How to Check the Readability Level of Your Survey Items? (2026 Guide)

If respondents cannot understand your questions, your data is compromised before anyone even answers. Knowing how to check the readability level of your survey items is one of the most practical skills a survey researcher, UX designer, or market researcher can develop. A question written at a 12th-grade reading level handed to a general adult audience will produce confusion, item nonresponse, and biased results.

Readability matters because it directly affects comprehension, response rates, and data quality. When a respondent encounters a question they cannot parse on the first read, they either skip it, guess, or drop out entirely. Each of those outcomes introduces error into your dataset that no amount of statistical weighting can fully fix.

In this guide, our team walks through the entire process step by step. We cover what readability means in the survey context, which formulas work best, how to score individual items using free tools, and what benchmarks to aim for based on your target audience. We also address the limitations of readability formulas so you can use them as part of a broader questionnaire validation strategy rather than a standalone solution.

Whether you are designing a health survey for patients, a customer satisfaction questionnaire for a consumer brand, an employee engagement survey, or an academic research instrument, the process outlined here applies universally. The principles of readable survey design cross every discipline that relies on self-report data.

Quick answer: To check the readability level of your survey items, extract each question individually, run it through a readability tool like Microsoft Word’s built-in readability statistics or a free online checker, and aim for a Flesch-Kincaid grade level of 6 to 8 for general adult populations. Score each item separately rather than averaging the whole survey, because readability can vary dramatically between questions.

What Is Readability and Why Does It Matter for Surveys?

Readability is a measure of how easy a piece of text is to read and understand. It is calculated using quantifiable text features like average sentence length, average syllables per word, and word frequency. The result is typically expressed as either a grade level (indicating the years of education needed to comprehend the text) or a reading ease score on a 0-to-100 scale.

For survey items, readability takes on a unique importance that general readability advice often misses. A survey question is not like a blog post or an essay. It must be understood quickly, accurately, and without the opportunity for re-reading in many cases, especially in interviewer-administered or mobile surveys. Respondents are not reading for pleasure or information. They are reading to perform a task, and any cognitive friction in the question wording directly translates into measurement error.

Unlike other forms of writing where readers can re-read a confusing passage, survey respondents often make snap judgments about what a question is asking. If the first reading does not produce a clear interpretation, the respondent will either guess at the meaning or disengage. This means the readability threshold for survey items should be even lower than for general informational text targeting the same audience.

Research published in the journal Medical Care found that readability within a single health survey (the VFQ-25) ranged from a 2nd-grade level to a 12th-grade level across individual items. That kind of variation means some questions are accessible to nearly everyone while others effectively exclude respondents with lower literacy. If the difficult items happen to cluster around a key construct you are measuring, your results for that construct will be systematically biased.

The same study analyzed the SF-36v2, one of the most widely used health-related quality of life instruments in the world. Even in this extensively validated survey, item-level readability varied significantly. Some items required college-level reading comprehension while others were accessible at an elementary school level. This finding underscores that even professionally developed, widely used surveys can have hidden readability problems that only item-level analysis reveals.

This is why item-level readability checking matters so much. Computing a readability score for the entire survey as one block of text hides the problem items. A survey can have an overall grade level of 7 while individual questions swing from grade 3 to grade 14. The only way to catch those outliers is to score each item on its own.

The consequences of poor survey readability are well documented in the research literature. Items written above the reading level of the target population produce higher item nonresponse rates, more straight-lining behavior, and greater dropout. For vulnerable populations, including older adults, individuals with limited formal education, and non-native speakers, the impact is even more pronounced. Health literacy research has shown that consent forms and patient surveys written above a 9th-grade level systematically exclude the populations most affected by health disparities.

Beyond data quality, there is an ethical dimension to survey readability. If you are conducting research on a population, that population deserves the ability to participate fully and accurately. When survey items are written above the reading level of the target audience, you are effectively silencing the voices of the people with the lowest literacy, which skews your sample toward more educated respondents and undermines the representativeness of your findings.

Key Readability Formulas Explained

Several readability formulas exist, each with its own strengths, weaknesses, and ideal use cases. Understanding the differences between them helps you choose the right tool for your survey population and interpret scores correctly. The formulas below are the ones most commonly used in survey research and academic literature.

Flesch Reading Ease (FRE)

The Flesch Reading Ease formula produces a score from 0 to 100, where higher scores indicate easier reading. The formula uses average sentence length and average syllables per word. A score of 60 to 70 is considered standard for plain English and is broadly readable by adults aged 13 to 15. Scores below 30 indicate very difficult text that requires graduate-level education to comprehend.

FRE is the formula most commonly built into free tools, including Microsoft Word. Its 0-to-100 scale is intuitive and makes it easy to compare items at a glance. The downside is that the score does not directly translate to a grade level, which can make benchmarking against audience literacy levels less precise.

The FRE scale can be interpreted as follows: 90 to 100 corresponds to 5th-grade reading level (very easy), 60 to 70 corresponds to 8th-grade level (standard), 30 to 50 corresponds to college level (difficult), and 0 to 30 corresponds to graduate-school level (very difficult). For most survey applications, you want your items to land in the 60 to 80 range.

Flesch-Kincaid Grade Level (FKGL)

The Flesch-Kincaid Grade Level formula converts the same text features into a U.S. school grade level. A score of 7.4 means the text is readable by someone in the middle of 7th grade. This formula is widely used in survey research because it maps directly to educational attainment data, making it easy to match question difficulty to your target population’s average reading ability.

FKGL is also the formula built into Microsoft Word’s readability statistics. For most survey applications targeting general adult populations, aim for a grade level between 6 and 8. For vulnerable populations or low-literacy audiences, target grade 5 or lower.

One thing to understand about FKGL is that it becomes less accurate at the extremes. Very short texts (under 100 words) can produce unstable scores because a single long word or sentence has an outsized effect on the average. Since individual survey items are typically short, run FKGL on groups of related items as a secondary check to confirm individual item scores.

SMOG Index (Simple Measure of Gobbledygook)

The SMOG Index estimates the years of education needed to understand a piece of writing. It is based on the number of polysyllabic words (three or more syllables) in a text sample. SMOG is considered one of the most reliable formulas for healthcare materials and is recommended by the National Institutes of Health for assessing patient education materials.

One limitation for survey researchers: SMOG requires a minimum of 30 sentences for accurate scoring. Individual survey items are typically single sentences, so SMOG works better when applied to groups of items or longer survey instructions rather than individual questions.

To use SMOG with survey items, group related questions together (such as all items in a scale or all items in a section) and score them as a block. This gives you a section-level SMOG score that is more reliable than trying to apply it to individual items. If a section scores high on SMOG, drill down to find which specific items are contributing polysyllabic words and revise those.

Gunning Fog Index

The Gunning Fog Index produces a grade-level score based on average sentence length and the percentage of complex words (three or more syllables). It is conceptually similar to FKGL but weights complex words more heavily. The formula assumes that shorter sentences and simpler vocabulary produce more accessible text.

Like SMOG, Gunning Fog works best on text samples of at least 100 words. For individual survey items, results may be unstable. Use it as a secondary check rather than a primary formula for short questions.

The name “Fog” comes from the idea that complex words and long sentences create a fog that obscures meaning. When revising survey items flagged by Gunning Fog, focus first on breaking long sentences and replacing multi-syllable words with shorter alternatives.

Coleman-Liau Index

The Coleman-Liau Index calculates grade level based on characters per word rather than syllables per word. This makes it well-suited for computerized assessment because counting characters is simpler than counting syllables. The formula does not require a syllable-counting algorithm, which removes a common source of error in automated readability tools.

For survey items, Coleman-Liau can produce slightly different results than FKGL because it focuses on word length rather than syllable complexity. Running both formulas and comparing results is a good practice. If they agree closely, you can be more confident in the assessment.

Automated Readability Index (ARI)

The Automated Readability Index uses characters per word and words per sentence to produce a grade-level score. Like Coleman-Liau, it relies on character counts rather than syllable counts. ARI is built into some readability tools and is useful as an additional data point when building a consensus score across multiple formulas.

Dale-Chall Formula

The Dale-Chall formula uses a different approach: it checks words against a list of 3,000 common words that are familiar to most 4th-grade readers. Words not on this list are counted as difficult. The formula combines the percentage of difficult words with average sentence length to produce a grade-level score.

For survey items, Dale-Chall can catch vocabulary problems that syllable-based formulas miss. A short word like “norm” has one syllable but may be unfamiliar to many respondents. Dale-Chall would flag it as difficult while FKGL would not. This makes Dale-Chall a valuable supplementary check, especially for surveys targeting general or vulnerable populations.

Which Formula Should You Use?

For most survey research applications, start with Flesch-Kincaid Grade Level as your primary formula because it maps directly to educational attainment benchmarks. Use Flesch Reading Ease as a secondary check because its 0-to-100 scale provides a different perspective on text difficulty. If you are working with health surveys or patient education materials, add SMOG as a third measure to align with healthcare readability standards.

Reddit users across multiple writing and research communities report that different readability tools can give results varying by 5 to 6 grade levels for the same text. This variance is a known issue in readability research. The best approach is to use multiple formulas and look for consensus rather than relying on a single score.

A practical workflow is to run each item through at least three formulas (FKGL, FRE, and Coleman-Liau) and calculate the average grade level. If the average is above your target threshold, flag the item for revision regardless of what any single formula says. This consensus approach gives you a more stable and defensible readability assessment than relying on one formula alone.

How to Check the Readability Level of Your Survey Items: Step-by-Step

Follow these steps to systematically check readability across every question in your survey. The process works for self-administered surveys, interviewer-administered surveys, and online questionnaires alike. Our team has refined this workflow through years of survey design work across academic, healthcare, and commercial research projects.

Step 1: Extract Each Survey Item Individually

Copy each question stem into a separate document or spreadsheet cell. Do not include response options, instructions, or transition text in the same cell as the question itself. Each item needs to be scored on its own so you can identify outliers.

If response options contain significant text (such as Likert scale descriptors or long multiple-choice options), score those separately. Response option readability matters too, but mixing them with the question stem will distort the score for both.

Set up a spreadsheet with columns for item number, item text, FKGL score, FRE score, SMOG score (where applicable), and a flagged column. This structured approach makes it easy to sort and filter by readability score, giving you a clear picture of which items need attention. Include a notes column to track revision decisions and rationale.

Step 2: Choose Your Readability Formula

Select a primary formula based on your survey context. For general population surveys, use Flesch-Kincaid Grade Level. For health-related surveys, use SMOG or add it alongside FKGL. For government or public sector surveys, Flesch Reading Ease is commonly used with a target score of 65 or higher.

Document your formula choice so the process is reproducible. If you publish your survey methodology, include which readability formula you used and what target threshold you applied. This transparency is increasingly expected in peer-reviewed research and institutional review board (IRB) submissions.

Consider running multiple formulas simultaneously. Most free online tools and programming libraries calculate scores for all major formulas at once, so there is little additional effort involved. Recording multiple scores per item gives you a richer dataset for decision-making.

Step 3: Score Each Item Using a Readability Tool

Paste each item into your chosen readability tool one at a time. Record the score for each item in your spreadsheet alongside the item text. If you are using multiple formulas, record each formula’s score in a separate column.

For free tools, our team recommends starting with Microsoft Word’s built-in readability statistics, which provide both FKGL and FRE scores. Alternatively, use a free online readability checker that supports multiple formulas. For researchers comfortable with programming, the R package koRpus and Python libraries like textstat can batch-process all items at once.

Be aware that very short items (under 15 words) may produce extreme or unreliable scores across some formulas. If a single short question produces a surprisingly high or low score, check it with a second formula before drawing conclusions. Sometimes a single multi-syllable word in an otherwise simple question can skew the score disproportionately.

Step 4: Calculate Survey-Level Statistics

After scoring all items, calculate the mean, median, standard deviation, and range of readability scores across your survey. The mean tells you the average difficulty level. The standard deviation tells you how much variation exists between items. A high standard deviation is a red flag, because it means some items are dramatically harder or easier than others.

Also identify the maximum and minimum scores. A single item scoring at grade 14 in a survey averaging grade 7 is a problem item that needs revision, even if the average looks acceptable. The range between your highest and lowest scoring items tells you how consistent your survey’s reading level is.

Create a histogram of your readability scores to visualize the distribution. Ideally, your scores should cluster tightly around your target grade level with very few outliers. A wide, flat distribution indicates inconsistent writing across items, which means different respondents may struggle with different parts of your survey depending on their reading ability.

Step 5: Flag Items Above Your Target Threshold

Set a readability target based on your audience (see the benchmarks in the next section). Flag every item that exceeds that target. These are the items you need to revise before fielding the survey.

For general adult populations, we recommend flagging any item above grade 8 on Flesch-Kincaid. For vulnerable populations, flag anything above grade 6. Be aggressive with flagging. It is much easier to simplify a flagged item than to deal with biased data after the survey is fielded.

Also flag items where different formulas disagree significantly. If FKGL says grade 5 but Coleman-Liau says grade 10, the item may have a specific feature (like a long technical word) that one formula catches and another misses. Investigate these disagreements case by case.

Step 6: Revise Flagged Items

Rewrite flagged items to bring their readability score within your target range. Common revision strategies include shortening sentences, replacing multi-syllable words with simpler alternatives, removing unnecessary qualifiers, and splitting double-barreled questions into two separate items.

After revising, re-score the rewritten items. Sometimes a revision that simplifies one aspect of the question inadvertently complicates another. Iterate until all items fall within your target readability range.

Keep a log of original and revised item text along with before-and-after scores. This documentation is valuable for methodology reports, IRB submissions, and future survey design work. It also helps you identify patterns in the types of readability problems your survey design process tends to produce, which can improve your first drafts over time.

Step 7: Conduct Qualitative Validation

Readability scores tell you about text features, not about whether respondents actually understand the question. Pair your readability check with cognitive interviews or pilot testing. Ask 5 to 10 members of your target population to read each question aloud and explain what they think it is asking.

This step catches comprehension problems that readability formulas miss. A question can score at grade 5 on Flesch-Kincaid but still be confusing because of cultural references, ambiguous pronouns, or unfamiliar technical jargon that happens to have few syllables.

Cognitive interviewing does not have to be expensive or time-consuming. Even a small number of interviews (5 to 8 participants) will surface the majority of comprehension problems in a survey. The combination of quantitative readability scoring and qualitative cognitive testing gives you the most comprehensive picture of whether your survey items are accessible to your target audience.

How to Interpret Readability Scores for Surveys

Interpreting readability scores requires knowing your audience. A score that is appropriate for an academic audience will be far too difficult for a general population survey. The benchmarks below are drawn from survey research literature, government content standards, and health literacy guidelines.

Benchmarks for General Adult Populations

For surveys targeting the general adult public, aim for a Flesch-Kincaid grade level of 6 to 8. This corresponds roughly to a Flesch Reading Ease score of 60 to 70. The average U.S. adult reads at approximately an 8th-grade level, and government communication standards typically target grade 8 or lower.

If your survey is going to a broad mix of education levels, err on the lower end of this range. A grade 6 target ensures you are accessible to the majority of adults, including those with lower literacy skills. Items at grade 9 or above will systematically exclude a portion of your sample.

The New Zealand Digital Government standards recommend aiming for a reading age of 12 (roughly grade 6 to 7) for public-facing content. This benchmark is widely adopted by government agencies and is a good default for any survey targeting the general public. If your items score above this level, revision is warranted.

Benchmarks for Vulnerable Populations

For surveys targeting vulnerable populations (older adults, individuals with limited formal education, non-native English speakers, or populations with known low health literacy), aim for grade 5 or lower on Flesch-Kincaid. This corresponds to a Flesch Reading Ease score of 80 or higher.

Health literacy research consistently shows that patient materials written above grade 6 exclude the populations most affected by health disparities. The same principle applies to surveys. If you are surveying older adults about health topics, readability is not just a data quality issue. It is an equity issue.

For cross-cultural surveys where respondents may be reading in a second language, target grade 4 to 5. This does not mean treating your audience as children. It means writing in clear, simple language that minimizes the cognitive load of processing a non-native language while also answering survey questions about potentially complex topics.

Benchmarks for Professional and Academic Audiences

For surveys targeting professionals, academics, or specialized populations, you can target grade 10 to 12. These audiences have higher average reading ability and may expect more technical language. However, do not assume that a professional audience gives you a free pass on readability. Even highly educated respondents prefer clear, concise questions over wordy ones.

For employee surveys within a specific organization, consider the full range of roles and education levels within the workforce. A manufacturing company’s employee survey should target a lower readability level than a survey sent exclusively to senior executives. When in doubt, aim for the lowest common denominator within your audience to maximize accessibility.

Addressing Common Score Interpretation Questions

A common question from researchers is whether a specific readability score is good or bad. The answer always depends on context. A Flesch Reading Ease score of 37 indicates fairly difficult text, roughly at a college-graduate reading level. That score would be appropriate for a survey of medical professionals but problematic for a general public health survey.

A Flesch-Kincaid grade level of 14.2 corresponds to approximately a sophomore year of college. For most general population surveys, this score is too high. Only surveys targeting college-educated professionals should tolerate items at this level, and even then, simplifying where possible will improve data quality.

When interpreting scores, always consider the full distribution across your survey rather than focusing on individual items in isolation. An item at grade 9 in a survey averaging grade 7 is less concerning than an item at grade 9 in a survey averaging grade 5. Context matters, and your interpretation should be relative to your target threshold and overall survey readability profile.

Free and Paid Tools for Checking Survey Readability

Multiple tools exist for checking readability, ranging from free utilities built into software you already own to dedicated paid platforms. Our team has tested the most commonly recommended options and compiled guidance for survey researchers specifically.

Microsoft Word Readability Statistics

Microsoft Word includes built-in readability statistics that report both Flesch Reading Ease and Flesch-Kincaid Grade Level. This is the most accessible option for most researchers because Word is already installed on most work computers.

To enable readability statistics in Word, go to File, Options, Proofing, and check the box for “Show readability statistics.” After enabling, run a spelling and grammar check on your document. When the check completes, Word displays a summary box with word count, average sentences per paragraph, average words per sentence, average syllables per word, Flesch Reading Ease score, and Flesch-Kincaid Grade Level.

For survey items, paste one question at a time into a blank Word document and run the check. This gives you item-level scores rather than document-level averages. The process is manual but reliable and free. The Microsoft support documentation on readability statistics provides step-by-step screenshots for users on both Windows and Mac.

One limitation of Word’s readability statistics is that the check runs as part of the spelling and grammar review. If Word flags spelling or grammar issues in your survey item (which can happen with technical terms or proper nouns), you need to resolve or dismiss each flag before seeing the readability scores. This makes the process slower for surveys with many specialized terms.

Free Online Readability Checkers

Several free online tools support multiple readability formulas simultaneously. These tools let you paste text and instantly see scores across Flesch-Kincaid, SMOG, Gunning Fog, Coleman-Liau, and ARI. Getting scores from multiple formulas in one place helps you build a consensus picture of each item’s difficulty.

When using free online tools, be aware that different tools may produce slightly different scores for the same text. This is because the underlying syllable-counting algorithms vary between implementations. For consistency, use the same tool for all items in your survey rather than switching between tools.

Free online tools are ideal for one-off checks or small surveys. For surveys with 50 or more items, the manual paste-and-record process becomes tedious and error-prone. At that scale, consider the programming approaches described below for batch processing.

Paid Readability Platforms

Paid platforms like Readable.com offer more extensive features, including bulk text scanning, API access, and up to 17 different readability algorithms. For researchers managing large survey programs or organizations that need readability checks across many documents, a paid platform may be worth the investment.

Readable.com is used by organizations including Shopify, Netflix, Harvard, Adobe, and NASA for content readability assessment. While those use cases are broader than survey research, the platform’s bulk scanning and API capabilities are directly applicable to checking large numbers of survey items efficiently.

For individual researchers or one-time survey projects, the free tools above are generally sufficient. The underlying formulas are the same regardless of whether you access them through a free or paid interface. Do not assume that a paid tool will give you more accurate scores. The value of paid platforms lies in workflow efficiency, not score accuracy.

Programming Approaches for Batch Scoring

If you are comfortable with programming, batch-processing survey items is much faster than checking each one manually. In Python, the textstat library provides one-line access to all major readability formulas. In R, the koRpus package offers similar functionality with more detailed linguistic analysis.

For a 50-item survey, a Python script using textstat can score every item across all formulas in under a second. This approach also makes it easy to generate a summary report with mean scores, standard deviations, and flagged items, all in a reproducible format.

A basic Python workflow looks like this: load your survey items from a CSV file, iterate through each item, calculate FKGL, FRE, SMOG, Coleman-Liau, and ARI for each one, then write the results to a new CSV with all scores appended. This takes about 20 lines of code and saves hours of manual checking. For R users, a similar workflow using koRpus produces a detailed linguistic analysis alongside readability scores.

Survey-Specific Readability Best Practices

Checking readability is not just about running a formula and recording a number. Survey researchers need to follow specific practices that account for the unique nature of questionnaire items. These best practices are informed by published survey methodology research and our team’s experience designing surveys across multiple domains.

Score Items Individually, Not as a Block

The single most important practice is to score each survey item separately. Pasting the entire survey into a readability tool gives you one average score that hides variation between items. As the published research on health surveys demonstrates, readability within a single instrument can span 10 grade levels. Only item-level scoring reveals that range.

Even if your survey’s average grade level looks acceptable, individual items at grade 12 or higher will cause problems for respondents with lower reading ability. Those respondents will answer the difficult items differently (or not at all) compared to the easier items, introducing systematic measurement error that compromises your analysis.

Use Multiple Formulas and Look for Consensus

Because readability formulas use different text features and produce different scores, running multiple formulas on each item gives you a more reliable picture. If Flesch-Kincaid, Coleman-Liau, and ARI all agree that an item is around grade 7, you can be confident in that assessment. If they disagree by more than 2 grade levels, investigate why.

Significant disagreement between formulas often points to a specific text feature causing the divergence. For example, an item with one very long word might score high on FKGL (which counts syllables) but lower on Coleman-Liau (which counts characters). Identifying the source of disagreement helps you make targeted revisions.

Understand the Limitations of Readability Formulas

Readability formulas measure text features, not comprehension. They cannot detect double-barreled questions, culturally specific references, ambiguous pronoun antecedents, or logical complexity in skip patterns. An item can score at grade 4 on every formula and still confuse respondents if it asks two things at once.

This is why readability checks should be one component of a broader questionnaire validation strategy. Pair them with cognitive interviewing, expert review, and pilot testing for a comprehensive approach to question quality.

Academic researchers have actively debated whether readability formulas are valid tools for assessing survey questions. The consensus is that they are useful but insufficient. They provide a quick, objective screen for vocabulary and sentence complexity issues, but they cannot replace human judgment about whether a question makes sense to the target audience.

Account for Response Option Readability

Response options are part of the survey item. If your Likert scale uses descriptors like “neither agree nor disagree” or “somewhat dissatisfied,” those phrases need readability checks too. Long response option labels can push the effective reading level of an item well above the question stem’s score.

For numeric rating scales (such as 0-to-10 scales), the response options themselves do not contribute to readability concerns. But for labeled scales with text descriptors, score the full set of response labels as a group to check whether any individual label is significantly more complex than the others.

How to Improve Survey Item Readability

When you find items that score above your target readability level, the following revision strategies will help bring them into range without changing the meaning of the question. These strategies are drawn from plain language guidelines, survey methodology best practices, and our team’s experience revising flagged survey items across multiple research projects.

Shorten Sentences

Sentence length is a major driver of readability scores across all formulas. If a survey item runs over 20 words, look for ways to split it into two shorter sentences or remove unnecessary clauses. “Did you experience any of the following symptoms during the past 30 days, including but not limited to headaches, fatigue, or difficulty sleeping?” becomes “In the past 30 days, did you have headaches? Did you feel tired? Did you have trouble sleeping?”

Watch for subordinate clauses that lengthen sentences without adding essential information. Phrases like “as a result of your participation in the program” can often be shortened to “because of the program” or eliminated entirely by reordering the question. Every word you remove will improve your readability score.

Replace Complex Vocabulary

Swap multi-syllable words for simpler alternatives. “Utilize” becomes “use.” “Demonstrate” becomes “show.” “Approximately” becomes “about.” Every polysyllabic word you replace will lower your readability score across multiple formulas. Keep a list of common complex-to-simple word swaps as a reference during survey design.

Be especially careful with technical jargon that may be unfamiliar to your audience. Even if a term is technically simple (few syllables, common in professional contexts), if your respondents do not know what it means, it functions as a barrier to comprehension regardless of what the readability score says. Always prioritize actual comprehension over formula scores when they conflict.

Avoid Double-Barreled Questions

Double-barreled questions ask about two things at once, which both reduces readability and introduces measurement error. “How satisfied are you with the quality and price of the product?” is double-barreled. Split it into two items: one about quality, one about price. This improves readability and produces cleaner data.

Double-barreled questions are often the result of trying to cover too much ground in a single item. When you find one during your readability check, do not just simplify the wording. Split the item into two or more separate questions. This will improve both the readability score and the validity of the resulting data.

Apply Plain English Principles

Write in active voice. Use common words. Be concrete rather than abstract. Avoid jargon unless you are certain your audience knows it. Use short paragraphs and clear formatting. These principles, promoted by government communication standards and plain language advocacy organizations, align with readability best practices and improve the overall respondent experience.

The Plain Language Action and Information Network, a group of U.S. federal employees who advocate for clear government communication, recommends writing for your audience, organizing information logically, using pronouns wisely, writing short sentences, and using the simplest tense possible. These guidelines translate directly to survey item design and will improve your readability scores as a side effect.

FAQs

Conclusion

Learning how to check the readability level of your survey items is a straightforward but powerful process. Extract each question individually, score it with Flesch-Kincaid Grade Level as your primary formula, flag items above your target threshold, revise those items, and validate with cognitive interviews. The entire workflow can be completed using free tools, and it directly improves your survey’s response rates and data quality.

Remember that readability formulas measure text features, not comprehension. Use them as one tool in a broader questionnaire validation toolkit that includes expert review, pilot testing, and respondent debriefing. The goal is not a perfect readability score but a survey that every member of your target audience can understand and answer accurately.

Start by scoring your most recent survey instrument item by item. You may be surprised by the variation you find. The items that score highest are your biggest opportunities for improvement, and fixing them will produce the most measurable gains in data quality. Make readability checking a standard part of your survey design workflow, and your data will be better for it.