In surveys, alongside closed-ended response options, there is often the “Other, please specify” field, designed to give participants the opportunity to add a personal choice. These open-ended responses offer valuable insights but can quickly become a messy collection of heterogeneous data that’s difficult to manage.
The critical issue is that, without proper coding, these contributions cannot be included in quantitative analysis: they remain isolated notes with no statistical weight.
Ignoring them means assigning partial meaning to the closed responses alone. The result is an incomplete analysis that considers only part of the data and risks producing a picture that does not truly represent the entire sample.

In this article, we’ll explore how IdSurvey’s AI, integrated with ChatGPT, offers a solution that brings order to the chaos. We’ll see how AI automates the categorization of “Other, please specify” responses, detects useful patterns, and transforms even marginal contributions into structured quantitative data—delivering practical benefits in speed, accuracy, and insight for researchers.

The challenges of “Other, please specify”: variety, time, and inconsistency

Open-ended responses from the “Other, please specify” field are, by nature, free-form: each participant can write whatever they want, resulting in a wide variety of answers. Let’s take a simple example.

In a survey question with these response options:

– Size/fit

– Defective product

– Does not meet expectations

– Other, please specify

Some respondents might type “color different from photo” or “fabric too thin,” which can be categorized under “Does not meet expectations.” Others might mention unexpected issues like “arrived late, don’t need it anymore” or “damaged box, unusable product.”
In these cases, AI can automatically assign the first set of answers to an existing category and propose new categories—like “Delivery issues” or “Damaged packaging”—for the uncategorized ones.
This diversity makes coding complex: it’s necessary to decide whether and how each response should fit into an existing category or if new ones need to be created.

Manually coding hundreds of these responses is a notoriously time-consuming and labor-intensive process. As a result, many researchers limit the use of open-ended questions to avoid the analytical burden.
It involves carefully reading each response, interpreting it, and assigning an appropriate code or category. This requires time and attention. With large data volumes, the risk of inconsistency increases: different analysts may apply slightly different criteria, or even the same person may not maintain consistent interpretation throughout. Manual coding is subjective and may introduce bias or inconsistency into the data.

IdSurvey’s AI: from the chaos of open-ended answers to consistent categories

Faced with these challenges, the AI integrated into IdSurvey marks a real step forward. Thanks to the integration of OpenAI’s language model, IdSurvey can automatically analyze and categorize open-ended responses, bringing structure to the disorder.
In practice, what once took hours of manual work can now be done by AI in just moments—with a level of consistency that’s hard to achieve manually.

The AI reads each “Other, please specify” response and understands its meaning through natural language processing. As a result, it systematically assigns each response to the appropriate category.
If the answer matches an existing option, AI will map it accordingly. If new topics or issues emerge that the original options didn’t cover, AI detects recurring patterns and suggests creating new categories.

This smart approach enables accurate and consistent coding at scale. Every open-ended response is treated with the same criteria, eliminating discrepancies caused by human interpretation.
The result is a solid categorization where similar information is grouped uniformly. What’s more, IdSurvey’s AI operates at unmatched speed: a traditionally long and complex process becomes fast and efficient.
This not only drastically reduces the time and resources needed for coding—it also frees research teams from repetitive tasks, allowing them to focus on high-level analysis.

Importantly, AI does not replace the researcher—it supports them. It generates coding suggestions and new categories, giving the researcher full control to accept or refine them based on their expertise and analytical goals.
This ensures the speed and consistency of automation is combined with the researcher’s critical judgment and oversight.

Practical benefits: speed, accuracy, and richer insights

Automating the coding of “Other, please specify” responses with AI brings several concrete benefits for researchers:

• Turning qualitative into quantitative
  AI allows you to transform open-text answers into structured data ready for quantitative analysis. Once “Other, please specify” responses are coded, even those that were unique or marginal can be integrated into the dataset as part of analyzable categories.
  This means participants’ verbatim comments become numbers, percentages, and charts.
  The researcher can see how many people mentioned a certain theme in open-ended feedback—just like with closed options.
  In short, AI creates an automatic coding scheme and returns the open-ended responses already categorized “as if they were quantitative data,” ready to be used in reports and statistical summaries.
  This process breaks down the barrier between qualitative and quantitative data, making it easy to include the richness of open text in structured analysis.
• Speed and efficiency
  The time savings are remarkable. AI can process thousands of responses in a fraction of the time required by a human team.
  This means that survey results can be delivered much faster, allowing teams to respond and make decisions quickly.
  Tasks that used to take days of manual coding can now be completed in minutes, with significant savings in staff time, outsourcing costs, and resources.
• Accuracy and consistency
  AI ensures consistent and accurate coding across the entire dataset.
  Unlike manual work—which can suffer from errors or inconsistent interpretations—AI applies the same logic to every response.
  This approach eliminates most human errors and subjective variations: for example, all versions of the same idea will be identified and coded the same way, ensuring uniformity.
  The result is a cleaner dataset where categories are applied consistently to all relevant responses.
• Pattern detection and new categories
  A key advantage of AI is its ability to detect meaningful patterns in open-ended text.
  If many respondents mention a recurring theme in the “Other” field that wasn’t originally listed, the AI will flag it.
  This enables researchers to create new categories where needed, avoiding the risk of missing emerging trends.
  For example, if multiple people write “price too high” in a product satisfaction survey, AI may group them together and suggest “Price” as a new feedback category—even if it wasn’t an option before.
  This way, hidden insights become visible, enriching the overall analysis.

Conclusion: greater efficiency, better data, deeper insights

Adopting AI to code “Other, please specify” responses in surveys represents a strategic leap in research quality.
In the past, managing open-ended answers meant a major investment of time and effort—with the risk of incomplete or inconsistent data.
Today, thanks to IdSurvey’s AI, that burden is lifted.
Automatic coding transforms a complex and time-consuming task into a fast, reliable process.
Research teams can now focus on interpreting data and making decisions, rather than spending time on repetitive classification work.

In a world where the best decisions are based on complete and reliable data, AI-powered coding of open responses becomes an essential ally.
It means having more time for strategic thinking, more consistent data, and the confidence of not missing any key insights hidden between the lines.
From “Other, please specify” to actionable insight: AI truly turns chaos into categories, helping companies and researchers extract maximum value from every survey.

L'articolo From chaos to categories: how AI simplifies the coding of open-ended responses proviene da IdSurvey.

When it comes to survey software, Qualtrics is undoubtedly one of the most well-known names globally. However, in recent years, many companies and research institutions have started exploring Qualtrics alternatives in search of more flexible, customizable, or simply better-suited solutions for their specific needs.
In this article, we’ll provide a comprehensive overview of the best alternatives to Qualtrics available on the market today, with a special focus on the experience of users who have chosen IdSurvey as their preferred platform.
By collecting real feedback from clients who have worked with both platforms, we aim to help you understand which Qualtrics competitor could be the right fit for your organization.

Top Qualtrics alternatives: the best competitors to explore

1 IdSurvey

2 SurveyMonkey

SurveyMonkey was one of the first to introduce a subscription model with self-service signup. Over time, it evolved its feature set but continues to focus on simplicity, making it ideal for quick and basic surveys.

3 QuestionPro

4 Alchemer (formerly SurveyGizmo)

5 Medallia

6 Typeform

7 Survio

8 SurveySparrow

9 Google Forms

10 Zoho Survey

Qualtrics alternatives: how IdSurvey stands out among the best competitors

Qualtrics is undoubtedly one of the most well-known survey platforms worldwide. It offers a modern and user-friendly interface designed to help even less experienced users create basic questionnaires quickly. However, this design choice often results in a rigid structure that can become a limitation when managing complex workflows, advanced logic, or surveys with specific technical requirements.

For organizations looking for a more flexible and powerful alternative to Qualtrics, IdSurvey stands out for its high degree of customization and control. Both platforms offer a wide variety of question types, but IdSurvey goes further by supporting advanced multimedia questions—including images, audio, and video—as well as interactive 3D grids. These features allow for more dynamic and structured respondent experiences and are not available in Qualtrics, which remains bound to a more standardized framework.

Another critical point of comparison among Qualtrics alternatives is the survey design environment. While Qualtrics prioritizes simplicity, it often comes at the expense of design freedom. Building complex conditional logic, custom paths, or advanced rules can become frustrating—or even impossible without compromising the original structure. As a result, users may find themselves forced to adapt their research project to fit the software, instead of the software adapting to their needs.

IdSurvey takes the opposite approach. It is designed for researchers, organizations, and professionals who need full control over every aspect of their survey. With proprietary tools like IdCode and FlowScript, users can set precise logic conditions, build sophisticated branching paths, and manage survey behavior with exceptional detail. This architecture makes IdSurvey an ideal Qualtrics competitor for enterprise projects, research institutes, agencies, and data teams looking for full freedom in data modeling.

Technical support is another key area where IdSurvey distinguishes itself from other alternatives to Qualtrics. While Qualtrics provides extensive documentation and support, its direct assistance can feel generic and sometimes slow—especially when hands-on help is needed. In contrast, IdSurvey offers a specialized support team with broad expertise, capable of assisting clients throughout the project lifecycle. The approach is consultative: the team does more than just answer questions—they actively contribute to the design and implementation of the survey, ensuring the final output fully aligns with client expectations.

Finally, while Qualtrics offers a ready-made set of features suitable for common use cases, it lacks the flexibility required for advanced research needs. IdSurvey is built for users who want to create complex, optimized, and fully customized surveys—without being constrained by the limitations of a standardized platform. If you’re searching for a true Qualtrics alternative that provides advanced capabilities and complete freedom in survey design, IdSurvey is a compelling choice.

Qualtrics alternatives: why choose IdSurvey over Qualtrics?

• More question types, including multimedia and interactive grids
• Maximum logic flexibility with IdCode and FlowScript
• Full support for complex workflows, no structural limits
• Dedicated, expert support team
• Ideal for advanced, enterprise, and customized research projects

L'articolo Qualtrics alternatives: the best competitors to consider today proviene da IdSurvey.

Difference between layout and styles

In the context of online questionnaires, layout and visual style are two distinct yet complementary elements.
Layout refers to the structural arrangement of components: the positioning of questions, spacing, alignment, and content flow.
Visual style, on the other hand, concerns the aesthetic appearance: colors, fonts, icons, borders, backgrounds, and all visual elements that define the questionnaire’s identity.

An effective layout ensures smooth and organized interaction, enhancing accessibility and providing a better experience across all devices. A well-crafted visual style, consistent with your brand identity, reinforces recognition and helps create a professional and pleasant experience for respondents.
When combined thoughtfully, both aspects can increase survey completion rates and improve the quality of collected data.

With IdSurvey’s theme editor, you can design questionnaires with a functional layout and a fully customized visual style—perfectly aligned with your goals and brand image.

A visual editor for full customization

The built-in visual editor allows you to modify every layout element easily and intuitively.
No coding skills are required—you can manage the entire look and feel of your questionnaire through a user-friendly interface with real-time preview.

Key features include:

• A wide library of ready-to-use themes
• Customization of colors, fonts, and text sizes
• Custom styling for radio buttons and checkboxes
• Editing of margins and spacing between elements
• Control over element widths, sizes, and alignment
• Insertion of logos, images, and custom backgrounds
• Responsive preview to optimize layout on mobile devices

Every change is instantly displayed in the preview panel, allowing you to test and fine-tune both layout and styles seamlessly.

Reusable themes

Every customization can be saved as a new theme.
This allows you to:

• Reuse layout and design in new projects, saving time
• Maintain visual consistency across different surveys
• Build a collection of branded themes for multiple clients

Additionally, you can edit a theme already applied to a survey without affecting other surveys using the same template, giving you full flexibility in project management.

Flexibility for every type of survey and device

Whether you’re creating a customer satisfaction survey, a market research study, a product test, or an internal data collection form, the layout and style must adapt to the context.
The IdSurvey editor is designed to manage every visual element of your questionnaire—even on mobile devices. For example, you can choose to display questions as buttons to make selection easier for users on smartphones and tablets.

CSS and javascript for advanced users

For those with more technical needs, IdSurvey allows the inclusion of custom CSS and JavaScript code, offering full control over the layout’s behavior and style.

This option is ideal for developers, web designers, or research institutes looking to finely customize specific aspects of their survey layout and design.

Conclusion

The visual appearance of a questionnaire is much more than an aesthetic detail—it’s a strategic tool to increase engagement, improve the respondent experience, and strengthen your organization’s identity.
With IdSurvey’s theme editor, you have all the tools you need to design online surveys that are visually effective, functional, and high-performing.

L'articolo Online surveys: the importance of an effective visual design proviene da IdSurvey.

Why doesn’t IdSurvey offer subscription plans like SurveyMonkey or QuestionPro?

SurveyMonkey and QuestionPro operate on rigid subscription models. Their platforms divide users into tiers—Basic, Standard, Premium, Enterprise—and each plan limits access to certain features. Want advanced logic or multilingual support? You’ll need to pay more. Want to remove their branding? Upgrade again.
IdSurvey rejects this business model entirely. Every user—regardless of company size, survey volume, or budget—gets full access to every feature. There are no artificial paywalls, no stripped-down versions, and no hidden upgrades. The same professional-grade platform is available to everyone.
This means researchers and teams don’t have to compromise. Whether you’re running a small internal survey or a complex international study, IdSurvey gives you 100% of the power, from day one.

What makes IdSurvey’s pricing better than subscription-based models?

With SurveyMonkey and QuestionPro, you’re paying for access—often on a monthly or annual basis—regardless of whether you’re using the software. These subscriptions can be costly and restrictive, especially when tied to feature-based plans or limited response counts.
IdSurvey takes a usage-based approach: you only pay for completed interviews. There are no recurring fees, no forced contracts, and no upselling. The more responses you collect over the year, the lower your per-response cost. It’s fair, transparent, and scalable.
Unlike its competitors, IdSurvey doesn’t charge for standard features—it charges for results. And every quote is customized based on your actual needs, with no surprises.

How does IdSurvey’s support compare to SurveyMonkey and QuestionPro?

One of the biggest frustrations with mainstream survey platforms is support. SurveyMonkey offers limited direct support unless you’re on an enterprise plan. QuestionPro has support, but much of it is scripted or outsourced. When problems arise, getting help can be slow and ineffective.
IdSurvey takes support seriously. Users get direct access to experienced technicians—not chatbots or call center agents. Support is fast, personal, and highly specialized. Technicians can assist with survey logic, platform configuration, integrations, and even custom feature development.
For clients who need more assistance, IdSurvey also offers turnkey services—fully programming the survey into the platform based on your specifications. This kind of partnership is rarely offered by mainstream tools.

Does IdSurvey offer the same flexibility as SurveyMonkey or QuestionPro when it comes to hosting?

Actually, IdSurvey offers more. SurveyMonkey and QuestionPro are cloud-only, meaning all data is stored on their servers. That may not work for organizations with strict data governance requirements or internal IT policies.
IdSurvey provides both cloud and on-premises deployment options. The cloud version is fully managed and ready to use. The on-premises version gives full control over hosting, data security, and system access—while still providing the exact same features and user experience.
No matter how you deploy it, IdSurvey delivers the same professional platform. That level of flexibility is rare among survey software vendors.

Is IdSurvey easier or harder to use than SurveyMonkey and QuestionPro?

SurveyMonkey and QuestionPro are known for their ease of use, but that simplicity often comes at the cost of flexibility. Advanced customizations can be hard to manage—or simply unavailable—without upgrading or using external tools.
IdSurvey balances usability with power. Its interface is intuitive enough for non-technical users, but the backend supports scripting, automation, complex logic building, and custom API access on request. Whether you’re creating a basic customer feedback form or a multi-language academic study, the platform adapts to your needs.
Plus, training is available on request to ensure your team gets the most out of the platform quickly.

How does IdSurvey handle data protection?

IdSurvey is designed to support compliance with the General Data Protection Regulation (GDPR) and follows recognized standards for data security and privacy. Data are hosted in ISO-certified data centers and are protected with commonly adopted security protocols, including encryption during transmission.
IdSurvey is structured to support responsible data management practices and to help protect respondents’ privacy throughout the data lifecyrcle.

Can IdSurvey build the survey for me, like a managed service?

Most platforms like SurveyMonkey or QuestionPro provide tools—but leave all the work to you. IdSurvey goes further.

For clients who prefer a hands-off experience, IdSurvey offers full-service support. Simply send us your questionnaire—typically as a Word or PDF file—and our team will take care of building it into a fully operational survey within the platform, exactly as specified. This includes implementing logic, quotas, and any other rules you define.

This service is especially valuable for agencies, institutions, and corporate departments that need fast, high-quality delivery without overloading internal teams. With IdSurvey, you’re not just using software—you’re partnering with experts who help bring your survey to life efficiently and accurately.

Other alternatives – and why they don’t compare

While SurveyMonkey and QuestionPro are among the most widely recognized survey platforms, other tools like Alchemer, SurveySparrow, Survio, and similar solutions also compete in the online survey space. However, these platforms offer comparable functionality and limitations—focusing mainly on basic survey creation with user-friendly interfaces and pre-built templates.

They may be suitable for simple use cases or quick polls, but when it comes to professional, large-scale research with complex logic, multi-language support, quota management, and integration flexibility, they provide little beyond what’s already covered in our comparison. That’s why we chose to focus on the tools that best represent this category—and to highlight how IdSurvey stands apart for advanced research needs.

What about Google Forms, Typeform, and similar tools?

Platforms like Google Forms, Typeform, and others in this category stand out for their clean design, ease of use, and ability to launch a simple survey in minutes. They’re often a go-to choice for individuals, educators, or small teams who need to collect quick feedback without technical overhead.

However, these tools are not designed with professional research in mind. They lack the depth needed for complex logic, quota management, multilingual support, panel integration, and advanced data control. While they may be “cool” and accessible, they simply don’t offer the features or scalability that professional researchers, agencies, and enterprise teams rely on. That’s why they fall outside the scope of this comparison.

Comparative Table: IdSurvey vs SurveyMonkey vs QuestionPro

Feature / Platform	IdSurvey	SurveyMonkey	QuestionPro
Access to all features	Included for all users	Limited to higher-tier plans	Limited to higher-tier plans
Pricing model	Pay-per-completed-interview	Monthly/annual subscriptions	Monthly/annual subscriptions
Feature restrictions	None	Many limitations in lower plans	Many limitations in lower plans
On-premises deployment	Available	Not supported	Not supported
Cloud deployment	Available	Available	Available
Advanced logic and scripting	Included	Limited to premium plans	Requires higher plans
Multilingual surveys	Included	Limited to higher-tier plans	Limited to higher-tier plans
Technical support	Direct access to expert technicians	Basic or delayed support	Mixed reviews
Survey scripting service	Available on request	Not available	Not available
CATI support (telephone surveys)	Native module included	Not supported	Not supported
CAPI support (offline face-to-face)	Native app with offline mode	Not supported	Limited support via add-ons
Mixed-mode methodology (CAWI, CATI, CAPI)	Fully supported	Not supported	Not supported
External panel integration	Supported	Supported	Supported
Advanced reporting	Customizable reports and exports	Many limitations in lower plans	Many limitations in lower plans
API access and integration	Available on request, custom on demand	Enterprise plan only	Limited, depends on plan
Advanced scripting (IdCode)	Proprietary scripting language for complex logic	Not available	Not available
Advanced logic editor (Flow-Script)	Built-in logic engine for advanced workflows	Not available	Not available
Theme customization (colors, layout, style)	Advanced theme editor with full design control	Limited customization options	Basic styling only
White label surveys	Brand logo is always removable, even in base setup	Limited to higher-tier plans	Limited to higher-tier plans

L'articolo How is IdSurvey different from SurveyMonkey, QuestionPro and other similar software? proviene da IdSurvey.

L'articolo Survey Software On Premise | Secure and Customizable On-Premise Surveys proviene da IdSurvey.

Crosstabs (or Crosstab tables) are one of the most powerful tools for data analysis in surveys and market research. Also known as contingency tables, Crosstabs allow researchers to analyze the relationship between two or more categorical variables, making it easier to identify trends and correlations.

In detail, a crosstab table (or contingency table) is a matrix showing the frequency distribution of two or more variables with at least one categorical variable. Each cell in the cross column and row (Banner and Stubs) shows the number of observations that fall into that combination or the frequency expressed as a percentage.

For example, by crossing the variables age group and educational qualification, we could observe how many people in the sample in the 30-49 age group had a bachelor’s degree.

Crosstabs example:

In a survey, we asked about the level of education and frequency of social media use. The hypothesis “level of education is related to frequency of social media use” was formulated. Therefore, we conducted the survey and collected the data to know the level of education and frequency of social media use. After collecting the data, we can then create the Crosstab table by setting the variable Degree of Education as Banner (Column) and the variable Frequency of Social Media Use as Stub (Row). The crosstab will show the distribution of cases in the various categories of the two variables.

Depending on the instrument used we activate the option to run the Pairwise Z-Test, a classic statistical test to highlight or test for correlation between multiple categorical variables.

How to interpret the test results of the crosstabs?

In the example below we see that in the Compulsory Schools column, on the Few row, the letters B and C are shown in the crosstab table. This means that for those with an education degree of compulsory schools, the likelihood that they would report spending little time on social is significantly higher than those with an education degree of “High School” (B) or a degree of “University” (C). Thus, in this example, a correlation emerges between education degree and self-reported time.

Interpreting Crosstabs: Chi-Square and P-Value

To simplify the previous example we skipped the Chi-square statistical significance test. With this preliminary test we can check whether the sample size and the results obtained can give us results attributable to chance or to a correlation between the variables. Depending on the confidence level of the research, generally set at 95% or 99%, we need to verify that the p-value resulting from the Chi-square test is less than 0.05 (95% confidence level) or 0.01 (99% confidence level), respectively.

For example, if the p-value is greater than 0.05, it means that the difference between the categories can be given by chance with a probability greater than 5%, thus greater than what is predetermined by the research. On the other hand, if the p-value is less than 0.05, the probability that the difference between categories is determined by chance is within the margin of tolerance of the research. Only in this case could we consider the initial hypothesis “level of education is correlated with frequency of social media use” to be true.

In our example we have a p-value well above 0.05 and therefore the hypothesis cannot be confirmed.

When to use crosstabs?

Crosstabs are useful in several contexts, especially when you want to:

• Compare subgroups: analyze how different categories (e.g., age, gender, geographic area) influence answers or behaviors.
• Detect hidden patterns: discover significant correlations or differences between variables or categories of variables. 
• Simplify complex data: represent large data sets in an understandable and visual way.
• Perform inferential analysis: evaluate statistical association between variables through tests such as Chi-Square, Z-test or Anova.

Examples of practical use of crosstab tables:

• Market research: understand whether purchase preferences are correlated with gender or age group. 
• Political surveys: analyze the correlation between voting intention and region of residence.
• Customer satisfaction: see if customer satisfaction is related to the product or service purchased.

Using Crosstabs for data analysis

To get the most out of crosstabs for your data analysis, it is essential to follow some best practices that will make the data clearer, more readable, and more meaningful. The first step is to choose the variables to be analyzed carefully, favoring those that might show a significant relationship. Selecting irrelevant or overly general variables could make the table unhelpful or even misleading, preventing interesting patterns from being detected.

Another crucial aspect is the clarity of the crosstab table. It is important to limit the number of categories to prevent the representation from becoming too complex and difficult to interpret. An overly dense table risks confusing rather than clarifying the data. To facilitate reading of crosstabs, it is essential to consider absolute frequencies, that is, the number of answers or observations in each cell. However, absolute numbers alone may not be sufficient to identify significant trends or differences between groups. For this reason, it is useful to include row or column percentages, which make it possible to normalize the data and easily compare categories with each other. For example, if in a survey 60 percent of male respondents preferred a particular product compared to 40 percent of female respondents, this information becomes much clearer expressed in percentage terms rather than simple absolute numbers. In addition to this, row and column totals provide the overall picture of the observations, helping to understand the weight of each subgroup relative to the total analysis.

Another essential step in the interpretation of crosstabs is to check the statistical significance of the results. This can be done, as seen in the previous example, by using statistical tests such as the Chi-Square, determining whether the observed differences between categories are statistically significant or simply the result of chance. This step is crucial to ensure that the conclusions drawn from the analysis have a sound basis.

When to use Crosstabs in research? Pros and cons

PROS

Simplicity: they are easy to construct and interpret.
Clarity: present complex data in an immediate visual format.
Flexibility: they can be used in various contexts and sectors.
Advanced analysis tools: enable statistical tests to validate observations.

CONS

Complexity reduction: they represent only part of the dataset and may oversimplify complex relationships.
Problems with continuous variables: work best with categorical variables and require grouping of numeric variables.
Information overload: if too many categories are used, tables can become difficult to read.

What key features should a good tool have for Crosstabs analysis

The essential features for analysis with Crosstabs are:

Bucket: it allows the creation of baskets i.e., groupings of several categories of a variable. For example, grouping the many regions of a nation into a few areas (north, center, south) in order to facilitate the interpretation and reading of data.

This feature can also be used to create categories of continuous variables, that is, to divide numerical data into specific ranges to simplify their analysis and understanding. For example, one can group respondents’ ages into ranges, such as 18-25 years, 26-35 years, 36-50 years, and over 50, instead of analyzing each age separately. This approach makes it possible to highlight general trends and make data interpretation clearer, avoiding excessive fragmentation of information.

Nested variables: this feature allows the Banner (columns) to be structured hierarchically, nesting two or more variables within a single cross table. Each category of the first variable is further subdivided according to the categories of the second variable, and so on, creating a more detailed and layered analysis.

For example, one can analyze the variable Degree of Education by breaking it down by geographic areas of a nation. In this way, instead of seeing the levels of education aggregated for the whole country, one can see how they are distributed within each geographical area (North, Central, South).

Statistical tests: in order to ensure a thorough and reliable analysis, it is essential that the instrument used offers the possibility of performing several statistical tests, including Chi-Square, Z-Test and ANOVA. These tests allow us to test whether the observed relationships between variables are truly significant or simply due to chance, providing a stronger basis for interpreting the data.

ANOVA (Analysis of Variance), which we have not discussed in this article, is particularly important when one wants to compare the mean of a numerical (continuous) variable among several distinct groups defined by a categorical variable. For example, it can be used to analyze whether satisfaction with a service varies by age group or whether average income differs among different geographic regions. Using ANOVA, it is possible to determine whether statistically significant differences exist between groups and, if so, to investigate which groups differ the most.

Other statistical metrics: for a complete and thorough analysis, it is essential that the instrument used provide a wide range of descriptive statistics, including mean, median, variance, and standard deviation. These indicators allow for a better understanding of the distribution of the data and to identify significant patterns that may not emerge from a simple reading of frequencies.

Management of multiple stubs: to conduct an in-depth analysis of survey data, it may be necessary to create contingency tables for multiple variables. The ideal tool should offer a streamlined approach, allowing users to set up multiple variable rows (stubs) to compare against the same banner and seamlessly switch between them while preserving metric settings.

Smart and Automatic Saving: modern tools efficiently manage automatic saving, a crucial feature when working with multiple crosstabs, as it ensures a more flexible and dynamic workflow. In contrast, outdated software requires frequent manual saves to external files, preset configurations for settings and metrics, and manual dataset imports—significantly slowing down the workflow and reducing efficiency in data analysis.

Advanced Crosstabs Export to Excel: most software allows exporting contingency tables in Excel format, but only a few provide clear and well-structured formatting within the file. Additionally, only the most advanced tools support simultaneous export of multiple crosstabs, generating an Excel file with multiple sheets, each containing a virtually unlimited number of crosstab tables. These tools enable users to export a complete analysis in a single file, streamlining data management and organization.

Conclusions

Crosstabs are a powerful and versatile tool in data analysis, essential for researchers, analysts, and anyone who needs to gain insight from a survey or complex dataset. When used correctly, they uncover meaningful relationships and facilitate strategic decisions based on hard data.

L'articolo Crosstabs: comprehensive guide to crosstab for data analysis proviene da IdSurvey.

What is a soft phone integrated in a phone survey software?

An embedded softphone is a VoIP calling feature built directly into the phone agent’s web interface. Unlike traditional softphone for phone surveys, it requires no external software, plugins, or complex configurations. It operates entirely within the CATI software, streamlining both call handling and operator management.
Because it’s a fully web-based system, both the phone survey software and the integrated softphone can be accessed directly from a browser without any local installation. This enables agents to start working immediately, whether they’re in the office or working remotely. The most advanced softphone for phone surveys leverage WebRTC technology for seamless audio streaming, eliminating the need for firewall and router configurations.

What Makes a Softphone Essential in Phone Survey Software?

Telephone surveys require reliable, scalable, and easy-to-use tools. Compared to traditional softphones, an integrated softphone for phone surveys offers several key advantages:

• No external software installation: Agents can make calls directly from the phone survey software without downloading or configuring additional programs or plugins.
• Greater flexibility: Agents can work remotely just as efficiently as they would in a centralized contact center.
• Seamless integration with the phone survey software: The soft phone is fully embedded in the survey workflow, enhancing the operator experience. With click-to-dial and predictive dialing, agents can make calls without manually dialing numbers.
• Better agent and call management: The best phone survey software provides automatic extension configuration, eliminating the need for administrators to manually create and configure PBX accounts.
• Zero setup time: Neither administrators nor agents need to configure routers, firewalls, or extensions the built-in softphone is instantly operational.

Standard Softphone vs. Built-in Softphone: Which One is Better for CATI?

Softphone for phone surveys and remote working: a perfect match

In recent years, remote work has become increasingly common in the market research industry. Web-based softphones and integrated softphones allow interviewers to conduct surveys from anywhere with an internet connection.
A key advantage of an integrated softphone for surveys is that operators can start working immediately by simply logging into the phone survey software without needing to install or configure any additional applications.

How to Choose the Best Phone Survey Software with an Integrated Softphone

To select the best CATI software with an integrated softphone, consider these factors:

• Native integration with the phone survey software: Choose phone survey software with a true built-in softphone that doesn’t require third-party plugins or applications.
• WebRTC protocol: Ensure that the softphone uses WebRTC technology for seamless operation without firewall or router configuration.
• Automatic extension setup: The best phone survey software automatically configures extensions for each agent, removing the need for manual PBX configurations.
• A single working window: The integrated softphone should operate directly within the main phone survey software interface, allowing agents to click-to-call and log call outcomes without switching between windows.

Since integrated softphone for surveys rely on deep PBX-CATI system integration, they may not be available for use with third-party PBXs.


Conclusion

Adopting an integrated softphone within your phone survey software paves the way for a more agile, cost-effective, and scalable survey model. By seamlessly integrating with survey workflows and enabling remote operations, an embedded softphone is an essential tool for modern contact centers.
If your goal is to enhance efficiency, reduce costs, and offer greater flexibility to your operators, CATI software with an integrated softphone is the ideal solution.

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Open-ended questions are designed to allow unconstrained answers, leaving the respondent free to express personal details, emotions and opinions. While closed questions are useful for collecting quantitative data through predefined answers, open-ended questions aim to provide deeper qualitative insights that are essential for better understanding the respondent’s needs, motivations and expectations. Open ended questions can also be integrated with quantitative questionnaires, thus expanding the quality of data collected and the depth of analysis.

Key differences between open-ended and closed-ended questions

Closed-ended questions provide defined and measurable answers, while open-ended questions allow the respondent to express himself freely. To make the difference immediate, here is a summary of the advantages and disadvantages of each type of question:

Characteristic	Open questions	Closed questions
Structure	No predefined answers	Answers limited to predefined options
Type of data	Qualitative	Quantitative
Analysis	Longer, requires interpretation	More immediate and faster
Goal	To explore opinions, feelings, and deep motivations	To measure preferences and collect standardized data
Ideal use	Exploratory research and detailed feedback	Quantitative surveys and easily comparable results
Example of analysis	Text mining, semantic networks, sentiment analysis, manual or AI-assisted analysis	Descriptive statistics (mean, standard deviation, etc.), graphs and Crosstabs
Respondent involvement	Requires more time and effort, more reasoned answers	Requires less effort, quick and immediate answers
Example	Briefly describe your experience with our service	Which of the following aspects of our service were you most satisfied with?

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Advantages of open ended questions

Open-ended questions are valuable because they allow in-depth exploration of the “why” behind customers’ choices, perceptions and behaviors, providing a level of understanding that closed answers cannot achieve. Unlike closed questions, which limit answers to predefined options, open questions give respondents the freedom to express themselves spontaneously and in detail, bringing to light aspects that are often overlooked or unknown. Here are some of the main benefits:

• Depth and richness of answers
  Open ended questions allow the collection of articulate answers that can reveal valuable details about personal experiences, preferences, and motivations. This type of answer helps to understand not only what customers think, but also why they think that way, providing crucial context for interpreting their opinions. For example, by asking customers “How was your experience with our service?” the answers could range from the quality of support to the room its speed, revealing elements that would not have emerged with a simple rating scale.
• Product Innovation and Improvement
  Open-ended responses can provide specific and unexpected suggestions that are extremely valuable for innovation. Through open-ended questions, customers often suggest features, improvements, or new uses for the product that the company hadn’t considered. This direct insight into customers’ desires and ideas can drive insights that inspire strategic changes or incremental improvements, leading to greater customer satisfaction. For instance, questions like “What would you improve about our product?” can reveal emerging needs and growth opportunities.

• Honest, Unmediated Feedback
  The absence of predefined answers encourages respondents to express themselves authentically, providing feedback that’s less influenced by the survey structure. Without imposed response options, customers feel free to share their viewpoints—even critical ones—offering a more realistic and genuine perspective on their experiences. This unfiltered feedback helps the company identify pain points and areas for improvement that might be overlooked in a closed-ended questionnaire. For example, requesting feedback with an open-ended question can reveal areas of dissatisfaction that predefined answers might not capture.

• Exploratory Research and Focus Groups
  Open-ended questions allow for exploring a wide range of topics without limitations imposed by the questionnaire, making them ideal in exploratory contexts or when the company seeks to understand new areas of interest through the opinions of interviewees or specific focus groups. This approach is particularly valuable in the early stages of developing a new product or campaign, where gathering insights on uncoded preferences and needs is crucial.

• Identification of Emerging Trends
  Open-ended responses can reveal hidden or emerging trends that might go unnoticed with only closed-ended responses. Since open-ended questions invite customers to freely express their opinions, a comprehensive view of shifts in customer expectations or tastes is more likely to emerge. Monitoring these responses over time can help the company stay aligned with market preferences and adjust its offerings accordingly.

 

Advantages and examples of open ended questions for various industries:

Sector	Main benefit	Open ended question example
Client assistance	Issues and critical areas identification	"Which difficulties you find in using our service?"
Marketing	Brand perception and company image	"How would you describe our company to your acquaintances?"
Product design	New ideas and improvements collection	"Which features would you add to our product?"
R&D	New client requirements	"What challenges do you regularly face, and how could we help you overcome them?"
Sales	Purchase reasons analysis	"Describe the main reasons that make you select our product"
Customer experience	Overall client experience tracking	"Describe a positive and a negative experience you had with our service."
Brand loyalty	Brand loyalty analysis	"What is the reason why you keep choosing our brand?"

Challenges and Limitations of Open-Ended Questions

Despite their benefits, open-ended questions pose several challenges that need to be carefully considered to ensure effective and consistent analysis of the collected data. Here are some of the main difficulties associated with using open-ended questions in surveys:

• Time and Resources Required for Analysis: The nature of open-ended responses implies a larger amount of textual data, which requires more complex and demanding analysis than closed responses. Manually analyzing these data can be lengthy and costly, requiring expert human resources in coding and interpretation, as well as the use of specific tools for qualitative research. Using software that leverages Natural Language Processing (NLP) with innovative AI tools requires careful manual configuration and constant monitoring.
• Ambiguity and Complexity of Responses: Open-ended responses often contain vague terms, irrelevant information, or ambiguous phrasing, making them difficult to interpret clearly and consistently. For example, a question like “What could improve our service?” may elicit generic answers such as “be better” or “offer more options,” which don’t provide concrete insights. Linguistic complexity, including the use of slang, metaphors, or cultural references, can increase the difficulty of analysis.

• Respondent Bias: Since open-ended questions allow a lot of expressive freedom, there is a risk that respondents might stray from the question’s focus, interpreting it in unforeseen ways. This can lead to lengthy, digressive answers that provide little value for analysis and can complicate categorization. Additionally, some respondents might be influenced by personal or situational biases, such as their emotional state or recent experiences, which can affect their answers and distort the overall results. AI automation can help detect off-topic or repetitive content, but even trained AI might reflect biases present in the training data.
• Variability in Response Quality: Open-ended responses can vary widely in quality, with some respondents providing in-depth details and others giving extremely brief or vague answers. This variability makes it difficult to obtain consistent, homogeneous data, complicating analysis. Some respondents may lack the motivation or time to provide complete answers, limiting the overall value of the collected data.
• Difficulty in Large-Scale Analysis: When collecting open-ended responses from a large number of participants, the volume of data can quickly become unmanageable without the use of advanced automation tools. However, even with AI and NLP support, interpreting large volumes of text can be challenging, especially when highly specific information is required or when a uniform analysis must be maintained. The need for additional resources for large-scale analysis can increase costs and project completion times.
• Challenges in Maintaining Objectivity: Since open-ended responses can be interpreted in different ways, there is a risk of introducing interpretive biases during coding and analysis. Even AI-based analysis is not immune to this challenge, as AI is influenced by the data with which it was trained. If interpretation isn’t standardized, the final results may reflect distorted respondent opinions, leading to potentially misleading conclusions.
• Difficulty in Synthesizing Qualitative Data: Collecting concise, easy-to-consult insights from qualitative data is more complex than with quantitative data. Open-ended responses often contain details, context, and opinions that may be hard to summarize in a clear, concise manner for decision-makers. Creating a report that accurately synthesizes sentiment or general opinions may require more time and skills than analyzing closed responses.
  
• Different Linguistic and Cultural Requirements: In a multilingual or multicultural context, open-ended responses may present linguistic or cultural differences that affect content and interpretation. For example, common terms or expressions in one culture might be difficult to understand or interpret correctly in another, requiring additional translation and interpretation work. AI tools based on LLM (Large Language Model) can be very helpful, but they cannot guarantee a correct interpretation of all cultural nuances.

 

Addressing these challenges requires an integrated approach that balances the use of technology with structured qualitative analysis methodologies and, if necessary, expert support to maintain accuracy and objectivity in the results.

Limitations and solutions for open ended questions

Limitation	Description	Proposed solution
Ambiguity	Unclear or off-topic answers	Clear wording of questions
Analysis time	Analysis requires more resources than closed questions	Use of tools that integrate AI capabilities with Natural Language Processing
High volume of data	Excessive amount of complex data to handle	Strategic selection of open-ended questions at design stage. Research on representative but small sample.
Variability in the quality of responses	Significant differences in the quality of responses, ranging from very detailed to superficial	Provide examples or guidelines to encourage more in-depth responses
Respondent bias	Risk of untruthful responses conditioned by bias, such as social desirability bias	Formulate neutral questions and ensure anonymity
Synthesis Difficulty	Gathering synthetic insights from qualitative responses	Structure the analysis with categories and main themes, facilitating synthesis
Linguistic and cultural needs	Difficulties in interpreting in multilingual or multicultural contexts	Use state-of-the-art translation tools and adapt questions to the respondent's cultural context

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Types of open-ended questions for specific goals

Open-ended questions can be structured differently depending on the survey’s specific goal, each type meeting different needs for qualitative data collection. Here is an in-depth look at the main types of open-ended questions and their most common uses:

• Explanatory Questions
  Explanatory questions are ideal when the goal is to understand better the context or background behind respondents’ choices and preferences. These questions allow exploration of motivations, past experiences, and factors influencing purchasing decisions or service use. For example, questions like “What elements led you to choose our service over others?” invite the respondent to reflect on key factors (such as price, quality, brand reputation, or recommendations from acquaintances). Responses to these questions can provide useful insights to adapt marketing strategies or product positioning based on what truly matters to customers.
• Suggestion Questions
  Suggestion questions are designed to gather practical feedback on desired improvements, additional features, or changes that could increase customer satisfaction. Often, these questions are asked to existing customers, as they have direct experience with the product or service and can offer useful input for optimization. An example of a suggestion question is: “What new features or characteristics would you like to see in our next update?” Responses to this question allow companies to identify improvement areas perceived by customers and anticipate future needs, enhancing the product or service based on what truly matters to users.
• Narrative Questions
  Narrative questions aim to gather personal, detailed stories that can help understand the product or service use context more deeply and engagingly. This type of question encourages respondents to share meaningful experiences, describing how the product has impacted their life or solved a specific problem. An example might be: “Tell us about a time our product helped you solve an important issue.” Narrative responses provide an emotional and contextual perspective on the customer experience, revealing aspects that might not emerge from other types of questions. These stories can also be used for brand storytelling, turning customer experiences into powerful testimonials that strengthen emotional connections with the audience.
• Exploratory Questions
  Exploratory questions are designed to uncover new, unconventional ideas that may not have surfaced in other circumstances. They are useful when you want to leave respondents complete freedom of expression to highlight concepts, needs, or trends that might escape more structured questions. For example: “Is there something you’d like to suggest or share about this product that we haven’t already considered?” Responses to these questions can lead to unexpected discoveries, inspiring innovations or new strategic directions.
• Evaluative Questions
  Evaluative questions ask the respondent to express a judgment or evaluation of a specific experience, product, or service. They are designed to gather direct opinions on specific aspects and are useful for understanding satisfaction levels or areas of dissatisfaction. For example: “How well did our customer service meet your expectations?” or “What do you think of the quality of our latest update?” Responses help measure satisfaction and provide indications for potential improvements.
• Comparative Questions
  These questions ask the respondent to compare two or more items and indicate differences or preferences. They are useful for understanding which product, service, or feature is preferred and for what reasons. For example: “How does our product differ from others you’ve used in the past?” or “If you’ve used Product A and Product B, what differences did you notice?” Responses to these questions can highlight strengths and weaknesses relative to competitors or internal alternatives.

• Predictive Questions
  Predictive questions ask the respondent to imagine the future or express opinions on potential changes. This type of question is useful for understanding the directions customers want the company to take or how they expect the product to evolve. For example: “How do you think you’ll use this service five years from now?” or “Which features do you think will be essential in the future?” Responses help guide product development and predict future trends.
• Need Identification Questions
  These questions aim to discover the respondent’s needs, desires, or challenges, providing key data for product or service improvement. They are useful in the development phase of new products or services, allowing the collection of information on unmet needs. For example: “What problems do you frequently face, and how do you think our service could help you solve them?” This type of response helps direct company offerings toward targeted solutions.

• Reflective Questions
  Reflective questions encourage respondents to think about past experiences or how they perceive a product or service in relation to their lives. They are useful for obtaining insights into how the product fits into the customer’s daily life and the emotions associated with its use. For example: “How has our product contributed to changing your life?” or “What emotions does our brand evoke for you?” Responses can help investigate and strengthen the emotional connection with the customer.

 

Using these different types of open ended questions allows for a more nuanced understanding of the audience, collecting data that goes beyond simple satisfaction assessments to understand needs, desires, expectations, and deep values.

Open ended question types

Type of question	Goal	Example
Explanatory	Understand the context and motivations	"What factors influenced your purchase decision?"
Suggestion	Get feedback on improvements	"What would you change in our service?"
Narrative	Collect personal experiences and stories	"Tell us about a time when our product helped you"
Exploratory	Discovering unconventional ideas and perspectives	"Is there anything you would like to add or suggest about this product?"
Evaluative	Gathering specific ratings or opinions	"How do you rate the quality of our latest update?"
Comparative	Compare items to identify preferences	"How does our service differ from that of competitors?"
Predictive	Gather visions about potential changes	"How do you think you will use this service five years from now?"
Need identification	Uncovering personal needs and challenges	"What problems do you often face and how do you think our service could help you?"
Reflective	Exploring personal or emotional impact	"How has using our product contributed to the change in your life?"

10 Tips for generating meaningful insights by integrating open ended questions into surveys

Balancing open- and closed-ended questions is crucial for effective surveys. Here are some practical tips:

1. Alternate with closed questions: A sequence of a closed question followed by an open question allows for contextualizing and further explaining responses. For example, “Are you satisfied with the service?” followed by “Why did you choose this rating?”
2. Position open-ended questions at the beginning or end: Placing open-ended questions at the start or end allows for collecting extensive or detailed feedback without impacting the survey’s length.
3. Know your audience: Adapt the complexity of the question to your target audience. Open-ended questions may require more cognitive effort, so they should be simple and relevant.
4. Be specific to avoid generic answers: A broad question like “How does your company manage internal resources?” risks yielding vague and varied responses. Define the context clearly to better direct responses. For example, ask: “What changes has your company made in personnel management over the past year to improve productivity?” Focusing the question avoids ambiguity and yields more precise and useful responses.
5. Give your question a clear purpose: Generic questions like “Why did you visit our site?” may lead to short, vague answers like “Out of curiosity.” To gather more meaningful data, guide respondents with more targeted questions, such as “What information were you looking for when you visited our site?” or “What aspects convinced you to choose our product over others?” This approach helps to gather more detailed and valuable answers.
6. Make the question simple and quick to answer: Avoid complex questions like “Can you describe all the steps you take to prepare a marketing campaign, including market analysis, target definition, and channel selection?” Such a question may be overly long and discourage respondents. If you need detailed information, break down the question into more manageable parts, like “What is the first step you take when preparing a marketing campaign?” or “What are the main criteria you use to define a campaign’s target?” This way, you get clearer and more specific answers without overwhelming respondents.
7. Do not require a minimum word count: Requesting a minimum number of words, such as “Explain why you chose to attend our event [Minimum 100 words],” may lead respondents to add irrelevant details just to meet the requirement. To obtain genuine responses, remove the word limit.
8. Ask only one question at a time: If you ask “How long have you been using our software? What features are most useful to you?”, you risk getting partial answers, as the respondent might focus on only one part of the question. To gather more accurate responses, split the query into two: first ask, “How long have you been using our software?” then follow up with “What features are most useful to you?” This approach helps to get complete information on each aspect.
9. Make open-ended questions optional: For a question like “What additional services would you like us to offer?”, it’s helpful to allow respondents to skip or select an option like “No suggestions at the moment.” This way, respondents won’t feel obligated to give forced or insincere answers, letting you collect only genuinely relevant and authentic suggestions.
10. Limit the number of open-ended questions: Questions like “What aspects do you like about our customer service?” and “What improvements would you suggest for our support hours?” can become burdensome if repeated in succession. Open-ended questions require time and thought, so it’s better to limit them to only the essential ones.
    
    
    Some examples of situations where open ended questions are useful include:

Surveys: If you want to explore interest in your product or service, you can include open-ended questions for more details. For example, “What do you like most about our menu?”

Research and Development: When testing a new product idea or improving an existing service, you can ask the audience about the features they appreciate most or what would make the product unique for them.

Quantitative Studies: Using open-ended questions during the preparation phase of a survey can help define the types of answers to include in a quantitative study, making the questionnaire more targeted and relevant.

When you need to gather more detailed information beyond a simple closed response, open-ended questions are the best option.

Language plays a key role; therefore, it’s helpful to include words that encourage more detailed responses, such as how, why, what, and describe. Formulating the question is up to you, but try to choose words and language that convey to the respondent that you want to know their personal opinion and thoughts.

How to analyze open ended questions

Analyzing open-ended responses requires a structured and specific approach, as qualitative data can be complex to process. Techniques like text mining or using natural language processing (NLP) software are advanced tools that help identify patterns, keywords, and recurring themes within responses. These tools help associate each concept or phrase in the responses with labels or categories, making the data quantifiable. This strategy facilitates the identification of trends and behaviors that can guide business decisions.

Another methodology, albeit more labor-intensive, is manual coding of responses. This approach allows researchers to examine responses in detail and assign specific categories to each content, achieving a deeper and more accurate interpretation. Although slower and requiring more human resources, manual coding is useful when you want to preserve the nuances and context of responses, as human judgment can perceive details that automated tools may miss.

In conclusion

Open ended questions are a powerful tool for gathering in-depth insights and gaining a more comprehensive understanding of participants’ opinions and experiences. Although they require more care in formulation and analysis, the benefits they offer in terms of detail and authenticity of data can make a difference in strategic decisions.

Open ended questions: FAQ

What are open ended questions?
An open ended question allows respondents to answer freely, without being limited to predefined response options. This type of question enables the collection of more detailed and personalized responses.

What’s the difference between an open-ended and a closed-ended question?
An open-ended question allows for free-form and detailed answers, while a closed-ended question limits responses to specific options, such as “yes” or “no” or a rating scale. Open-ended questions are useful for gathering qualitative data, while closed-ended ones focus on quantitative data.

When are open-ended questions generally most useful?
Open-ended questions are ideal when you want to explore respondents’ detailed opinions, motivations, or experiences. They are particularly useful in market research, customer support, and exploratory research.

What are the advantages of open-ended questions?
The main advantages of open-ended questions include collecting authentic and unfiltered feedback, gaining in-depth details, and discovering innovative ideas and unexpected perspectives.

What are the main challenges of using open-ended questions?
The main challenges include the time and resources required for response analysis, variability in response quality, and the difficulty in synthesizing data into actionable insights.

How can I effectively analyze open-ended question responses?
Open-ended responses can be analyzed using techniques like manual coding, text analysis, or AI-based software to categorize and interpret the data.

What is an example of an open ended question?
Common examples include: “What improvements would you suggest for our product?”, “What do you think of our customer service?” and “What was your overall experience with our brand?”

Can I use only open-ended questions in a survey?
It’s possible, but not always advisable. Alternating between open- and closed-ended questions can make data collection more balanced, reduce respondent fatigue, and ease result analysis.

How can I encourage respondents to provide detailed answers to open-ended questions?
To encourage in-depth responses, it helps to explain the importance of their opinion and ask specific questions that guide towards a detailed answer. Additionally, avoid imposing a word minimum or making all open-ended questions mandatory.

What common mistakes should I avoid when formulating open ended questions?
Common mistakes include asking overly general or vague questions, combining multiple questions into one, and requiring a minimum word count in responses.

L'articolo Open ended questions: benefits, limitations tips and examples. proviene da IdSurvey.

The Likert scale is a key tool in psychometrics and social research, used in surveys to collect data on people’s opinions, perceptions and attitudes. Introduced by Rensis Likert in 1932, it allows participants to express their degree of agreement or disagreement through questions posed in the form of statements and answers that allow them to express their opinions.
In this article we will explore how to create effective questions with the Likert scale and how to analyze the results in depth.

What is a likert scale?

The Likert scale is a psychometric scale that measures the intensity of an opinion through a series of ordered answers, called items. Answers range from extremely positive to extremely negative, for example, “Strongly Agree,” “Agree,” “Neutral,” “Disagree,” and “Strongly Disagree.” This method is useful to get a clear idea of respondents’ feelings about a particular issue.

How to create an effective likert scale?

• Define the goal of the survey

  Before you start creating questions, it is essential to have a clear goal for the survey. What do you want to find out? What aspects do you wish to explore? Having a well-defined objective helps to structure the questions in a clear and relevant way.

  Make clear and specific statements

  Likert Scale questions should be phrased as statements that respondents can answer indicating their degree of agreement or disagreement. Avoid vague wording, which could confuse participants and lead to inaccurate answers. Statements should be designed to be clear, specific and targeted to the construct you intend to measure. For example, to measure job satisfaction, it is preferable to use statements such as “I am satisfied with my work environment” rather than generic questions such as “Are you happy at work?”.

• Balance the statements

  Include both positive and negative statements to reduce the risk of acquiescence, i.e., the tendency of respondents to always answer positively or negatively. This approach helps to mitigate answer bias and get a more accurate picture of opinions. If you want to interpret positive and negative statements in the same way, the coding of answers to negative statements should be reversed so that a high score always represents a positive assessment and a low score a negative assessment, or vice versa.

• Avoid bias

  It is important to write questions while avoiding influencing the respondent, for example suggesting a particular answer or introducing an implicit bias. Instead of asking “This product is outstanding,” use a neutral statement such as “This product meets my expectations.”

  Some of the most common biases in this context are:
  Central tendency bias: the tendency to choose central options, such as “neutral” or “indifferent,” to avoid taking a clear position. To reduce this bias, you can use scales without neutral options (e.g., a 4-point scale), and avoid extreme item definitions.
  Acquiescence bias: the tendency of participants to agree with statements regardless of their content. This bias can be avoided by alternating positive and negative statements to encourage more reflective answers.
  Social desirability bias: participants may respond in a way that makes them appear socially desirable or morally correct, rather than expressing their true thoughts. To reduce this bias, it is helpful to ensure anonymity and to phrase questions neutrally.

• Choose the number of options:

  usually, the Likert scale provides 5 or 7 answer options. An odd number of options allows for a neutral option in the middle, while an even number forces participants to take a position. The choice depends on the type of analysis you want to perform. If the goal is to measure the indifference regarding a specific topic, it is important to include the neutral option.

• Maintain consistency:

  if you use multiple Likert scale questions within the same survey, it is important to maintain the same format and order of answers to avoid confusion and to facilitate data analysis.

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Likert Scale data analysis

From a statistical point of view, we should remember that Likert scales produce ordinal data.

After collecting the data, it’s crucial to analyze them systematically to draw meaningful conclusions. Below are some statistical metrics that can be computed from Likert Scale questions:

• Mode analysis

  The mode represents the value that occurs most frequently in a dataset. If multiple values share the highest frequency, the dataset is considered multimodal.

• Median Analysis

  The median is the value that lies in the middle of an ordered data set. It divides the data into two halves: 50% of the answers are lower than the median and 50% are higher. This value gives us a clue about the distribution of the data, for example, a high median suggests that the distribution of answers tends toward positive or high values.

• Correlation analysis

  Checking whether there are correlations between answers to different questions can offer interesting insights. For example, if a strong correlation emerges between customer service satisfaction and brand loyalty, this could indicate an area on which to focus improvement efforts. The use of correlation coefficients (such as Spearman’s coefficient) can help identify relationships between answers to different Likert questions, providing an in-depth view of the interactions between the constructs being measured.

• Other non-parametric tests

  To test for statistical significance between groups of data collected through Likert scale questions, specific tests for ordinal data are typically applied. When comparing two groups, the Mann-Whitney U Test is often used to obtain a p-value, which is then compared to a significance threshold to determine whether the differences between groups suggest a meaningful relationship or are due to chance. For comparisons involving three or more groups, the Kruskal-Wallis Test is commonly employed. Although the Chi-square Test can also be used, it is designed for categorical data, meaning it disregards the ordinal nature of the responses. However, the Chi-square Test can still be effective if the data are grouped into broader categories, such as combining responses into “positive” and “negative” opinions, rather than analyzing each Likert scale level individually.

Other types of data analysis

Although this practice is incorrect, some researchers treat Likert scales as interval data, assuming that the distances between response options are equal. This approach attempts to force the analysis of ordinal data as though they were numerical (or interval) scale data. Instead of using the item values (e.g., “satisfied,” “very satisfied,” etc.), numerical scores are assigned to each option, allowing arithmetic calculations, such as mean and standard deviation, and enabling the application of tests typically used for quantitative variable analysis.

• Mean Analysis

  The arithmetic mean is a measure of central tendency that represents the average value of answers. Using the mean, other statistics typical of quantitative variables can be calculated.

• Variance and Standard Deviation

  The standard deviation is the mean distance of values with the arithmetic mean. It is important for understanding the dispersion of answers. A high standard deviation indicates a large variability in opinions, while a low one suggests a consensus among participants.

• Parametric tests

  Even if one chooses to force the interpretation of Likert scale data as interval-scale – allowing for the calculation of the mean and standard deviation – the use of parametric tests is strongly discouraged. Tests like the T-test, ANOVA, or Pearson’s Correlation Test are designed for quantitative data that follow a normal distribution, a condition that is not met by data collected using a Likert scale. Thus, applying these tests can lead to inaccurate or misleading results.

Interpreting data collected using the Likert scale requires caution, as it is essential to consider the context and characteristics of the sample. For instance, a high level of job satisfaction observed within one company may not necessarily be applicable or generalizable to other industries or cultural settings. Understanding these nuances is key to drawing accurate and relevant conclusions.

Types for likert scale survey

Avoid cognitive overload: do not include too many likert scale questions in a row, as this may tire respondents and reduce the quality of answers.
Test the survey: before distributing it, test the survey with a small group to check the clarity of the questions and understandability of the answers.

Examples of application of the Likert Scale question

Below are some areas of application that show how the Likert scale can be a versatile and powerful tool for collecting detailed and meaningful data on a wide range of issues, facilitating strategic decisions based on structured feedback.

Customer satisfaction surveys

Likert scales are commonly used to assess customer satisfaction with products, services or brand experiences. Companies can ask questions to obtain detailed feedback that allows them to identify strengths and areas for improvement.

Example question:
“I feel satisfied with customer service.”

Answer options:

• Strongly disagree
• Disagree
• Neutral
• Agree
• Strongly agree

Assessment of employee experience

Likert scales are ideal for collecting employees’ opinions on aspects of their work environment, such as the level of support received from superiors, opportunities for professional growth, and the quality of internal communications. This type of survey helps organizations measure employee engagement and satisfaction.

Sample question:
“Does my supervisor support me in my professional development?”

Answer options:

• Not at all
• Slightly
• Medium
• Fairly
• Very

Measuring brand perceptions and corporate image

Companies can use Likert scales to understand how the public perceives their brand, products or advertising campaigns. Questions such as “Does our brand represent sustainability values” allow them to assess the effectiveness of communication and marketing strategies.

Example question:
“How much do you agree with the statement: Our brand stands for innovation and quality?”

Answer options:

• Strongly disagree
• Disagree
• Neutral
• Agree
• Strongly agree

Purchasing behavior analysis

The Likert scale is useful for exploring consumer buying habits and preferences. For example, structured questions such as the example below can provide important data on consumer trends, helping companies adapt their offerings to market needs.

Example question:
“I often buy organic products.”

Answer options:

• Strongly disagree
• Disagree
• Neutral
• Agree
• Strongly agree

Evaluation of the effectiveness of training and learning

In education and business, Likert scales can be used to measure the effectiveness of training courses and educational programs. Questions such as “Do I feel prepared after completing this course?” allow educators and trainers to assess the impact of their program and make any improvements.

Example question:
“I feel prepared to use the skills learned during this course”

Answer options:

• Strongly disagree
• Disagree
• Neutral
• Agree
• Strongly agree

Likert phrased in the form of a question

Deviating from the original model, it is common to find questionnaires with Likert questions not phrased in the affirmative form. This form is not properly correct but nevertheless has become commonly used.

Example of a non-affirmative question:

“How likely are you to purchase our products again?”
Answer options:

• Extremely unlikely
• Unlikely
• Neutral
• Probable
• Extremely likely

Conclusions of likert scale guide

Likert scales are a versatile and powerful tool for collecting data on opinions and attitudes.

It is critical to consider ethical implications in the design and interpretation of Likert scale surveys. Questions should be phrased in a respectful and neutral manner, avoiding influencing answers. It is essential to ensure the anonymity and confidentiality of participants, especially when addressing sensitive issues.

A rigorous and scientific approach to survey design and analysis can provide valuable insights and support strategic decisions in academic, business, and social settings.

Likert scale FAQ

What is the likert scale?
The Likert scale is a rating scale used in surveys to measure respondents’ opinions. Each Likert scale question consists of a statement and a set of answer options that allow respondents to express their degree of agreement or disagreement. The answers are structured with an ordinal scale, for example from “Strongly disagree” to “Strongly agree.”

What are the advantages of using the Likert scale?
Advantages include ease of use and understanding and the ability to measure the complexity of respondents’ feelings and perceptions. The range of answers a likert scale allows can be easily adapted to different research needs. For example, you can create questions with options ranging from “Strongly disagree” to “Strongly agree,” or from “Extremely dissatisfied” to “Extremely satisfied,” etc..

How to analyze Likert scale data statistically?
Likert scale data can be analyzed using descriptive statistics such as mode and median. By forcing the type of data, some researchers turn the ordinal scale into an interval scale, allowing the calculation of mean and standard deviation. Nonparametric tests can also be used to compare groups or check correlations. The use of correlation coefficients such as Spearman can help identify relationships between variables.

How many answer options should I use in a Likert scale question?
Generally, Likert scales use 5 or 7 answer options. A 5-point scale offers a good balance between simplicity and accuracy, while a 7-point scale can provide greater sensitivity in measurement. Odd-numbered scales allow a neutral answer to be included, while even-numbered scales force the respondent to take a positive or negative position.

When to use a Likert scale?
Likert scales are ideal for assessing respondents’ feelings on specific topics, such as customer satisfaction, service quality, employee experience, and effectiveness of products or programs. They are especially useful when you want to get a detailed view of opinions while avoiding the use of open-ended answers.

What is the difference between the Likert scale and other rating scales?
The Likert scale differs from other scales – such as the nominal scale or the ordinal scale – in that it allows measurement not only of the presence of an opinion, but also the degree of intensity with which it is expressed. Unlike a dichotomous variable (Yes/No), the Likert scale allows a wider range of answers, providing more detail on the degree of agreement or disagreement.

What common mistakes can be made when designing Likert questions?
Common mistakes include: using ambiguous or complex statements, formulating questions that suggest an answer (wording bias), and using too many or too few answer options. It is important to maintain clear, neutral language and make sure that all answer options are easily understood.

Why is the Likert scale preferred over other question types?
The Likert scale is preferred because it allows the collection of detailed and easily quantifiable data on opinions, easy to implement, and allows a large volume of answers to be collected quickly. In addition, the use of closed questions facilitates data analysis compared to open-ended questions, reducing the time and resources required for interpretation.

How can I improve the quality of Likert scale surveys?
It is important to: formulate clear questions focused on a specific topic, balance positive and negative statements to reduce bias, test the survey with a small group before distribution, and make sure the answer options are consistent and understandable.

What are the disadvantages of the Likert scale?
Disadvantages include a greater possibility of acquiescence bias (tendency to answer positively), the risk of respondents choosing the neutral answer to avoid taking a position (central tendency bias), and the difficulty in dealing with ordinal data in statistical analysis. In addition, the Likert scale may not be suitable for measuring complex opinions that require numerical or more detailed answers.

L'articolo How to create and analyze likert scale data proviene da IdSurvey.

A variable is a concept that represents a characteristic or aspect of a person, object, system or phenomenon, and can take on different values. Age, gender, screen size of a smartphone, grade on an exam, and level of satisfaction with a purchase are examples of variables.

For example, the variable “age” can take values expressed in years (18, 21, 40, etc.). The level of satisfaction with an online purchase can range from 1 to 5, as measured by a star rating system.

Independent variable vs dependent variable: definition

The independent variable is the item that researchers decide to change or manipulate in an experiment (or research) to observe how those changes affect another variable, known as the dependent variable.

The dependent variable is the one that is measured, and represents the effect or result of changes in the independent variable

Let’s assume that we want to determine whether the color of a package affects the sales of a product. In this case, the color of the package is the independent variable, which can take different values, such as red, green or blue. To conduct the experiment, we will expose customers to packages of different colors and measure the number of sales for each color. Sales represent the dependent variable, which we expect to vary according to the color of the package.

In short, by changing the independent variable we can check for any effects on the dependent variable. This helps us find out if there is a relationship between the variables.

Correlation and the Cause-Effect relationship

In research, it is crucial to distinguish between correlation and causation. A correlation implies that two variables are associated in some way, but this association does not necessarily imply that one causes the other. On the other hand, the cause-effect relationship shows how as the independent variable changes, a direct effect on the dependent variable can be measured.
The distinction is critical for correctly interpreting data collected in studies that do not directly manipulate variables.

Let’s suppose we want to assess how residents’ perceived sense of safety varies with the crime rate in their cities. To do this, we decide to administer questionnaires measuring the level of perceived safety to a representative sample of residents in different cities with different crime rates.

In this study, we indirectly manipulate the independent variable “crime rate” by choosing cities with different levels of crime, but we cannot directly intervene on this variable. After collecting data, we analyze the correlation between perceived safety scores and crime rates of cities to see if there is a statistical relationship between these two factors.

We also assume that the results show that residents of cities with lower crime rates have a higher perception of safety. This is not sufficient to prove a cause-and-effect relationship for several reasons:

• Independent variable not directly manipulated 

  The independent variable (crime rate) is not directly manipulated, but is only observed in different cities. This type of study, observational rather than experimental, may reveal correlations but does not automatically establish causality.
• Confounding variables
  
Other variables, not considered in the study, could influence both crime rates and perceptions of safety. For example, factors such as economic well-being, quality of public services, or social cohesion could play an important role in both variables. Without controlling for these potential confounding variables, we cannot be sure that it is crime rates that directly influence perceptions of safety.
• Non-random sample selection

  In this study, although the residents for each city were chosen randomly, the formation of the groups based on the different crime rates of the cities was predetermined by the researcher. This is not equivalent to true random selection of participants in a controlled experiment, where each participant is equally likely to be assigned to any experimental condition. Instead, groups were formed based on pre-existing city characteristics (crime rates), not through random assignment. This methodology may introduce limitations in interpreting causality, as the groups may differ on unmeasured or unconsidered variables that influence both crime rates and perceptions of safety.

 
Thus, in order to confirm a cause-and-effect relationship, it is necessary to conduct research that adopts the experimental method, which involves controlled manipulation of the independent variable and random assignment of participants to different treatment conditions, such as in multiple experimental groups and a control group. This approach minimizes the influences of confounding variables and allows the effect of the independent variable on the dependent variable to be isolated.
For the same reasons, observational methods, such as those that rely on data collection through questionnaires, thus without direct manipulation of variables, are unable to establish causality (cause-effect relationship). Such methods can reveal correlations, that is, relationships in which the presence or change in one characteristic is associated with changes in another, but without being able to confirm that one directly causes the other.
 

Practical examples of case studies for dependent variables and independent variables

Understanding the use of independent and dependent variables is crucial in various fields of research. Let’s look at some use cases:

Case 1

Hypothesis: Excessive use of social media (independent variable) increases levels of anxiety and depression (dependent variable) in adolescents.

Study: Researchers may divide a group of adolescents into two subgroups: one with unlimited access to social media and the other with limited access. After a period of observation, they would measure levels of anxiety and depression using validated questionnaires. This study would help to understand whether limiting social media use can actually improve adolescents’ emotional well-being.
 

Case 2

Hypothesis: Exposure to urban green spaces (independent variable) correlates with higher levels of life satisfaction (dependent variable) in adults.

Study: To investigate this correlation, researchers could administer questionnaires to a large sample of adults living in different urban areas with varying levels of accessibility and proximity to parks and green spaces. The questionnaires would assess the frequency of visiting these green spaces and levels of overall life satisfaction. By analyzing the collected data, researchers can examine whether there is a statistically significant correlation between frequency of access to green spaces and life satisfaction, while keeping in mind that this type of observational study does not allow for direct causality, only an association between the variables.
 

Case 3

Hypothesis: Regular coffee consumption (independent variable) reduces the risk of developing type 2 diabetes (dependent variable) in adults.

Study: To examine this possible correlation, researchers can conduct an observational survey by administering questionnaires to a large group of adults. These questionnaires investigate the frequency of coffee consumption and collect information on general health status and the presence of type 2 diabetes. Through statistical analysis of the collected data, an attempt is made to identify whether there is a correlation between coffee consumption and a lower prevalence of diabetes. Since the study is observational in nature, it can only indicate whether people who drink coffee regularly tend to have lower rates of diabetes, but without confirming that coffee consumption directly causes a reduction in diabetes risk.
 

Case 4

Hypothesis: The effect of logo prominence (independent variable) on brand recognition (dependent variable).

Study: To evaluate the effectiveness of this marketing strategy, researchers distribute questionnaires to consumers who have seen ads with the logo in positions of different visibility. The questionnaires measure how easily participants recognize the brand after seeing the ads. This study helps to understand whether higher logo visibility contributes to better brand recognition by illustrating the relationship between the independent variable (logo visibility) and the dependent variable (brand recognition).
 

Recognizing the dependent variable and the independent variable

Understanding the distinction between independent variable and dependent variable is critical to the proper setting and interpretation of any research study. The independent variable is one that the researcher deliberately manipulates or controls for in order to observe the effects it may have on another variable. In other words, it is the putative cause of an observed change. The dependent variable, on the other hand, is the effect or outcome that is measured in the course of the study; it responds to changes in the independent variable.

Education study

• Hypothesis: more hours of study per day will lead to higher grades on exams.
• Independent Variable: number of hours of study per day.
• Dependent Variable: grades obtained on exams.
• Description: the researcher analyzes how variations in study time affect students’ academic performance.

Marketing research

• Hypothesis: emotional advertising messages will increase product sales more effectively than informational messages.
• Independent Variable: type of advertising message used.
• Dependent Variable: number of products sold.
• Description: the effect of different advertising messages on sales of a new product is examined.

 
Health study

• Hypothesis: daily intake of this vitamin will improve specific health indicators in participants.
• Independent Variable: daily intake of a specific vitamin.
• Dependent Variable: level of concentration of certain health indicators in the blood.
• Description: researchers evaluate how regular intake of a vitamin affects certain biological parameters in participants

 
Behavioral research

• Hypothesis: exposure to stressful stimuli increases anxiety levels.
• Independent variable: Exposure to stressful stimuli.
• Dependent variable: Anxiety levels measured by questionnaire.
• Description: this study investigates the effect of stress on various degrees of anxiety in individuals subjected to certain conditions.

Notes on statistical methods for analyzing relationships between variables

The statistical analysis of relationships between variables is essential for interpreting the data collected in a research study. To do this, there are several statistical methods that help researchers understand the nature and strength of these relationships:

To analyze relationships between variables in a research study, statistical methods play a key role. Some of the most common methods include:

Correlation: This technique measures the degree of relationship between two variables. A correlation coefficient near +1 or -1 indicates a strong relationship, while a value near 0 indicates no relationship.
 
Linear regression: It is used to predict the value of a dependent variable based on an independent variable. This method helps to understand how much one variable affects the other and what the future trend may be.
 
ANOVA (Analysis of Variance): This method is useful for comparing the means of multiple groups and determining whether there are statistically significant differences between them.
 
Chi-square test: It is applied to examine whether there is a significant relationship between two categorical variables.

These tools allow researchers to test hypotheses and accurately interpret collected data, providing a solid foundation on which to make informed decisions or pursue further investigation.
 

Dependent and independent variables: conclusions

Understanding independent and dependent variables is crucial to the design and analysis of any research study. Correctly identifying these variables not only clarifies the research framework, but also helps to make accurate conclusions about cause-and-effect relationships or correlations.

The appropriate use of statistical methods to analyze these relationships adds another level of precision, allowing researchers to test hypotheses with greater confidence and interpret data in a more informative manner. If correlation and regression provide insights into the degree and direction of relationships, methods such as ANOVA and chi-square tests allow exploration of differences between groups, further enriching the analysis.

In conclusion, a proper understanding and application of the principles governing independent and dependent variables, along with a well-considered methodological choice, are critical to the success of a scientific investigation. Together, these elements provide a solid foundation for advancing knowledge and innovation, regardless of the field of study.
 

FAQ – Dependent and independent variables

What is an independent variable?
An independent variable is a factor in an experiment that is manipulated or controlled by the researcher to observe its effect on the dependent variable.

What is a dependent variable?
A dependent variable is the outcome or response that is measured in an experiment; it is affected by changes in the independent variable.

What is the difference between independent and dependent variables?
Independent variables are the conditions manipulated by the researcher, while dependent variables are the observed results that change in response to the independent variables.

Why are independent and dependent variables important in research?
They are crucial for establishing cause-and-effect relationships, allowing researchers to determine how changes in one factor influence another.

What are variables in research?
Variables in research are elements, traits, or conditions that can vary or change. They are fundamental in scientific studies and experiments, allowing researchers to measure, manipulate, and analyze different aspects of their research subjects. Variables can be classified into different types, such as independent, dependent, controlled, and extraneous, each playing a distinct role in the research process.

What are dependent and independent variables examples?
In an experiment to test the effect of sunlight on plant growth, the amount of sunlight is the independent variable because it is controlled by the researcher. The dependent variable is the plant’s growth, measured in terms of height or biomass, as it changes in response to the amount of sunlight.

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