Are you looking for ways to analyze Likert scale data but don’t know how? A Likert scale is a survey tool that measures people’s attitudes or feelings toward a topic. The respondents choose from a range of options like “strongly agree” to “strongly disagree,” typically with a neutral midpoint like “neither agree nor disagree.” This allows researchers to quantify the subjective data and draw significant conclusions. Invented by psychologist Rensis Likert, the scale typically has five or seven points.
Analyzing Likert scale data involves interpreting the responses to gain valuable insights. In this comprehensive article, we will delve into the step-by-step process of “how to analyze Likert scale data” and make meaningful conclusions.
Understanding the Likert Scale
Before diving into the analysis, let’s take a closer look at the Likert scale and its characteristics:
- The Likert scale presents a series of statements or questions to respondents, who then indicate their level of agreement or disagreement on a numerical scale.
- Commonly used Likert scale response options include “Strongly Disagree,” “Disagree,” “Neutral,” “Agree,” and “Strongly Agree.”
- The responses are usually coded numerically, with higher values representing higher agreement or positive attitudes.
The Likert scale is popular due to its simplicity, ease of administration, and ability to capture nuanced opinions. Researchers can gain insights into the distribution of responses, as well as the central tendency and variability of the data, to understand participants’ attitudes toward a specific topic.
You can find more details about the Likert scale on the following Wikipedia page.
Steps to Analyze Likert Scale Data
1. Data Collection and Preparation
- Design the Survey: Creating a well-structured survey is crucial to obtaining reliable data. Craft clear and concise Likert scale questions aligned with your research objectives. Consider the wording of the statements to avoid bias and ambiguity.
- Data Entry and Cleaning: Once the survey is completed, enter the responses into a data spreadsheet or statistical software. Ensure that the data is cleaned, checking for any missing or incorrect entries that could affect the analysis.
2. Calculate Descriptive Statistics
- Frequency Distribution: To understand the distribution of responses, calculate the frequency of each Likert scale option for each question. Organize the data into a table or chart to visualize the distribution effectively.
- Calculate Mean and Standard Deviation: The mean (average) and standard deviation provide essential information about the central tendency and dispersion of responses for each question. The mean represents the typical response, while the standard deviation measures the spread of responses around the mean. A high standard deviation indicates greater variability in opinions.
For example, consider a survey question on a 5-point Likert scale about the satisfaction level with a product, with responses coded as follows: 1 – Strongly Dissatisfied, 2 – Dissatisfied, 3 – Neutral, 4 – Satisfied, and 5 – Highly Satisfied. After collecting the data, calculate the frequency of each response option and the mean satisfaction level to understand the overall sentiment toward the product.
3. Assess Data Normality
- Normality Test: To apply appropriate statistical techniques, check if the data follows a normal distribution. Normality tests, such as the Shapiro-Wilk or Kolmogorov-Smirnov test, assess whether the data deviates significantly from a normal distribution.
- Interpretation: If the data is normally distributed (p > 0.05), parametric statistical tests like t-tests and ANOVA can be used. If the data is not normally distributed (p < 0.05), non-parametric tests like the Mann-Whitney U test or the Kruskal-Wallis test are more suitable.
For instance, if we analyze the Likert scale responses for two different groups using a normality test and find that the data is not normally distributed, we may choose to use the non-parametric Mann-Whitney U test to compare the groups’ attitudes.
4. Group Comparisons and Analysis
- Comparing Groups: If your survey includes multiple groups or variables, compare their responses to identify significant differences or patterns. For normally distributed data, use techniques like t-tests or analysis of variance (ANOVA). While non-normally distributed data can be analyzed using non-parametric tests like the Mann-Whitney U test or the Kruskal-Wallis test.
For instance, consider a survey that evaluates the effectiveness of two different teaching methods, with responses collected on a 7-point Likert scale. Use ANOVA to determine if there are any significant differences in the perceived effectiveness between the two teaching methods.
- Correlation Analysis: Explore relationships between Likert scale questions or variables using correlation coefficients. There are two most common types of correlation coefficients, the Pearson correlation coefficient & the Spearman correlation coefficient. The Pearson correlation coefficient is suitable for normally distributed data, while the Spearman correlation coefficient is appropriate for non-normally distributed data.
For example, you may want to assess if there is a correlation between job satisfaction and work performance using Likert scale responses from employees. Calculate the correlation coefficient to determine the strength and direction of the relationship.
5. Interpretation and Visualization
- Data Visualization: Present your findings using charts and graphs, such as bar charts or stacked bar charts, to visually represent the distribution of responses for each Likert scale option. Visualizations aid in better understanding the distribution of attitudes and facilitate data interpretation. Read How to Present Likert Scale Data for more details on presenting Likert scale data
- Interpretation: Carefully interpret the results and draw meaningful conclusions based on the data analysis. Pay attention to patterns, trends, and significant differences between groups. Discuss the implications of the findings and how they relate to your research objectives.
How to Analyze Likert Scale Data – Practical Example
Let’s consider a practical example to illustrate the process of analyzing Likert scale data. Imagine conducting a survey on employee satisfaction with workplace facilities. You are using a 5-point Likert scale (1 – Strongly Dissatisfied, 2 – Dissatisfied, 3 – Neutral, 4 – Satisfied, 5 – Highly Satisfied). After collecting the data and calculating the mean satisfaction level and standard deviation, you find that the mean is 4.2, indicating a generally positive sentiment among employees. However, the high standard deviation of 0.9 suggests that opinions vary significantly. That means some employees are extremely satisfied while others are less so.
Next, you decide to compare satisfaction levels between two different departments – the HR department and the Marketing department. Using an appropriate statistical test, such as the t-test for normally distributed data or the Mann-Whitney U test for non-normally distributed data, you find that there is a significant difference in satisfaction levels between the two departments. The Marketing department employees report higher satisfaction levels compared to the HR department.
Finally, you want to explore if there is a correlation between job satisfaction and employee retention rates. Using the appropriate correlation coefficient based on data normality, you discover a strong positive correlation between job satisfaction and retention rates. This suggests that, if job satisfaction is high, the employee retention rate will also be high within the organization.
In conclusion, analyzing Likert scale data involves several critical steps to derive meaningful insights from respondents’ attitudes and opinions. By understanding the Likert scale, researchers can unlock valuable information to inform decision-making and further research. Accurate analysis of Likert scale data is essential in various fields, from social sciences to market research. This helps researchers understand human behavior, preferences, and perceptions in a quantitative manner. With a thorough understanding of Likert scale data analysis, researchers can draw reliable conclusions.