questionnaires, surveys, and observation methods.

 Compared to secondary data, primary data is gathered from first-hand sources and is more reliable. However, the process of primary data collection is both costly and time-consuming.

Secondary Data

Survey research is generally used to collect first-hand information from a respondent. However, surveys can also be designed to collect and process secondary data. It’s collected from third-party sources or primary sources in the past.

 This type of data is usually generic, readily available, and cheaper than primary data collection. Some common sources of secondary data are books, data collected from older surveys, online data, and data from government archives. Beware that you might compromise the validity of your findings if you end up with irrelevant or inflated data.

Based on Research Method

Quantitative research.

Quantitative research is a popular research methodology that is used to collect numeric data in a systematic investigation. It’s frequently used in research contexts where statistical data is required, such as sciences or social sciences. Quantitative research methods include polls, systematic observations, and face-to-face interviews.

Qualitative Research

Qualitative research is a research methodology where you collect non-numeric data from research participants. In this context, the participants are not restricted to a specific system and provide open-ended information. Some common qualitative research methods include focus groups, one-on-one interviews, observations, and case studies.

Based on Deployment Method

Online surveys.

With technology advancing rapidly, the most popular method of survey research is an online survey. With the internet, you can not only reach a broader audience but also design and customize a survey and deploy it from anywhere. Online surveys have outperformed offline survey methods as they are less expensive and allow researchers to easily collect and analyze data from a large sample.

Paper or Print Surveys

As the name suggests, paper or print surveys use the traditional paper and pencil approach to collect data. Before the invention of computers, paper surveys were the survey method of choice.

Though many would assume that surveys are no longer conducted on paper, it's still a reliable method of collecting information during field research and data collection. However, unlike online surveys, paper surveys are expensive and require extra human resources.

Telephonic Surveys

Telephonic surveys are conducted over telephones where a researcher asks a series of questions to the respondent on the other end. Contacting respondents over a telephone requires less effort, human resources, and is less expensive.

What makes telephonic surveys debatable is that people are often reluctant in giving information over a phone call. Additionally, the success of such surveys depends largely on whether people are willing to invest their time on a phone call answering questions.

One-on-one Surveys

One-on-one surveys also known as face-to-face surveys are interviews where the researcher and respondent. Interacting directly with the respondent introduces the human factor into the survey.

Face-to-face interviews are useful when the researcher wants to discuss something personal with the respondent. The response rates in such surveys are always higher as the interview is being conducted in person. However, these surveys are quite expensive and the success of these depends on the knowledge and experience of the researcher.

Based on Distribution

The easiest and most common way of conducting online surveys is sending out an email. Sending out surveys via emails has a higher response rate as your target audience already knows about your brand and is likely to engage.

Buy Survey Responses

Purchasing survey responses also yields higher responses as the responders signed up for the survey. Businesses often purchase survey samples to conduct extensive research. Here, the target audience is often pre-screened to check if they're qualified to take part in the research.

Embedding Survey on a Website

Embedding surveys on a website is another excellent way to collect information. It allows your website visitors to take part in a survey without ever leaving the website and can be done while a person is entering or exiting the website.

Post the Survey on Social Media

Social media is an excellent medium to reach abroad range of audiences. You can publish your survey as a link on social media and people who are following the brand can take part and answer questions.

Based on Frequency of Deployment

Cross-sectional studies.

Cross-sectional studies are administered to a small sample from a large population within a short period of time. This provides researchers a peek into what the respondents are thinking at a given time. The surveys are usually short, precise, and specific to a particular situation.

Longitudinal Surveys

Longitudinal surveys are an extension of cross-sectional studies where researchers make an observation and collect data over extended periods of time. This type of survey can be further divided into three types:

-       Trend surveys are employed to allow researchers to understand the change in the thought process of the respondents over some time.

-       Panel surveys are administered to the same group of people over multiple years. These are usually expensive and researchers must stick to their panel to gather unbiased opinions.

-       In cohort surveys, researchers identify a specific category of people and regularly survey them. Unlike panel surveys, the same people do not need to take part over the years, but each individual must fall into the researcher’s primary interest category.

Retrospective Survey

Retrospective surveys allow researchers to ask questions to gather data about past events and beliefs of the respondents. Since retrospective surveys also require years of data, they are similar to the longitudinal survey, except retrospective surveys are shorter and less expensive.

Why Should You Conduct Research Surveys?

“In God we trust. All others must bring data” - W. Edwards Deming

 In the information age, survey research is of utmost importance and essential for understanding the opinion of your target population. Whether you’re launching a new product or conducting a social survey, the tool can be used to collect specific information from a defined set of respondents. The data collected via surveys can be further used by organizations to make informed decisions.

Furthermore, compared to other research methods, surveys are relatively inexpensive even if you’re giving out incentives. Compared to the older methods such as telephonic or paper surveys, online surveys have a smaller cost and the number of responses is higher.

 What makes surveys useful is that they describe the characteristics of a large population. With a larger sample size , you can rely on getting more accurate results. However, you also need honest and open answers for accurate results. Since surveys are also anonymous and the responses remain confidential, respondents provide candid and accurate answers.

Common Uses of a Survey

Surveys are widely used in many sectors, but the most common uses of the survey research include:

-       Market research : surveying a potential market to understand customer needs, preferences, and market demand.

-       Customer Satisfaction: finding out your customer’s opinions about your services, products, or companies .

-       Social research: investigating the characteristics and experiences of various social groups.

-       Health research: collecting data about patients’ symptoms and treatments.

-       Politics: evaluating public opinion regarding policies and political parties.

-       Psychology: exploring personality traits, behaviors, and preferences.

6 Steps to Conduct Survey Research

An organization, person, or company conducts a survey when they need the information to make a decision but have insufficient data on hand. Following are six simple steps that can help you design a great survey.

Step 1: Objective of the Survey

The first step in survey research is defining an objective. The objective helps you define your target population and samples. The target population is the specific group of people you want to collect data from and since it’s rarely possible to survey the entire population, we target a specific sample from it. Defining a survey objective also benefits your respondents by helping them understand the reason behind the survey.

Step 2: Number of Questions

The number of questions or the size of the survey depends on the survey objective. However, it’s important to ensure that there are no redundant queries and the questions are in a logical order. Rephrased and repeated questions in a survey are almost as frustrating as in real life. For a higher completion rate, keep the questionnaire small so that the respondents stay engaged to the very end. The ideal length of an interview is less than 15 minutes. ( 2 )

Step 3: Language and Voice of Questions

While designing a survey, you may feel compelled to use fancy language. However, remember that difficult language is associated with higher survey dropout rates. You need to speak to the respondent in a clear, concise, and neutral manner, and ask simple questions. If your survey respondents are bilingual, then adding an option to translate your questions into another language can also prove beneficial.

Step 4: Type of Questions

In a survey, you can include any type of questions and even both closed-ended or open-ended questions. However, opt for the question types that are the easiest to understand for the respondents, and offer the most value. For example, compared to open-ended questions, people prefer to answer close-ended questions such as MCQs (multiple choice questions)and NPS (net promoter score) questions.

Step 5: User Experience

Designing a great survey is about more than just questions. A lot of researchers underestimate the importance of user experience and how it affects their response and completion rates. An inconsistent, difficult-to-navigate survey with technical errors and poor color choice is unappealing for the respondents. Make sure that your survey is easy to navigate for everyone and if you’re using rating scales, they remain consistent throughout the research study.

Additionally, don’t forget to design a good survey experience for both mobile and desktop users. According to Pew Research Center, nearly half of the smartphone users access the internet mainly from their mobile phones and 14 percent of American adults are smartphone-only internet users. ( 3 )

Step 6: Survey Logic

Last but not least, logic is another critical aspect of the survey design. If the survey logic is flawed, respondents may not continue in the right direction. Make sure to test the logic to ensure that selecting one answer leads to the next logical question instead of a series of unrelated queries.

How to Effectively Use Survey Research with Starlight Analytics

Designing and conducting a survey is almost as much science as it is an art. To craft great survey research, you need technical skills, consider the psychological elements, and have a broad understanding of marketing.

The ultimate goal of the survey is to ask the right questions in the right manner to acquire the right results.

Bringing a new product to the market is a long process and requires a lot of research and analysis. In your journey to gather information or ideas for your business, Starlight Analytics can be an excellent guide. Starlight Analytics' product concept testing helps you measure your product's market demand and refine product features and benefits so you can launch with confidence. The process starts with custom research to design the survey according to your needs, execute the survey, and deliver the key insights on time.

• Survey research in the United States: roots and emergence, 1890-1960 https://searchworks.stanford.edu/view/10733873    
• How to create a survey questionnaire that gets great responses https://luc.id/knowledgehub/how-to-create-a-survey-questionnaire-that-gets-great-responses/    
• Internet/broadband fact sheet https://www.pewresearch.org/internet/fact-sheet/internet-broadband/    

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• Survey Research: Types, Examples & Methods

Surveys have been proven to be one of the most effective methods of conducting research. They help you to gather relevant data from a large audience, which helps you to arrive at a valid and objective conclusion. 

Just like other research methods, survey research had to be conducted the right way to be effective. In this article, we’ll dive into the nitty-gritty of survey research and show you how to get the most out of it. 

What is Survey Research? 

Survey research is simply a systematic investigation conducted via a survey. In other words, it is a type of research carried out by administering surveys to respondents. 

Surveys already serve as a great method of opinion sampling and finding out what people think about different contexts and situations. Applying this to research means you can gather first-hand information from persons affected by specific contexts. 

Survey research proves useful in numerous primary research scenarios. Consider the case whereby a restaurant wants to gather feedback from its customers on its new signatory dish. A good way to do this is to conduct survey research on a defined customer demographic. 

By doing this, the restaurant is better able to gather primary data from the customers (respondents) with regards to what they think and feel about the new dish across multiple facets. This means they’d have more valid and objective information to work with. 

Why Conduct Survey Research?  

One of the strongest arguments for survey research is that it helps you gather the most authentic data sets in the systematic investigation. Survey research is a gateway to collecting specific information from defined respondents, first-hand.  

Surveys combine different question types that make it easy for you to collect numerous information from respondents. When you come across a questionnaire for survey research, you’re likely to see a neat blend of close-ended and open-ended questions, together with other survey response scale questions. 

Apart from what we’ve discussed so far, here are some other reasons why survey research is important: 

• It gives you insights into respondents’ behaviors and preferences which is valid in any systematic investigation.
• Many times, survey research is structured in an interactive manner which makes it easier for respondents to communicate their thoughts and experiences. 
• It allows you to gather important data that proves useful for product improvement; especially in market research. 

Characteristics of Survey Research

• Usage : Survey research is mostly deployed in the field of social science; especially to gather information about human behavior in different social contexts. 
• Systematic : Like other research methods, survey research is systematic. This means that it is usually conducted in line with empirical methods and follows specific processes.
• Replicable : In survey research, applying the same methods often translates to achieving similar results. 
• Types : Survey research can be conducted using forms (offline and online) or via structured, semi-structured, and unstructured interviews . 
• Data : The data gathered from survey research is mostly quantitative; although it can be qualitative. 
• Impartial Sampling : The data sample in survey research is random and not subject to unavoidable biases.
• Ecological Validity : Survey research often makes use of data samples obtained from real-world occurrences. 

Types of Survey Research

Survey research can be subdivided into different types based on its objectives, data source, and methodology. 

Types of Survey Research Based on Objective

• Exploratory Survey Research

Exploratory survey research is aimed at finding out more about the research context. Here, the survey research pays attention to discovering new ideas and insights about the research subject(s) or contexts. 

Exploratory survey research is usually made up of open-ended questions that allow respondents to fully communicate their thoughts and varying perspectives on the subject matter. In many cases, systematic investigation kicks off with an exploratory research survey. 

• Predictive Survey Research

This type of research is also referred to as causal survey research because it pays attention to the causative relationship between the variables in the survey research. In other words, predictive survey research pays attention to existing patterns to explain the relationship between two variables. 

It can also be referred to as conclusive research because it allows you to identify causal variables and resultant variables; that is cause and effect. Predictive variables allow you to determine the nature of the relationship between the causal variables and the effect to be predicted. 

• Descriptive Survey Research

Unlike predictive research, descriptive survey research is largely observational. It is ideal for quantitative research because it helps you to gather numeric data. 

The questions listed in descriptive survey research help you to uncover new insights into the actions, thoughts, and feelings of survey respondents. With this data, you can know the extent to which different conditions can be obtained among these subjects. 

Types of Survey Research Based on Data Source

• Secondary Data

Survey research can be designed to collect and process secondary data. Secondary data is a type of data that has been collected from primary sources in the past and is readily available for use. It is the type of data that is already existing.

Since secondary data is gathered from third-party sources, it is mostly generic, unlike primary data that is specific to the research context. Common sources of secondary data in survey research include books, data collected through other surveys, online data, data from government archives, and libraries. 

• Primary Data

This is the type of research data that is collected directly; that is, data collected from first-hand sources. Primary data is usually tailored to a specific research context so that reflects the aims and objectives of the systematic investigation.

One of the strongest points of primary data over its secondary counterpart is validity. Because it is collected directly from first-hand sources, primary data typically results in objective research findings. 

You can collect primary data via interviews, surveys, and questionnaires, and observation methods. 

Types of Survey Research Based on Methodology

• Quantitative Research

Quantitative research is a common research method that is used to gather numerical data in a systematic investigation. It is often deployed in research contexts that require statistical information to arrive at valid results such as in social science or science. 

For instance, as an organization looking to find out how many persons are using your product in a particular location, you can administer survey research to collect useful quantitative data. Other quantitative research methods include polls, face-to-face interviews, and systematic observation. 

• Qualitative Research

This is a method of systematic investigation that is used to collect non-numerical data from research participants. In other words, it is a research method that allows you to gather open-ended information from your target audience. 

Typically, organizations deploy qualitative research methods when they need to gather descriptive data from their customers; for example, when they need to collect customer feedback in product evaluation. Qualitative research methods include one-on-one interviews, observation, case studies, and focus groups. 

Survey Research Scales

• Nominal Scale

This is a type of survey research scale that uses numbers to label the different answer options in a survey. On a nominal scale , the numbers have no value in themselves; they simply serve as labels for qualitative variables in the survey. 

In cases where a nominal scale is used for identification, there is typically a specific one-on-one relationship between the numeric value and the variable it represents. On the other hand, when the variable is used for classification, then each number on the scale serves as a label or a tag. 

Examples of Nominal Scale in Survey Research 

1. How would you describe your complexion? 

2. Have you used this product?

• Ordinal Scale

This is a type of variable measurement scale that arranges answer options in a specific ranking order without necessarily indicating the degree of variation between these options. Ordinal data is qualitative and can be named, ranked, or grouped. 

In an ordinal scale , the different properties of the variables are relatively unknown, and it also identifies, describes, and shows the rank of the different variables. With an ordered scale, it is easier for researchers to measure the degree of agreement and/or disagreement with different variables. 

With ordinal scales, you can measure non-numerical attributes such as the degree of happiness, agreement, or opposition of respondents in specific contexts. Using an ordinal scale makes it easy for you to compare variables and process survey responses accordingly. 

Examples of Ordinal Scale in Survey Research

1. How often do you use this product?

• Prefer not to say

2. How much do you agree with our new policies? 

• Totally agree
• Somewhat agree
• Totally disagree
• Interval Scale

This is a type of survey scale that is used to measure variables existing at equal intervals along a common scale. In some way, it combines the attributes of nominal and ordinal scales since it is used where there is order and there is a meaningful difference between 2 variables. 

With an interval scale, you can quantify the difference in value between two variables in survey research. In addition to this, you can carry out other mathematical processes like calculating the mean and median of research variables. 

Examples of Interval Scale in Survey Research

1. Our customer support team was very effective. 

• Completely agree
• Neither agree nor disagree
• Somewhat disagree
• Completely disagree 

2. I enjoyed using this product.

Another example of an interval scale can be seen in the Net Promoter Score.

• Ratio Scale

Just like the interval scale, the ratio scale is quantitative and it is used when you need to compare intervals or differences in survey research. It is the highest level of measurement and it is made up of bits and pieces of the other survey scales. 

One of the unique features of the ratio scale is it has a true zero and equal intervals between the variables on the scale. This zero indicates an absence of the variable being measured by the scale. Common occurrences of ratio scales can be seen with distance (length), area, and population measurement. 

Examples of Ratio Scale in Survey Research

1. How old are you?

• Below 18 years
• 41 and above

2. How many times do you shop in a week?

• Less than twice
• Three times
• More than four times

Uses of Survey Research

• Health Surveys

Survey research is used by health practitioners to gather useful data from patients in different medical and safety contexts. It helps you to gather primary and secondary data about medical conditions and risk factors of multiple diseases and infections. 

In addition to this, administering health surveys regularly helps you to monitor the overall health status of your population; whether in the workplace, school, or community. This kind of data can be used to help prevent outbreaks and minimize medical emergencies in these contexts. 

Survey research is also useful when conducting polls; whether online or offline. A poll is a data collection tool that helps you to gather public opinion about a particular subject from a well-defined research sample.

By administering survey research, you can gather valid data from a well-defined research sample, and utilize research findings for decision making. For example, during elections, individuals can be asked to choose their preferred leader via questionnaires administered as part of survey research.

• Customer Satisfaction

Customer satisfaction is one of the cores of every organization as it is directly concerned with how well your product or service meets the needs of your clients. Survey research is an effective way to measure customer satisfaction at different intervals. 

As a restaurant, for example, you can send out online surveys to customers immediately when they patronize your business. In these surveys, encourage them to provide feedback on their experience and to provide information on how your service delivery can be improved. 

Survey research makes data collection and analysis easy during a census. With an online survey tool like Formplus , you can seamlessly gather data during a census without moving from a spot. Formplus has multiple sharing options that help you collect information without stress. 

Survey Research Methods

Survey research can be done using different online and offline methods. Let’s examine a few of them here.

• Telephone Surveys

This is a means of conducting survey research via phone calls. In a telephone survey, the researcher places a call to the survey respondents and gathers information from them by asking questions about the research context under consideration. 

A telephone survey is a kind of simulation of the face-to-face survey experience since it involves discussing with respondents to gather and process valid data. However, major challenges with this method include the fact that it is expensive and time-consuming. 

• Online Surveys

An online survey is a data collection tool used to create and administer surveys and questionnaires using data tools like Formplus. Online surveys work better than paper forms and other offline survey methods because you can easily gather and process data from a large sample size with them. 

• Face-to-Face Interviews

Face-to-face interviews for survey research can be structured, semi-structured, or unstructured depending on the research context and the type of data you want to collect. If you want to gather qualitative data , then unstructured and semi-structured interviews are the way to go. 

On the other hand, if you want to collect quantifiable information from your research sample, conducting a structured interview is the best way to go. Face-to-face interviews can also be time-consuming and cost-intensive. Let’s mention here that face-to-face surveys are one of the most widely used methods of survey data collection. 

How to Conduct Research Surveys on Formplus 

With Formplus, you can create forms for survey research without any hassles. Follow this step-by-step guide to create and administer online surveys for research via Formplus. 

1. Sign up at www.formpl.us to create your Formplus account. If you already have a Formplus account, click here to log in.

5. Use the form customization options to change the appearance of your survey. You can add your organization’s logo to the survey, change the form font and layout, and insert preferred background images.

Advantages of Survey Research

• It is inexpensive – with survey research, you can avoid the cost of in-person interviews. It’s also easy to receive data as you can share your surveys online and get responses from a large demographic
• It is the fastest way to get a large amount of first-hand data
• Surveys allow you to compare the results you get through charts and graphs
• It is versatile as it can be used for any research topic
• Surveys are perfect for anonymous respondents in the research 

Disadvantages of Survey Research

• Some questions may not get answers
• People may understand survey questions differently
• It may not be the best option for respondents with visual or hearing impairments as well as a demographic with no literacy levels
• People can provide dishonest answers in a survey research

Conclusion 

In this article, we’ve discussed survey research extensively; touching on different important aspects of this concept. As a researcher, organization, individual, or student, it is important to understand how survey research works to utilize it effectively and get the most from this method of systematic investigation. 

As we’ve already stated, conducting survey research online is one of the most effective methods of data collection as it allows you to gather valid data from a large group of respondents. If you’re looking to kick off your survey research, you can start by signing up for a Formplus account here. 

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Methodology

• What Is a Research Design | Types, Guide & Examples

What Is a Research Design | Types, Guide & Examples

Published on June 7, 2021 by Shona McCombes . Revised on November 20, 2023 by Pritha Bhandari.

A research design is a strategy for answering your   research question  using empirical data. Creating a research design means making decisions about:

• Your overall research objectives and approach
• Whether you’ll rely on primary research or secondary research
• Your sampling methods or criteria for selecting subjects
• Your data collection methods
• The procedures you’ll follow to collect data
• Your data analysis methods

A well-planned research design helps ensure that your methods match your research objectives and that you use the right kind of analysis for your data.

Table of contents

Step 1: consider your aims and approach, step 2: choose a type of research design, step 3: identify your population and sampling method, step 4: choose your data collection methods, step 5: plan your data collection procedures, step 6: decide on your data analysis strategies, other interesting articles, frequently asked questions about research design.

• Introduction

Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.

There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities—start by thinking carefully about what you want to achieve.

The first choice you need to make is whether you’ll take a qualitative or quantitative approach.

Qualitative research designs tend to be more flexible and inductive , allowing you to adjust your approach based on what you find throughout the research process.

Quantitative research designs tend to be more fixed and deductive , with variables and hypotheses clearly defined in advance of data collection.

It’s also possible to use a mixed-methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.

Practical and ethical considerations when designing research

As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics .

• How much time do you have to collect data and write up the research?
• Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?
• Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?
• Will you need ethical approval ?

At each stage of the research design process, make sure that your choices are practically feasible.

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Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.

Types of quantitative research designs

Quantitative designs can be split into four main types.

• Experimental and   quasi-experimental designs allow you to test cause-and-effect relationships
• Descriptive and correlational designs allow you to measure variables and describe relationships between them.

With descriptive and correlational designs, you can get a clear picture of characteristics, trends and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation ).

Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.

Types of qualitative research designs

Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.

The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analyzing the data.

Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.

In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.

Defining the population

A population can be made up of anything you want to study—plants, animals, organizations, texts, countries, etc. In the social sciences, it most often refers to a group of people.

For example, will you focus on people from a specific demographic, region or background? Are you interested in people with a certain job or medical condition, or users of a particular product?

The more precisely you define your population, the easier it will be to gather a representative sample.

• Sampling methods

Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.

To select a sample, there are two main approaches: probability sampling and non-probability sampling . The sampling method you use affects how confidently you can generalize your results to the population as a whole.

Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.

For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.

Case selection in qualitative research

In some types of qualitative designs, sampling may not be relevant.

For example, in an ethnography or a case study , your aim is to deeply understand a specific context, not to generalize to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.

In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question .

For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.

Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.

You can choose just one data collection method, or use several methods in the same study.

Survey methods

Surveys allow you to collect data about opinions, behaviors, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews .

Observation methods

Observational studies allow you to collect data unobtrusively, observing characteristics, behaviors or social interactions without relying on self-reporting.

Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.

Other methods of data collection

There are many other ways you might collect data depending on your field and topic.

If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what kinds of data collection methods they used.

Secondary data

If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected—for example, datasets from government surveys or previous studies on your topic.

With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.

Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.

However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.

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As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.

Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are high in reliability and validity.

Operationalization

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalization means turning these fuzzy ideas into measurable indicators.

If you’re using observations , which events or actions will you count?

If you’re using surveys , which questions will you ask and what range of responses will be offered?

You may also choose to use or adapt existing materials designed to measure the concept you’re interested in—for example, questionnaires or inventories whose reliability and validity has already been established.

Reliability and validity

Reliability means your results can be consistently reproduced, while validity means that you’re actually measuring the concept you’re interested in.

For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.

If you’re developing a new questionnaire or other instrument to measure a specific concept, running a pilot study allows you to check its validity and reliability in advance.

Sampling procedures

As well as choosing an appropriate sampling method , you need a concrete plan for how you’ll actually contact and recruit your selected sample.

That means making decisions about things like:

• How many participants do you need for an adequate sample size?
• What inclusion and exclusion criteria will you use to identify eligible participants?
• How will you contact your sample—by mail, online, by phone, or in person?

If you’re using a probability sampling method , it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?

If you’re using a non-probability method , how will you avoid research bias and ensure a representative sample?

Data management

It’s also important to create a data management plan for organizing and storing your data.

Will you need to transcribe interviews or perform data entry for observations? You should anonymize and safeguard any sensitive data, and make sure it’s backed up regularly.

Keeping your data well-organized will save time when it comes to analyzing it. It can also help other researchers validate and add to your findings (high replicability ).

On its own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyze the data.

Quantitative data analysis

In quantitative research, you’ll most likely use some form of statistical analysis . With statistics, you can summarize your sample data, make estimates, and test hypotheses.

Using descriptive statistics , you can summarize your sample data in terms of:

• The distribution of the data (e.g., the frequency of each score on a test)
• The central tendency of the data (e.g., the mean to describe the average score)
• The variability of the data (e.g., the standard deviation to describe how spread out the scores are)

The specific calculations you can do depend on the level of measurement of your variables.

Using inferential statistics , you can:

• Make estimates about the population based on your sample data.
• Test hypotheses about a relationship between variables.

Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs ) look for differences in the outcomes of different groups.

Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.

Qualitative data analysis

In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.

Two of the most common approaches to doing this are thematic analysis and discourse analysis .

There are many other ways of analyzing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.

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

• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

A research design is a strategy for answering your   research question . It defines your overall approach and determines how you will collect and analyze data.

A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.

Quantitative research designs can be divided into two main categories:

• Correlational and descriptive designs are used to investigate characteristics, averages, trends, and associations between variables.
• Experimental and quasi-experimental designs are used to test causal relationships .

Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs.

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 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 )

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

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

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.

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Survey descriptive research: Method, design, and examples

• November 2, 2022

What is survey descriptive research?

The observational method: monitor people while they engage with a subject, the case study method: gain an in-depth understanding of a subject, survey descriptive research: easy and cost-effective, types of descriptive research design, what is the descriptive survey research design definition by authors, 1. quantitativeness and qualitatively, 2. uncontrolled variables, 3. natural environment, 4. provides a solid basis for further research, describe a group and define its characteristics, measure data trends by conducting descriptive marketing research, understand how customers perceive a brand, descriptive survey research design: how to make the best descriptive questionnaire, create descriptive surveys with surveyplanet.

Survey descriptive research is a quantitative method that focuses on describing the characteristics of a phenomenon rather than asking why it occurs. Doing this provides a better understanding of the nature of the subject at hand and creates a good foundation for further research.

Descriptive market research is one of the most commonly used ways of examining trends and changes in the market. It is easy, low-cost, and provides valuable in-depth information on a chosen subject.

This article will examine the basic principles of the descriptive survey study and show how to make the best descriptive survey questionnaire and how to conduct effective research.

It is often said to be quantitative research that focuses more on the what, how, when, and where instead of the why. But what does that actually mean?

The answer is simple. By conducting descriptive survey research, the nature of a phenomenon is focused upon without asking about what causes it.

The main goal of survey descriptive research is to shed light on the heart of the research problem and better understand it. The technique provides in-depth knowledge of what the research problem is before investigating why it exists.

Survey descriptive research and data collection methods

Descriptive research methods can differ based on data collection. We distinguish three main data collection methods: case study, observational method, and descriptive survey method.

Of these, the descriptive survey research method is most commonly used in fields such as market research, social research, psychology, politics, etc.

Sometimes also called the observational descriptive method, this is simply monitoring people while they engage with a particular subject. The aim is to examine people’s real-life behavior by maintaining a natural environment that does not change the respondents’ behavior—because they do not know they are being observed.

It is often used in fields such as market research, psychology, or social research. For example, customers can be monitored while dining at a restaurant or browsing through the products in a shop.

When doing case studies, researchers conduct thorough examinations of individuals or groups. The case study method is not used to collect general information on a particular subject. Instead, it provides an in-depth understanding of a particular subject and can give rise to interesting conclusions and new hypotheses.

The term case study can also refer to a sample group, which is a specific group of people that are examined and, afterward, findings are generalized to a larger group of people. However, this kind of generalization is rather risky because it is not always accurate.

Additionally, case studies cannot be used to determine cause and effect because of potential bias on the researcher’s part.

The survey descriptive research method consists of creating questionnaires or polls and distributing them to respondents, who then answer the questions (usually a mix of open-ended and closed-ended).

Surveys are the easiest and most cost-efficient way to gain feedback on a particular topic. They can be conducted online or offline, the size of the sample is highly flexible, and they can be distributed through many different channels.

When doing market research , use such surveys to understand the demographic of a certain market or population, better determine the target audience, keep track of the changes in the market, and learn about customer experience and satisfaction with products and services.

Several types of survey descriptive research are classified based on the approach used:

• Descriptive surveys gather information about a certain subject.
• Descriptive-normative surveys gather information just like a descriptive survey, after which results are compared with a norm.
• Correlative surveys explore the relationship between two variables and conclude if it is positive, neutral, or negative.

A descriptive survey research design is a methodology used in social science and other fields to gather information and describe the characteristics, behaviors, or attitudes of a particular population or group of interest. While there may not be a single definition provided by specific authors, the concept is widely understood and defined similarly across the literature.

Here’s a general definition that captures the essence of a descriptive survey research design definition by authors:

A descriptive survey research design is a systematic and structured approach to collecting data from a sample of individuals or entities within a larger population, with the primary aim of providing a detailed and accurate description of the characteristics, behaviors, opinions, or attitudes that exist within the target group. This method involves the use of surveys, questionnaires, interviews, or observations to collect data, which is then analyzed and summarized to draw conclusions about the population of interest.

It’s important to note that descriptive survey research is often used when researchers want to gain insights into a population or phenomenon, but without manipulating variables or testing hypotheses, as is common in experimental research. Instead, it focuses on providing a comprehensive overview of the subject under investigation. Researchers often use various statistical and analytical techniques to summarize and interpret the collected data in descriptive survey research.

The characteristics and advantages of a descriptive survey questionnaire

There are numerous advantages to using a descriptive survey design. First of all, it is cheap and easy to conduct. A large sample can be surveyed and extensive data gathered quickly and inexpensively.

The data collected provides both quantitative and qualitative information , which provides a holistic understanding of the topic. Moreover, it can be used in further research on this or related topics.

Here are some of the most important advantages of conducting a survey descriptive research:

The descriptive survey research design uses both quantitative and qualitative research methods. It is used primarily to conduct quantitative research and gather data that is statistically easy to analyze. However, it can also provide qualitative data that helps describe and understand the research subject.

Descriptive research explores more than one variable. However, unlike experimental research, descriptive survey research design doesn’t allow control of variables. Instead, observational methods are used during research. Even though these variables can change and have an unexpected impact on an inquiry, they will give access to honest responses.

The descriptive research is conducted in a natural environment. This way, answers gathered from responses are more honest because the nature of the research does not influence them.

The data collected through descriptive research can be used to further explore the same or related subjects. Additionally, it can help develop the next line of research and the best method to use moving forward.

Descriptive survey example: When to use a descriptive research questionnaire?

Descriptive research design can be used for many purposes. It is mainly utilized to test a hypothesis, define the characteristics of a certain phenomenon, and examine the correlations between them.

Market research is one of the main fields in which descriptive methods are used to conduct studies. Here’s what can be done using this method:

Understanding the needs of customers and their desires is the key to a business’s success. By truly understanding these, it will be possible to offer exactly what customers need and prevent them from turning to competitors.

By using a descriptive survey, different customer characteristics—such as traits, opinions, or behavior patterns—can be determined. With this data, different customer types can be defined and profiles developed that focus on their interests and the behavior they exhibit. This information can be used to develop new products and services that will be successful.

Measuring data trends is extremely important. Explore the market and get valuable insights into how consumers’ interests change over time—as well as how the competition is performing in the marketplace.

Over time, the data gathered from a descriptive questionnaire can be subjected to statistical analysis. This will deliver valuable insights.

Another important aspect to consider is brand awareness. People need to know about your brand, and they need to have a positive opinion of it. The best way to discover their perception is to conduct a brand survey , which gives deeper insight into brand awareness, perception, identity, and customer loyalty .

When conducting survey descriptive research, there are a few basic steps that are needed for a survey to be successful:

• Define the research goals.
• Decide on the research method.
• Define the sample population.
• Design the questionnaire.
• Write specific questions.
• Distribute the questionnaire.
• Analyze the data .
• Make a survey report.

First of all, define the research goals. By setting up clear objectives, every other step can be worked through. This will result in the perfect descriptive questionnaire example and collect only valuable data.

Next, decide on the research method to use—in this case, the descriptive survey method. Then, define the sample population for (that is, the target audience). After that, think about the design itself and the questions that will be asked in the survey .

If you’re not sure where to start, we’ve got you covered. As free survey software, SurveyPlanet offers pre-made themes that are clean and eye-catching, as well as pre-made questions that will save you the trouble of making new ones.

Simply scroll through our library and choose a descriptive survey questionnaire sample that best suits your needs, though our user-friendly interface can help you create bespoke questions in a process that is easy and efficient.

With a survey in hand, it will then need to be delivered to the target audience. This is easy with our survey embedding feature, which allows for the linking of surveys on a website, via emails, or by sharing on social media.

When all the responses are gathered, it’s time to analyze them. Use SurveyPlanet to easily filter data and do cross-sectional analysis. Finally, just export the results and make a survey report.

Conducting descriptive survey research is the best way to gain a deeper knowledge of a topic of interest and develop a sound basis for further research. Sign up for a free SurveyPlanet account to start improving your business today!

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Home » Research Design – Types, Methods and Examples

Research Design – Types, Methods and Examples

Table of Contents

Research Design

Definition:

Research design refers to the overall strategy or plan for conducting a research study. It outlines the methods and procedures that will be used to collect and analyze data, as well as the goals and objectives of the study. Research design is important because it guides the entire research process and ensures that the study is conducted in a systematic and rigorous manner.

Types of Research Design

Types of Research Design are as follows:

Descriptive Research Design

This type of research design is used to describe a phenomenon or situation. It involves collecting data through surveys, questionnaires, interviews, and observations. The aim of descriptive research is to provide an accurate and detailed portrayal of a particular group, event, or situation. It can be useful in identifying patterns, trends, and relationships in the data.

Correlational Research Design

Correlational research design is used to determine if there is a relationship between two or more variables. This type of research design involves collecting data from participants and analyzing the relationship between the variables using statistical methods. The aim of correlational research is to identify the strength and direction of the relationship between the variables.

Experimental Research Design

Experimental research design is used to investigate cause-and-effect relationships between variables. This type of research design involves manipulating one variable and measuring the effect on another variable. It usually involves randomly assigning participants to groups and manipulating an independent variable to determine its effect on a dependent variable. The aim of experimental research is to establish causality.

Quasi-experimental Research Design

Quasi-experimental research design is similar to experimental research design, but it lacks one or more of the features of a true experiment. For example, there may not be random assignment to groups or a control group. This type of research design is used when it is not feasible or ethical to conduct a true experiment.

Case Study Research Design

Case study research design is used to investigate a single case or a small number of cases in depth. It involves collecting data through various methods, such as interviews, observations, and document analysis. The aim of case study research is to provide an in-depth understanding of a particular case or situation.

Longitudinal Research Design

Longitudinal research design is used to study changes in a particular phenomenon over time. It involves collecting data at multiple time points and analyzing the changes that occur. The aim of longitudinal research is to provide insights into the development, growth, or decline of a particular phenomenon over time.

Structure of Research Design

The format of a research design typically includes the following sections:

• Introduction : This section provides an overview of the research problem, the research questions, and the importance of the study. It also includes a brief literature review that summarizes previous research on the topic and identifies gaps in the existing knowledge.
• Research Questions or Hypotheses: This section identifies the specific research questions or hypotheses that the study will address. These questions should be clear, specific, and testable.
• Research Methods : This section describes the methods that will be used to collect and analyze data. It includes details about the study design, the sampling strategy, the data collection instruments, and the data analysis techniques.
• Data Collection: This section describes how the data will be collected, including the sample size, data collection procedures, and any ethical considerations.
• Data Analysis: This section describes how the data will be analyzed, including the statistical techniques that will be used to test the research questions or hypotheses.
• Results : This section presents the findings of the study, including descriptive statistics and statistical tests.
• Discussion and Conclusion : This section summarizes the key findings of the study, interprets the results, and discusses the implications of the findings. It also includes recommendations for future research.
• References : This section lists the sources cited in the research design.

Example of Research Design

An Example of Research Design could be:

Research question: Does the use of social media affect the academic performance of high school students?

Research design:

• Research approach : The research approach will be quantitative as it involves collecting numerical data to test the hypothesis.
• Research design : The research design will be a quasi-experimental design, with a pretest-posttest control group design.
• Sample : The sample will be 200 high school students from two schools, with 100 students in the experimental group and 100 students in the control group.
• Data collection : The data will be collected through surveys administered to the students at the beginning and end of the academic year. The surveys will include questions about their social media usage and academic performance.
• Data analysis : The data collected will be analyzed using statistical software. The mean scores of the experimental and control groups will be compared to determine whether there is a significant difference in academic performance between the two groups.
• Limitations : The limitations of the study will be acknowledged, including the fact that social media usage can vary greatly among individuals, and the study only focuses on two schools, which may not be representative of the entire population.
• Ethical considerations: Ethical considerations will be taken into account, such as obtaining informed consent from the participants and ensuring their anonymity and confidentiality.

How to Write Research Design

Writing a research design involves planning and outlining the methodology and approach that will be used to answer a research question or hypothesis. Here are some steps to help you write a research design:

• Define the research question or hypothesis : Before beginning your research design, you should clearly define your research question or hypothesis. This will guide your research design and help you select appropriate methods.
• Select a research design: There are many different research designs to choose from, including experimental, survey, case study, and qualitative designs. Choose a design that best fits your research question and objectives.
• Develop a sampling plan : If your research involves collecting data from a sample, you will need to develop a sampling plan. This should outline how you will select participants and how many participants you will include.
• Define variables: Clearly define the variables you will be measuring or manipulating in your study. This will help ensure that your results are meaningful and relevant to your research question.
• Choose data collection methods : Decide on the data collection methods you will use to gather information. This may include surveys, interviews, observations, experiments, or secondary data sources.
• Create a data analysis plan: Develop a plan for analyzing your data, including the statistical or qualitative techniques you will use.
• Consider ethical concerns : Finally, be sure to consider any ethical concerns related to your research, such as participant confidentiality or potential harm.

When to Write Research Design

Research design should be written before conducting any research study. It is an important planning phase that outlines the research methodology, data collection methods, and data analysis techniques that will be used to investigate a research question or problem. The research design helps to ensure that the research is conducted in a systematic and logical manner, and that the data collected is relevant and reliable.

Ideally, the research design should be developed as early as possible in the research process, before any data is collected. This allows the researcher to carefully consider the research question, identify the most appropriate research methodology, and plan the data collection and analysis procedures in advance. By doing so, the research can be conducted in a more efficient and effective manner, and the results are more likely to be valid and reliable.

Purpose of Research Design

The purpose of research design is to plan and structure a research study in a way that enables the researcher to achieve the desired research goals with accuracy, validity, and reliability. Research design is the blueprint or the framework for conducting a study that outlines the methods, procedures, techniques, and tools for data collection and analysis.

Some of the key purposes of research design include:

• Providing a clear and concise plan of action for the research study.
• Ensuring that the research is conducted ethically and with rigor.
• Maximizing the accuracy and reliability of the research findings.
• Minimizing the possibility of errors, biases, or confounding variables.
• Ensuring that the research is feasible, practical, and cost-effective.
• Determining the appropriate research methodology to answer the research question(s).
• Identifying the sample size, sampling method, and data collection techniques.
• Determining the data analysis method and statistical tests to be used.
• Facilitating the replication of the study by other researchers.
• Enhancing the validity and generalizability of the research findings.

Applications of Research Design

There are numerous applications of research design in various fields, some of which are:

• Social sciences: In fields such as psychology, sociology, and anthropology, research design is used to investigate human behavior and social phenomena. Researchers use various research designs, such as experimental, quasi-experimental, and correlational designs, to study different aspects of social behavior.
• Education : Research design is essential in the field of education to investigate the effectiveness of different teaching methods and learning strategies. Researchers use various designs such as experimental, quasi-experimental, and case study designs to understand how students learn and how to improve teaching practices.
• Health sciences : In the health sciences, research design is used to investigate the causes, prevention, and treatment of diseases. Researchers use various designs, such as randomized controlled trials, cohort studies, and case-control studies, to study different aspects of health and healthcare.
• Business : Research design is used in the field of business to investigate consumer behavior, marketing strategies, and the impact of different business practices. Researchers use various designs, such as survey research, experimental research, and case studies, to study different aspects of the business world.
• Engineering : In the field of engineering, research design is used to investigate the development and implementation of new technologies. Researchers use various designs, such as experimental research and case studies, to study the effectiveness of new technologies and to identify areas for improvement.

Advantages of Research Design

Here are some advantages of research design:

• Systematic and organized approach : A well-designed research plan ensures that the research is conducted in a systematic and organized manner, which makes it easier to manage and analyze the data.
• Clear objectives: The research design helps to clarify the objectives of the study, which makes it easier to identify the variables that need to be measured, and the methods that need to be used to collect and analyze data.
• Minimizes bias: A well-designed research plan minimizes the chances of bias, by ensuring that the data is collected and analyzed objectively, and that the results are not influenced by the researcher’s personal biases or preferences.
• Efficient use of resources: A well-designed research plan helps to ensure that the resources (time, money, and personnel) are used efficiently and effectively, by focusing on the most important variables and methods.
• Replicability: A well-designed research plan makes it easier for other researchers to replicate the study, which enhances the credibility and reliability of the findings.
• Validity: A well-designed research plan helps to ensure that the findings are valid, by ensuring that the methods used to collect and analyze data are appropriate for the research question.
• Generalizability : A well-designed research plan helps to ensure that the findings can be generalized to other populations, settings, or situations, which increases the external validity of the study.

Research Design Vs Research Methodology

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Handling sensitive questions in surveys and screeners.

August 9, 2024 2024-08-09

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Frequently, surveys and screeners contain questions that some respondents may find sensitive or be reluctant to answer. Even questions that might seem innocuous to a researcher, like age, gender, or income level, could cause an emotional response from your respondents.

This article guides you through handling sensitive questions in surveys and screeners and provides some example wording for you to use in the future.

In This Article:

Which questions are sensitive, guidelines for handling sensitive questions, specific question wordings.

Researchers should always attempt to avoid any possible discomfort experienced by their research participants. In addition to the obvious ethical implications, sensitive questions could cause participants to either abandon the survey or, worse, provide unreliable answers.

A sensitive question is one that respondents might find embarrassing or invasive.

There are multiple categories of sensitive questions to be aware of, but two are particularly worthy of consideration for user researchers: demographic questions and questions about socially undesirable behaviors.

Demographic Questions

Survey researchers often add demographic questions  to a questionnaire without considering how they might be construed. While many survey takers might breeze through demographic questions, they can be potentially triggering or offensive for some respondents.

Demographic questions that have the potential to be perceived as sensitive include those asking about:

• Income level

Income level can be especially problematic and lead to high rates of nonresponse. One study by Jeffrey Moore and colleagues found that questions about income are 10 times more likely to be left blank than other demographic questions.

Sex and gender are other categories of questions that can be sensitive depending on one’s gender identity. Take this question from a recent Nielsen Radio Ratings survey.

Many people would easily complete this question without a second thought. However, consider this account from genderqueer essayist s.e. smith:

“For some trans* folk, it is a place of endless heartbreak. Every. Single. Time. I fill out a form, I stop here. There is a long pause. A hesitation. A sigh. I am not male. I am not female. On paper forms, I often leave it blank [...] Imagine dreading the filling out of forms not because it’s a hassle and it’s repetitive and it’s not very fun. Imagine dreading it because you know that you are going to have to lie and erase yourself every time you fill out a form.”

User researchers like to refer to themselves as user advocates. This lofty designation extends not only to designing delightful user experiences and interfaces but also to protecting the human experiences of our research participants. This includes all participants, not just those that fall into a perceived norm.

Questions About Socially Undesirable Behaviors

Social-desirability bias is a cognitive bias that dictates that people are less likely to disclose behaviors or preferences that are deemed to be undesirable by society. Behaviors that are considered socially undesirable can vary from person to person and, therefore, are likely to slip undetected through a survey creation process.

Common behaviors known to lead to underreporting in surveys include:

• Alcohol consumption
• Cigarette smoking (especially during pregnancy)

However, there are less obvious behaviors as well. For example, voting is also considered a socially desirable behavior, and in many surveys, the reported voting rate is higher than the real one.

Beyond these categories, additional topics that might include sensitive questions include the following:

• Questions about illegal activity
• Identifying information (e.g., home address)
• Emotionally upsetting topics (e.g., health concerns or victimization)
• Information that might be threatening in the wrong hands (e.g., disclosing a preexisting health condition)

While the strategy for addressing sensitive survey questions will vary based on multiple factors, some general guidelines are always helpful.

Determine if You Really Need to Ask

Researchers frequently include demographic questions in a survey just out of habit, without considering whether asking is truly necessary. To optimize response rate, surveys should be kept as short as possible, and therefore any question should be included only if necessary for the research at hand.

Here are 2 questions you should ask yourself before including any question in a questionnaire, regardless of sensitivity.

1. Do You Truly Care About the Answer?

Don’t ask questions because you’re merely curious, or assume all questionnaires should include basic demographic questions. If you have no plans to make decisions based on the outcome, you probably shouldn’t include the question.

2. Can You Find the Answer Another Way?

Are you using a recruiting panel like UserTesting.com or User Interviews? If so, a lot of demographic data about respondents is already included in their profiles and, therefore, doesn’t need to be asked.

Are you asking about browsing behavior on your site, data that could be gathered more accurately from analytics? Don’t ask users to provide you with information that you can access in another, less intrusive way.

Emphasize Confidentiality and Anonymity

Whether to keep participant data confidential, anonymous, or both is an important consideration in survey methodology. While often conflated, these two terms describe two different concepts.

Anonymity means a participant's data cannot be traced back to their identity. Ensuring anonymity may mean using a pseudonym or code number instead of the participant’s name in reporting.

Confidentiality limits the people who are permitted to view the response. Ensuring confidentiality might mean that no one beyond the immediate research team will ever view the survey data.

Anonymity is typically required in most cases of user research, and confidentiality is typically overkill. That said, adding the promise of confidentiality can sometimes increase the likelihood of complete and honest responses to sensitive questions.

Respondents should never have to guess whether a survey is anonymous, confidential, both, or neither. This information should be readily available to respondents when they are deciding whether to take the survey. For example:

Your privacy is important to us. All responses to this survey will be kept strictly confidential. Individual answers will be anonymized and aggregated for analysis. Your personal information will not be shared with any third parties, and will only be used for the purpose of this research. Thank you for your participation.

Lead Up to Sensitive Questions

Sensitive questions placed too early in a questionnaire or screener are more likely to lead to dropoffs. Build respondent trust with nonsensitive questions first, allowing them to invest effort in the process.

This guideline also applies to demographic questions, which should be optional and placed at the end of a questionnaire, not at the beginning, as is often the case. Demographic questions at the beginning are much more likely to lead to dropoffs than demographic questions at the end, even when they are not deemed to be sensitive.

Also, avoid placing particularly sensitive questions at the very end of a survey. If you inadvertently offend a respondent, you don’t want that to be their lasting impression of the survey.

Provide Context and Use “Question Loading”

UXers love to remove unnecessary text from interfaces and forms. Sometimes, however, adding more information is necessary.

First, you may use question loading (not to be confused with a leading question ). Question loading is the inclusion of additional context that may assuage respondent guilt or shame around questionable behavior.

Original question: Do you save a portion of your income each month?

With question loading: Given the rising cost of living and various financial commitments, many people find it challenging to save regularly. How often are you able to save a portion of your income each month?

Additionally, if a question or topic may raise eyebrows, consider explaining the purpose of asking it and the benefit that could come from answering it honestly. For example, if a survey asks a question about abortion, the drafter may include language such as the following:

We are conducting a survey to understand people's experiences with abortion. Please know that your responses are completely anonymous and will be used solely to improve access to abortion care for those who need it. We approach this topic with sensitivity and without judgment.

Use Ranges Rather than Specific Values

Imagine you want to ask about someone’s income — something they may reasonably feel sensitive about sharing. Imagine someone’s reaction to reading the following question:

What is your annual household income: __________

A respondent would likely feel very apprehensive about sharing their specific income for several reasons. They may:

• Worry about how their income will compare to the rest of the sample and whether they would be an outlier on either end of the scale
• Be confused about input formats (Do I include a dollar sign? Do I include “.00 ”? Do I include “ per year ”?)
• Worry about whether giving an exact number may be somehow identifying or overly revealing
• Be reluctant to do math if their income is variable or complex

Now consider the following format instead:

What is your total annual household income?

• $24,999 or less
• $25,000 to $49,999
• $50,000 to $74,999
• $75,000 to $99,999
• $100,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

This tweak addresses all the previous concerns. With this question format, a respondent would:

• Have a sense of how their income compares to the range, and hopefully get some reassurance that they are not alone in whatever range they fall into
• Not need to worry about an input format
• Not need to provide an exact number
• Be less likely to need to do math

Ranges feel less sensitive than asking for specific values. The larger the ranges provided, the less sensitive the request feels.

Ask About Frequency Rather than Yes/No Questions

Consider the following question from the 2019 Youth Risk Behaviors Survey.

During your life, how many times have you used marijuana?

• 1 or 2 times
• 3 to 9 times
• 10 to 19 times
• 20 to 39 times
• 40 to 99 times
• 100 or more times

Rather than simply asking Have you ever used marijuana? (which, admittedly, would probably have been easier for respondents to answer correctly if they had been willing to share that information) , the researchers asked about the frequency of use. While the Yes/No formulation may imply a moral judgment and encourage lying, the frequency question makes a slight assumption that the respondent has indeed used marijuana in the past, which encourages honesty.

Additionally, you may wish to provide additional response options at the end of the range that may be perceived as undesirable, to skew the responses away from bias. For example, consider the following question about exercise frequency.

How often do you engage in physical exercise each month?

• 0 to 3 times a month
• 4 to 7 times a month
• 8 to 12 times a month
• 13 to 20 times a month
• More than 20 times a month

Someone who only works out once or twice per month may feel shame around selecting the bottommost option, and dishonestly select a response towards the middle of the range (see central-tendency bias ).

The following formulation would likely encourage more honest responses from infrequent exercisers.

• 0 times a month
• 1 to 2 times a month
• 3 to 4 times a month
• 5 to 7 times a month

Ask Indirectly

For particularly sensitive topics, researchers may wish to employ an indirect surveying technique, such as the item-count method.

In this approach, the respondent population is divided into two groups. Each group is asked an identical question about how many behaviors from a list they have engaged in, with the addition of the behavior in question for only one of the groups.

For example, consider this question from a 2014 study by Jouni Kuha and Jonathan Jackson at the London School of Economics:

I am now going to read you a list of five [six] things that people may do or that may happen to them. Please listen to them and tell me how many of them you have done or have happened to you in the last 12 months. Do not tell me which ones are and are not true for you. Just tell me how many you have done at least once. Attended a religious service, except for a special occasion like a wedding or funeral Went to a sporting event Attended an opera Visited a country outside [your country] Had personal belongings such as money or a mobile phone stolen from you or from your house? Treatment group only: Bought something you thought might have been stolen

During the analysis, the research will then compare the differences between the two groups in order to estimate the frequency of the behavior in question.

Embed the Sensitive Question

A single sensitive question in an otherwise mundane questionnaire can stand out and be jarring. Researchers may attempt to disarm the respondent with other slightly sensitive questions to mask the question of interest.

Some questions (e.g., demographic questions) are very common and potentially sensitive. The following section includes recommendations for specific wording for these questions. You are welcome to utilize these wordings in your own surveys and screeners.

Sex and Gender

Sex and gender, while frequently conflated and confused, are different (sex is biological characteristics, whereas gender is one’s social identity). The first question you need to ask yourself when asking about sex or gender (after asking, “Do I really need to be asking at all?”) is, “Which one do I care about?” Most of the time, UX researchers care about gender, not sex.

The most important thing to remember when asking about gender is to use inclusive and gender-expansive language that captures the full breadth of gender identity that society now recognizes.

First, consider the question wording. Please select your gender implies that one of the options provided will be a perfect match for the respondent’s gender identity and, therefore, requires the inclusion of an Other:_______ option.

On the other hand, Which of the following best represents your gender? implies only a best fit, so the use of a fill-in-the-blank Other option, while still advisable, is not required.

Next, consider the options provided. It is no longer acceptable to limit gender identity options to simply man and woman . Society’s understanding of gender identity has expanded, and research methods must reflect that.

In most instances, it is sufficient to limit options to the following categories:

• Prefer to self-describe:_______
• Prefer not to disclose

If you additionally have a need to know whether someone is transgender (e.g., in order to differentiate between cisgender men and transgender men, both of whom would select Man from the above options), ask a followup question:

Do you identify as transgender?

If you are conducting research for which a granular and inclusive capturing of one’s gender identity is necessary, consider using the following options to the first question and omitting the followup transgender question.

• Cisgender man
• Cisgender woman
• Transgender man
• Transgender woman
• Nonbinary person
• Genderqueer/genderfluid person
• Agender person
• Two-spirit person
• Intersex person
• Prefer to self-describe:______

Age is a common question in in user-research surveys, as age may correlate to distinct persona traits. Adhere to the following best practices when asking about age:

One recommended wording is:

How old are you? (single select)

• 17 or under

Race and Ethnicity

Like sex and gender, race and ethnicity are often confused but are different (race refers to physical traits; ethnicity refers to cultural identity and heritage).

Determine first which you care about. It is possible to formulate questions that ask about race only, about ethnicity only, or about both.

Additionally, be aware that many people identify with more than one racial and ethnic group and will be excluded if you force them to pick one. For this reason, use multiselect question formats for these questions.

How do you describe yourself? (multiselect)

• American Indian or Alaska Native
• Black or African American
• Native Hawaiian or Other Pacific Islander
• White or Caucasian
• Other:______

Just Ethnicity

Are you of Hispanic, Latinx, or of Spanish origin? (single select)

You can ask about both race and ethnicity separately, using the two questions above, or you can combine them into a single question:

How do you describe yourself? (multi-select)

• Hispanic or Latinx

Accessibility Needs and Disabilities

When asking about accessibility needs or disability, do so directly, using language used by the communities in question.

Do you have any of the following accessibility needs? (Select all that apply)

• Cognitive: such as dyslexia or ADHD
• Emotional: such as anxiety or depression
• Hearing: such as deafness or hearing loss
• Motor: such as cerebral Palsy or arthritis
• Visual: such as blindness or vision loss
• Other:________
• No, I don’t have any accessibility needs

Make sure to avoid language that implies negativity (e.g., Do you have diabetes? instead of Do you suffer from diabetes?; Do you use a wheelchair ? instead of Are you confined to a wheelchair? ).

Language Proficiency

Suppose you need to know about someone’s qualitative assessment of their own language proficiency. In that case, it is helpful to provide a brief description of each answer option to ensure understanding, particularly if the reader is less proficient with the language.

How would you describe your English language level (or proficiency)?

• Native / Mother-tongue: English is my first language
• Fluent: I can speak, read, and write in English fluently
• Proficient: I am skilled in English but not fluent
• Conversational: I can communicate effectively in English in most situations
• Basic knowledge: I have a basic knowledge of the language

Groves, R.M., Fowler Jr, F.J., Couper, M.P., Lepkowski, J.M., Singer, E., and Tourangeau, R. 2009. Survey Methodology . 2nd ed. Wiley.

Jarrett, C. 2021. Surveys That Work . Rosenfeld Media.

Kuha, J. and Jackson, J. 2014. The item count method for sensitive survey questions: modelling criminal behaviour. Journal of the Royal Statistical Society: Series C (Applied Statistics) 63, 2 (Feb. 2014), 321-341. Published by Oxford University Press.

Moore, J., Stinson, L., and Welniak, E. 1997. Income Measurement Error in Surveys: A Review. In Cognition and Survey Research, Sirken, M., Herrmann, D., Schechter, S., Schwarz, N., Tanur, J., and Tourangeau, R. (Eds.). Wiley, New York, 155-174.

smith, s.e. 2009. Beyond the Binary: Forms. Retrieved March 31, 2021, from meloukhia.net/2009/12/beyond_the_binary_forms/.

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• Published: 17 August 2024

Social determinants of health and hypertension screening among women in The Gambia: an evaluation of 2019-2020 demographic and health survey data

• Heather F. McClintock   ORCID: orcid.org/0009-0007-8411-5524 1 ,
• Victoria Peacock 2 &
• Rose Nkiri Asong 1  

Journal of Human Hypertension ( 2024 ) Cite this article

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• Preventive medicine
• Risk factors

Hypertension is a leading modifiable risk factor for morbidity and mortality among women in Sub-Saharan Africa. Social determinants of health (SDH) are associated with sex-based differences in access to preventative screenings globally. Little research has assessed the influence of SDH on screening for hypertension among women in The Gambia. The aim of this study was to identify SDH associated with the utilization of hypertension screening among women in The Gambia. Data was examined from the 2019–2020 Gambia Demographic and Health Survey. Weighted multivariate logistic was used to identify whether SDH were associated with hypertension screening. Among 4116 women, over one-fifth (21.1%) had not been screened for hypertension in their lifetime. In fully adjusted models, older age, rural residence, higher than secondary educational attainment, employment, identification with specific ethnic groups, richer wealth status, parity (1 or more), and antenatal care visits increased the likelihood of lifetime hypertension screening. Women who indicated that others made their healthcare decisions for them (partners or someone else) were significantly less likely to have been screened for hypertension in their lifetime than women who made their healthcare decisions alone (adjusted odds ratio = 0.552, 95% confidence interval = (0.384–0.794)). SDH influence access to screening for hypertension among women in The Gambia. Initiatives may need to address the role of SDH to improve access and uptake of hypertension screening.

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McClintock, H.F., Peacock, V. & Nkiri Asong, R. Social determinants of health and hypertension screening among women in The Gambia: an evaluation of 2019-2020 demographic and health survey data. J Hum Hypertens (2024). https://doi.org/10.1038/s41371-024-00945-y

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Hazard ratios were estimated using Cox proportional hazard regression models and the proportional hazards assumption was confirmed for moderate-to-vigorous physical activity intensity (MVPA). Hazard ratios (95% CIs) were used to generate the population attributable fractions (PAFs). When calculating the PAFs, physical activity levels for participants identified as having frailty or needing special equipment to walk were held constant. Bars represent 95% CIs for both the estimated number of deaths and the proportion of total deaths. Hazard ratios and the estimated number of deaths per year were adjusted for age, sex, race and ethnicity, education level, body mass index, diet quality, alcohol consumption, smoking status, self-reported diabetes, heart disease, heart failure, stroke, cancer, chronic bronchitis, emphysema, mobility limitations, and general health. The number of deaths per year was computed using the 2003 annual mortality for US adults aged 40 to 84 years. Models included US population and study design weights to account for the complex survey. Sample weights also included poststratification adjustments from loss of observations attributable to missing accelerometry data, and all participants were eligible for mortality linkage through the National Death Index.

a Total number of minutes per day recorded by the accelerometer that were at or above the cutpoint of 760 counts per minute 4 (ie, MVPA).

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Saint-Maurice PF , Graubard BI , Troiano RP, et al. Estimated Number of Deaths Prevented Through Increased Physical Activity Among US Adults. JAMA Intern Med. 2022;182(3):349–352. doi:10.1001/jamainternmed.2021.7755

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Estimated Number of Deaths Prevented Through Increased Physical Activity Among US Adults

• 1 Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland
• 2 Division of Cancer Control and Population Sciences, National Cancer Institute, Rockville, Maryland
• 3 Division of Nutrition, Physical Activity, and Obesity, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia

Previous studies suggest that a substantial number of deaths could be prevented annually by increasing population levels of physical activity. 1 - 3 However, previous estimates have relied on convenience samples, 2 , 3 used self-reported physical activity data, 1 - 3 and assumed relatively large increases in activity levels (eg, more than 30 minutes per day). 1 - 3 The potential public health benefit of changing daily physical activity by a manageable amount is not yet known. In this study, we used accelerometer measurements (1) to examine the association of physical activity and mortality in a population-based sample of US adults and (2) to estimate the number of deaths prevented annually with modest increases in moderate-to-vigorous physical activity intensity (MVPA).

This cohort study was approved by the National Center for Health Statistics Ethics Review Board. This study used data from the National Health and Nutrition Examination Survey (NHANES), and written informed consent was obtained for all NHANES participants. The study followed the Strengthening the Reporting of Observational Studies in Epidemiology ( STROBE ) reporting guideline.

The NHANES is a representative survey of the US civilian, noninstitutionalized population, including oversampling for non-Hispanic Black participants and Mexican American participants. Race and ethnicity was determined by self-report and classified using preferred terminology from the National Center for Health Statistics as Mexican American, non-Hispanic Black, non-Hispanic White, or other. Race and ethnicity was included in this study to better characterize the US population. In 2003 to 2006, NHANES participants aged 6 years or older were asked to wear an accelerometer for 7 days. For this study, we evaluated 4840 of 6355 adults aged 40 to 85 years or older with accelerometer data. The remaining 1515 individuals were excluded because they were not eligible or refused to participate in the monitoring protocol (853 [13%]), had monitors that malfunctioned or lost calibration (360 [6%]), or had no valid days with monitor data (302 [5%]). Mortality follow-up was completed via National Death Index linkage through December 31, 2015. We estimated MVPA by summing accelerometer minutes at or above an established cutpoint 4 and creating 8 physical activity categories (0-19, 20-39, 40-59, 60-79, 80-99, 100-119, 120-139, or ≥140 minutes per day).

The number of deaths per year prevented with increased physical activity was estimated as the adjusted population attributable fraction (PAF) 5 multiplied by the US population annual number of deaths for 2003 (for individuals aged 40-84 years). To calculate the PAFs, we used population prevalence estimates and hazard ratios adjusted for age, sex, race and ethnicity, education level, body mass index (calculated as weight in kilograms divided by height in meters squared), diet, alcohol use, smoking status, and self-reported chronic conditions, mobility limitations, and general health. Hazard ratios were estimated using Cox proportional hazard regression models, and the proportional hazards assumption was confirmed for our main exposure (ie, MVPA). Counterfactuals for increased activity were set to 10, 20, and 30 minutes per day higher than participants’ observed values. Those classified as frail 6 or who required equipment to walk were assumed to be unable to increase their activity (eMethods in the Supplement ); when PAFs were calculated, physical activity levels for these participants were held constant. Data were analyzed with SAS version 9.4 (SAS Institute Inc), accounting for the NHANES complex sample design.

This analysis included 4840 participants. Of these, 2435 (53%) were women, 993 (10.4%) were non-Hispanic Black, and 887 (5.1%) were Mexican American ( Table ). A total of 1165 deaths occurred during a mean (SEM) follow-up of 10.1 (0.1) years.

Adjusted hazard ratios changed from 0.69 to 0.28 across increasing activity categories (vs 0-19 minutes per day). Hazard ratios used to generate the PAFs for the 8 activity categories were as follows: 1.00 (reference) for 0 to 19 (548 [7.9%]), 0.69 (95% CI, 0.55-0.85) for 20 to 39 (616 [10.0%]), 0.51 (95% CI, 0.42-0.63) for 40 to 59 (635 [11.8%]), 0.40 (95% CI, 0.29-0.55) for 60 to 79 (614 [12.7%]), 0.34 (95% CI, 0.25-0.47) for 80-99 (633 [14.4%]), 0.32 (95% CI, 0.21-0.48) for 100 to 119 (508 [12.1%]), 0.30 (95% CI, 0.19-0.48) for 120-139 (384 [9.3%]), and 0.28 (95% CI, 0.18-0.42) for 140 or more (902 [21.7%]) minutes per day. The number of participants with frailty or needing special equipment was 280 (49.4%) for 0 to 19, 164 (26.3%) for 20 to 39, 94 (12.4%) for 40 to 59, 66 (9.5%) for 60 to 79, 42 (5.1%) for 80 to 99, 31 (4.7%) for 100 to 119, 20 (2.9%) for 120 to 139, and 35 (2.7%) for 140 or more minutes per day.

Increasing MVPA by 10, 20, or 30 minutes per day was associated with a 6.9%, 13.0%, and 16.9% decrease in the number of deaths per year, respectively. Adding 10 minutes per day of physical activity resulted in an estimated 111 174 preventable deaths per year (95% CI, 79 594-142 754), with greater benefits associated with the addition of more physical activity (209 459 preventable deaths [95% CI, 146 299-272 619] for 20 minutes and 272 297 preventable deaths [95% CI, 177 557-367 037] for 30 minutes) ( Figure ).

The PAFs indicate that the addition of 10 minutes per day of MVPA was associated with the prevention of 8.0% (95% CI, 6.0-10.0) of total deaths per year among men, 5.9% (95% CI, 2.0-9.8) among women, 4.8% (95% CI, 0.0-10.7) among Mexican American individuals, 6.1% (95% CI, 2.2-10.0) among non-Hispanic Black individuals, and 7.3% (95% CI, 5.3-9.3) among non-Hispanic White individuals.

In this cohort study, we estimated that approximately 110 000 deaths per year could be prevented if US adults aged 40 to 85 years or older increased their MVPA by a small amount (ie, 10 minutes per day). Similar benefits were observed for men and women and for Mexican American, non-Hispanic Black, and non-Hispanic White adults. To our knowledge, this is the first study to estimate the number of preventable deaths through physical activity using accelerometer-based measurements among US adults while recognizing that increasing activity may not be possible for everyone. However, 1 week of monitoring may not reflect changes in activity over time, and the observational study design limits the direct determination of causality.

These findings support implementing evidence-based strategies to improve physical activity for adults and potentially reduce deaths in the US.

Accepted for Publication: October 30, 2021.

Published Online: January 24, 2022. doi:10.1001/jamainternmed.2021.7755

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2022 Saint-Maurice PF et al. JAMA Internal Medicine .

Corresponding Author: Pedro F. Saint-Maurice, PhD, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, 9609 Medical Center Dr, Rm 6E-572, Bethesda, MD 20892-9762 ( [email protected] ).

Author Contributions: Drs Saint-Maurice, Graubard, and Matthews had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Saint-Maurice, Galuska, Matthews.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Saint-Maurice, Troiano, Matthews.

Critical revision of the manuscript for important intellectual content: Saint-Maurice, Graubard, Berrigan, Galuska, Fulton, Matthews.

Statistical analysis: Saint-Maurice, Graubard.

Obtained funding: Troiano.

Administrative, technical, or material support: Fulton, Matthews.

Supervision: Matthews.

Conflict of Interest Disclosures: None reported.

Funding/Support: Drs Saint-Maurice, Graubard, and Matthews were supported by the National Institutes of Health Intramural Research Program of the National Cancer Institute.

Role of the Funder/Sponsor: The National Center for Health Statistics was responsible for all data collection and management of baseline and mortality follow-up data but had no role in the design of this study, the analysis and interpretation of the results, or drafting of the manuscript.

Disclaimer: The findings and conclusion of this work are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention or the National Institutes of Health.

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• Published: 07 August 2024

Management training programs in healthcare: effectiveness factors, challenges and outcomes

• Lucia Giovanelli 1 ,
• Federico Rotondo 2 &
• Nicoletta Fadda 1  

BMC Health Services Research volume  24 , Article number:  904 ( 2024 ) Cite this article

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Different professionals working in healthcare organizations (e.g., physicians, veterinarians, pharmacists, biologists, engineers, etc.) must be able to properly manage scarce resources to meet increasingly complex needs and demands. Due to the lack of specific courses in curricular university education, particularly in the field of medicine, management training programs have become an essential element in preparing health professionals to cope with global challenges. This study aims to examine factors influencing the effectiveness of management training programs and their outcomes in healthcare settings, at middle-management level, in general and by different groups of participants: physicians and non-physicians, participants with or without management positions.

A survey was used for gathering information from a purposive sample of professionals in the healthcare field attending management training programs in Italy. Factor analysis, a set of ordinal logistic regressions and an unpaired two-sample t-test were used for data elaboration.

The findings show the importance of diversity of pedagogical approaches and tools and debate, and class homogeneity, as effectiveness factors. Lower competencies held before the training programs and problems of dialogue and discussion during the course are conducive to innovative practice introduction. Interpersonal and career outcomes are greater for those holding management positions.

Conclusions

The study reveals four profiles of participants with different gaps and needs. Training programs should be tailored based on participants’ profiles, in terms of pedagogical approaches and tools, and preserve class homogeneity in terms of professional backgrounds and management levels to facilitate constructive dialogue and solution finding approach.

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Several healthcare systems worldwide have identified management training as a precondition for developing appropriate strategies to address global challenges such as, on one hand, poor health service outcomes in front of increased health expenditure, particularly for pharmaceuticals, personnel shortages and low productivity, and on the other hand in terms of unbalanced quality and equal access to healthcare across the population [ 1 ]. The sustainability of health systems itself seems to be associated with the presence of leaders, at all levels of health organizations, who are able to correctly manage scarce resources to meet increasingly complex health needs and demands, at the same time motivating health personnel under an increasing amount of stress and steering their behaviors towards the system’s goals, in order to drive the transition towards more decentralized, interorganizational and patient-centered care models [ 2 ].

Recently, professional training as an activity aimed at increasing learning of new capabilities (reskilling) and improving existing ones (upskilling) during the lifetime of individuals (lifelong learning) has been identified by the European Commission as one of the seven flagship programs to be developed in the National Recovery and Resilience Plans (NRRP) to support the achievement of European Union’s goals, such as green and digital transitions, innovation, economic and social inclusion and occupation [ 3 ]. As a consequence, many member states have implemented training programs to face current and future challenges in health, which often represents a core mission in their NRRPs.

The increased importance of developing management training programs is also related to the rigidity and focalization of university degree courses in medicine, which do not provide physicians with the basic tools for fulfilling managerial roles [ 4 ]. Furthermore, taking on these roles does not automatically mean filling existing gaps in management capabilities and skills [ 5 ]. Several studies have demonstrated that, in the health setting, management competencies are influenced by positions and management levels as well as by organization and system’s features [ 6 , 7 ]. Hence, training programs aimed at increasing management competencies cannot be developed without considering these differences.

To date, few studies have focused on investigating management training programs in healthcare [ 8 ]. In particular, much more investigation is required on methods, contents, processes and challenges determining the effectiveness of training programs addressed to health managers by taking into account different environments, positions and management levels [ 1 ]. A gap also exists in the assessment of management training programs’ outcomes [ 9 ]. This study aims to examine factors influencing the effectiveness and outcomes of management training, at the middle-management level, in healthcare. It intends to answer the following research questions: which factors influence the management training process? Which relationships exist between management competencies held before the program, factors of effectiveness, critical issues encountered, and results achieved or prefigured at the end of the program? Are there differences, in terms of factors of effectiveness, challenges and outcomes, between the following groups of management training programs’ participants: physicians and non-physicians, participants with or without management positions?

Management training in healthcare

Currently, there is a wide debate about the added value of management to health organizations [ 10 ] and thus about the importance of spreading management competencies within health organizations to improve their performance. Through a systematic review, Lega et al. [ 11 ] highlighted four approaches to examine the impact of management on healthcare performance, focusing on management practices, managers’ characteristics, engagement of professionals in performance management and organizational features and management styles.

Although findings have not always been univocal, several studies suggest a positive relationship between management competencies and practices and outcomes in healthcare organizations, both from a clinical and financial point of view [ 12 ]. Among others, Vainieri et al. [ 13 ] found, in the Italian setting, a positive association between top management’s competencies and organizational performance, assessed through a multidimensional perspective. This study also reveals the mediating effect of information sharing, in terms of strategy, results and organization structure, in the relationship between managerial competencies and performance.

The key role of management competencies clearly emerges for health executives, who have to turn system policies into a vision, and then articulate it into effective strategies and actions within their organizations to steer and engage professionals [ 14 , 15 , 16 , 17 , 18 , 19 ]. However, health systems are increasingly complex and continually changing across contexts and health service levels. This means the role of health executives is evolving as well and identifying the capacities they need to address current and emerging issues becomes more difficult. For instance, a literature review conducted by Figueroa et al. [ 20 ] sheds light on priorities and challenges for health leadership at three structural levels: macro context (international and national), meso context (organizations) and micro context (individual healthcare managers).

Doctor-managers are requested to carry both clinical tasks and tasks related to budgeting, goal setting and performance evaluation. As a consequence, a growing stream of research has speculated whether managers with a clinical background actually affect healthcare performance outcomes, but studies have produced inconclusive findings. In relation to this topic, Sarto and Veronesi [ 21 ] carried out a literature review showing a generally positive impact of clinical leadership on different types of outcome measures, with only a few studies reporting negative impacts on financial and social performance. Morandi et al. [ 22 ] focused on doctor-managers who have become middle managers and investigated the potential bias in performance appraisal due to the mismatch between self-reported and official performance data. At the individual level, the role played by managerial behavior, training, engagement, and perceived organizational support was analyzed. Among others indications they suggested that training programs should be revised to reduce bias in performance appraisal. Tasi et al. [ 23 ] conducted a cross-sectional analysis of the 115 largest U.S. hospitals, divided into physician-led and non-physician-led, which revealed that physician-led hospital systems have higher quality ratings across all specialities and more inpatient days per hospital bed than non-physician-led hospitals. No differences between the groups were found in total revenue and profit margins. The main implication of their study is that hospital systems may benefit from the presence of physician leadership to improve the quality and efficiency of care delivered to patients as long as education and training are able to adequately prepare them. The main issue, as also observed by others [ 4 , 24 ], is that university education in medicine still includes little focus on aspects such as collaborative management, communication and coordination, and leadership skills. Such a circumstance motivates the call for further training. Regarding the implementation of training programs, Liang et al. [ 1 ] have recently shown how it is hindered, among others, by a lack of sufficient knowledge about needed competencies and existing gaps. Their analysis, which focuses on senior managers from three categories in Chinese hospitals, shows that before commencing the programs senior managers had not acquired adequate management competencies either through formal or informal training. It is worth noticing that significant differences exist between hospital categories and management levels. For this reason, they recommend using a systemic approach to design training programs, which considers different hospital types, management levels and positions. Yarbrough et al. [ 6 ] examined how competence training worked in healthcare organizations and the competencies needed for leaders at different points of their careers at various organizational levels. They carried out a cross-sectional survey of 492 US hospital executives, whose most significant result was that competence training is effective in healthcare organizations.

Walston and Khaliq [ 25 ], from a survey of 2,001 hospital CEOs across the US concluded that the greatest contribution of continuing education is to keep CEOs updated on technological and market changes that impact their current job responsibilities. Conversely, it does not seem to be valued for career or succession planning. About the methods of continuing education, an increasing use of some internet-based tools was found. Walston et al. [ 26 ] identified the factors affecting continuing education, finding, among others, that CEOs from for-profit and larger hospitals tend to take less continuing education, whereas senior managers' commitment to continuing education is influenced by region, gender, the CEO's personal continuing education hours and the focus on change.

Furthermore, the principles that inspire modern healthcare models, such as dehospitalization, horizontal coordination and patient-centeredness, imply the increased importance of middle managers, within single structures but also along clinical pathways and projects, to create and sustain high performances [ 27 , 28 , 29 ].

Whaley and Gillis [ 8 ] investigated the development of training programs aimed at increasing managerial competencies and leadership of middle managers, both from clinical and nonclinical backgrounds, in the US context. By adopting the top managers’ perspective, they found a widespread difficulty in aligning training needs and program contents. A 360° assessment of the competencies of Australian middle-level health service managers from two public hospitals was then conducted by Liang et al. [ 7 ] to identify managerial competence levels and training and development needs. The assessment found competence gaps and confirmed that managerial strengths and weaknesses varied across management groups from different organizations. In general, several studies have shown that leading at various organizational levels, in healthcare, does not necessarily require the same levels and types of competencies.

Liang et al. [ 30 ] explored the core competencies required for middle to senior-level managers in Victorian public hospitals. By adopting mixed methods, they confirmed six core competencies and provided guidance to the development of the competence-based educational approach for training the current and future management workforce. Liang et al. [ 31 ] then focused on the poorly investigated area of community health services, which are one of the main solutions to reducing the increasing demand for hospital care in general, and, in particular, in the reforms of the Australian health system. Their study advanced the understanding of the key competencies required by senior and mid-level managers for effective and efficient community health service delivery. A following cross-sectional study by AbuDagga et al. [ 32 ] highlighted that some community health services, such as home healthcare and hospice agencies, also need specific cultural competence training to be effective, in terms of reducing health disparities.

Using both qualitative and quantitative methods, Liang et al. [ 33 ] developed a management competence framework. Such a framework was then validated on a sample of 117 senior and middle managers working in two public hospitals and five community services in Victoria, Australia [ 34 ]. Fanelli et al. [ 35 ] used mixed methods to identify the following specific managerial competencies, which healthcare professionals perceive as crucial to improve their performance: quality evaluation based on outcomes, enhancement of professional competencies, programming based on process management, project cost assessment, informal communication style and participatory leadership.

Loh [ 5 ], through a qualitative analysis conducted in Australian hospitals, examined the motivation behind the choice of medically trained managers to undertake postgraduate management training. Interesting results stemming from the analysis include the fact that doctors often move into management positions without first undertaking training, but also that clinical experience alone does not lead to required management competencies. It is also interesting to remark that effective postgraduate management training for doctors requires a combination of theory and practice, and that doctors choose to undertake training mostly to gain credibility.

Ravaghi et al. [ 36 ] conducted a literature review to assess the evidence on the effectiveness of different types of training and educational programs delivered to hospital managers. The analysis identifies a set of aspects that are impacted by training programs. Training programs focus on technical, interpersonal and conceptual skills, and positive effects are mainly reported for technical skills. Numerous challenges are involved in designing and delivering training programs, including lack of time, difficulty in employing competencies in the workplace, also due to position instability, continuous changes in the health system environment, and lack of support by policymakers. One of the more common flaws concerns the fact that managers are mainly trained as individuals, but they work in teams. The implications of the study are that increased investments and large-scale planning are required to develop the knowledge and competencies of hospital managers. Another shortage concerns the outcome measurement of training programs, which is a usually neglected issue in the literature [ 9 ]. It also emerges that the training programs performing best are specific, structured and comprehensive.

Kakemam and Liang [ 2 ] conducted a literature review to shed light on the methods used to assess management competencies, and, thus, professional development needs in healthcare. Their analysis confirms that most studies focus on middle and senior managers and demonstrate great variability in methods and processes of assessment. As a consequence, they elaborate a framework to guide the design and implementation of management competence studies in different contexts and countries.

In the end, the literature has long pointed out that developing and strengthening the competencies and skills of health managers represent a core goal for increasing the efficiency and effectiveness of health systems, and management training is crucial for achieving such a goal [ 37 ]. The reasons can be summarized as follows: university education has scarcely been able to provide physicians and, in general, health operators, with adequate, or at least basic, managerial competencies and skills; over time, professionals have been involved in increasingly complex and rapidly changing working environments, requiring increased management responsibilities as well as new competencies and skills; in many settings, for instance in Italy, delays in the enforcement of law requiring the attendance of specific management training courses to take up a leadership position, hindered the acquisition of new competencies and the improvement of existing ones by those already managing health organizations, structures and services.

For the purposes of this study, management competencies refer to the possession and ability to use skills and tools for service organization and service planning, control and evaluation, evidence-informed decision-making and human resource management in the healthcare field.

Management training in the Italian National Health System

The reform of the Italian National Health System (INHS), implemented by Legislative Decree No. 502/1992 and inspired by neo-managerial theories, introduced the role of the general manager and assigned new responsibilities to managers.

However, the inadequate performance achieved in the first years of the application of the reform highlighted the cultural gap that made the normative adoption of managerial approach and tools unproductive on the operational level. Legislation evolved accordingly, and in order to hold management positions, management training became mandatory. Decree-Law No. 583/1996 (converted into Law No. 4/1997) provided that the requirements and criteria for access to the top management level were to be determined. Therefore, Presidential Decree No. 484/1997 determined these requirements and also the requirements and criteria to access the middle-management level of INHS’ healthcare authorities. This regulation also imposed the acquisition of a specific management training certificate, dictated rules concerning the duration, contents, and teaching methods of management training courses issuing this certificate, and indicated the requirements for attendance. Immediately afterwards, Legislative Decree No. 229/1999 amended the discipline of medical management and health professions and promoted continuous training in healthcare. It also regulated management training, which became an essential requirement for the appointments of health directors and directors of complex structures in the healthcare authorities, for the categories of physicians, dentists, veterinarians, pharmacists, biologists, chemists, physicists and psychologists.

The second pillar of the INHS reform was the regionalization of the INHS. Therefore, the Regions had to organize the courses to achieve management training certificates on the basis of specific agreements with the State, which regulated the contents, the methodology, the duration and the procedures for obtaining certification. The State-Regions Conference approved the first interregional agreement on management training in July 2003, whereas the State-Regions Agreement of 16 May 2019 regulated the training courses. The mandatory contents of the management training outlined the skills and behaviors expected from general managers and other top management key players (Health Director, Administrative Director and Social and Health Director), but also for all middle managers.

A survey was used to gather information from a purposive sample of professionals in the healthcare field taking part in management training programs. In particular, a structured questionnaire was submitted to 140 participants enrolled in two management programs organized by an Italian university: a second-level specializing master course and a training program carried out in collaboration with the Region. The programs awarded participants the title needed to be appointed as a director of a ward or administrative unit in a public healthcare organization, and share the same scientific committee, teaching staff, administrative staff and venue. The respondents’ profile is shown in Table  1 .

It is worth pointing out that the teaching staff is characterized by diversity: teachers have different educational and professional backgrounds, are practitioners or academics, and come from different Italian regions.

The questionnaire was submitted and completed in presence and online between November 2022 and February 2023. All participants decided to take part in the analysis spontaneously and gave their consent, being granted total anonymity.

The questionnaire, which was developed for this study and based on the literature, consisted of 64 questions shared in the following five sections: participant profile (10 items), management competencies held by participants before the training program (4 items), effectiveness factors of the training program (23 items), challenges to effectiveness (10 items), and outcomes of the training program (17 items) (an English language version of the questionnaire is attached to this paper as a supplementary file). In particular, the second section aimed to shed light on the participants’ situation regarding management competencies held before the start of the training program and how they were acquired; the third section aimed to collect participants’ opinions regarding how the program was conducted and the factors influencing its effectiveness; the fourth section aimed to collect participants’ opinions regarding the main obstacles encountered during the program; and the fifth section aimed to reveal the main outcomes of the program in terms of knowledge, skills, practices and career.

Except for those of the first section, which collected personal information, all the items of the next four categories – management competencies, effectiveness factors, challenges and outcome — were measured through a 5-point Likert scale. To ensure that the content of the questionnaire was appropriate, clear and relevant, a pre-testing was conducted in October 2022 by asking four academics and four practitioners, both physicians and not, with and without management positions, to fill it out. The aim was to understand whether the questionnaire really addressed the information needs behind the study and was easily and correctly understood by respondents. Therefore, the four individuals involved in the pre-testing were asked to fill it out simultaneously but independently, and at the end of the compilation, a focus group that included them and the three authors was used to collect their opinions and suggestions. After this phase, the following changes were made: in the ‘Participant profile’ section, ‘Veterinary medicine’ was added to the fields accounting for the ‘Educational background’ (item 3); in Sect. 2, it was decided to modify the explanation given to ‘basic management competencies’ and align it to what required by Presidential Decree No. 484/1997; in Sect. 3, item 25 was added to catch a missing aspect that respondents considered important, and brackets were added to the description of items 15, 16 and 29 to clarify the concepts of mixed and homogenous class and pedagogical approaches and tools; in Sect. 4, in the description of item 40, the words ‘find the energy required’ were added to avoid confusion with items 38 and 39, whereas brackets were added to items 41 and 45 to provide more explanation; in Sect. 5, brackets were added to the description of item 51 to increase clarity, and the last item was divided into two (now items 63 and 64) to distinguish the training program’s impact on career at different times.

With reference to the methods, first, a factor analysis based on the principal component method was conducted within each section of the questionnaire (except for the first again), in order to reduce the number of variables and shed light on the factors influencing the management training process. Bartlett's sphericity test and the Kaiser–Meyer–Olkin (KMO) value were performed to assess sampling adequacy, whereas factors were extracted following the Kaiser criterion, i.e., eigenvalues greater than unity, and total variance explained. The rotation method used was the Varimax method with Kaiser normalization, except for the second section (i.e., management competencies held by participants before the training program) that), which did not require rotation since a single factor emerged from the analysis. Bartlett's sphericity test was statistically significant ( p  < 0.001) in all sections, KMO values were all greater than 0.65 (average value 0.765), and the total variances explained were all greater than 65% (average value of approximately 70.89%), which are acceptable values for such analysis.

Second, a set of ordinal logistic regressions were performed to assess the relationships existing between management competencies held before the start of the course, effectiveness factors, challenges, and outcomes of the training program.

The factors that emerged from the factor analysis were used as independent variables, whereas some significant outcome items accounting for different performance aspects were selected as dependent variables: improved management competencies, innovation practices, professional relationships, and career prospects. Ordered logit regressions were used because the dependent variables (outcomes) were measured on ordinal scales. Some control variables for the respondent profiles were included in the regression models: age, gender, educational background, management position, and working in the healthcare field.

With the aim of understanding which explanatory variables could exert an influence, a backward elimination method was used, adopting a threshold level of significance values below 0.20 ( p  < 0.20). Table 4 shows the results of regressions with independent variables obtained following the criterion mentioned above. All four models respected the null hypothesis, which means that the proportional odds assumption behind the ordered logit regressions had not been rejected ( p  > 0.05). Third and last, an unpaired two-sample t-test was used to examine the differences between groups of participants in the management training programs selected based on two criteria: physicians and non-physicians, and participants with or without management positions.

First, descriptive statistics is useful for understanding the aspects participants considered the most and least important by category. This can be done by focusing on the items of the four sections of the questionnaire (except for the first one depicting participant profiles) that were given the highest and lowest scores at the sample level and by different groups of participants (physicians and non-physicians, participants with or without management positions). Table 2 summarizes the mean values and standard deviations by group of these higher and lower scores. Focusing on management competencies, all groups reported having mainly acquired them through professional experience, except for non-physicians who attributed major significance to postgraduate training programs, with a mean value of 3.05 out of 5. All groups agreed on the poor role of university education in providing management competencies, with mean values for the sample and all four groups below 2.5. It is worth noting that this item exhibits the lowest value for physicians (1.67) and the highest for non-physicians (2.37). In addition, physicians are the group attributing the lowest values to postgraduate education and professional experience for acquiring management competencies. In reference to factors of effectiveness, all groups also agree on the necessity of mixing theoretical and practical lessons during the training program with mean values of well above 4.5, whereas exclusive use of self-assessment is generally viewed as the most ineffective practice, except for non-physician, who attribute the lowest value to remote lessons (mean 1.82). Among the challenges, the whole sample and physicians and participants without management positions see the lack of financial support from their organization as the main problem (mean 4.10), while non-physicians and participants with management positions believe this is represented by a lack of time, with mean values, respectively, of 3.75 and 4. All agree that dialogue and discussion during the course have been the least relevant of the problems, with mean values below 1.5. Outcomes show generally high values, as revealed by the fact that the lowest values exhibit mean values around 3.5. It is worth noting that an increased understanding of the healthcare systems has been the main benefit gained from the program, with mean values equal to or higher than 4.50. The lowest positive impact is attributed by all attendees to improved relationships with superiors and top management, with mean values between 3.44 and 3.74, with the exception of participants without management positions who mention improved career prospects.

To shed light on the factors influencing the management training process, the findings of the factor analyses conducted by category are reported. Starting from the management competencies held before the training program, the following single factor was extracted from the four items, named and interpreted as follows:

Basic management competencies, which measures the level of management competencies acquired by participants through higher education, post-graduate training and professional experience.

The effectiveness factors are then grouped into six factors, named and explained as follows:

Diversity and debate, which aggregates five items assessing the importance of diversity in participants’ and teachers’ educational and professional backgrounds and pedagogical approaches and tools, as well as level of participant engagement and discussion during lessons and in carrying out the project work required to complete the program.

Specialization, which includes three items accounting for a robust knowledge of healthcare systems by focusing on teachers’ profiles and lessons’ theoretical approaches.

Lessons in presence, which groups three items explaining that in-presence lessons increase learning outcomes and discussion among participants.

Final self-assessment, made up of three items asserting that learning outcomes should be assessed by participants themselves at the end of the course.

Written intermediate assessment, composed of two items explaining that mid-terms assessment should only be written.

Homogeneous class, which is made up of a single component accounting for participants’ similarity in terms of professional backgrounds and management levels, tasks and responsibilities.

The challenges are aggregated into the following four factors:

Lack of time, which includes three items reporting scarce time and energy for lessons and study.

Problems of dialogue and discussion, which groups three items focusing on difficulties in relating to and debating with other participants and teachers.

Low support from organization, which is made up of two items reporting poor financial support and low value given to the initiative from participants’ own organizations.

Organizational issues, which aggregates two items demonstrating scarce flexibility and collaboration by superiors and colleagues of participants’ own organizations and unfamiliarity to study.

Table 3 shows the component matrix with saturation coefficients and factors obtained for the management competencies held before the training program (unrotated), effectiveness factors (rotated), and challenges (rotated).

A set of ordinal logistic regressions was performed to examine the relationships between management competencies held before the start of the course, effectiveness factors, challenges and outcomes of the training program. The results, shown in Table  4 , are articulated into four models, one for each selected outcome. In relation to model 1, the factors ‘diversity and debate’ ( p  < 0.001), ‘written intermediate assessment’ ( p  < 0.05) and ‘homogeneous class’ ( p  < 0.001) have a significant positive impact on the improvement of management competencies, which is also increased by low values attributed to ‘problems of dialogue and discussion’ ( p  < 0.01). In model 2, the change of professional practices in light of lessons learned during the program, selected as an innovation outcome, is then positively affected by ‘diversity and debate’ ( p  < 0.001), ‘homogeneous class’ ( p  < 0.05) and ‘organizational issues’ ( p  < 0.01), while it was negatively influenced by a high value of ‘basic management competencies’ held before the course ( p  < 0.05). Regarding model 3, ‘Diversity and debate’ ( p  < 0.001) and ‘homogeneous class’ ( p  < 0.01) have a significant positive effect on the improvement of professional relationships as well, whereas the same is negatively affected by ‘lessons in presence’ ( p  < 0.05). Finally, concerning model 4, the outcome career prospects benefit from ‘diversity and debate’ ( p  < 0.05) and ‘homogeneous class’ ( p  < 0.01), since both factors exert a positive effect. ‘Low support from organization’ negatively influences career prospects ( p  < 0.001). Table 4 also shows that the LR test of proportionality of odds across the response categories cannot be rejected (all four p  > 0.05).

Finally, it is worth noting that none of the control variables reflecting the respondent profiles (age, gender, management position, working in the healthcare field, and educational background) was found to be statistically significant. These variables are not reported in Table  4 because regression models were obtained following a backward elimination method, as explained in the method section.

In the end, the t-test reveals significant differences between physicians and non-physicians, as well as between participants with or without management positions. Table 5 shows only figures of t-test statistically significant with regards to competencies held before the attendance of the course, the factors of effectiveness, challenges of the training program, and outcomes achieved. In the first comparison, non-physicians show higher management competencies at the start of the program, with a mean value of 0.31, while physicians suffer from less support from their own organization with a mean value of 0.13 compared to -0.18, the mean value of the non-physicians. Concerning the second comparison, participants with management positions have higher management competencies at the start of the program (0.19 versus -0.13) and suffer more from lack of time, with higher mean values compared to participants without managerial positions, respectively 0.23 and -0.16. For what concerns the factors related to the effectiveness of the training program, participants with management positions exhibit a lower mean value in relation to written mid-term assessments, -0.24 versus 0.17, reported by participants with management positions. Differently, the final self-assessment at the end of the program is higher for participants with management positions, 0.24 compared to -0.17, the mean value of the participants without management positions. This latter category feels more the problem of low support from their organizations, with a mean value of 0.16 compared to -0.23, and is slightly less motivated by possible career improvement, with a mean value of 3.31 compared to 3.73 reported by participants with management positions.

The results stemming from the different analyses are now considered and interpreted in the light of the extant literature. Personal characteristics such as gender and age, differently from what was found by Walston et al. [ 26 ] for executives’ continuing education, and professional characteristics such as seniority and working in public or private sectors, do not seem to affect participation in management training programs.

The findings clearly show the outstanding importance of ‘diversity and debate’ and ‘class homogeneity’ as factors of effectiveness, since they positively impact all outcomes: competencies, innovation, professional relationships and career. These factors capture two key aspects complementing each other: on the one hand, participants and teachers’ different backgrounds provide the class with a wider pool of resources and expertise, whereas the use of pedagogical tools fostering discussion enriches the educational experience and stimulates creativity. On the other hand, due to the high level of professionalism in the setting, sharing common management levels means similar tasks and responsibilities, as well as facing similar problems. Consequently, speaking the same language leads to deeper knowledge and effective technical solutions.

In relation to the improvement of management competencies, it also emerges the critical role of a good class atmosphere, that is, the absence of problems of dialogue and discussion. ‘Diversity and debate’ and ‘class homogeneity’, as explained before, seem to contribute to this, since they enhance freedom of expression and fair confrontation, leading to improved learning outcomes. It is interesting to notice that the problems of dialogue and discussion turned out to be the least relevant challenge across the sample.

Two interesting points come from the factors affecting innovation. First, it seems that lower competencies before the training programs lead to the development of more innovative practices. The reason is that holding fewer basic competencies means a greater scope for action once new capabilities are learned: the spirit of openness is conducive to breaking down routines, and innovative practices hindered by a lack of knowledge and tools can thus be introduced. The reason is that holding fewer basic competencies means greater scope for action once new capabilities are learned: the spirit of openness is conducive to breaking down routines, and innovative practices hindered by a lack of knowledge and tools can thus be introduced. This extends the findings of previous studies since the employment of competencies in the workplace is influenced by the starting competence equipment of professionals [ 36 ], and those showing gaps have more room to recover, also in terms of motivation to change, that is, understanding the importance of meeting current and future challenges [ 26 ]. Second, more innovative practices are introduced by participants perceiving more organizational issues. This may reveal, on the one side, a stronger individual motivation towards professional growth of participants who suffer from lack of flexibility and collaboration from their own superiors and colleagues. In this regard, poor tolerance, flexibility and permissions in their workplace act as a stimulus to innovation, which can be viewed as a way of challenging the status quo. On the other side, in line with the above-mentioned concept, this confirms that unfamiliarity with the study increases the innovative potential of participants. Since this study reveals that physicians are neither adequately educated from a management point of view nor incentivized to attend post-graduation training programs, it points out how important is extending continuing education to all health professional categories [ 25 , 26 ].

The topic of competencies held by different categories needs more attention. The study reveals that physicians and participants without management positions start the program with less basic competencies. At the sample level, higher education is viewed as the most ineffective tool to provide such competencies, whereas professional experience is seen as the best way to gather them. Actually, non-physicians give the highest value to postgraduate education, which suggests they are those more interested or incentivized to take part in continuing education. Although holding managerial positions does not automatically mean having higher competencies [ 5 ], it is evident that such a professional experience contributes to filling existing gaps. Physicians stand out as the category for which university education, postgraduate education and professional experience exert the lowest impact on management competence improvement. Considering the relationship between competence held before the course and innovation, as described above, engaging physicians in training programs, even more if they do not have management responsibilities, has a major impact on health organizations’ development prospects. The findings also point out that effective management training requires a combination of theory and practice for all categories of professionals, not just for physicians, as observed by Loh [ 5 ].

The main outcome, in general and for all participant categories, is an increased understanding of how healthcare systems work, which anticipates increased competencies. This confirms the importance of knowledge on the healthcare environment [ 31 ], and clarifies the order of aspects impacted by training programs as reported by Ravaghi et al. [ 36 ]: first conceptual, then technical, and finally interpersonal. However, interpersonal outcomes are by far greater for those holding management positions, which extends the findings by Liang et al. [ 31 ]. In particular, participants already managing units report the greatest impacts in terms of ability to understand colleagues’ problems, improvement of professional relationships and collaboration with colleagues from other units. Obviously, participants with management positions, more than others, feel the lack of collaborative and communication skills, which represents one of the main flaws of university education in the field of medicine [ 4 ] and is also often neglected in management training [ 36 ]. This also confirms that different management levels show specific competence requirements and education needs [ 6 , 7 ]. 

It is then important to discuss the negative effect of lessons in presence on the improvement of professional relationships. At first glance, it may sound strange, but its real meaning emerges from a comprehensive interpretation of all the findings. First, it does not mean that remote lessons are more effective, as revealed by the fact that they, as a factor of effectiveness, are attributed very low values and, for all categories of participants, lower values than those attributed to lessons in presence and hybrid lessons. Non-physicians, in particular, attribute them the lowest value at all. At most, remote lessons are viewed as convenient rather than effective. The negative influence of lessons in presence can be explained by the fact that a specific category, i.e., those with management positions, rate this aspect much more important than other participants and, as reported above, find much more benefits in terms of improved relationships from management training. Participants with management positions, due to their tasks and responsibilities, suffer more than others from lack of time to be devoted to course participation. For them, as for the category of non-physicians, lack of time represents the main challenge to effectively attending the course. In the literature, such a problem is well considered, and lack of time is also viewed as a challenge to apply the skills learned during the course [ 36 ]. Considering that class discussion and homogeneity contribute to fostering relationships, a comprehensive reading of the findings reveals that due to workload, participants with management positions see particularly convenient and still effective remote lessons. Furthermore, if the class is formed by participants sharing similar professional backgrounds and management levels, debate is not precluded and interpersonal relationships improved as a consequence. From the observation of single items, it can be concluded that participants with management positions and in general those with higher basic management competencies at the start of the program, prefer more flexible and leaner training programs: intermediate assessment through conversation, self-assessment at the end of the course, more concentrated scheduled lessons and greater use of remote lessons.

Differently from what was found by Walston and Khaliq [ 25 ], the findings highlight that participants with management positions value the impact of management training on career prospects positively. These participants are also those more supported by their own organizations. Conversely, the lack of support, especially in terms of inadequate funds devoted to these initiatives, strongly affects physicians and participants without management positions, which clarifies what this challenge is about and who is mainly affected by it [ 36 ]. Low incentives mean having attended fewer training programs in the past, which, together with less management experience, explains why they have developed less competencies. Among the outcomes of the training program, the little attention paid by organizations is also testified by the lowest values attributed by all categories, except for participants without management positions, to the improvement of relationships with superiors and top management.

In general, the study contributes to a better understanding of the outcomes of management training programs in healthcare and their determinants [ 9 ]. In particular, it sheds light on gaps and education needs [ 1 ] by category of health professionals [ 2 ]. The research findings have major implications for practice, which can be drawn after identifying the four profiles of participants revealed by the study. All profiles share common characteristics, such as value given to debate, diversity of pedagogical approaches and tools and class homogeneity, rather than the need for a deeper comprehension of healthcare systems. However, they present characteristics that determine specific issues and education gaps, which are summarized as follows:

Physicians without management positions: low competencies at the start of the program and scarce incentives for attending the course from their own organization;

Physicians with management positions: they partially compensate for competence gaps through professional experience, suffer from lack of time, and are motivated by the chance to improve their career prospects;

Non-physicians without management positions: they partially fill competence gaps through postgraduate education, suffer from lack of time, and have scarce incentives for attending the course from their own organization;

Non-physicians with management positions: they partially bridge competence gaps through postgraduate education and professional experience, are the most affected by a lack of time, and are motivated by the chance to improve their career prospects.

Recommendations are outlined for different levels of action:

For policymakers, it is suggested to strengthen the ability of higher education courses in medicine and related fields to advance the understanding of healthcare systems’ structure and operation, as well as their current and future challenges. Such a new approach in the design curricula should then have as a main goal the provision of adequate management competencies.

For healthcare organizations, it is suggested to incentivize the acquisition of management competencies by all categories of professionals through postgraduate education and training programs. This means supporting them from both financial and organizational point of view, for instance, in terms of more flexible working conditions. Special attention should be paid to physicians who, even without executive roles, manage resources and directly impact the organization's effectiveness and efficiency levels through their day-by-day activity, and are the players holding the greatest innovative potential within the organization. Concerning the executives, especially in the current changing context of healthcare systems, much higher attention should be paid to fostering interpersonal skills, in terms of communication and cooperation.

For those designing training programs, it is suggested to tailor courses on the basis of participants’ profiles, using different pedagogical approaches and tools, for instance, in terms of teacher composition, lesson delivery methods and learning assessment methods, while preserving class homogeneity in terms of professional backgrounds and management levels to facilitate constructive dialogue and solution finding approaches. Designing ad hoc training programs would give the possibility to meet the needs of participants from an organizational point of view as well as, for instance, in terms of program length and lesson concentration.

Limitations

This study has some limitations, which pave the way for future research. First, it is context-specific by country, since it is carried out within the INHS, which mandatorily requires health professionals to attend management training programs to hold certain positions. It is then context-specific by training program, since it focuses on management training programs providing participants with the title to be appointed as a director of a ward or administrative unit in a public healthcare organization. This determines the kind of management competencies included in the study, which are those mandatorily required for such a middle-management category. Therefore, there is a need to extend research and test these findings on different types of management training programs, participants and countries. Second, this study is based on a survey of participants’ perceptions, which causes two kinds of unavoidable issues: although based on the literature and pre-tested, the questionnaire could not be able to measure what it intends to or capture detailed and nuanced insights from respondents, and responses may be affected by biases due to reactive effects. Third, a backward elimination method was adopted to select variables in model building. Providing a balance between simplicity and fit of models, this variable selection technique is not consequences-free. Despite advantages such as starting the process with all variables included, removing the least important early, and leaving the most important in, it also has some disadvantages. The major is that once a variable is deleted from the model, it is not included anymore, although it may become significant later [ 38 ]. For these reasons, it is intended to reinforce research with new data sources, such as teachers’ perspectives and official assessments, and different variable selection strategies. A combination of qualitative and quantitative methods for data elaboration could then be used to deepen the analysis of the relationships between motivations, effectiveness factors and outcomes. Furthermore, since the investigation of competence development, acquisition of new competencies and the transfer of acquired competencies was beyond the purpose of this study, a longitudinal approach will be used to collect data from participants attending future training programs to track changes and identify patterns.

Availability of data and materials

An English-language version of the questionnaire used in this study is attached to this paper as a supplementary file. The raw data collected via the questionnaire are not publicly available due to privacy and other restrictions. However, datasets generated and analyzed during the current study may be available from the corresponding author upon reasonable request.

Abbreviations

Italian National Health System

Kaiser–Meyer–Olkin

National Recovery and Resilience Plan

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Giovanelli, L., Rotondo, F. & Fadda, N. Management training programs in healthcare: effectiveness factors, challenges and outcomes. BMC Health Serv Res 24 , 904 (2024). https://doi.org/10.1186/s12913-024-11229-z

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Lau F, Kuziemsky C, editors. Handbook of eHealth Evaluation: An Evidence-based Approach [Internet]. Victoria (BC): University of Victoria; 2017 Feb 27.

Handbook of eHealth Evaluation: An Evidence-based Approach [Internet].

Chapter 13 methods for survey studies.

Francis Lau .

13.1. Introduction

The survey is a popular means of gauging people’s opinion of a particular topic, such as their perception or reported use of an eHealth system. Yet surveying as a scientific approach is often misconstrued. And while a survey seems easy to conduct, ensuring that it is of high quality is much more difficult to achieve. Often the terms “survey” and “questionnaire” are used interchangeably as if they are the same. But strictly speaking, the survey is a research approach where subjective opinions are collected from a sample of subjects and analyzed for some aspects of the study population that they represent. On the other hand, a questionnaire is one of the data collection methods used in the survey approach, where subjects are asked to respond to a predefined set of questions.

The eHealth literature is replete with survey studies conducted in different health settings on a variety of topics, for example the perceived satisfaction of ehr systems by ophthalmologists in the United States ( Chiang et al., 2008 ), and the reported impact of emr adoption in primary care in a Canadian province ( Paré et al., 2013 ). The quality of eHealth survey studies can be highly variable depending on how they are designed, conducted, analyzed and reported. It is important to point out there are different types of survey studies that range in nature from the exploratory to the predictive, involving one or more groups of subjects and an eHealth system over a given time period. There are also various published guidelines on how survey studies should be designed, reported and appraised. Increasingly, survey studies are used by health organizations to learn about provider, patient and public perceptions toward eHealth systems. As a consequence, the types of survey studies and their methodological considerations should be of great interest to those involved with eHealth evaluation.

This chapter describes the types of survey studies used in eHealth evaluation and their methodological considerations. Also included are three case examples to show how these studies are done.

13.2. Types of Survey Studies

There are different types of survey study designs depending on the intended purpose and approach taken. Within a given type of survey design, there are different design options with respect to the time period, respondent group, variable choice, data collection and analytical method involved. These design features are described below ( Williamson & Johanson, 2013 ).

13.2.1. The Purpose of Surveys

There are three broad types of survey studies reported in the eHealth literature: exploratory, descriptive, and explanatory surveys. They are described below.

• Exploratory Surveys – These studies are used to investigate and understand a particular issue or topic area without predetermined notions of the expected responses. The design is mostly qualitative in nature, seeking input from respondents with open-ended questions focused on why and/or how they perceive certain aspects of an eHealth system. An example is the survey by Wells, Rozenblum, Park, Dunn, and Bates (2014) to identify organizational strategies that promote provider and patient uptake of phr s.
• Descriptive Surveys – These studies are used to describe the perception of respondents and the association of their characteristics with an eHealth system. Perception can be the attitudes, behaviours and reported interactions of respondents with the eHealth system. Association refers to an observed correlation between certain respondent characteristics and the system, such as prior eHealth experience. The design is mostly quantitative and involves the use of descriptive statistics such as frequency distributions of Likert scale responses from participants. An example is the survey on change in end user satisfaction with cpoe over time in intensive care ( Hoonakker et al., 2013 ).
• Explanatory Surveys – These studies are used to explain or predict one or more hypothesized relationships between some respondent characteristics and the eHealth system. The design is quantitative, involving the use of inferential statistics such as regression and factor analysis to quantify the extent to which certain respondent characteristics lead to or are associated with specific outcomes. An example is the survey to model certain residential care facility characteristics as predictors of ehr use ( Holup, Dobbs, Meng, & Hyer, 2013 ).

13.2.2. Survey Design Options

Within the three broad types of survey studies one can further distinguish their design by time period, respondent group, variable choice, data collection and analytical method. These survey design options are described below.

• Time Period – Surveys can take on a cross-sectional or longitudinal design based on the time period involved. In cross-sectional design the survey takes place at one point in time giving a snapshot of the participant responses. In longitudinal design the survey is repeated two or more times within a specified period in order to detect changes in participant responses over time.
• Respondent Group – Surveys can involve a single or multiple cohorts of respondents. With multiple cohorts they are typically grouped by some characteristics for comparison such as age, sex, or eHealth use status (e.g., users versus non-users of emr ).
• Variable Choice – In quantitative surveys one needs to define the dependent and independent variables being studied. A dependent variable refers to the perceived outcome that is measured, whereas an independent variable refers to a respondent characteristic that may influence the outcome (such as age). Typically the variables are defined using a scale that can be nominal, ordinal, interval, or ratio in nature ( Layman & Watzlaf, 2009 ). In a nominal scale, a value is assigned to each response such as 1 or F for female and 2 or M for male. In an ordinal scale, the response can be rank ordered such as user satisfaction that starts from 1 for very unsatisfied to 4 for very satisfied. Interval and ratio scales have numerical meaning where the distance between two responses relate to the numerical values assigned. Ratio is different from interval in that it has a natural zero point. Two examples are weight as a ratio scale and temperature as an interval scale.
• Data Collection – Surveys can be conducted by questionnaire or by interview with structured, semi-structured or non-structured questions. Questionnaires can be administered by postal mail, telephone, e-mail, or through a website. Interviews can be conducted in-person or by phone individually or in groups. Pretesting or pilot testing of the instrument should be done with a small number of individuals to ensure its content, flow and instructions are clear, consistent, appropriate and easy to follow. Usually there are one or more follow-up reminders sent to increase the response rate.
• Analytical Method – Survey responses are analyzed in different ways depending on the type of data collected. For textual data such qualitative analyses as content or thematic analysis can be used. Content analysis focuses on classifying words and phrases within the texts into categories based on some initial coding scheme and frequency counts. Thematic analysis focuses on identifying concepts, relationships and patterns from texts as themes. For numeric data, quantitative analysis such as descriptive and inferential statistics can be used. Descriptive statistics involves the use of such measures as mean, range, standard deviation and frequency to summarize the distribution of numeric data. Inferential statistics involve the use of a random sample of data from the study population to make inferences about that population. The inferences are made with parametric and non-parametric tests and multivariate methods. Pearson correlation, t -test and analysis of variance are examples of parametric tests. Sign test, Mann-Witney U test and χ 2 are examples of non-parametric tests. Multiple regression, multivariate analysis of variance, and factor analysis are examples of multivariate methods ( Forza, 2002 ).

13.3. Methodological Considerations

The quality of survey studies is dependent on a number of design parameters. These include population and sample, survey instrument, sources of bias, and adherence to reporting standards. These considerations are described below ( Williamson & Johanson, 2013 ).

13.3.1. Population and Sample

For practical reasons, survey studies are often done on a sample of individuals rather than the entire population. Sampling frame refers to the population of interest from which a representative sample is drawn for the study. The two common strategies used to select the study sample are probability and non-probability sampling. These are described below.

• Probability sampling – This is used in descriptive and explanatory surveys where the sample selected is based on the statistical probability of each individual being included under the assumption of normal distribution. They include such methods as simple random, systematic, stratified, and cluster sampling. The desired confidence level and margin of error are used to determine the required sample size. For example, in a population of 250,000 at 95% confidence level and a ±5% margin of error, a sample of 384 individuals is needed (Research Advisors, n.d.).
• Non-probability sampling – This is used in exploratory surveys where individuals with specific characteristics that can help understand the topic being investigated are selected as the sample. They include such non-statistical methods as convenience, snowball, quota, and purposeful sampling. For example, to study the effects of the Internet on patients with chronic conditions one can employ purposeful sampling where only individuals known to have a chronic disease and access to the Internet are selected for inclusion.

13.3.2. Survey Instrument

The survey instrument is the tool used to collect data from respondents on the topic being investigated. Ideally one should demonstrate that the survey instrument chosen is both valid and reliable for use in the study. Validity refers to whether the items (i.e., predefined questions and responses) in the instrument are accurate in what they intend to measure. Reliability refers to the extent to which the data collected are reproducible when repeated on the same or similar groups of respondents. These two constructs are elaborated below.

• Validity – The four types of validity are known as face, content, criterion, and construct validity. Face and content validity are qualitative assessments of the survey instrument for its clarity, comprehensibility and appropriateness. While face validity is typically assessed informally by non-experts, content validity is done formally by experts in the subject matter under study. Criterion and construct validity are quantitative assessments where the instrument is measured against other schemes. In criterion validity the instrument is compared with another reputable test on the same respondents, or against actual future outcomes for the survey’s predictive ability. In construct validity the instrument is compared with the theoretical concepts that the instrument purports to represent to see how well the two align with each other.
• Reliability – The tests for reliability include test-retest, alternate form and internal consistency. Test-retest reliability correlates results from the same survey instrument administered to the same respondents over two time periods. Alternate form reliability correlates results from different versions of the same instrument on the same or similar individuals. Internal consistency reliability measures how well different items in the same survey that measure the same construct produce similar results.

13.3.3. Sources of Bias

There are four potential sources of bias in survey studies. These are coverage, sampling, non-response, and measurement errors. These potential biases and ways to minimize them are described below.

• Coverage bias – This occurs when the sampling frame is not representative of the study population such that certain segments of the population are excluded or under-represented. For instance, the use of the telephone directory to select participants would exclude those with unlisted numbers and mobile devices. To address this error one needs to employ multiple sources to select samples that are more representative of the population. For example, in a telephone survey of consumers on their eHealth attitudes and experience, Ancker, Silver, Miller, and Kaushal (2013) included both landline and cell phone to recruit consumers since young adults, men and minorities tend to be under-represented among those with landlines.
• Sampling bias – This occurs when the sample selected for the study is not representative of the population such that the sample values cannot be generalized to the broader population. For example, in their survey of provider satisfaction and reported usage of cpoe , Lee, Teich, Spurr, and Bates (1996) reported different response rates between physicians and nurses, and between medical and surgical staffs, which could affect the generalizability of the results. To avoid sampling bias one should clearly define the target population and sampling frame, employ systematic methods such as stratified or random sampling to select samples, identify the extent and causes of response differences, and adjust the analysis and interpretation accordingly.
• Non-response bias – This occurs when individuals who responded to the survey have different attributes than those who did not respond to the survey. For example, in their study to model nurses’ acceptance of barcoded medication administration technology, Holden, Brown, Scanlon, and Karsh (2012) acknowledged their less than 50% response rate could have led to non-response bias affecting the accuracy of their prediction model. To address this error one can offer incentives to increase response rate, follow up with non-respondents to find out the reasons for their lack of response, or compare the characteristics of non-respondents with respondents or known external benchmarks for differences ( King & He, 2005 ). Adjustments can then be made when the cause and extent of non-response are known.
• Measurement bias – This occurs when there is a difference between the survey results obtained and the true values in the population. One major cause is deficient instrument design due to ambiguous items, unclear instructions, or poor usability. To reduce measurement bias one should apply good survey design practices, adequate pretesting or pilot testing of the instrument, and formal tests for validity and reliability. An example of good Web-based eHealth survey design guidelines is the Checklist for Reporting Results of Internet E-Surveys ( cherries ) by Eysenbach (2004) . The checklist has eight item categories and 31 individual items that can be used by authors to ensure quality design and reporting of their survey studies.

13.3.4. Adherence to Reporting Standards

Currently there are no universally accepted guidelines or standards for reporting survey studies. In the field of management information systems ( mis ), Grover, Lee, and Durand (1993) published nine ideal survey methodological attributes for analyzing the quality of mis survey research. In their review of ideal survey methodological attributes, Ju, Chen, Sun, and Wu (2006) found two frequent problems in survey studies published in three top mis journals to be the failure to perform statistical tests for non-response errors and not using multiple data collection methods. In healthcare, Kelly, Clark, Brown, and Sitzia (2003) published a checklist of seven key points to be covered when reporting survey studies. They are listed below:

• Explain the purpose of the study with explicit mention of the research question.
• Explain why the research is needed and mention previous work to provide context.
• Provide detail on how study was done that covers: the method and rationale; the instrument with its psychometric properties and references to original development/testing; sample selection and data collection processes.
• Describe and justify the analytical methods used.
• Present the results in a concise and factual manner.
• Interpret and discuss the findings.
• Present conclusions and recommendations.

In eHealth, Bassi, Lau, and Lesperance (2012) published a review of survey-based studies on the perceived impact of emr in physician office practices. In the review they used the 9-item assessment tool developed by Grover and colleagues (1993) to appraise the reporting quality of 19 emr survey studies. Using the 9-item tool a score from 0 to 1 was assigned depending on whether the attribute was present or absent, giving a maximum score of 9. Of the 19 survey studies appraised, the quality scores ranged from 0.5 to 8. Over half of the studies did not include a data collection method, the instrument and its validation with respect to pretesting or pilot testing, and non-respondent testing. Only two studies scored 7 or higher which suggested the reporting of the 19 published emr survey studies was highly variable. The criteria used in the 9-item tool are listed below.

• Report the approach used to randomize or select samples.
• Report a profile of the sample frame.
• Report characteristics of the respondents.
• Use a combination of personal, telephone and mail data collection methods.
• Append the whole or part of the questionnaire in the publication.
• Adopt a validated instrument or perform a validity or reliability analysis.
• Perform an instrument pretest.
• Report on the response rate.
• Perform a statistical test to justify the loss of data from non-respondents.

13.4. Case Examples

13.4.1. clinical informatics governance for ehr in nursing.

Collins, Alexander, and Moss (2015) conducted an exploratory survey study to understand clinical informatics ( ci ) governance for nursing and to propose a governance model with recommended roles, partnerships and councils for ehr adoption and optimization. The study is summarized below.

• Setting – Integrated healthcare systems in the United States with at least one acute care hospital that had pioneered enterprise-wide ehr implementation projects and had reached the Health Information Management Systems Society ( himss ) Analytics’ emr Adoption Model ( emram ) level 6 or greater, or were undergoing enterprise-wide integration, standardization and optimization of existing ehr systems across sites.
• Population and samples – Nursing informatics leaders in the role of an executive in an integrated healthcare system who could offer their perspective and lessons learned in their organization’s clinical and nursing informatics governance structure and its evolution. The sampling frame was the himss Analytics database that contains detailed information on most u.S. healthcare organizations and their health it status.
• Design – A cross-sectional survey conducted through semi-structured telephone interviews with probing questions.
• Measures – The survey had four sections: (a) organizational characteristics; (b) participant characteristics; (c) governance structure; and (d) lessons learned. Questions on governance covered decision-making, committees, collaboration, roles, and facilitators/barriers for success in overall and nursing-specific ci governance.
• Analysis – Grounded theory techniques of open, axial and selective coding were used to identify overlapping themes on governance structures and ci roles. Data were collected until thematic saturation in open coding was reached. The ci structures of each organization were drawn, compared and synthesized into a proposed model of ci roles, partnerships and councils for nursing. Initial coding was independently validated among the researchers and group consensus was used in thematic coding to develop the model.
• Results – Twelve nursing executives (made up of six chief nursing information officers, four directors of nursing informatics, one chief information officer, and one chief ci officer) were interviewed by phone. For analysis 128 open codes were created and organized into 18 axial coding categories where further selective coding led to four high-level themes for the proposed model. The four themes (with lessons learned included) identified as important are: inter-professional partnerships; defining role-based levels of practice and competence; integration into existing clinical infrastructure; and governance as an evolving process.
• Conclusion – The proposed ci governance model can help understand, shape and standardize roles, competencies and structures in ci practices for nursing, as well as be extended to other domains.

13.4.2. Primary Care EMR Adoption, Use and Impacts

Paré et al. (2013) conducted a descriptive survey study to examine the adoption, use and impacts of primary care emr s in a Canadian province. The study is summarized below.

• Setting – Primary care clinics in the Canadian Province of Quebec that had adopted electronic medical records under the provincial government’s emr adoption incentive and accreditation programs.
• Population and samples – The population consisted of family physicians as members of the Quebec Federation of General Practitioners that practice in primary care clinics in the province. The sample had three types of physician respondents that: (a) had not adopted emr (type-1); (b) had emr in their clinic but were not using it to support their practice (type-2); or (c) used emr in their clinic to support their practice (type-3).
• Design – A cross-sectional survey in the form of a pretested online questionnaire in English and French accessible via a secure website. E-mail invitations were sent to all members followed by an e-mail reminder. With a sampling frame of 9,166 active family physicians in Quebec, 370 responses would be needed to obtain a representative sample with a 95% confidence interval and a margin of error of ±5%.
• Measures – For all three respondent types the measures were clinic and socio-demographic profiles and comments. For type-2 and type-3 respondents, the measures were emr brand and year of implementation. For type-1 the measures were barriers and intent to adopt emr . For type-2 the measures were reasons and influencing factors for not using emr , and intent to use emr in future. For type-3 the measures were emr use experience, level and satisfaction, ease of use with advanced emr features, and individual/organizational impacts associated with emr use.
• Analysis – Descriptive statistics in frequencies, per cent and mean Likert scores were used on selected measures. Key analyses included comparison of frequencies by: socio-demographic and clinic profiles; barrier and adoption intent; emr feature availability and use; and comparison of mean Likert scores for satisfaction and individual and organizational impacts. Individual impacts included perceived efficiency, quality of care and work satisfaction. Organizational impacts included effects on clinical staff, the clinic’s financial position, and clients.
• Results – Of 4,845 invited physicians, 780 responded to the survey (16% response rate) that was representative of the population. Just over half of emr users reported the high cost and complexity in emr acquisition and deployment as the main barriers. Half of non-users reported their clinics intended to deploy emr in the next year. emr users made extensive use of basic emr features such as clinical notes, lab results and scheduling, but few used clinical decision support and data sharing features. For work organization, emr s addressed logistical issues with paper systems. For care quality, emr s improved the quality of clinical notes and safety of care provided but not clinical decision-making. For care continuity, emr s had poor ability to transfer clinical data among providers.
• Conclusion – emr impacts related to a physician’s experience where the perceived benefits were tied to the duration of emr use. Health organizations should continue to certify emr products to ensure alignment with the provincial ehr .

13.4.3. Nurses’ Acceptance of Barcoded Medication Administration Technology

Holden and colleagues (2012) conducted an explanatory survey study to identify predictors of nurses’ acceptance of barcoded medication administration ( bcma ) in a u.S. pediatric hospital. The study is summarized below.

• Setting – A 236-bed free standing academic pediatric hospital in the midwestern U.S. that had recently adopted bcma . The hospital also had cpoe , a pharmacy information system and automated medication-dispensing units.
• Population and Sample – The population consisted of registered nurses that worked at least 24 hours per week at the hospital. The sample consisted of nurses from three care units that had used bcma for three or more months.
• Design – A cross-sectional paper survey with reminders was conducted to test the hypothesis that bcma acceptance would be best predicted by a larger set of contextualized variables than the base variables in the Technology Acceptance Model ( tam ). A multi-item scales survey instrument, validated in previous studies with several added items, was used. The psychometric properties of the survey scales were pretested with 16 non-study nurses.
• Measures – Seven bcma -related perceptions: ease of use, usefulness for the job, non-specific social influence, training, technical support, usefulness for patient care, and social influence from patients/families. Responses were 7-point scales from not-at-all to a-great-deal. Also tracked were variables for age in five categories, as well as experience measured as job tenure in years and months. Two bcma acceptance variables: behavioural intention to use and satisfaction.
• Analysis – Regression of all subsets of perceptions to identify the best predictors of bcma acceptance using five goodness-of-fit indicators (i.e., R 2 , root mean square error, Mallow’s Cp statistics, Akaike information criterion, and Bayesian information criterion). An a priori α criterion of 0.05 was used and 95% confidence intervals were computed around parameter estimates.
• Results – Ninety-four of 202 nurses returned a survey (46.5% response rate) but 11 worked less than 24 hours per week and were excluded, leaving a final sample of 83 respondents. Nurses perceived moderate ease of use and low usefulness of bcma . They perceived moderate or higher social influence to use bcma , and were moderately positive about bcma training and technical support. Behavioural intention to use bcma was high but satisfaction was low. Behavioural intention to use bcma was best predicted by perceived ease of use, non-specific social influence and usefulness for patient care (56% variance explained). Satisfaction was best predicted by perceived ease of use, usefulness for patient care and social influence from patients/families (76% variances explained).
• Conclusion – Predicting bcma acceptance benefited from using a larger set of perceptions and adapting variables.

13.5. Summary

This chapter introduced three types of surveys, namely exploratory, descriptive and explanatory surveys. The methodological considerations addressed included population and sample, survey instrument, variable choice and reporting standards. Three case examples were also included to show how eHealth survey studies are done.

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This publication is licensed under a Creative Commons License, Attribution-Noncommercial 4.0 International License (CC BY-NC 4.0): see https://creativecommons.org/licenses/by-nc/4.0/

• Cite this Page Lau F. Chapter 13 Methods for Survey Studies. In: Lau F, Kuziemsky C, editors. Handbook of eHealth Evaluation: An Evidence-based Approach [Internet]. Victoria (BC): University of Victoria; 2017 Feb 27.
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• Introduction
• Types of Survey Studies
• Methodological Considerations
• Case Examples

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