The answers depends on many factors. First, what is your measurement. Secondly, what size sample. Thirdly, and based upon 1 and 2, what statistical test. For example, an independent t test will require a much different sample size than confirmatory factor analysis.

Another key point: Differentiate statistical significance versus practical significance. For example, statistical significance is a factor partly based on sample size.

Dear Camacho,

I believe that designing a quasi-experimental study would be a preferable approach. One group would receive intervention-based instruction, while the other group would undergo traditional teaching.

Statistical analysis would involve using an independent t-test.

Sincerely,

Tzu-Hsiang Peng

I don't know but I asked the hive-mind (GPT-4):

To determine if there's a significant relationship between curriculum enhancements and learning growth rate, you can use various statistical tools depending on the nature of your data and your research design. Here are some popular options:

When selecting a statistical tool, consider the following:

• The level of measurement of your data (nominal, ordinal, interval, ratio).
• The distribution of your data (normal vs. non-normal).
• The design of your study (paired vs. independent samples).
• The number of groups or conditions you're comparing.

Remember to also check the assumptions of each statistical test and ensure that your data meets these assumptions before conducting the analysis. If the assumptions are not met, consider using an alternative test or transformation methods.

Before thinking about the statistical method to use, the question to ask is how are you going to measure those variables? Do you have the tool to measure it? If yes, then you can proceed from there.

T-test statistics may be appropriate if there are two variables but if they are more than two variables, then use ANOVA

It depends on the variables that have been recorded in the operationalization of variables, the objectives and hypotheses.

You can use Pearson's R, Spearman's Rho, chi square, or linear regression analysis.

Pearson is used for correlational analysis. If it is effect, then regression. However, I believe the gold standard now is PLS- SEM which already includes the correlation and regression. Just be mindful of the number of respondents for the result to be generalizable. Minimum is 250 or thereabouts.