types of scale in research questionnaire

• Survey Scale: Definitions, Types + [Question Examples]

Survey scales have become one of the most common elements of quantitative data collection. These scales help you to gather and organize large volumes of data during data collection; especially in quantitative research. 

At one point or the other, you must have come across a survey scale during data collection ; whether as a survey respondent or administrator. Although survey scales are common, only a few know how to properly use them to carry out research. 

What is a Survey Scale?

A survey scale is an orderly arrangement of different survey response options. It typically consists of a specific range of verbal or numerical options that respondents can choose from as they provide answers to questions in a survey or questionnaire . 

Survey scales are important because they help respondents to quantify what they think or how they feel about certain things. In other words, it allows respondents to assign specific quantifiable values to feelings, ideas, experiences, and expectations. 

If you want to ask customers to provide feedback about your organization’s service delivery, you need to use a survey scale. It makes it easier for them to communicate their answers according to the descriptive or numerical values in the scale, instead of providing vague or ambiguous responses. 

To a large extent, survey scales help you to measure variables that are inferred; that is, variables that cannot be communicated directly. More properly, it is a composite score of several survey questions that each measure the same attribute.  

Types of Surveys Scale

Dichotomous scale.

A dichotomous scale is a type of survey response scale that provides two options, which lie at opposite ends. On a dichotomous scale, the survey respondent can not give a neutral answer because it is a case of either one or the other. 

As a result of its binary layout, a dichotomous scale is used when you need to gather precise data in research. In such situations, any answer that is indifferent, neutral, or described as “sitting on the fence” will not serve the purpose of the data collection process. 

Pros of Dichotomous Scale

• It helps you gather direct and precise responses in research.
• It prevents vague or ambiguous answers that may not prove useful in the research.
• To a large extent, it results in valid and objective survey data.

Cons of Dichotomous Scale

• It is quite restrictive and can prevent respondents from fully communicating their thoughts.
• It can lead to survey response bias due to fatigue. When this happens, respondents tend to tilt towards positive answers.
• They can be used for leading or loaded questions.

Dichotomous Scale Question Samples

Did you enjoy using our product?

Our service delivery is top-notch.

Was this article helpful to you?

Rating Scale

A rating scale is a type of survey response scale that allows respondents to match specific qualitative values with different assertions, products, or features. With a rating scale, you simply answer the survey question by picking one of the rating options on the scale. 

A rating scale can be categorized as ordinal or interva l. Ordinal rating scales present the values and options in an orderly manner; for example, ascending or descending order. In an interval scale, the options are presented in an orderly manner and the difference between each option can be measured. 

Types of Rating Scales  

• Graphic Rating Scale

In a graphic rating scale, the answer options provided are placed on a scale of 1-3, 1-5, and so on. Respondents can choose an option on the scale that reflects their rating for a specific assertion in the data collection context. 

A good example of this type of rating scale is the Likert scale . 

Graphic Rating Scale Question Sample

This product meets your needs effectively.

• Strongly agree
• Strongly disagree
• Numerical Rating Scale

As the name suggests, a numerical rating scale is a type of rating scale with numbers as answer options. This means that respondents would have to choose a number on the scale that corresponds with their perceptions in the specific research context. 

A good example of this is the semantic differential scale . 

• Comparative Rating Scale

This is a type of rating scale that requires respondents to answer questions in specific comparative contexts. For example, respondents can be asked to rate a specific product feature functionality in comparison with another product or feature. 

• Descriptive Rating Scale

Here, the answer options listed are accompanied by descriptive explanations to help respondents choose objective responses. This type of rating scale can be found in customer satisfaction surveys. 

Pros of Rating Scales

• Rating scales are easy to understand and fill out.
• It allows you to collect and process large volumes of data.
• It is quick and time-efficient.

Cons of Rating Scales 

• It does not allow for adequate observation and representation of data.
• It is subject to ambiguity.
• Rating scales can result in the collection of generic data from survey respondents.

Ranking Scale

A ranking scale is a type of close-ended scale that measures people’s preferences by asking them to rank their views on a list of related items. In other words, respondents simply evaluate and rank different items in a row based on the criterion stated in a specific column.

This type of survey response scale is typically used in market research to gather feedback on different sets of product features. Depending on the context of your survey and research needs, there are different types of ranking scales you can include in your questionnaire and survey. 

Types of Ranking Scales

• Scale Ranking

This type of ranking is commonly used with multiple-choice questions. Here, respondents are asked to rank a set of items such as product features or customer experience categories, against each other. 

Scale ranking takes numerous forms including a drop-down scale, emoji scale, and heart scale, just to mention a few. 

Scale Ranking Question Sample

• Rank the following in order of preference

Product Feature A.

Product Feature B.

Product Feature C

• Constant Sum

This is a common type of ranking scale that is used for financial surveys or surveys that involve some degree of summation or calculation. With constant sum, there is a predetermined total and when filling the survey, respondents are required to input numbers for each variable being considered. 

• Drag and Drop Scale

For this scale, respondents need to drag and drop different survey variables and rearrange them in the ranking order that they prefer. This makes it easier for respondents to communicate their perceptions and provide valid survey responses. 

Pros of Ranking Scales

• It provides a vivid picture of the perceptions of respondents.
• It allows for swift data collection and analysis.

Cons of Ranking Scales

• It does not provide specific insights into the responses gathered. It allows you to understand what matters most to your respondents.
• It can be time-consuming and difficult to administer.

Likert Scale 

This is a type of psychometric scale that is used to collect information about people’s opinions and perceptions on specific subjects and contexts. It is used to measure the degree to which people agree or disagree with a question or statement. 

As we’ve mentioned earlier, a Likert scale is a type of rating scale. It is considered one of the most effective types of ranking scales; especially in social and educational research . Likert scales commonly have a 3-point, 4-point, or 5-point scale structure. 

The options on a Likert scale can be numeric or verbal; respondents choose answer options that best represent how they feel or what they think about the statement or assertion in question. This type of survey response scale is commonly deployed in customer satisfaction surveys. 

Pros of Likert Scale

• It provides a wide range of options that covers varying perceptions and points of view.
• It makes it easy for you to gather and process data.
• It is very easy to set up and administer surveys with Likert scales.

Cons of Likert Scale

• It can lead to survey response bias.
• It can take a long time to analyze data gathered via a Likert scale.

Likert Scale Question Sample

How likely are you to buy from us?

• Very likely
• Somewhat likely
• Very unlikely

Semantic Differential Scale

A semantic differential scale is a rating scale that requires respondents to rate a product, feature, entity, or team based on semantic variables listed as scale options. These variables are typically opposite adjectives at each end of the scale. 

With this type of survey scale, respondents need to choose options that best reflect their emotions within the defined context. Let’s look at a few types of semantic differential scales you can include in your survey: 

• Matrix Rating Scale

This type of scale requires survey respondents to provide answers to closed-ended questions by evaluating a set of items. It is usually presented in a grid-like format consisting of rows and columns. 

• Open-ended Question

This is a type of survey question that does not restrict respondents to a set of premeditated answers. In other words, respondents are allowed to communicate their thoughts and experiences, completely, without any limitations. 

• Slider Scale

This type of semantic differential scale allows respondents to choose preferred survey response options by simply sliding the scale’s cursor to the option they want. 

• Satisfaction Scale

This is a type of semantic differential scale that allows respondents to communicate their degree of satisfaction with a product, feature, or service. It is common in customer satisfaction and feedback surveys. 

Semantic Differential Scale Question Sample

• How satisfied are you with our services?
• Tell us how you feel about our service delivery.
• How would you rate our service delivery?

Pros of Semantic Differential Scale

• It allows you to collect valid and objective data.
• It allows respondents to express their thoughts clearly.
• The findings from a semantic differential scale are more authentic than other types of rating scales.

Cons of Semantic Differential Scale

• Lack of standardization in terms of the number of divisions that should be included in the scale.
• It is difficult to represent neutral responses with this scale.

Also Read: 7 Types of Data Measurement Scales in Research

How to Create Surveys with Scales on Formplus 

Formplus is a data collection tool that helps you to create and administer surveys seamlessly. With our drag-and-drop form builder, you can easily add different types of survey response scales to your questionnaire and collect data effectively from numerous respondents. 

Follow this step-by-step guide to create surveys with scales on Formplus: 

• Sign in to your Formplus account to access your dashboard and start creating your survey with scales. If you do not have a Formplus account, you can sign up for one for free here.
• In your dashboard, click the “create new form” button to access the form builder. You can also edit any of the available form templates for your survey.
• In the form fields section, drag and drop preferred response scales to add them to your form. Formplus has numerous response scales including matrix and Likert scale options.
• After adding the desired response scales, save your form to access the customization section of the builder. Here, you can change the appearance of your form by adding your organization’s logo and changing the survey’s background image.
• Copy the form link and share it with survey respondents to collect information from them. You can use any of the multiple form sharing options to make data collection easier for you.
• For instance, you can send out email invitations to respondents or share your form with your online community via the Share page.

Tips to Increase Response Rate on Your Surveys  

A good survey response rate lends credibility to your survey and allows you to gather enough data to arrive at valid research findings. This is why you must take extra care to ensure a good response rate for your surveys. 

Getting a good survey response rate isn’t difficult even though it can be challenging. The tips we’ll share here would help you to increase your survey response rate and gather objective information during data collection. 

• Your survey should be concise, direct, and straight to the point. A lengthy survey can discourage respondents from completing your survey, which can negatively impact your response rate.  Be sure to include only important questions in your survey. If your survey is lengthy, you should split your questionnaire into different sections and multiple pages to make it easier for respondents to complete it. 
• Provide incentives to respondents for completing your survey. This is an easy way to encourage respondents to fill out your questionnaire and also share the survey with their network.
• Target the right audience for your survey as this would help you get a large volume of valid responses. Ensure that the target audience for your survey has direct or indirect knowledge of the survey context or subjects.
• Leverage multiple form sharing options to get your survey across to a large audience both online and offline. You can send out email invitations to respondents or encourage them to fill out the survey using Formplus social media direct sharing buttons.
• Unless necessary, do not ask respondents to provide personal information in your surveys. Gathering data anonymously makes it easier for respondents to freely communicate their thoughts and beliefs about the subject matter.
• Send out your survey at the right time.

Knowing how to utilize response scales in your survey is essential to your data collection process. This is because survey response scales, when used the right way, play an important role in qualitative and quantitative research by helping you to collect large volumes of data. 

As we’ve mentioned in this article, there are several response scales that you can use in your surveys including dichotomous scales and semantic differential scales. Adding one or more of these scales to the survey can make a difference in your data collection process. 

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15 Common Rating Scales Explained

Variety in question types can be both a blessing and a curse.

Having many ways to ask questions provides better options to the researcher to assess the opinion of the respondent.

But the wrong type of question can fail to capture what’s intended, confuse respondents, or even lead to incorrect decisions.

In surveys, questions can be broadly classified as open-ended (free responses) or closed-ended. Closed-ended questions themselves can be classified into multiple choice questions or rating scales.

Multiple choice questions (e.g. age, education level, or electronic devices owned) are usually more straightforward for researchers with little survey experience. When properly written, they’re also straightforward to the respondents as they usually involve concrete selections.

Rating scales however usually involve asking participants to rate abstract concepts, such as satisfaction, ease, or likelihood to recommend. The item selection can have a big impact on both the responses and interpretation.

There are different ways of classifying rating scales and slight variations can result in different looking rating scales, even though they’re variations on the same scale. For example, our MUIQ platform offers over 30 question types but I’ve identified 15 distinct ones.

I’ve adapted a classification scheme based on our experience at MeasuringU and from the classic text on survey research by Alreck and Settle . Here are 15 scales, in roughly the order of most to least commonly used.

1. Linear Numeric Scale

In a linear numeric scale, participants provide some numeric response to a question or statement. This can include things like satisfaction , ease, brand favorability , feature importance, or likelihood to recommend. The Single Ease Question (SEQ) and likelihood to recommend item used in the NPS are examples of linear numeric scales. Linear numeric scales usually have at least the endpoints labeled. (Labeling, neutral points, and number of response options are the topics of other articles.)

The classic Likert scale has participants agree or disagree (or approve/disapprove) to multiple statements. When numbers are associated with each response option, the Likert item can be seen as a special case of the linear numeric scale. The classic Likert item uses a 5-point response scale, but you can use 7, 9, or other points, too. (Although someone will have a strong opinion about the “right” number of steps. ) Because the response scale is about agreement, be sure items are phrases participants can agree or disagree to. The System Usability Scale (SUS) , SUPR-Q , and UMUX-Lite use a Likert scale with numbered values .

3. Multiple Rating Matrix

The matrix question is a compact way of presenting multiple linear numeric items and is the typical method for displaying Likert items, too. It’s probably not technically different from a linear numeric scale but I’ve separated it out because they’re so popular for online surveys. For example, when having participants rate their brand attitude, it’s common to use a matrix similar to the following one.

4. Frequency Scales

Understanding how often people perform (or think they perform) actions helps when product planning as in the example below. When listing the frequency of actions, consider both specific number of times (e.g. every day) as well as more general timeframes (sometimes, always, never—referred to as a verbal frequency scale). Also, be sure the frequencies are sequentially ordered and well understood. For example, is occasional more frequent than sometimes?

When we measure users’ attitudes toward the ease of use of websites or software using the SUS or UMUX-Lite, we ask how frequently participants use the software with a verbal frequency scale similar to the one below. ( Frequency of use often predicts attitudes. )

5. Forced Ranking Scale

Forced ranking scales are good for prioritizing product features. Having participants rate their interest on a linear numeric scale may result in the problem of every feature being important because there’s no disincentive for rating everything high.

I recommend keeping the number of items to fewer than 10 when possible and randomize their presentation order. With each option, respondents have to review the list to make a decision on ranking. To rank 20 items, for example, participants need to make 19 passes through the continually shrinking list. This process is easier with a drag-and-drop interface as in the MUIQ item below, but forcing people to rank items they have little opinion on may lead to drop out or error. It gets quite laborious to rank many items. If your list is long, consider a “pick some” question type (see #6).

6. Pick Some (a.k.a Top Task)

When you have a long list for participants to prioritize (e.g. more than 10 and especially more than 20) but don’t want them to have to rank all of the items, have participants select a fixed subset, such as 3 or 5. This is what we do for a top-tasks analysis . Again, it’s important to randomize the order to avoid items near the top being favored. Surprisingly, we’ve found that this crude technique takes a fraction of the time as forced ranking and yields very similar results.

7. Paired Comparison Scale

When you want to force a choice between two alternatives (sort of a mini-rank) such as a preference for a website, brand, or design, use a paired comparison scale.

Items don’t have to be just text. You can present pictures (like alternate designs) or videos for respondents to select their preference.

8. Comparative Scale/Comparative Intensity

You can have participants rate their preference and strength of preference all in one item using a comparative scale. The scale below asks participants to rate their preference and intensity for two rental car companies on four website attributes.

9. Semantic Differential Scale

When you want to assess where participants fall on a continuum of adjectives or attributes, use a semantic differential scale. You need to provide clear polar opposite terms (like hot to cold)—which can be easy in principle but hard in practice. For this reason, we don’t use these as often and prefer the next two options.

The semantic differential scale below asks participants to rate their experience with Netflix on two items.

10. Adjective Checklist

When assessing brand attitude, the adjective checklist is a staple. It’s also the technique used in the Microsoft Desirability Toolkit.  Instead of aligning opposite adjectives, you can list them (usually a mix of positive and negative) for participants to select. Again, randomize the presentation order.

11. Semantic Distance Scale

A way to avoid the problem of having to find polar opposites on the semantic differential scale but still have participants rate each adjective is to have respondents rate an adjective, term, or phrase and provide some level of intensity. It’s sort of a cross between the adjective scale and semantic differential scale.

12. Fixed Sum

When you need responses to add up to a fixed amount, such as 100% or an amount spent (e.g. $100), a fixed sum approach might work. It can be another way to force respondents to decide which features are more important than others and is a popular technique for assessing the importance of new features, or even how participants allocate their budget (like the example below).

13. Compound Matrix

You can really cram a lot of things into one question by using drop-down lists or text fields instead of radio buttons or checkboxes. The compound matrix has participants rate two dimensions at the same time; for example, the importance of features by device type for online banking.

14. Pictorial/Graphic Scales

Instead of picking a number, participants can respond to pictures, such as stars as done on Amazon and Netflix. The stars represent a quantity that can be averaged similar to linear numeric scales.

The Wong-Baker faces pain scale is a common scale used to assess patient discomfort.

Pictorial scales can be particularly helpful when participants might not speak the target language well or even have trouble communicating (hence its widespread use in medical settings).

15. Visual Analog/Slider Scale

Imagine a linear numeric scale that didn’t have discrete points (e.g. 1 through 7) but instead allowed participants to select any value in between. This is the idea behind the Visual Analog Scale (VAS), often just called a slider scale. The analog is the continuum the slider represents; for example, from extremely difficult to extremely easy in the example below.

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• Researcher's guide to 4 measurement scales: Nominal, ordinal, interval, ratio

Understanding the type of data you're working on is crucial for your data gathering and analysis. Data can be qualitative or quantitative , coming from a variety of sources. Data that is descriptive and extensive is referred to as qualitative data . Numerical data is quantitative . 

There will be several factors in your dataset that can be captured with different degrees of accuracy. There are four primary levels of measurement scale type: nominal , ordinal , interval , and ratio . This article will explain the definition of a scale of measurement, a description of four scales and examples, and the best question types for measurement scales in research.

• What is a scale of measurement?

The scales of measurement show the accuracy with which variables are captured. A scale of measurement is a system or framework for classifying and quantifying mutable characteristics. Numbers or categories express the features of the variables evaluated in this technique.  

The scale of measurement,  also known as the level of measurement , describes the accuracy level that may be achieved while recording data. These four measuring scales were created by  Stanley Smith Stevens  in  1946 .

The four main measuring scales are nominal, ordinal, interval, and ratio. These levels are listed in increasing order of the detailed information they provide. The complexity and accuracy of the degree of measurement are ranked from low ( nominal ) to high ( ratio ) in a hierarchy.

The four main measuring scales

• 1. Nominal scale

The nominal scale is the first level of measurement . Numbers are used solely for identifying an object . This assessment addresses only non-numeric factors or situations where numbers have no meaning. The nominal scale is the simplest of the four variable measuring scales.

Your data can be categorized by grouping them into mutually exclusive labels; however, there is no hierarchy among the categories. This scale’s variable numbers are only labels for grouping or dividing the variables . If these values have no quantitative meaning , making any calculations based on them is useless. 

Nominal scale examples

Notice that all the scales are mutually exclusive, and none have any numerical significance. Some examples are given below to better explain the nominal scale. 

#1 - What is your favorite car brand?

•  Toyota
• Mercedes-Benz

Only the brand names are significant to the poll's consumer researcher in this question. For these brands, there is no need for a particular order . When collecting nominal data, however, researchers examine the data using pertinent tags .

When a survey respondent chooses Toyota as their favorite brand, the selected option will be “1” in the measurement scale example above. This made it easier to quantify and respond to the last query, which asked how many respondents selected Toyota, Audi, and Ford , and which was the highest. The nominal scale is the most fundamental research scale and is the foundation for quantitative research.  

• 2. Ordinal scale

The ordinal scale, the second measurement level , reports the ranking and ordering of the data without determining the degree of variance among them . Ordinal data is quantitative information with naturally existing orders , and how they vary is uncertain. 

An ordinal scale is a variable measurement scale in statistics used to show the order of variables rather than the differences between them. Generally, these scales represent non-mathematical concepts like pleasure , happiness , and frequency .

Ordinal scale examples

The ordinal scale often measures satisfaction , preferences , frequency , and agreement . Below, we have shared a sample question that a health research company used to measure the monthly exercise frequency of individuals. You can better understand the ordinal scale by examining the example below:

#2 - How often do you exercise?

Researchers can use the ordinal scale to acquire more data than they would with a nominal scale. The order of the answer options in this case, as well as their labeling, is crucial. The researcher finds it convenient to analyze the findings according to the results’ order and name.

• 3. Interval scale

The interval scale is the third of four measurement level s. The interval scale is a quantitative measuring scale with order, significant and equal differences between the two variables , and arbitrary zero presence .

The interval scale measures variables along a standard scale at equal intervals. The measures used to calculate the distance between the variables are highly reliable. These scales are effective as they open doors for the statistical analysis of provided data. 

Interval scale examples

Interval measurements can be used to assign metrics to data values. No real zero point exists , but you may categorize , rank , and infer equal gaps between adjacent data points. The following example may make the interval scale easier to understand. 

The Celsius and Fahrenheit temperature scale is a well-known example of an interval scale since “0” is arbitrary because negative temperature values can exist . The difference between these two temperatures, 50 and 30 degrees, is equal to the difference between 30 and 10 degrees; hence 50 is always higher than 30 .  

• 4. Ratio scale

The ratio scale is the fourth level of measurement in research and has a zero point or character of origin . A ratio scale of measurement is quantitative , with absolute zero and equal gaps between nearby points. The ratio scale has no negative values since there is a zero value.

Researchers can use the ratio scale to work on data by determining the mean, median, and mode for a central tendency. The ratio scale cannot be “0” because of zero. When a researcher wants to utilize a ratio scale, he must consider the qualities of the variable and if it has all of the required features. 

Ratio scale examples

Physical properties of people and objects may be quantified using ratio scales; consequently, height, weight, and kilogram calories are instances of ratio measurement. Below are examples of ratio scales; you can better understand the ratio scale by looking at these examples:

# 3 - What is your current weight in kilograms?  

• Less than 49 kilograms
• 50- 69 kilograms
• 70- 89 kilograms
• 90- 109 kilograms
• More than 110 kilograms

Weight in kilograms is an excellent example of ratio data. If something weighs 0 kilos, it weighs nothing, especially when compared to temperature, where a figure of zero degrees represents no temperature but rather extremely cold .

• Best question types for measurement scales

The question type determines the data type created by a survey, and the data type limits the data analysis that may be conducted. The most frequent survey question is an interval rating scale question, which we utilize to record the respondent’s degree of sentiment about the issue of interest.   

Ordinal questions commonly feature a list of response alternatives , but the answer list is distinguished by the fact that the response options are ordered somehow. The ratio scale questions occur when respondents are requested to answer to a physical measure . Here are the best question types listed for measurement scales:

1 - Open-ended questions

Asking open-ended questions is a great way to learn more about a topic. When you request an open question, you allow the other person to elaborate and provide a detailed explanation. A nominal scale can be used for open-ended question type. You can ask how , what , why , when , explain , and describe questions like the example below.

• What is your favorite color?

Open-ended question example

2 - Rating questions

Rating questions enable participants to weigh or assign numerical values to responses using a graphical interface, employing a basic 1-5 star rating system or 0-100 slider scale where a higher number equals a better score. Ordinal scales and interval scales can be used for rating question types.

• How likely are you to recommend this product to a friend? 

Rating question example

3 - Likert scale questions

Likert scale questions are critical in determining a respondent's opinion or attitude toward a particular issue and are essential to market research. A Likert is a five, seven, or nine-point agreement scale used to assess respondents’ agreement with various claims. Interval scales and ordinal scales can be used for Likert scale question types. 

• What was your level of satisfaction with our product?

- Extremely satisfied

- Very satisfied

- A little satisfied

- A little dissatisfied

- Not satisfied at all

Likert scale question example

4 - Multiple choice questions

Multiple-choice questions are classic surveys because they provide respondents with multiple choices. They can contain single or multi-select options . Ratio scales and nominal scales can be used for multiple-choice questions:

• How much time do you spend on social media per day?

- Less than 1 hour

- 2-3 hours

- 3-4 hours

- 4-6 hours

- More than 6 hours

Ratio scale multiple-choice question example

What is your favorite phone brand? 

Nominal scale multiple-choice question example

• Frequently asked questions about levels of measurement

Is Likert scale ordinal or nominal?

The Likert scale is a type of ordinal scale. The Likert scale assesses respondents’ attitudes or levels of agreement and disagreement on a subject by asking them to select from a list of answer alternatives such as “ strongly agree ,” “ agree ,” “ neutral ,” “ disagree ,” and “ strongly disagre e.” The selections are arranged in a positive to negative sequence, reflecting a rating of views or levels of agreement.

Nominal scale vs. Interval scale

A nominal scale is a measuring scale that divides variables into different categories or groups with no regard for the order or size of the types . On the other hand, an interval scale is a measuring scale that categorizes variables and measures the magnitude or distance between them meaningfully and consistently . 

The significant distinction between a nominal scale and an interval scale is that a nominal scale does not quantify the distance or size between the categories , but an interval scale does.

Nominal scale vs. ratio scale

The nominal scale is the most basic measure for categorizing data into groups or categories. In contrast, the ratio scale is a more sophisticated measuring scale that categorizes data, specifies the order, and creates meaningful gaps between values.

The ratio scale offers a more accurate variable measurement with meaningful intervals and a zero point , allowing for mathematical operations , as opposed to the nominal scale, which categorizes data into categories without any order or ranking.

Are Likert scales ordinal or interval?

Ordinal and interval are differentiated based on the particular requirements of the analysis being done. Because the items in Likert scale inquiries have a distinct rank order but an uneven distribution, Likert scales are typically regarded as ordinal data .

Sometimes, the overall Likert scale scores are seen as interval data . These scores have equal spacing between them and directionality.

Ordinal vs. interval scale

Ordinal and interval scales are two of the four primary categories of data or classifications. Both data kinds accommodate the necessity to categorize and communicate information . Data quantities are also measured in terms of ordinal and interval data.

Ordinal scales have unordered categories with a defined order or ranking , but interval scales have ordered categories with equally measured intervals between them. This is the primary distinction between ordinal and interval scales.

Nominal scale vs. ordinal scale

A nominal scale is a scale in which variables are only “ named ” or “ labeled ” without regard to their order. Beyond merely identifying them, the variables on an ordinal scale have a precise order.

A nominal scale is a measuring scale that divides data into unique, unrelated groups without any innate hierarchy or order . On the contrary, an ordinal scale is a measuring scale that ranks or orders data according to some trait or feature .

Interval scale vs. ratio scale

The interval scale and the ratio scale are two quantitative scales that are used to quantify variables in research or statistics. While they have certain similarities, they also have significant distinctions. 

The interval scale lacks a genuine zero point , whereas the ratio scale has one. When compared to variables measured on an interval scale, this distinction enables more complex mathematical operations on variables recorded on a ratio scale.

How is the interval scale used?

The interval scale provides a controlled and regulated method for collecting, analyzing, and interpreting data, which aids in the generation of insights and the making of informed decisions in a variety of sectors. 

Interval scales work well in surveys where respondents must provide temperature , time , and date variables . Interval scales may be readily integrated into multiple-choice or rating scale questions by asking respondents to rate using a numerical scale. Here are some common uses of interval scale:

• Surveys and questionnaires: The interval scale is extensively used in the design and administration of surveys or questionnaires. You can use an interval scale when create a survey. Respondents can score or rank objects on a scale, which allows researchers to obtain quantitative data for the study.
• Market research: In market research, an interval scale is used to assess client preferences, satisfaction levels, and buy intent. It aids in the analysis and interpretation of client feedback or ratings, helping organizations to make educated decisions. 
• Measurement and quantification: By giving numerical values to distinct properties or variables, the interval scale provides for exact measurement and quantification of data.

Sena is a content writer at forms.app. She likes to read and write articles on different topics. Sena also likes to learn about different cultures and travel. She likes to study and learn different languages. Her specialty is linguistics, surveys, survey questions, and sampling methods.

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Survey Response Scales

The response formats used in surveys vary depending on the type of question being asked. Responses can be as simple as a choice between “Yes” or “No” or as complex as choosing an answer among seven response options.

This article is a part of the guide:

• Example - Questionnaire
• Advantages and Disadvantages
• Surveys and Questionnaires - Guide
• Types of Surveys
• Personal Interview

Browse Full Outline

• 1 Surveys and Questionnaires - Guide
• 2.1 Research and Surveys
• 2.2 Advantages and Disadvantages
• 2.3 Survey Design
• 2.4 Sampling
• 3.1 Defining Goals
• 4.1 Survey Layout
• 4.2 Types of Questions
• 4.3 Constructing Questions
• 4.4 Response Formats
• 4.5 Response Scales
• 5.1 Selecting Method
• 5.2 Personal Interview
• 5.3 Telephone
• 5.4.1 Preparing Online Surveys
• 5.4.2 Online Tools
• 5.5 Focus Group
• 5.6 Panel Study
• 6.1 Pilot Survey
• 6.2 Increasing Response Rates
• 7.1 Analysis and Data
• 7.2 Conclusion
• 7.3 Presenting the Results
• 8 Example - Questionnaire
• 9 Checklist

The response options for each question in your survey may include a dichotomous, a three-point, a five-point, a seven-point or a semantic differential scale. Each of these response scales has its own advantages and disadvantages, but the rule of thumb is that the best response scale to use is the one which can be easily understood by respondents and interpreted by the researcher.

Dichotomous Scales

A dichotomous scale is a two-point scale which presents options that are absolutely opposite each other. This type of response scale does not give the respondent an opportunity to be neutral on his answer in a question.

• True - False
• Fair - Unfair
• Agree – Disagree

Rating Scales

Three-point, five-point, and seven-point scales are all included in the umbrella term “rating scale”. A rating scale provides more than two options, in which the respondent can answer in neutrality over a question being asked.

1. Three-point Scales

• Good - Fair – Poor
• Agree – Undecided - Disagree
• Extremely- Moderately - Not at all
• Too much - About right - Too little

2. Five-point Scales (e.g. Likert Scale)

• Strongly Agree – Agree – Undecided / Neutral - Disagree - Strongly Disagree
• Always – Often – Sometimes – Seldom – Never
• Extremely – Very - Moderately – Slightly - Not at all
• Excellent - Above Average – Average - Below Average - Very Poor

3. Seven-point Scales

• Exceptional – Excellent – Very Good – Good – Fair – Poor – Very Poor
• Very satisfied - Moderately satisfied - Slightly satisfied – Neutral - Slightly dissatisfied - Moderately Dissatisfied- Very dissatisfied

Semantic Differential Scales

A semantic differential scale is only used in specialist surveys in order to gather data and interpret based on the connotative meaning of the respondent’s answer. It uses a pair of clearly opposite words, and can either be marked or unmarked.

1. Marked Semantic Differential Scale

Please answer based on your opinion regarding the product:

2. Unmarked Semantic Differential Scale

The central line serves as the neutral point:

Inexpensive __________________|__________________ Expensive

Effective __________________|__________________ Ineffective

Useful __________________|__________________ Useless

Reliable __________________|__________________ Unreliable

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Sarah Mae Sincero (Jun 6, 2012). Survey Response Scales. Retrieved Apr 03, 2024 from Explorable.com: https://explorable.com/survey-response-scales

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4 Measurement Scales Every Researcher Should Remember

One of the standard features offered by QuestionPro’s online survey software is a wide variety of scales that you can use to measure customer response.

At a first glance all the different scales that might seem similar and easily replaceable by each other. However, as you study them in depth, you realize the diversity of their natures and differences in their uses and their findings. There are over 20 different types of scales that are used by researchers in online surveys.  They can be categorized in two classes – comparative scales and non-comparative scales.

There are a number of factors you might consider when deciding on which scales to incorporate in a questionnaire and which ones to use while analyzing data. Some of the factors are:

• The type of data that is required from the respondent – ratio, interval, ordinal or nominal .
• How the information will be used once it is acquired.
• Number of divisions in the scale – odd or even.
• Types of statistical analysis methods to be used after data is acquired.

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• The physical form of the scale – vertical, linear, horizontal, etc.
• Details to be provided in the scale as labels.
• Whether or not response to a question is mandatory.

Since non-comparative scaling techniques are easier and simpler to understand, we’ll introduce to you the most important four scales. You’ll be delighted to see how easy it is to understand and use them. Those who already know about it them are encouraged to comment on the post and let us know any tips that might further help our readers in using these scales.

1.     Graphic Rating Scale

2.     Likert Scale

A Likert scale typically contains an odd number of options, usually 5 to 7. One end is labeled as the most positive end while the other one is labeled as the most positive one with the label of ‘neutral’ in the middle of the scale.

The phrases ‘purely negative’ and ‘mostly negative’ could also have been ‘extremely disagree’ and ‘slightly disagree’.

3.     Semantic Differential Scale (Max Diff)

A semantic differential scale is a combination of more than one continuum. It usually contains an odd number of radio buttons with labels at opposite ends.  Max Diff scales are often used in trade-off analysis such as conjoint.

MaxDiff analysis can be used in new product features research or or even market segmentation research to get accurate orderings of the most important product features. Discriminate among feature strengths more effectively than derived importance methodologies. Like other trade-off analyses, the analysis derives utilities for each of the most important product features which can be used to derive optimal products, using market segmentation to put respondents into groups with similar preference structures, or to prioritize strategic product goals.

You can have your respondents perform Forced-choice nature of the tasks, and disentangle the relative feature importance in cases where average Likert-style ratings might all have very similar ratings.

4.     Side-by-Side Matrix

Another very commonly used scale in questionnaires is the side-by-side matrix.  A common and powerful application of the side-by-side matrix is the importance/satisfaction type of question.

First, ask the respondent how important an attribute is, then ask them how satisfied they are with your performance in this area.  QuestionPro’s logic and loop functions also allow you to run through this question multiple times with other alternatives that the respondent might consider.  This yields benchmark data that will allow you to compare your performance against other competing alternatives.

Here is an example of data from an importance/satisfaction question.  The importance rating is the line and the performance ratings are the bars.  With this type of data, you can actually see where your company needs to increase its efforts to more closely meet the needs of the customer.

While there are many online survey tools and online survey software to choose from, you’ll find that not all of them have these different types of scales available to them.

As you’re designing your survey, be sure to offer a variety of scales.  Using different scales in your survey will engage the respondent more fully and prevent them from clicking the highest, lowest or middle rating all the time.  Another benefit to using different kinds of scales in your survey is that each scale provides you a unique perspective on the data that you are analyzing.

You can also find best alternatives of Conjoint.ly for your business.

Before designing your survey, review the different types of scales and question types inside of your online survey tool and be sure to pick the one that will best help you make your decision.

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The Ultimate Guide to Scale Questions Examples: 10 Proven Techniques

Examples of scale questions for surveys and assessments

Introduction  

Likert Scale questions (also known as scale questions), are crucial instruments for researchers and professionals across a myriad of fields. Whether you’re gauging customer satisfaction, conducting academic research, or seeking feedback on a new product, scale questions provide quantitative and qualitative insights. This guide will unfold the art and science behind scale questions, supported by relevant examples and backed by real-world applications.

Scale Questions Examples  

That’s a classic example of a scale question, is being asked to rate a product or service on a scale of 1 to 5. They help capture the intensity of feelings or opinions about a particular topic. A typical scale question might look something like: 

>”On a scale of 1 to 7, where 1 is ‘Not Satisfied at All’ and 7 is ‘Extremely Satisfied’, how would you rate our customer service?”

This allows respondents to give nuanced feedback rather than a simple yes or no.

Why Scale Questions Are Crucial  

Quantifiable Data: They convert qualitative feedback into quantifiable data, enabling statistical analysis.

Flexible: Scales can be adjusted depending on the granularity of feedback required. 

Consistent Feedback Mechanism: Ensures a uniform feedback mechanism across different respondent groups.

Crafting the Perfect Scale Question  

The devil, they say, is in the details. Crafting an effective scale question involves:

Choosing the Right Scale: From binary scales (yes/no) to 7-point scales, choose what aligns with your objective.

Clarity is King: Keep the language simple and avoid jargon.

Neutral Options: Offering a middle or neutral option can be crucial for those on the fence.

Examples of Different Scale Types  

Binary Scale: Did you like our service? Yes/No.

5-Point Scale: How satisfied are you with our product? (Very Dissatisfied to Very Satisfied)

7-Point Scale: Rate your experience with our website. (1-7)

Balancing Scale Questions with Open-Ended Queries  

Scale questions should be paired with open-ended questions so that we can acquire both quantifiable data, and deeper insights. For instance, after asking someone to rate a service, a follow-up could be: “What did you like most about our service?”

Potential Pitfalls and How to Avoid Them  

Leading Questions: Avoid framing questions that lead respondents to a particular answer.

Too Many Options: Overwhelming respondents with too many scale points can lead to inaccurate feedback.

Ambiguous Language: Ensure each scale point is distinct from the others.

Analyzing and Interpreting Scale Data  

The very next step after procuring your responses is to:

Calculating Averages: Gives an overall sense of sentiment.

Identifying Outliers: Understanding extreme opinions can offer valuable insights.

Segmented Analysis: Break down data by demographics or other parameters to glean specific insights.

The Evolution of Scale Questions  

Scale questions have changed over time and have presently settled on visual elements like emojis. It’s fascinating how a simple “😃 to 😢” scale can communicate feelings effectively.

Scale Questions in Digital Feedback Tools  

Today’s digital platforms, from Google Forms to advanced Customer Relationship Management (CRM) systems, have in-built scale questions templates, making it easier for users to incorporate them.

FAQs  

• How many scale points should I include in my question?  

Typically, 5 to 7 scale points are recommended. However, it depends on the depth of feedback you’re seeking.

• What is the difference between a scale question and a multiple-choice question?  

While both can have multiple options, scale questions gauge intensity, whereas multiple-choice questions offer distinct, unrelated choices.

• Is it essential to have an odd number of scale points?  

Not necessarily, though having a middle option can be beneficial for respondents who feel neutral.

• How do I know if my scale question is effective?  

If it provides clear, actionable feedback that aligns with your research goals, it’s effective.

• Can scale questions be used in any industry or field?  

Absolutely! From academic research to customer feedback, scale questions have diverse applications.

• Why do some online surveys use emojis in their scale questions?  

Emojis provide a visual, relatable way to express feelings, especially on digital platforms.

Conclusion  

More than serving as a tool to collect feedback, scale questions act as a really powerful means of connecting providers with users educators with students and so on. Understanding their nuances and applying the insights from this guide, you’re well on your way to mastering the art of scale questions.

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Scales Used in Social Science Research

Constructing Scales to Survey Opinion

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A scale is a type of composite measure that is composed of several items that have a logical or empirical structure among them. That is, scales make use of differences in intensity among the indicators of a variable. For example, when a question has the response choices of "always," "sometimes," "rarely," and "never," this represents a scale because the answer choices are rank-ordered and have differences in intensity. Another example would be "strongly agree," "agree," "neither agree nor disagree," "disagree," "strongly disagree."

There are several different types of scales. We’ll look at four commonly used scales in social science research and how they are constructed.

Likert Scale

Likert scales are one of the most commonly used scales in social science research . They offer a simple rating system that is common to surveys of all kinds. The scale is named for the psychologist who created it, Rensis Likert. One common use of the Likert scale is a survey that asks respondents to offer their opinion on something by stating the level to which they agree or disagree. It often looks like this:

• Strongly agree
• Neither agree nor disagree
• Strongly disagree

Within the scale, the individual items that compose it are called Likert items. To create the scale, each answer choice is assigned a score (for instance, 0-4), and the answers for several Likert items (that measure the same concept) can be added together for each individual to obtain an overall Likert score.

For example, let’s say that we're interested in measuring prejudice against women . One method would be to create a series of statements reflecting prejudiced ideas, each with the Likert response categories listed above. For example, some of the statements might be, "Women shouldn’t be allowed to vote," or "Women can’t drive as well as men." We would then assign each of the response categories a score of 0 to 4 (for example, assign a score of 0 to "strongly disagree," a 1 to "disagree," a 2 to "neither agree or disagree," etc.). The scores for each of the statements would then be totaled for each respondent to create an overall score of prejudice. If we had five statements and a respondent answered "strongly agree" to each item, his or her overall prejudice score would be 20, indicating a very high degree of prejudice against women.

Bogardus Social Distance Scale

The Bogardus social distance scale was created by sociologist Emory S. Bogardus as a technique for measuring the willingness of people to participate in social relations with other kinds of people. (Incidentally, Bogardus established one of the first departments of sociology on American soil at the University of Southern California in 1915.) Quite simply, the scale invites people to state the degree to which they are accepting of other groups.

Let’s say we are interested in the extent to which Christians in the U.S. are willing to associate with Muslims. We might ask the following questions:

• Are you willing to live in the same country as Muslims?
• Are you willing to live in the same community as Muslims?
• Are you willing to live in the same neighborhood as Muslims?
• Are you willing to live next door to a Muslim?
• Are you willing to let your son or daughter marry a Muslim?

The clear differences in intensity suggest a structure among the items. Presumably, if a person is willing to accept a certain association, he is willing to accept all those that precede it on the list (those with lesser intensities), though this is not necessarily the case as some critics of this scale point out.

Each item on the scale is scored to reflect the level of social distance, from 1.00 as a measure of no social distance (which would apply to question 5 in the above survey), to 5.00 measuring maximize social distance in the given scale (though the level of social distance could be higher on other scales). When the ratings for each response are averaged, a lower score indicates a greater level of acceptance than does a higher score.

Thurstone Scale

The Thurstone scale, created by Louis Thurstone, is intended to develop a format for generating groups of indicators of a variable that have an empirical structure among them. For example, if you were studying discrimination , you would create a list of items (10, for example) and then ask respondents to assign scores of 1 to 10 to each item. In essence, respondents are ranking the items in order of the weakest indicator of discrimination all the way to the strongest indicator.

Once the respondents have scored the items, the researcher examines the scores assigned to each item by all the respondents to determine which items the respondents agreed upon most. If the scale items were adequately developed and scored, the economy and effectiveness of data reduction present in the Bogardus social distance scale would appear.

Semantic Differential Scale

The semantic differential scale asks respondents to answer a questionnaire and choose between two opposite positions, using qualifiers to bridge the gap between them. For instance, suppose you wanted to get respondents’ opinions about a new comedy television show. You'd first decide what dimensions to measure and then find two opposite terms that represent those dimensions. For example, "enjoyable" and "unenjoyable," "funny" and "not funny," "relatable" and "not relatable." You would then create a rating sheet for respondents to indicate how they feel about the television show in each dimension. Your questionnaire would look something like this:

                Very Much     Somewhat     Neither     Somewhat     Very Much Enjoyable                             X                                                                     Unenjoyable Funny                                                                                             X          Not Funny Relatable                                                 X                                                  Unrelatable

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Types of measurement scales in survey questionnaires

Measurement scales while framing a questionnaire play an important role to understand the characteristics of the variables. A questionnaire is a collection of a set of questions. A survey, on the other hand, includes the process of making the questionnaire, collecting the responses, aggregating the data and analysing it (SurveyMonkey, 2019). Therefore, the questionnaire is a subset of a survey. A previous article discussed the important points to remember while making a questionnaire. It also mentioned about two different types of questionnaires.

• closed-ended or structured and,
• open-ended or semi-structured or unstructured questionnaires.

This article focuses on the types of measurement scales used in a closed-ended questionnaire.

A closed-ended questionnaire developed for a research purpose typically consists of a number of sections. The sections depend on the study topic and its objectives. But the questions contained in these sections can broadly be classified into two categories. These are

• Questions for descriptive analysis.
• Questions for inferential analysis.

The questions for descriptive analysis collect information on the demographic profile (age, gender, educational qualification among others) and general background of the survey participants. In contrast, the questions for inferential analysis are framed to collect data on the dependent and independent variables of the study. However, each question in a questionnaire uses one of the specific measurement scales.

Importance of measurement scales

Take the example of research to investigate the effect of organisational factors on job satisfaction among employees working in the IT industry in Delhi-NCR. For the primary study make a closed-ended questionnaire to collect data. The following measurement scales can be used for different variables in the questionnaire.

Nominal scale

A nominal scale classifies variables that do not involve any numerical value. The following questions use a nominal scale. The corresponding variables are gender, place of residence and area of work.

What is your gender?

Where do you live?

What is the area of your work in the IT industry?

• Web development
• Applications development
• Cyber security
• Consultancy

Ordinal scale

An ordinal scale is used to measure variables that involve an order or a rank. However, these variables are not measurable by a standardized unit. The Likert scale is an example of an ordinal scale. It is typically a 5-point or a 7-point scale that includes a number of options. The following statements use a Likert scale. The corresponding variables are organizational factors and job satisfaction.

The rate of scale involves choices of:

• strongly disagree = 1,
• disagree = 2,
• slightly disagree = 3,
• neither agree nor disagree = 4,
• slightly agree = 5,
• strongly agree = 7.

Please put a tick mark in the corresponding box against your choice.

Interval scale

An interval scale is used when the variable involves an order or a rank. In addition, the variable involves numerical values and is measurable by a standardized unit. The following question uses an interval scale. Here, the corresponding variable is annual income which is measurable in a currency unit.

What is your annual income in Indian Rupees?

• Less than 4,00,000
• 4,00,000 – 6,00,000
• 6,00,001 – 8,00,000
• 8,00,001 – 10,00,000
• More than 10,00,000

Ratio scales

A ratio scale is a numerical scale. It is an interval scale with the additional property that the variable can take a value of zero. The following question uses a ratio scale. The corresponding variables are age and duration of employment.

What is your age?

For how many years have you been employed in the IT industry?

• Less than 5 years
• 5 years – 10 years
• 11 years – 15 years
• 16 years – 20 years
• More than 20 years

Use of these scales

Important points to remember.

• Use the nominal scale when only labelling the variable is required.
• An ordinal scale has all the properties of a nominal scale. In addition, it involves the ranking of variables.
• An interval scale has all the properties of an ordinal scale. In addition, it involves proportional intervals of values of variables and the values are measurable.
• A ratio scale has all the properties of an interval scale. In addition, the variables that are expressed in ratio scales can take values of zero.
• SurveyMonkey. (2019). Survey vs Questionnaire: What’s the difference? Retrieved January 24, 2019, from https://www.surveymonkey.com/mp/survey-vs-questionnaire/
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How to Design Effective Research Questionnaires for Robust Findings

As a staple in data collection, questionnaires help uncover robust and reliable findings that can transform industries, shape policies, and revolutionize understanding. Whether you are exploring societal trends or delving into scientific phenomena, the effectiveness of your research questionnaire can make or break your findings.

In this article, we aim to understand the core purpose of questionnaires, exploring how they serve as essential tools for gathering systematic data, both qualitative and quantitative, from diverse respondents. Read on as we explore the key elements that make up a winning questionnaire, the art of framing questions which are both compelling and rigorous, and the careful balance between simplicity and depth.

Table of Contents

The Role of Questionnaires in Research

So, what is a questionnaire? A questionnaire is a structured set of questions designed to collect information, opinions, attitudes, or behaviors from respondents. It is one of the most commonly used data collection methods in research. Moreover, questionnaires can be used in various research fields, including social sciences, market research, healthcare, education, and psychology. Their adaptability makes them suitable for investigating diverse research questions.

Questionnaire and survey  are two terms often used interchangeably, but they have distinct meanings in the context of research. A survey refers to the broader process of data collection that may involve various methods. A survey can encompass different data collection techniques, such as interviews , focus groups, observations, and yes, questionnaires.

Pros and Cons of Using Questionnaires in Research:

While questionnaires offer numerous advantages in research, they also come with some disadvantages that researchers must be aware of and address appropriately. Careful questionnaire design, validation, and consideration of potential biases can help mitigate these disadvantages and enhance the effectiveness of using questionnaires as a data collection method.

Structured vs Unstructured Questionnaires

Structured questionnaire:.

A structured questionnaire consists of questions with predefined response options. Respondents are presented with a fixed set of choices and are required to select from those options. The questions in a structured questionnaire are designed to elicit specific and quantifiable responses. Structured questionnaires are particularly useful for collecting quantitative data and are often employed in surveys and studies where standardized and comparable data are necessary.

Advantages of Structured Questionnaires:

• Easy to analyze and interpret: The fixed response options facilitate straightforward data analysis and comparison across respondents.
• Efficient for large-scale data collection: Structured questionnaires are time-efficient, allowing researchers to collect data from a large number of respondents.
• Reduces response bias: The predefined response options minimize potential response bias and maintain consistency in data collection.

Limitations of Structured Questionnaires:

• Lack of depth: Structured questionnaires may not capture in-depth insights or nuances as respondents are limited to pre-defined response choices. Hence, they may not reveal the reasons behind respondents’ choices, limiting the understanding of their perspectives.
• Limited flexibility: The fixed response options may not cover all potential responses, therefore, potentially restricting respondents’ answers.

Unstructured Questionnaire:

An unstructured questionnaire consists of questions that allow respondents to provide detailed and unrestricted responses. Unlike structured questionnaires, there are no predefined response options, giving respondents the freedom to express their thoughts in their own words. Furthermore, unstructured questionnaires are valuable for collecting qualitative data and obtaining in-depth insights into respondents’ experiences, opinions, or feelings.

Advantages of Unstructured Questionnaires:

• Rich qualitative data: Unstructured questionnaires yield detailed and comprehensive qualitative data, providing valuable and novel insights into respondents’ perspectives.
• Flexibility in responses: Respondents have the freedom to express themselves in their own words. Hence, allowing for a wide range of responses.

Limitations of Unstructured Questionnaires:

• Time-consuming analysis: Analyzing open-ended responses can be time-consuming, since, each response requires careful reading and interpretation.
• Subjectivity in interpretation: The analysis of open-ended responses may be subjective, as researchers interpret and categorize responses based on their judgment.
• May require smaller sample size: Due to the depth of responses, researchers may need a smaller sample size for comprehensive analysis, making generalizations more challenging.

Types of Questions in a Questionnaire

In a questionnaire, researchers typically use the following most common types of questions to gather a variety of information from respondents:

1. Open-Ended Questions:

These questions allow respondents to provide detailed and unrestricted responses in their own words. Open-ended questions are valuable for gathering qualitative data and in-depth insights.

Example: What suggestions do you have for improving our product?

2. Multiple-Choice Questions

Respondents choose one answer from a list of provided options. This type of question is suitable for gathering categorical data or preferences.

Example: Which of the following social media/academic networking platforms do you use to promote your research?

• ResearchGate
• Academia.edu

3. Dichotomous Questions

Respondents choose between two options, typically “yes” or “no”, “true” or “false”, or “agree” or “disagree”.

Example: Have you ever published in open access journals before?

4. Scaling Questions

These questions, also known as rating scale questions, use a predefined scale that allows respondents to rate or rank their level of agreement, satisfaction, importance, or other subjective assessments. These scales help researchers quantify subjective data and make comparisons across respondents.

There are several types of scaling techniques used in scaling questions:

i. Likert Scale:

The Likert scale is one of the most common scaling techniques. It presents respondents with a series of statements and asks them to rate their level of agreement or disagreement using a range of options, typically from “strongly agree” to “strongly disagree”.For example: Please indicate your level of agreement with the statement: “The content presented in the webinar was relevant and aligned with the advertised topic.”

• Strongly Agree
• Strongly Disagree

ii. Semantic Differential Scale:

The semantic differential scale measures respondents’ perceptions or attitudes towards an item using opposite adjectives or bipolar words. Respondents rate the item on a scale between the two opposites. For example:

• Easy —— Difficult
• Satisfied —— Unsatisfied
• Very likely —— Very unlikely

iii. Numerical Rating Scale:

This scale requires respondents to provide a numerical rating on a predefined scale. It can be a simple 1 to 5 or 1 to 10 scale, where higher numbers indicate higher agreement, satisfaction, or importance.

iv. Ranking Questions:

Respondents rank items in order of preference or importance. Ranking questions help identify preferences or priorities.

Example: Please rank the following features of our app in order of importance (1 = Most Important, 5 = Least Important):

• User Interface
• Functionality
• Customer Support

By using a mix of question types, researchers can gather both quantitative and qualitative data, providing a comprehensive understanding of the research topic and enabling meaningful analysis and interpretation of the results. The choice of question types depends on the research objectives , the desired depth of information, and the data analysis requirements.

Methods of Administering Questionnaires

There are several methods for administering questionnaires, and the choice of method depends on factors such as the target population, research objectives , convenience, and resources available. Here are some common methods of administering questionnaires:

Each method has its advantages and limitations. Online surveys offer convenience and a large reach, but they may be limited to individuals with internet access. Face-to-face interviews allow for in-depth responses but can be time-consuming and costly. Telephone surveys have broad reach but may be limited by declining response rates. Researchers should choose the method that best suits their research objectives, target population, and available resources to ensure successful data collection.

How to Design a Questionnaire

Designing a good questionnaire is crucial for gathering accurate and meaningful data that aligns with your research objectives. Here are essential steps and tips to create a well-designed questionnaire:

1. Define Your Research Objectives : Clearly outline the purpose and specific information you aim to gather through the questionnaire.

2. Identify Your Target Audience : Understand respondents’ characteristics and tailor the questionnaire accordingly.

3. Develop the Questions :

• Write Clear and Concise Questions
• Avoid Leading or Biasing Questions
• Sequence Questions Logically
• Group Related Questions
• Include Demographic Questions

4. Provide Well-defined Response Options : Offer exhaustive response choices for closed-ended questions.

5. Consider Skip Logic and Branching : Customize the questionnaire based on previous answers.

6. Pilot Test the Questionnaire : Identify and address issues through a pilot study .

7. Seek Expert Feedback : Validate the questionnaire with subject matter experts.

8. Obtain Ethical Approval : Comply with ethical guidelines , obtain consent, and ensure confidentiality before administering the questionnaire.

9. Administer the Questionnaire : Choose the right mode and provide clear instructions.

10. Test the Survey Platform : Ensure compatibility and usability for online surveys.

By following these steps and paying attention to questionnaire design principles, you can create a well-structured and effective questionnaire that gathers reliable data and helps you achieve your research objectives.

Characteristics of a Good Questionnaire

A good questionnaire possesses several essential elements that contribute to its effectiveness. Furthermore, these characteristics ensure that the questionnaire is well-designed, easy to understand, and capable of providing valuable insights. Here are some key characteristics of a good questionnaire:

1. Clarity and Simplicity : Questions should be clear, concise, and unambiguous. Avoid using complex language or technical terms that may confuse respondents. Simple and straightforward questions ensure that respondents interpret them consistently.

2. Relevance and Focus : Each question should directly relate to the research objectives and contribute to answering the research questions. Consequently, avoid including extraneous or irrelevant questions that could lead to data clutter.

3. Mix of Question Types : Utilize a mix of question types, including open-ended, Likert scale, and multiple-choice questions. This variety allows for both qualitative and quantitative data collections .

4. Validity and Reliability : Ensure the questionnaire measures what it intends to measure (validity) and produces consistent results upon repeated administration (reliability). Validation should be conducted through expert review and previous research.

5. Appropriate Length : Keep the questionnaire’s length appropriate and manageable to avoid respondent fatigue or dropouts. Long questionnaires may result in incomplete or rushed responses.

6. Clear Instructions : Include clear instructions at the beginning of the questionnaire to guide respondents on how to complete it. Explain any technical terms, formats, or concepts if necessary.

7. User-Friendly Format : Design the questionnaire to be visually appealing and user-friendly. Use consistent formatting, adequate spacing, and a logical page layout.

8. Data Validation and Cleaning : Incorporate validation checks to ensure data accuracy and reliability. Consider mechanisms to detect and correct inconsistent or missing responses during data cleaning.

By incorporating these characteristics, researchers can create a questionnaire that maximizes data quality, minimizes response bias, and provides valuable insights for their research.

In the pursuit of advancing research and gaining meaningful insights, investing time and effort into designing effective questionnaires is a crucial step. A well-designed questionnaire is more than a mere set of questions; it is a masterpiece of precision and ingenuity. Each question plays a vital role in shaping the narrative of our research, guiding us through the labyrinth of data to meaningful conclusions. Indeed, a well-designed questionnaire serves as a powerful tool for unlocking valuable insights and generating robust findings that impact society positively.

Have you ever designed a research questionnaire? Reflect on your experience and share your insights with researchers globally through Enago Academy’s Open Blogging Platform . Join our diverse community of 1000K+ researchers and authors to exchange ideas, strategies, and best practices, and together, let’s shape the future of data collection and maximize the impact of questionnaires in the ever-evolving landscape of research.

Frequently Asked Questions

A research questionnaire is a structured tool used to gather data from participants in a systematic manner. It consists of a series of carefully crafted questions designed to collect specific information related to a research study.

Questionnaires play a pivotal role in both quantitative and qualitative research, enabling researchers to collect insights, opinions, attitudes, or behaviors from respondents. This aids in hypothesis testing, understanding, and informed decision-making, ensuring consistency, efficiency, and facilitating comparisons.

Questionnaires are a versatile tool employed in various research designs to gather data efficiently and comprehensively. They find extensive use in both quantitative and qualitative research methodologies, making them a fundamental component of research across disciplines. Some research designs that commonly utilize questionnaires include: a) Cross-Sectional Studies b) Longitudinal Studies c) Descriptive Research d) Correlational Studies e) Causal-Comparative Studies f) Experimental Research g) Survey Research h) Case Studies i) Exploratory Research

A survey is a comprehensive data collection method that can include various techniques like interviews and observations. A questionnaire is a specific set of structured questions within a survey designed to gather standardized responses. While a survey is a broader approach, a questionnaire is a focused tool for collecting specific data.

The choice of questionnaire type depends on the research objectives, the type of data required, and the preferences of respondents. Some common types include: • Structured Questionnaires: These questionnaires consist of predefined, closed-ended questions with fixed response options. They are easy to analyze and suitable for quantitative research. • Semi-Structured Questionnaires: These questionnaires combine closed-ended questions with open-ended ones. They offer more flexibility for respondents to provide detailed explanations. • Unstructured Questionnaires: These questionnaires contain open-ended questions only, allowing respondents to express their thoughts and opinions freely. They are commonly used in qualitative research.

Following these steps ensures effective questionnaire administration for reliable data collection: • Choose a Method: Decide on online, face-to-face, mail, or phone administration. • Online Surveys: Use platforms like SurveyMonkey • Pilot Test: Test on a small group before full deployment • Clear Instructions: Provide concise guidelines • Follow-Up: Send reminders if needed

Thank you, Riya. This is quite helpful. As discussed, response bias is one of the disadvantages in the use of questionnaires. One way to help limit this can be to use scenario based questions. These type of questions may help the respondents to be more reflective and active in the process.

Thank you, Dear Riya. This is quite helpful.

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5 Essential Types of Questionnaire in Research to Improve Your Survey

Leah Nguyen • 11 Sep 2023 • 6 min read

Questionnaires are clutch for rounding up details from people all over the place.

Even though questionnaires are everywhere, people still aren’t sure which kinds of queries to add down.

We’ll show you the types of questionnaire in research, plus how and where to use one.

Let’s get down to it👇

• #1. Open-ended questionnaire in research

#2. Rating scale questionnaire in research

#3. closed-ended questionnaire in research, #4. multiple choice questionnaire in research, #5. likert scale questionnaire in research.

• Key Takeaways 

Frequently Asked Questions

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Types of Questionnaire in Research

When making your questionnaire, you have to think about what type of information you trying to get from people.

If you want rich, exploratory details to help prove or debunk a theory, go with a qualitative survey with open-ended questions. This lets people freely explain their thoughts.

But if you already have a hypothesis and just need numbers to test it, a quantitative questionnaire is the jam. Use closed questions where folks pick answers to get measurable, quantifiable stats.

Once you’ve got it, now it’s time to choose what type of questionnaire in research you’d like to include.

#1. Open-ended question naire in research

Open-ended questions are a valuable tool in research as they allow subjects to fully express their perspectives without limitations.

The unstructured format of open-ended questions, which do not provide predefined answer choices, makes them well-suited for exploratory research early on.

This allows investigators to uncover nuanced insights and potentially identify new avenues for investigation that had not been conceived previously.

While open-ended questions generate qualitative rather than quantitative data, requiring more in-depth coding methods for analysis across large samples, their strength lies in revealing a wide range of thoughtful responses.

Commonly used as introductory questions in interviews or pilot studies to explore explanatory factors, open-ended queries are most useful when a topic needs to be understood from all angles before designing more direct closed-question surveys.

Opinion questions:

• What are your thoughts on [topic]?
• How would you describe your experience with [topic]?

Experience questions:

• Tell me about a time when [event] occurred.
• Walk me through the process of [activity].

Feeling questions:

• How did you feel about [event/situation]?
• What emotions are evoked when [stimulus] is present?

Recommendation questions:

• How could [issue] be improved?
• What suggestions do you have for [proposed solution/idea]?

Impact questions:

• In what ways has [event] affected you?
• How have your views on [topic] changed over time?

Hypothetical questions:

• How do you think you would react if [scenario]?
• What factors do you think would influence [outcome]?

Interpretation questions:

• What does [term] mean to you?
• How would you interpret the finding that [result]?

Rating scale questions are a valuable tool in research for measuring attitudes, opinions, and perceptions that exist on a continuum rather than as absolute states.

By presenting a question followed by a numbered scale for respondents to indicate their level of agreement, importance, satisfaction, or other ratings, these questions capture the intensity or direction of feelings in a structured yet nuanced way.

Common types include Likert scales involving labels like strongly disagree to strongly agree as well as visual analogue scales.

The quantitative metric data they provide can then be easily aggregated and statistically analysed to compare mean ratings, correlations, and relationships.

Rating scales are well suited for applications like market segmentation analysis, pre-testing, and post-implementation program evaluation through techniques such as A/B testing.

While their reductive nature may lack the context of open responses, rating scales still efficiently gauge sentiment dimensions for examination of predictive links between attitude facets when appropriately placed after initial descriptive inquiry.

Closed-ended questions are commonly used in research to collect structured, quantitative data through standardised answer choices.

By providing a restricted set of response options for subjects to select from, such as true/false, yes/no, rating scales or predefined multiple choice answers, closed-ended questions yield responses that can be more easily coded, aggregated, and statistically analysed across large samples compared to open-ended questions.

This makes them suitable during later validation phases after factors have already been identified, such as hypothesis testing, measuring attitudes or perceptions, subject ratings, and descriptive inquiries relying on fact-based data.

While limiting responses simplifies surveying and allows for direct comparison, it risks omitting unanticipated issues or losing context beyond the given alternatives.

Multiple choice questions are a useful tool in research when administered properly through closed questionnaires.

They present respondents with a question along with four to five pre-defined answer options from which to select.

This format allows for easy quantification of responses that can be statistically analysed across large sample groups.

While quick for participants to complete and straightforward to code and interpret, multiple-choice questions also carry some limitations.

Most notably, they risk overlooking important nuances or missing relevant options if not carefully pilot-tested beforehand.

To minimise the risk of bias, answer choices must be mutually exclusive and collectively exhaustive.

With considerations for wording and options, multiple choice questions can efficiently yield measurable descriptive data when the key possibilities are pre-identified, such as for classifying behaviours, and demographic profiles or assessing knowledge on topics where variations are known.

The Likert scale is a commonly used type of Rating scale in research to quantitatively measure attitudes, opinions, and perceptions on various topics of interest.

Utilising a symmetrical agree-disagree response format where participants indicate their level of agreement with a statement, Likert scales typically feature a 5-point design although more or fewer options are possible depending on the needed sensitivity of measurement.

By assigning numeric values to each level of the response scale, Likert data allows for statistical analysis of patterns and relationships between variables.

This yields more consistent results than simple yes/no or open-ended questions for certain types of questions aimed at gauging the intensity of sentiments on a continuum.

While Likert scales provide easily collectable metric data and are straightforward for respondents, their limitation is oversimplifying complex viewpoints, though they still offer valuable insight when properly applied in research.

A researcher wants to understand the relationship between job satisfaction (dependent variable) and factors like pay, work-life balance, and supervision quality (independent variables).

A 5-point Likert scale is used for questions like:

• I am satisfied with my pay (Strongly disagree to Strongly agree)
• My job allows for a good work-life balance (Strongly disagree to Strongly agree)
• My supervisor is supportive and a good manager (Strongly disagree to Strongly agree)

We cover all types of questionnaire in research. Get started right away with AhaSlides’ free survey templates !

General Event Feedback Survey

Team Engagement Survey

Environmental Issues Survey

Key takeaways.

These types of questionnaire in research are typically common and easy for people to fill out.

When your queries are straightforward to grasp and your options are uniform, everyone’s on the same page. Answers then compile nicely whether you got one response or a million.

The key is making sure respondents always know exactly what you’re asking, then their replies will slide right into place for the smooth assembling of sweet survey scoops.

What are the 4 types of questionnaire in research?

The four main types of questionnaires used in research are structured questionnaires, unstructured questionnaires, surveys and interviews. The appropriate type depends on the research objectives, budget, timeline and whether qualitative, quantitative or mixed methods are most suitable.

What are the 6 main types of survey questions?

The six main types of survey questions are closed-ended questions, open-ended questions, rating scale questions, ranking scale questions, demographic questions and behavioural questions.

What are the three types of questionnaires?

The three main types of questionnaires are structured questionnaires, semi-structured questionnaires and unstructured questionnaires.

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• Knowledge Base

Levels of Measurement | Nominal, Ordinal, Interval and Ratio

Published on July 16, 2020 by Pritha Bhandari . Revised on June 21, 2023.

Levels of measurement, also called scales of measurement, tell you how precisely variables are recorded. In scientific research, a variable is anything that can take on different values across your data set (e.g., height or test scores).

There are 4 levels of measurement:

• Nominal : the data can only be categorized
• Ordinal : the data can be categorized and ranked
• Interval : the data can be categorized, ranked, and evenly spaced
• Ratio : the data can be categorized, ranked, evenly spaced, and has a natural zero.

Depending on the level of measurement of the variable, what you can do to analyze your data may be limited. There is a hierarchy in the complexity and precision of the level of measurement, from low (nominal) to high (ratio).

Table of contents

Nominal, ordinal, interval, and ratio data, why are levels of measurement important, which descriptive statistics can i apply on my data, quiz: nominal, ordinal, interval, or ratio, other interesting articles, frequently asked questions about levels of measurement.

Going from lowest to highest, the 4 levels of measurement are cumulative. This means that they each take on the properties of lower levels and add new properties.

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The level at which you measure a variable determines how you can analyze your data.

The different levels limit which descriptive statistics you can use to get an overall summary of your data, and which type of inferential statistics you can perform on your data to support or refute your hypothesis .

In many cases, your variables can be measured at different levels, so you have to choose the level of measurement you will use before data collection begins.

• Ordinal level: You create brackets of income ranges: $0–$19,999, $20,000–$39,999, and $40,000–$59,999. You ask participants to select the bracket that represents their annual income. The brackets are coded with numbers from 1–3.
• Ratio level: You collect data on the exact annual incomes of your participants.

At a ratio level, you can see that the difference between A and B’s incomes is far greater than the difference between B and C’s incomes.

Descriptive statistics help you get an idea of the “middle” and “spread” of your data through measures of central tendency and variability .

When measuring the central tendency or variability of your data set, your level of measurement decides which methods you can use based on the mathematical operations that are appropriate for each level.

The methods you can apply are cumulative; at higher levels, you can apply all mathematical operations and measures used at lower levels.

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

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval

Methodology

• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Likert scale

Research bias

• Implicit bias
• Framing effect
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic

Levels of measurement tell you how precisely variables are recorded. There are 4 levels of measurement, which can be ranked from low to high:

• Nominal : the data can only be categorized.
• Ordinal : the data can be categorized and ranked.
• Interval : the data can be categorized and ranked, and evenly spaced.
• Ratio : the data can be categorized, ranked, evenly spaced and has a natural zero.

Depending on the level of measurement , you can perform different descriptive statistics to get an overall summary of your data and inferential statistics to see if your results support or refute your hypothesis .

Some variables have fixed levels. For example, gender and ethnicity are always nominal level data because they cannot be ranked.

However, for other variables, you can choose the level of measurement . For example, income is a variable that can be recorded on an ordinal or a ratio scale:

• At an ordinal level , you could create 5 income groupings and code the incomes that fall within them from 1–5.
• At a ratio level , you would record exact numbers for income.

If you have a choice, the ratio level is always preferable because you can analyze data in more ways. The higher the level of measurement, the more precise your data is.

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Bhandari, P. (2023, June 21). Levels of Measurement | Nominal, Ordinal, Interval and Ratio. Scribbr. Retrieved April 2, 2024, from https://www.scribbr.com/statistics/levels-of-measurement/

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Practical Guidelines to Develop and Evaluate a Questionnaire

Kamal kishore.

Department of Biostatistics, Post Graduate Institute of Medical Education and Research (PGIMER), Chandigarh, India

Vidushi Jaswal

1 Department of Psychology, MCM DAV College for Women, Chandigarh, India

Vinay Kulkarni

2 Department of Dermatology, PRAYAS Health Group, Amrita Clinic, Karve Road, Pune, Maharashtra, India

Dipankar De

3 Department of Dermatology, Post Graduate Institute of Medical Education and Research (PGIMER), Chandigarh, India

Life expectancy is gradually increasing due to continuously improving medical and nonmedical interventions. The increasing life expectancy is desirable but brings in issues such as impairment of quality of life, disease perception, cognitive health, and mental health. Thus, questionnaire building and data collection through the questionnaires have become an active area of research. However, questionnaire development can be challenging and suboptimal in the absence of careful planning and user-friendly literature guide. Keeping in mind the intricacies of constructing a questionnaire, researchers need to carefully plan, document, and follow systematic steps to build a reliable and valid questionnaire. Additionally, questionnaire development is technical, jargon-filled, and is not a part of most of the graduate and postgraduate training. Therefore, this article is an attempt to initiate an understanding of the complexities of the questionnaire fundamentals, technical challenges, and sequential flow of steps to build a reliable and valid questionnaire.

Introduction

There is an increase in the usage of the questionnaires to understand and measure patients' perception of medical and nonmedical care. Recently, with increased interest in quality of life associated with chronic diseases, there is a surge in the usage and types of questionnaires. The questionnaires are also known as scales and instruments. Their significant advantage is that they capture information about unobservable characteristics such as attitude, belief, intention, or behavior. The multiple items measuring specific domains of interest are required to obtain hidden (latent) information from participants. However, the importance of questions or items needs to be validated and evaluated individually and holistically.

The item formulation is an integral part of the scale construction. The literature consists of many approaches, such as Thurstone, Rasch, Gutmann, or Likert methods for framing an item. The Thurstone scale is labor intensive, time-consuming, and is practically not better than the Likert scale.[ 1 ] In the Guttman method, cumulative attributes of the respondents are measured with a group of items framed from the “easiest” to the “most difficult.” For example, for a stem, a participant may have to choose from options (a) stand, (b) walk, (c) jog, and (d) run. It requires a strict ordering of items. The Rasch method adds the stochastic component to the Guttman method which lay the foundation of modern and powerful technique item response theory for scale construction. All the approaches have their fair share of advantages and disadvantages. However, Likert scales based on classical testing theory are widely established and preferred by researchers to capture intrinsic characteristics. Therefore, in this article, we will discuss only psychometric properties required to build a Likert scale.

A hallmark of scientific research is that it needs to meet rigorous scientific standards. A questionnaire evaluates characteristics whose value can significantly change with time, place, and person. The error variance, along with systematic variation, plays a significant part in ascertaining unobservable characteristics. Therefore, it is critical to evaluate the instruments testing human traits rigorously. Such evaluations are known as psychometric evaluations in context to questionnaire development and validation. The scientific standards are available to select items, subscales, and entire scales. The researchers can broadly segment scientific criteria for a questionnaire into reliability and validity.

Despite increasing usage, many academicians grossly misunderstand the scales. The other complication is that many authors in the past did not adhere to the rigorous standards. Thus, the questionnaire-based research was criticized by many in the past for being a soft science.[ 2 ] The scale construction is also not a part of most of the graduate and postgraduate training. Given the previous discussion, the primary objective of this article is to sensitize researchers about the various intricacies and importance of each step for scale construction. The emphasis is also to make researcher aware and motivate to use multiple metrics to assess psychometric properties. Table 1 describes a glossary of essential terminologies used in context to questionnaire.

Glossary of important terms used in context to psychometric scale

The process of building a questionnaire starts with item generation, followed by questionnaire development, and concludes with rigorous scientific evaluation. Figure 1 summarizes the systematic steps and respective tasks at each stage to build a good questionnaire. There are specific essential requirements which are not directly a part of scale development and evaluation; however, these improve the utility of the instrument. The indirect but necessary conditions are documented and discussed under the miscellaneous category. We broadly segment and discuss the questionnaire development process under three domains, known as questionnaire development, questionnaire evaluation, and miscellaneous properties.

Flowchart demonstrating the various steps involved in the development of a questionnaire

Questionnaire Development

The development of the list of items is an essential and mandatory prerequisite for developing a good questionnaire. The researcher at this stage decides to utilize formats such as Guttman, Rasch, or Likert to frame items.[ 2 ] Further, the researcher carefully identifies the appropriate member of the expert panel group for face and content validity. Broadly, there are six steps in the scale development.

It is crucial to select appropriate questions (items) to capture the latent trait. An exhaustive list of items is the most critical and primary requisite to lay the foundation of a good questionnaire. It needs considerable work in terms of literature search, qualitative study, discussion with colleagues, other experts, general and targeted responders, and other questionnaires in and around the area of interest. General and targeted participants can also advise on items, wording, and smoothness of questionnaire as they will be the potential responders.

It is crucial to arrange and reword the pool of questions for eliminating ambiguity, technical jargon, and loading. Further, one should avoid using double-barreled, long, and negatively worded questions. Arrange all items systematically to form a preliminary draft of the questionnaire. After generating an initial draft, review the instrument for the flow of items, face validity and content validity before sending it to experts. The researcher needs to assess whether the items in the score are comprehensive (content validity) and appear to measure what it is supposed to measure (face validity). For example, does the scale measuring stress is measuring stress or is it measuring depression instead? There is no uniformity on the selection of a panel of experts. However, a general agreement is to use anywhere from a minimum of 5–15 experts in a group.[ 3 ] These experts will ascertain the face and content validity of the questionnaire. These are subjective and objective measures of validity, respectively.

It is advisable to prepare an appealing, jargon-free, and nontechnical cover letter explaining the purpose and description of the instrument. Further, it is better to include the reason/s for selecting the expert, scoring format, and explanations of response categories for the scale. It is advantageous to speak with experts telephonically, face to face, or electronically, requesting their participation before mailing the questionnaire. It is good to explain to them right in the beginning that this process unfolds over phases. The time allowed to respond can vary from hours to weeks. It is recommended to give at least 7 days to respond. However, a nonresponse needs to be followed up by a reminder email or call. Usually, this stage takes two to three rounds. Therefore, it is essential to engage with experts regularly; else there is a risk of nonresponse from the study. Table 2 gives general advice to researchers for making a cover letter. The researcher can modify the cover letter appropriately for their studies. The authors can consult Rubio and coauthors for more details regarding the drafting of a cover letter.[ 4 ]

General overview and the instructions for rating in the cover letter to be accompanied by the questionnaire

The responses from each round will help in rewording, rephrasing, and reordering of the items in the scale. Few questions may need deletion in the different rounds of previous steps. Therefore, it is better to evaluate content validity ratio (CVR), content validity index (CVI), and interrater agreement before deleting any question in the instrument. Readers can consult formulae in Table 2 for calculating CVR and CVI for the instrument. CVR is calculated and reported for the overall scale, whereas CVI is computed for each item. Researchers need to consult Lawshe table to determine the cutoff value for CVR as the same depends on the number of experts in the panel.[ 5 ] CVI >0.80 is recommended. Researchers interested in detail regarding CVR and CVI can read excellent articles written by Zamanzadeh et al . and Rubio et al .[ 4 , 6 ] It is crucial to compute CVR, CVI, and kappa agreement for each item from the rating of importance, representativeness, and clarity by experts. The CVR and CVI do not account for a chance factor. Since interrater agreement (IRA) incorporates chance factor; it is better to report CVR, CVI, and IRA measures.

The scholars require to address subtle issues before administering a questionnaire to responders for pilot testing. The introduction and format of the scale play a crucial role in mitigating doubts and maximizing response. The front page of the questionnaire provides an overview of the research without using technical words. Further, it includes roles and responsibilities of the participants, contact details of researchers, list of research ethics (such as voluntary participation, confidentiality and withdrawal, risks and benefits), and informed consent for participation in the study. It is also better to incorporate anchors (levels of Likert item) in each page at the top or bottom or both for ease and maximizing response. Readers can refer to Table 3 for detail.

A random set of questions with anchors at the top and bottom row

Pilot testing of an instrument in the target population is an important and essential requirement before testing on a large sample of individuals. It helps in the elimination or revision of poorly worded items. At this stage, it is better to use floor and ceiling effects to eliminate poorly discriminating items. Further, random interviews of 5–10 participants can help to mitigate the problems such as difficulty, relevance, confusion, and order of the questions before testing it on the study population. The general recommendations are to recruit a sample size between 30 and 100 for pilot testing.[ 4 ] Inter-question (item) correlation (IQC) and Cronbach's α can be assessed at this stage. The values less than 0.3 and 0.7, respectively, for IQC and reliability, are suspicious and candidate for elimination from the questionnaire. Cronbach's α, a measure of internal consistency and IQC of a scale, indicates researcher about the quality of items in measuring latent attribute at the initial stage. This process is important to refine and finalize the questionnaire before starting the testing of a questionnaire in study participants.

Questionnaire Evaluation

The preliminary items and the questionnaire until this stage have addressed issues of reliability, validity, and overall appeal in the target population. However, researchers need to rigorously evaluate the psychometric properties of the primary instrument before finally adopting. The first step in this process is to calculate the appropriate sample size for administering a preliminary questionnaire in the target group. The evaluations of various measures do not follow a sequential order like the previous stage. Nevertheless, these measures are critical to evaluate the reliability and validity of the questionnaire.

Correct data entry is the first requirement to evaluate the characteristics of a manually administered questionnaire. The primary need is to enter the data into an appropriate spreadsheet. Subsequently, clean the data for cosmetic and logical errors. Finally, prepare a master sheet, and data dictionary for analysis and reference to coding, respectively. Authors interested in more detail can read “Biostatistics Series.”[ 7 , 8 ] The data entry process of the questionnaire is like other cross-sectional study designs. The rows and columns represent participants and variables, respectively. It is better to enter the set of items with item numbers. First, it is tedious and time-consuming to find suitable variable names for many questions. Second, item numbers help in quick identification of significantly contributing and non-contributing items of the scale during the assessment of psychometric properties. Readers can see Table 4 for more detail.

A sample of data entry format

Descriptive statistics

Spreadsheets are easy and flexible for routine data entry and cleaning. However, the same lack the features of advanced statistical analysis. Therefore, the master sheet needs to be exported to appropriate software for advanced statistical analysis. Descriptive analysis is the usual first step which helps in understanding the fundamental characteristics of the data. Thus, report appropriate descriptive measures such as mean and standard deviation, and median and interquartile/interdecile range for continuous symmetric and asymmetric data, respectively.[ 9 ] Utilize exploratory tabular and graphical display to inspect the distribution of various items in the questionnaire. A stacked bar chart is a handy tool to investigate the distribution of data graphically. Further, ascertain linearity and lack of extreme multicollinearity at this stage. Any value of IQC >0.7 warrants further inspection for deletion or modification. Help from a good biostatistician is of great assistance for data analysis and reporting.

Missing data analysis

Missing data is the rule, not the exception. Majority of the researchers face difficulties of finding missing values in the data. There are usually three approaches to analyze incomplete data. The first approach is to “take all” which use all the available data for analysis. In the second method, the analyst deletes the participants and variables with gross missingness or both from the analysis process. The third scenario consists of estimating the percentage and type of missingness. The typically recommended threshold for the missingness is 5%.[ 10 ] There are broadly three types of missingness, such as missing completely at random, missing at random, and not missing at random. After identification of a missing mechanism, impute the data with single or multiple imputation approaches. Readers can refer to an excellent article written by Graham for more details about missing data.[ 11 ]

Sample size

The optimum sample size is a vital requisite to build a good questionnaire. There are many guidelines in the literature regarding recruiting an appropriate sample size. Literature broadly segments sample size approaches into three domains known as subject to variables ratio (SVR), minimum sample size, and factor loadings (FL). The factor analysis (FA) is a crucial component of questionnaire designing. Therefore, recent recommendations are to use FLs to determine sample size. Readers can consult Table 5 for sample size recommendations under various domains. Interested readers can refer to Beavers and colleagues for more detail.[ 12 ] The stability of the factors is essential to determine sample size. Therefore, data analysis from questionnaires validates the sample size after data collection. The Kaiser–Meyer–Olkin (KMO) criterion testing the adequacy of sample size is available in the majority of the statistical software packages. A higher value of KMO is an indicator of sufficient sample size for stable factor solution.

Sample size recommendations in the literature

SVR→Subject to variable ratio, FL→Factor loading

Correlation measures

The strength of relationships between the items is an imperative requisite for a stable factor solution. Therefore, the correlation matrix is calculated and ascertained for same. There are various recommendations of correlation coefficient; however, a value greater than 0.3 is a must.[ 13 ] A lower value of the correlation coefficient will fail to form a stable factor due to lack of commonality. The determinant and Bartlett's test of sphericity can be used to ascertain the stability of the factors. The determinant is a single value which ranges from zero to one. A nonzero determinant indicates that factors are possible. However, it is small in most of the studies and not easy to interpret. Therefore, Bartlett's test of sphericity is routinely used to infer that determinant is significantly different than zero.

Physical quantities such as height and weight are observable and measurable with instruments. However, many tools need regular calibration to be precise and accurate. The standardization in context to the questionnaire development is known as reliability and validity. The validity is the property which indicates that an instrument is measuring what it is supposed to measure. Validation is a continuous process which begins with the identification of domains and goes on till generalization. There are various measures to establish the validity of the instrument. Authors can consult Table 6 for different types of validity and their metrics.

Scientific standards to evaluate and report for constructing a good scale

MCAR: Missing completely at random; MAR: Missing at random; NMAR: Not missing at random; KMO: Kaiser-Meyer-Olkin; SD: Standard deviation; IQR: Interquartile range

Exploratory FA

FA assumes that there are underlying constructs (factors) which cannot be measured directly. Therefore, the investigator collects the exhaustive list of observed variables or responses representing underlying constructs. Researchers expect that variables or questions in the questionnaire correlate among themselves and load on the corresponding but a small number of factors. FA can be broadly segmented in exploratory factor analysis (EFA) and confirmatory factor analysis. The EFA is applied on the master sheet after assessing descriptive statistics such as tabular and graphical display, missing mechanism, sample size adequacy, IQC, and Bartlett's test in step 7 [ Figure 1 ]. The value of EFA is used at the initial stages to extract factors while constructing a questionnaire. It is especially important to identify an adequate number of factors for building a decent scale. The factors represent latent variables that explain variance in the observed data. First and the last factor explain maximum and minimum variance, respectively. There are multiple factor selection criteria, each with its advantages and disadvantages. It is better to utilize more than one approach for retaining factors during the initial extraction phase. Readers can consult Sindhuja et al . for the practical application of more than one-factor selection criteria.[ 14 ]

Kaiser's criterion

Kaiser's criterion is one of the most popular factor retention criteria. The basis of the Kaiser criterion is to explain the variance through the eigenvalue approach. A factor with more than one eigenvalue is the candidate for retention.[ 15 ] An eigenvalue bigger than one simply means that a single factor is explaining variance for more than one observed variable. However, there is a dearth of scientifically rigorous studies to declare a cutoff value for Kaiser's criterion. Many authors highlighted that the Kaiser criterion over-extract and under-extract factors.[ 16 , 17 ] Therefore, investigators need to calculate and consider other measures for extraction of factors.

Cattell's scree plot

Cattell's scree plot is another widespread eigenvalue-based factor selection criterion used by researchers. It is popularly known as scree plot. The scree plot assigns the eigenvalues on the y -axis against the number of factors in the x -axis. The factors with highest to lowest eigenvalues are plotted from left to right on the x -axis. Usually, the scree plots form an elbow which indicates the cutoff point for factor extraction. The location or the bend at which the curve first begins to straighten out indicates the maximum number of factors to retain. A significant disadvantage of the scree plot is the subjectivity of the researcher's perception of the “elbow” in the plot. Researchers can see Figure 2 for detail.

A hypothetical example showing the researcher's dilemma of selecting 6, 10, or 15 factors through scree plot

Percentage of variance

The variance extraction criterion is another criterion to retain the number of factors. The literature recommendation varies from more than a minimum of 50–70% onward.[ 12 ] However, both the number of items and factors will increase dramatically if there are a large number of manifest (observed) variables. Practically, the percentage of variance explained mechanism should be used judiciously along with FL. The FLs with greater than 0.4 value are preferred; however, there are recommendations to use a value higher than 0.30.[ 3 , 15 , 18 ]

Very simple structure

Very simple structure (VSS) approach is a symbiosis of theory, psychometrics, and statistical analysis. The VSS criterion compares the fit of the simplified model to the original correlations. It plots the goodness-of-fit value as a function of several factors rather than statistical significance. The number of factors that maximizes the VSS criterion suggests the optimal number of factors to extract. VSS criterion facilitates comparison of a different number of factors for varying complexity. VSS will be highest at the optimum number of factors.[ 19 ] However, it is not efficient for factorially complex data.

Parallel analysis

Parallel analysis (PA) is a statistical theory-based robust technique to identify the appropriate number of factors. It is the only technique which accounts for the probability that a factor is due to chance. PA simulates data to generate 95 th percentile cutoff line on a scree plot restricted upon the number of items and sample size in original data. The factors above the cutoff line are not due to chance. PA is the most robust empirical technique to retain the appropriate number of factors.[ 16 , 20 ] However, it should be used cautiously for the eigenvalue near the 95 th percentile cutoff line. PA is also robust to distributional assumptions of the data. Since different techniques have their fair share of advantages and disadvantages, researchers need to assess information on the basis of multiple criteria.

Reliability

Reliability, an essential requisite of a scale, is also known as reproducibility, repeatability, and consistency. It identifies that the instrument is consistently measuring the attribute under identical conditions. Reliability is a necessary characteristic of a tool. The trustworthiness of a scale can be increased by increasing and decreasing the systematic and random component, respectively. The reliability of an instrument can be further segmented and measured with various indices. Reliability is important but it is secondary to validity. Therefore, it is ideal to calculate and report reliability after validity. However, there are no hard and fast rules except that both are necessary and important measures. Readers may consult Table 6 for multiple types of indices for reliability.

Internal consistency

Cronbach's alpha (α), also known as α-coefficient, is one of the most used statistics to report internal consistency reliability. The internal consistency using the interitem correlations suggests the cohesiveness of items in a questionnaire. However, the α-coefficient is sample-specific; thus, the literature recommends the same to calculate and report for all the studies. Ideally, a value of α >0.70 is preferred; however, the value of α >0.60 is also accepted for construction of new scale.[ 21 , 22 ] Researchers can increase the α-coefficient by adding items in the scale. However, a value can either reduce with the addition of non-correlated items or deletion of correlated items. Corrected interitem correlation is another popular measure to report for internal consistency. A value of α <0.3 indicates the presence of nonrelated items. The studies claim that coefficient beta (β) and omega (Ω) are better indices than coefficient-α, but there is a scarcity of literature reporting these indices.[ 23 ]

Test–retest

Test–retest reliability measures the stability of an instrument over time. In other words, it measures the consistency of scores over time. However, the appropriate time between repeated measures is a debatable issue. Pearson's product-moment and intraclass correlation coefficient measure and report test–retest reliability. A high value of correlation >0.70 represents high reliability.[ 21 ] The change in study condition (recovery of patients after intervention) over time can decrease test–retest reliability. Therefore, it is important to report the time between repeated measures while reporting test–retest reliability.

Parallel forms and split-half reliability

Parallel form reliability is also known as an alternate form of consistency. There are two types of option to report parallel form reliability. In the first method, different but similar items make alternative forms of the test. The assumptions of both the assessment are that they measure the same phenomenon or underlying construct. It addresses the twin issues of time and knowledge acquisition of test in test–retest reliability. In the second approach, the researcher randomly divides the total items of an instrument into two halves. The calculation of parallel form from two halves is known as split-half reliability. However, randomly divided half may not be similar. The parallel from and split-half reliability are reported with the correlation coefficient. The recommendations are to use a value higher than 0.80 to assess the alternate form of consistency.[ 24 ] It is challenging to generate two types of tests in clinical studies. Therefore, researchers rarely report reliability from two analogous but separate tests.

General Questionnaire Properties

The major issues regarding the reliability and validity of scale development have already been discussed. However, there are many other subtle issues for developing a good questionnaire. These delicate issues may vary from a choice of Likert items, length of the instrument, cover letter, web or internet mode of data collection, and weighting of scale. The immediately preceding issues demand careful deliberation and attention from the researcher. Therefore, the researcher should carefully think through all these issues to build a good questionnaire.

Likert items

The Likert items are the fixed choice ordinal items which capture attitude, belief, and various other latent domains. The subsequent step is to rank the questions of the Likert scale for further analysis. The numerals for ranking can either start from 0 or 1. It does not make a difference. The Likert scale is primarily bipolar as opposite ends endorse the contrary idea.[ 2 ] These are the type of items which express opinions on a measure from strong disagreement to strong agreement. The adjectival scales are unipolar scale that tends to measure variables like pain intensity (no pain/mild pain/moderate pain/severe pain) in one direction. However, the Likert scale (most likely–least likely) can measure almost any attribute. The Likert scale can either have odd or even categories; however, odd categories are more popular. The number of classifications in the Likert scale can vary from anywhere between 3 and 11,[ 2 ] although the scale with 5 and 7 classes have displayed better statistical properties for discriminating between responses.[ 2 , 24 ]

Length of questionnaire

A good questionnaire needs to include many items to capture the construct of interest. Therefore, investigators need to collect as many questions as possible. However, the lengthier scale increases both time and cost. The response rate also decreases with an increase in the length of the questionnaire.[ 25 ] Although what is lengthy is debatable and varies from more than 4 pages to 12 pages in various studies,[ 26 ] the longer scales increase the false positivity rate.[ 27 ]

Translating a questionnaire

Many a time, there are already existing reliable and valid questionnaires for use. However, the expert needs to assess two immediate and important criteria of cultural sensitivity and language of the scale. Many sensitive questions on sexual preferences, political orientations, societal structure, and religion may be open for discussion in certain societies, religions, and cultures, whereas the same may be taboo or receive misreporting in others. The sensitive questions need to be reframed considering regional sentiments and culture in mind. Further, a questionnaire in different language needs to be translated by a minimum of two independent bilingual translators. Similarly, the translated questionnaire needs to be translated back into the original language by a minimum of two independent and different bilingual experts who converted the original questionnaire. The process of converting the original questionnaire to the targeted language and then back to the original language is known as forward and backward translation. The subsequent steps such as expert panel group, pilot testing, reliability, and validity for translating a questionnaire remain the same as in constructing a new scale.

Web-based or paper-based

Broadly, paper and electronic format are the two modes of administering a questionnaire to the participants. Both techniques have advantages and disadvantages. The response rate is a significant issue in self-administered scales. The significant benefits of electronic format are the reduction in cost, time, and data cleaning requirements. In contrast, paper-based administration of questionnaire increases external generalization, paper feel, and no need of internet. As per Greenlaw and Welty, the response rate improves with the availability of both the options to participants. However, cost and time increase in comparison to the usage of electronic format alone.[ 27 ]

Item order and weights

There are multiple ways to order an item in a questionnaire. The order of questions becomes more critical for a lengthy questionnaire. There are different opinions about either grouping or mixing the issues in an instrument.[ 24 ] Grouping inflates intra-scale correlation, whereas mixing inflates inter-scale correlation.[ 28 ] Both the approaches have empirically shown to give similar results for at least 20 or more items. The questions related to a particular domain can be assigned either equal or unequal weights. There are two mechanisms to assign unequal weights in a questionnaire. In the first situation, researchers affix different importance to items. In the second method, the investigators frame more or fewer questions as per the importance of subscales in the scale.

The fundamental triad of science is accuracy, precision, and objectivity. The increasing usage of questionnaires in medical sciences requires rigorous scientific evaluations before finally adopting it for routine use. There are no standard guidelines for questionnaire development, evaluation, and reporting in contrast to guidelines such as CONSORT, PRISMA, and STROBE for treatment development, evaluation, and reporting. In this article, we emphasize on the systematic and structured approach for building a good questionnaire. Failure to meet the questionnaire development standards may lead to biased, unreliable, and inaccurate study finding. Therefore, the general guidelines given in this article can be used to develop and validate an instrument before routine use.

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Columbia-Suicide Severity Rating Scale (C-SSRS)

The Columbia-Suicide Severity Rating Scale (C-SSRS) is a unique suicide risk assessment tool that supports suicide risk assessment through a series of simple, plain-language questions that anyone can ask. The answers help users identify whether someone is at risk for suicide, assess the severity and immediacy of that risk, and gauge the level of support that the person needs. Users of the C-SSRS tool ask people:

• Whether and when they have thought about suicide (ideation)
• What actions they have taken—and when—to prepare for suicide
• Whether and when they attempted suicide or began a suicide attempt that was either interrupted by another person or stopped of their own volition

Suicide Prevention Benefits

The first step in effective suicide prevention is to identify everyone who needs help. The C-SSRS was the first scale to address the full range of suicidal thoughts and behaviors that point to heightened risk. That means it identifies risk not only if someone has previously attempted suicide, but also if he or she has considered suicide, prepared for an attempt (for example, buying a gun, collecting pills, or writing a suicide note), or aborted plans for suicide because of a last-minute change of heart or a friend’s intervention.

The C-SSRS screens for this wide range of risk factors without becoming unwieldy or overwhelming, because it includes the most essential, evidence-supported questions required for a thorough assessment. The C-SSRS is:

• Simple - Ask all the questions in a few moments or minutes—with no mental health training required to ask them.
• Efficient - Use of the scale redirects resources to where they’re needed most. It reduces unnecessary referrals and interventions by more accurately identifying who needs help—and it makes it easier to correctly identify the level of support a person needs, such as patient safety monitoring procedures, counseling, or emergency room care.
• Effective - Real-world experience and data show the scale has helped prevent suicide.
• Evidence-supported - An unprecedented amount of research has validated the relevance and effectiveness of the questions used in the C-SSRS to assess suicide risk, making it the most evidence-based tool of its kind.
• Universal - The C-SSRS is suitable for all ages and special populations in different settings and is available in more than 100 country-specific languages.
• Free - The scale and the training on how to use it are available free of charge for use in community and healthcare settings, as well as in federally funded or nonprofit research.

The Columbia Lighthouse Project

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ORIGINAL RESEARCH article

This article is part of the research topic.

Orthorexia Nervosa: New Insights into Clinical Management and Social Environment Aspects

Orthorexic Tendency and Its Association with Weight Control Methods and Dietary Variety in Polish Adults: A Cross-Sectional Study Provisionally Accepted

• 1 Department of Food Market and Consumer Research, Institute of Human Nutrition Sciences, Warsaw University of Life Sciences, Poland
• 2 Department of Human Nutrition, Faculty of Food Science, University of Warmia and Mazury in Olsztyn, Poland

The final, formatted version of the article will be published soon.

The methods for controlling weight play a central role in formally diagnosed eating disorders (EDs) and appear to be important in the context of other nonformally recognized disorders, such as orthorexia nervosa (ON). These methods also have an impact on eating behaviors, including dietary variety. Our study aimed to: (i) assess the intensity of ON tendency by sex and BMI groups, (ii) evaluate the associations between ON tendency, weight control methods, and dietary variety, and (iii) determine the extent to which weight control methods and dietary variety contribute to the ON tendency among both females and males. Data were gathered from a sample of 936 Polish adults (463 females and 473 males) through a cross-sectional quantitative study conducted in 2019. Participants were requested to complete the ORTO-6, the Weight Control Methods Scale, and the Food Intake Variety Questionnaire (FIVeQ). Multiple linear regression analysis was employed to evaluate associations between ON tendency, weight control methods, and dietary variety. Females exhibited a higher ON tendency than males (14.4 ± 3.4 vs. 13.5 ± 3.7, p < 0.001, d = 0.25). In the regression model, the higher ON tendency was predicted by more frequent use of weight control methods, such as restricting the amount of food consumed, using laxatives, and physical exercise among both females and males as well as following a starvation diet in females, and drinking teas to aid bowel movements among males. Moreover, the higher ON tendency was predicted by higher dietary variety, lower age in both sexes, and higher level of education among males. However, there were no differences in ON tendency across BMI groups. In conclusion, the findings showed that ON tendency was predicted by a higher frequency of weight control methods commonly used by individuals with anorexia nervosa (AN) and bulimia nervosa (BN). The resemblance to these two EDs is also suggested by the higher intensity of ON tendency among females and younger people. However, the prediction of ON tendency by dietary variety indicates that the obsessive preoccupation with healthy eating may not be advanced enough to observe a decrease in the dietary variety among these individuals.

Keywords: orthorexic tendency, Weight control methods, dietary variety, adults, Poland

Received: 14 Dec 2023; Accepted: 02 Apr 2024.

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

* Correspondence: Dr. Marta Plichta, Department of Food Market and Consumer Research, Institute of Human Nutrition Sciences, Warsaw University of Life Sciences, Warsaw, Poland

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Drought prediction and water availability: A report on the 2022 ​​USGS-NIDIS National Listening Session Series

The U.S. Geological Survey (USGS) and NOAA’s National Integrated Drought Information System (NIDIS) conducted a series of four Listening Sessions in 2022 – each with a different application or topical focus – to seek input on priorities and needs related to predicting water availability changes under drought conditions at national and regional scales. This input was gathered to help inform the USGS Drought Program, regional and national drought efforts at NIDIS, and other national drought efforts. The series started with a February 2022 kick-off that introduced the series of Listening Sessions being held from March through September 2022. This kickoff also provided an overview of the USGS Drought Program’s work to characterize hydrological (e.g., streamflow and groundwater) drought, drought variability, drivers, and trends over the past century. Participants in these Listening Sessions included diverse stakeholder representation and perspectives.

The first of the four Listening Sessions focused on streamflow (March 3, 2022), and included a short introduction to the USGS national streamflow drought research, the properties of a national drought prediction system, as well as presentations by other agencies on different drought prediction and forecasting efforts. The second session focused on groundwater (May 5, 2022), and included presentations on groundwater drought, sustainable groundwater management, and improving our understanding of soil moisture, groundwater, and surface water drought. The third session focused on water use (July 14, 2022), and included a discussion of the different drought types, as well as an introduction to several key projects, including the USGS Upper Colorado River Basin Study, the Ogallala Data Directory project, and a multi-agency drought prediction partnership in Oklahoma. The fourth and final Listening Session focused on water availability prediction for ecosystems (September 8, 2022), and included presentations on the development of a national capacity for eco-hydrological and drought science, building climate resilience, and actionable ecodrought resources.

Citation Information

Related content, erik smith, ph.d., hydrologist, water-use data and research program coordinator, john c. hammond, phd.