How to Create and Analyze Likert Scale Surveys in Microsoft Forms Using Excel

Likert Scale Surveys in Microsoft Forms, organizations rely on surveys to understand customer satisfaction, employee engagement, product feedback, academic research, and market opinions. Among the many survey methods available, the Likert scale remains one of the most reliable techniques for measuring attitudes, perceptions, and opinions in a structured and quantifiable way.

Microsoft Forms provides a simple way to build professional surveys, while Microsoft Excel offers powerful analytical capabilities for transforming survey responses into meaningful insights.

This guide walks through the complete process of designing statistically sound Likert scale surveys in Microsoft Forms, exporting responses to Excel, performing descriptive analysis, and presenting results using charts and summary statistics.

What Is a Likert Scale?

A Likert scale is a response scale used to measure the degree of agreement, satisfaction, importance, frequency, or likelihood regarding a particular statement.

Unlike a single survey question, a true Likert scale consists of several related statements measuring the same underlying concept. Individual responses are typically combined by summing or averaging the scores to create a composite measurement.

The most common formats include:

5-Point Scale

• Very Satisfied
• Satisfied
• Neutral
• Dissatisfied
• Very Dissatisfied

or

• Strongly Agree
• Agree
• Neutral
• Disagree
• Strongly Disagree

Some research studies use a 7-point scale, offering additional response levels for greater precision.

Likert Scale Surveys in Microsoft Forms

Why Use Likert Scale Surveys?

Likert surveys provide several advantages:

• Easy for respondents to understand
• Simple to create in Microsoft Forms
• Suitable for customer, employee, and academic research
• Generates structured ordinal data
• Enables statistical analysis and visualization
• Produces measurable insights for decision-making

Common applications include:

• Customer satisfaction surveys
• Employee engagement studies
• Product evaluation
• Training effectiveness
• User experience (UX) research
• Healthcare questionnaires
• Educational assessments
• Market research

Step 1: Design a Statistically Balanced Survey

The quality of your analysis depends heavily on the quality of your survey design.

Write Clear and Simple Questions

Questions should be concise, direct, and easy to understand.

Instead of writing:

The software interface adequately facilitates efficient workflow management.

Use:

The software is easy to use.

Avoid technical jargon whenever possible.

Measure Only One Idea Per Question

Each question should evaluate a single concept.

Avoid:

The product is affordable and reliable.

This asks about both price and reliability.

Instead, separate it into two questions:

• The product is affordable.
• The product is reliable.

This approach improves measurement accuracy.

Choose the Appropriate Scale

For most organizations, a 5-point scale provides the best balance between simplicity and analytical value.

Whichever scale you choose, use it consistently throughout the questionnaire.

Avoid Leading Questions

Questions should never encourage respondents toward a particular answer.

Poor example:

Don’t you think our customer support is excellent?

Better example:

How would you rate our customer support?

Neutral wording produces more reliable data.

Eliminate Ambiguity

Always specify exactly what respondents are evaluating.

Instead of:

How satisfied are you?

Use:

How satisfied are you with the delivery process?

Specific questions generate more meaningful responses.

Step 2: Create the Survey in Microsoft Forms

Microsoft Forms makes it easy to build professional Likert-style questionnaires.

Create a New Form

1. Open Microsoft Forms.
2. Select New Form.
3. Enter a descriptive title.
4. Add an optional survey description.

Add Survey Questions

For each survey item:

1. Select Add New.
2. Choose Choice.
3. Enter the survey statement.
4. Add the response options:

• Very Satisfied
• Satisfied
• Neutral
• Dissatisfied
• Very Dissatisfied

Repeat this process for every survey question.

Require Responses

Enable the Required option to prevent unanswered questions.

Mandatory responses reduce missing data and simplify analysis.

Include Optional Open-Ended Questions

Quantitative scores explain what respondents think.

Open-ended questions help explain why they think that way.

For example:

What could we improve?

These qualitative comments often provide valuable business insights.

Step 3: Export Responses to Excel

After distributing the survey and collecting responses:

1. Open your Microsoft Form.
2. Select the Responses tab.
3. Click Open Results in Excel.
4. Download the workbook.

Each respondent will appear as one row, while each survey question occupies a separate column.

This structure is ideal for statistical analysis.

Step 4: Convert Text Responses into Numerical Scores

Likert responses should be converted into numerical values before analysis.

Example encoding:

Excel’s SWITCH function provides a convenient way to automate the conversion.

=SWITCH([@[Overall Satisfaction]],
"Very Satisfied",5,
"Satisfied",4,
"Neutral",3,
"Dissatisfied",2,
"Very Dissatisfied",1)

Repeat this process for every Likert question.

Step 5: Calculate Descriptive Statistics

Once responses are converted into numbers, Excel can summarize the data.

Median

Since individual Likert items represent ordinal data, the median is generally the preferred measure of central tendency.

=MEDIAN(FormsResponse[Satisfaction Score])

Mean

Although many organizations report averages, means are more appropriate when combining several related Likert items into a composite score.

=AVERAGE(FormsResponse[Satisfaction Score])

Number of Responses

=COUNTIF(FormsResponse[Satisfaction Score],"<>"&"")

Standard Deviation

Measure response variability using:

=STDEV(FormsResponse[Satisfaction Score])

A smaller standard deviation indicates stronger agreement among respondents.

Understanding Mean vs. Median

One of the most debated topics in Likert scale analysis is whether averages should be reported.

Because a single Likert item is ordinal, the distance between response categories cannot be assumed to be equal. Therefore:

• Median is the preferred statistic for individual survey questions.
• Mean can be reported as supplementary information.
• For composite scales created by summing multiple related questions, using the mean becomes statistically more acceptable.

Reporting both values provides a more complete picture of the survey results.

Step 6: Analyze Response Distribution

Descriptive statistics summarize the data, but frequency distributions reveal how respondents actually answered.

Create Frequency Tables

Count responses for each category using COUNTIF.

Very Satisfied:

=COUNTIF(FormsResponse[Satisfaction Score],5)

Satisfied:

=COUNTIF(FormsResponse[Satisfaction Score],4)

Neutral:

=COUNTIF(FormsResponse[Satisfaction Score],3)

Dissatisfied:

=COUNTIF(FormsResponse[Satisfaction Score],2)

Very Dissatisfied:

=COUNTIF(FormsResponse[Satisfaction Score],1)

Frequency tables make it easy to identify dominant opinions and unusual response patterns.

Step 7: Visualize Survey Results

Charts make survey findings easier to communicate.

Column Charts

Column charts are ideal for displaying the number of responses within each category.

To create one:

1. Select the frequency table.
2. Open the Insert tab.
3. Choose Column Chart.

Pie Charts

Pie charts provide a quick overview of response proportions.

They work well for executive summaries and presentations.

Recommended Visualizations

For larger surveys, consider:

• Stacked bar charts
• Diverging Likert charts
• Heat maps
• Radar charts
• Pivot charts
• Dashboard scorecards

These visualizations help stakeholders quickly identify strengths, weaknesses, and trends.

Step 8: Report Your Findings

A good survey report should include more than just numbers.

Summarize:

• Overall satisfaction levels
• Most common responses
• Questions with high disagreement
• Areas requiring improvement
• Recommendations based on the data

For example:

Customer satisfaction was generally positive. The median satisfaction score was 4, while the average score was 4.18 with a standard deviation of 0.74, indicating consistent responses across participants. Approximately 81% of respondents selected either “Satisfied” or “Very Satisfied,” suggesting a strong overall customer experience.

Supporting charts make these findings much easier to understand.

Advanced Analysis Techniques

Researchers and data analysts often perform additional statistical analyses.

Handle Missing Data

Document your approach clearly.

Common methods include:

• Listwise deletion
• Pairwise deletion
• Imputation (for advanced studies)

Compare Multiple Groups

When comparing departments, customer segments, or demographic groups, non-parametric methods are generally more appropriate than traditional parametric tests.

Recommended methods include:

• Mann–Whitney U Test
• Kruskal–Wallis Test
• Spearman Rank Correlation

These tests respect the ordinal nature of Likert responses.

Validate Multi-Item Scales

When multiple questions measure the same construct, consider:

• Cronbach’s Alpha
• Exploratory Factor Analysis (EFA)
• Principal Component Analysis (PCA)

These techniques help determine whether survey items consistently measure the intended concept.

Protect Respondent Privacy

Always inform participants about:

• Survey purpose
• Data storage
• Confidentiality
• Anonymity
• Applicable privacy regulations such as GDPR or organizational data governance policies

Transparency increases response quality and trust.

Conclusion

Likert scale surveys remain one of the most effective methods for collecting structured feedback across business, education, healthcare, and research environments. Microsoft Forms simplifies survey creation, while Microsoft Excel provides a familiar platform for organizing, summarizing, and visualizing responses.

By designing unbiased questions, using consistent response scales, converting responses into numerical values, and applying appropriate statistical techniques, you can transform raw survey data into reliable, evidence-based insights. Whether you are evaluating customer satisfaction, employee engagement, product performance, or academic research, a well-designed Likert survey combined with thoughtful Excel analysis enables informed decisions backed by meaningful data.