How to Make a Likert Scale A Step-by-Step Guide for Survey Design

Likert scales are powerful tools for measuring opinions and attitudes in surveys. Developed by psychologist Rensis Likert in the 1930s, these scales typically consist of a statement followed by a range of response options. To create a Likert scale, start by defining the attitude or opinion you want to measure, then craft a clear statement or question with 5-7 response options ranging from strong agreement to strong disagreement.

Designing effective Likert scale questions requires careful consideration of wording and response options. The statement should be unambiguous and directly related to the topic being measured. Response options should be balanced, offering an equal number of positive and negative choices, with a neutral midpoint. This symmetry allows respondents to express their opinions accurately.

When implementing Likert scales in surveys, it’s crucial to provide clear instructions to respondents. Explain how to interpret the scale and emphasize the importance of choosing the option that best reflects their true feelings. Consistent formatting and labeling of response options across all Likert items in a survey help reduce confusion and improve data quality.

Designing a Likert Scale Survey

Creating an effective Likert scale survey requires careful planning and attention to detail. The design process involves understanding the scale’s structure, crafting clear questions, selecting an appropriate range, and formatting the survey for ease of use.

Understanding the Likert Scale

Likert scales measure attitudes, opinions, or behaviors using a symmetric agree-disagree scale. These scales typically range from “Strongly Disagree” to “Strongly Agree.” A standard Likert item consists of a statement and a set of response options.

Bipolar scales measure both positive and negative responses, while unipolar scales focus on a single dimension. The most common Likert scales use 5 or 7 points, though other variations exist.

Including a neutral midpoint option allows respondents to express a lack of opinion or ambivalence. This can reduce forced responses but may also encourage non-committal answers.

Crafting Your Questions

Writing clear, concise questions is crucial for accurate data collection. Each item should focus on a single concept and avoid double-barreled statements.

Use simple language and avoid jargon or technical terms. Keep questions short and specific to minimize confusion.

Examples of well-crafted Likert items:

• “The customer service was helpful.”
• “I feel confident using the new software.”
• “The training materials were easy to understand.”

Balance positively and negatively worded items to reduce response bias. This encourages respondents to read each question carefully.

Choosing the Scale Range

The scale range affects the level of detail in responses. Five-point scales are common and provide a good balance between precision and ease of use.

Five-point scale example:

1. Strongly Disagree
2. Disagree
3. Neither Agree nor Disagree
4. Agree
5. Strongly Agree

Seven-point scales offer more nuanced responses but may be more challenging for respondents. They can be useful for complex topics or when finer distinctions are needed.

Consider the target audience and survey goals when selecting a scale range. Longer scales may be appropriate for expert respondents or detailed research studies.

Formatting Your Survey

A well-formatted survey enhances readability and encourages completion. Use consistent formatting throughout the questionnaire to avoid confusion.

Group related items together and consider using section headers to organize the survey. This helps respondents understand the context of each question.

Present the scale consistently, either horizontally or vertically. Horizontal scales work well for shorter surveys, while vertical scales may be easier to read on mobile devices.

Include clear instructions at the beginning of the survey and for each section if needed. Explain how to interpret the scale and provide examples if necessary.

Online form builders like Formifyr offer customizable templates for Likert scale surveys. These tools can streamline the design process and ensure a professional appearance.

Analyzing Likert Scale Data

Analyzing Likert scale data involves quantitative methods to interpret responses and draw meaningful conclusions. Key aspects include examining central tendencies and addressing potential biases in the data.

Quantitative Data Analysis

Likert scale responses can be analyzed as numerical data. Each response option is assigned a value, typically 1-5 for a 5-point scale. This allows for statistical calculations and comparisons across groups or questions.

To analyze Likert data:

1. Calculate response frequencies
2. Compute percentages for each option
3. Create visual representations (e.g. bar charts)

Researchers often treat Likert data as interval data, enabling more advanced statistical tests. However, this approach is debated among statisticians.

Measuring Central Tendency

Central tendency measures provide insights into overall response patterns. The three main measures are:

1. Mean: Average of all responses
2. Median: Middle value when responses are ordered
3. Mode: Most frequent response

The median is often preferred for Likert data as it’s less affected by extreme values. The mode can be useful for identifying the most common opinion.

Example:
Responses: 2, 3, 3, 4, 5
Mean: 3.4
Median: 3
Mode: 3

Addressing Response Bias

Response bias can skew Likert scale results. Common biases include:

1. Central tendency bias: Respondents avoid extreme options
2. Acquiescence bias: Tendency to agree with statements
3. Social desirability bias: Answering to appear favorable

To mitigate bias:

• Use balanced scales with equal positive and negative options
• Include reverse-coded items
• Randomize question order

Analyzing response patterns across items can help identify inconsistent or biased responses. Formifyr offers features to randomize question order and include reverse-coded items, helping researchers create more robust Likert scales.