We want to analyze the relationship and impact between two variables in the education sector, The first variable is the independent variable (intellectual capital), which is measured by a sample of workers and leaders, size 150, and the second variable is the dependent variable (quality of service provided), and it is measured by a sample of Students and parents, its size 330
Is this a nested sampling design where the students and/or parents from the second sample are nested within the workers and/or leaders? If that is the case, then perhaps multilevel (hierarchical linear) regression modeling could be an option. However, this requires nesting of observations, e.g., that the parents in Sample 2 can be linked to workers/leaders in Sample 1.
If the data aren't nested but there's some other connection/dependency between the observations in both samples, perhaps you could account for missing data by using full information maximum likelihood estimation or multiple imputation so that you wouldn't waste any data. But again, this requires that at least some observations can be linked across the two samples.
First, you need to ensure that the data for both variables is collected in a similar way. This means that the data should be collected under similar conditions, using the same measurement scales and techniques.
Next, you will need to check for outliers and missing data, and handle them appropriately. This is important because outliers and missing data can have a large impact on the results of the regression analysis.
Once the data has been cleaned and prepared, you can run the regression analysis. One approach to dealing with different sample size is to use weighted least squares regression. This is a method that gives more weight to observations with larger sample sizes, which can help to balance out the impact of the different sample sizes on the results.
Another approach is to use bootstrapping technique, which is a resampling method that can be used to generate multiple samples from the original data set, with replacement. This can help to account for the different sample sizes by allowing you to run the regression analysis multiple times and then combine the results.
It's also important to consider the assumptions of the linear regression analysis, such as normality and linearity, and check whether they are met. If not, other techniques such as non-parametric methods or non-linear regression should be considered.
It's also essential to interpret the results of the regression analysis with care, considering the sample size and the relationship between the independent and dependent variables, as well as the assumptions of the model and the practical significance of the results.
- Badges
- Science topic
- Similar topics
- Mathematics
- Statistics