Comparing means: Likert scale analysis

Comparing means: Likert scale analysis

Table of Contents

1. Introduction
2. Method 1: Analysis of Means
3. Method 2: Independent Samples t-test
4. Method 3: Non-parametric Tests
5. Interpretation of Results
6. Conclusion
7. References

Introduction

In this tutorial, we will explore three methods to compare the means of two or more independent groups using Record Scale data. The goal is to compare male and female respondents with regard to different items of the liquid scale. We will examine each method step by step, discussing their advantages and limitations.

Method 1: Analysis of Means

To begin, we will use the Analysis of Means feature in SPSS. This method allows us to compare the mean and median scores of the groups. We will start by moving the record scale items to the dependent list and the gender groups to the independent list. We can then select the number of cases, mean, standard deviation, and median as desired. After selecting the options, we can proceed with the analysis.

Pros:

• Provides mean and median scores for comparison
• Allows customization of analysis options

Cons:

• Assumes normal distribution of data

Method 2: Independent Samples t-test

Next, we will use the Independent Samples t-test in SPSS. This method is suitable for normally distributed data when the assumption of homogeneity of variances holds. We will move the groups to the grouping variable and define them accordingly. The options allow us to exclude cases with missing values. The results will include descriptive statistics such as mean, standard deviation, standard error, and sample size for each group.

Pros:

• Suitable for normally distributed data
• Provides comprehensive descriptive statistics

Cons:

• Assumes homogeneity of variances

Method 3: Non-parametric Tests

If the assumptions of the Independent Samples t-test are violated, we can use non-parametric tests. In SPSS, the Mann-Whitney U test, a popular non-parametric test, can be employed. Before running the test, we need to ensure that the variables are coded as ordinal or scale. We can then move the groups and the items to the test fields. The results will include statistical significance and visual representation of the difference between the groups.

Pros:

• Does not assume normal distribution or homogeneity of variances
• Provides visual representation of differences between groups

Cons:

• Limited to ordinal or scale variables

Interpretation of Results

After performing the analysis, we need to interpret the results. The p-value from the Independent Samples t-test indicates the significance of the difference between the groups. The Mann-Whitney U test also provides a significant difference if the asymptotic significance is violated. Visual representation, such as mean rank or graphs, can aid in understanding the results.

Conclusion

In conclusion, we have explored three methods to compare the means of independent groups using record scale data. Each method has its advantages and limitations, and the choice depends on the nature of the data and assumptions. By following the step-by-step instructions in SPSS, researchers can effectively analyze and interpret their data to gain valuable insights.

References

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