Likert scale, Likert type questions, levels of measurement, Google Forms, Qualtrics and SPSS: Which statistical tests to choose and how to interpret the results

When using Likert-type scales in surveys and analyzing the results with tools like Google Forms, Qualtrics, or SPSS, understanding the levels of measurement and choosing the appropriate statistical tests is crucial for accurate interpretation. Here’s a guide to help you navigate these elements effectively.

Understanding Likert Scales and Likert-Type Questions

Likert Scales are a popular rating system used in questionnaires to measure respondents’ attitudes or opinions. These scales typically have 5 or 7 points ranging from “strongly disagree” to “strongly agree,” which allow respondents to express the intensity of their feelings about a given statement.

Likert-Type Questions refer to individual questions that use a Likert scale. They are considered ordinal data because the response categories have a meaningful order but unknown intervals between each category.

Levels of Measurement

The levels of measurement important for statistical analysis are:

  • Nominal: Categories with no logical order (e.g., types of fruit).
  • Ordinal: Categories with a logical order but without consistent intervals (e.g., Likert scales).
  • Interval: Numeric scales with equal intervals between values but no true zero (e.g., temperature in Celsius).
  • Ratio: Numeric scales with equal intervals and a true zero point (e.g., height, weight).

Likert scales are typically treated as ordinal. However, when several Likert items are summed to create a scale score, researchers often treat the resulting scale score as interval data under the assumption of equidistant points between scale levels.

Choosing Statistical Tests for Likert-Type Data

When analyzing Likert-type data, selecting the right statistical test depends on whether the data is analyzed at the item level or the scale level. Here’s how you can proceed:

1. Descriptive Statistics

  • Median and Mode: Suitable for ordinal data to describe central tendency.
  • Frequency Counts: Useful for understanding the distribution of responses across categories.

2. Comparing Groups (Two or More)

  • Mann-Whitney U Test (two groups) or Kruskal-Wallis H Test (more than two groups): Non-parametric tests ideal for comparing ordinal data across independent groups.
  • Wilcoxon Signed-Rank Test (two related groups) or Friedman Test (more than two related groups): Suitable for related samples or repeated measures.

3. Correlation

  • Spearman’s Rank Correlation Coefficient: Appropriate for assessing the relationship between two sets of ranked data or ordinal scales.

4. Regression Analysis

  • If treated as interval data (e.g., summed scores): Linear regression can be used.
  • For ordinal outcomes: Ordinal logistic regression is more appropriate.

Tools for Analysis

  • Google Forms: Collects data but lacks built-in advanced statistical analysis features. Data often need to be exported to another tool like SPSS for detailed analysis.
  • Qualtrics: Offers robust data collection and some analysis features. Can generate frequency distributions and mean scores, but for advanced statistics, data exporting to a statistical package is recommended.
  • SPSS: Highly versatile in handling both descriptive and inferential statistics. You can perform all the above tests and more detailed analyses like factor analysis or multivariate regression.

Interpreting Results

Interpretation should consider the level of measurement:

  • Ordinal Data: Emphasize medians and modes rather than means. Differences between groups should be interpreted in terms of rank rather than absolute differences.
  • Summed Scores Treated as Interval: You can discuss mean differences and use parametric tests, but it’s important to acknowledge the assumption of equidistant intervals.

Conclusion

Choosing the right statistical methods for Likert-type data involves understanding the nature of your data and the assumptions underlying different statistical tests. By selecting appropriate tests and carefully interpreting the results, you can ensure that your findings are both valid and robust, providing valuable insights into your research questions.

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