The classic tool for this kind of test is Analysis of Variance. In general, you would be hypothesizing that each of your experimental groups performed different from the control group. You can then use "post-hoc" analyses with ANOVA to make group-by-group comparisons to test those hypotheses.

When conducting a quasi-experimental design to test two treatments and one control group, you need to use statistical tools that allow you to compare the outcomes across the three groups while accounting for any potential confounding variables. Here are some recommended statistical tools and approaches:

1. Descriptive Statistics

• Means and Standard Deviations: Calculate means and standard deviations for the outcome variables in each group to get an initial understanding of the data.
• Frequency Distributions: Check the distribution of categorical variables across groups.

2. Inferential Statistics

• ANOVA (Analysis of Variance):Use ANOVA to determine if there are statistically significant differences in the means of the outcome variable among the three groups (two treatments and one control). One-Way ANOVA: If you have a single outcome variable and you want to compare it across the three groups. Two-Way ANOVA: If you have more than one factor or want to include interaction terms.
• Post-Hoc Tests:If ANOVA indicates significant differences, use post-hoc tests (e.g., Tukey’s HSD, Bonferroni correction) to determine which specific groups differ from each other.
• ANCOVA (Analysis of Covariance):Use ANCOVA to adjust for potential confounding variables and to assess the differences among group means after accounting for these covariates.
• Multivariate Analysis:MANOVA (Multivariate Analysis of Variance): If you have multiple dependent variables, use MANOVA to assess the differences across groups while considering the correlation between dependent variables.

3. Regression Analysis

• Multiple Regression:Use multiple regression if you want to predict the outcome variable based on group membership and other covariates. This helps in adjusting for confounders.
• Generalized Linear Models (GLM):GLMs can be useful if your outcome variable is not normally distributed (e.g., binary outcomes, count data).

4. Propensity Score Matching (PSM):

• Matching Techniques: Use PSM to create comparable groups based on covariates. This can help reduce selection bias in quasi-experimental designs.
• Analysis After Matching: Conduct ANOVA or regression analyses on the matched sample to assess treatment effects.

5. Effect Size Measurement:

• Cohen’s d, Eta Squared, and Partial Eta Squared: Calculate effect sizes to understand the magnitude of differences between groups.

6. Mixed-Effects Models:

• Linear Mixed-Effects Models: Useful if you have repeated measures or hierarchical data structures.
• Random and Fixed Effects: These models account for both fixed effects (e.g., treatment) and random effects (e.g., subject variability).

7. Non-Parametric Tests:

• If the data do not meet the assumptions of parametric tests (e.g., normality), use non-parametric alternatives like the Kruskal-Wallis test for comparing medians across groups.

Steps for Analysis:

Software Tools:

• Statistical Software: SPSS, R, SAS, Stata, or Python (with libraries like statsmodels and scipy).
• Data Visualization: ggplot2 in R, matplotlib and seaborn in Python.

These statistical tools and approaches will help you rigorously test the effects of the two treatments compared to the control group in your quasi-experimental research.

Federico Alonso-Trujillo The next time you cut and paste an answer from Chat-GPT or equivalent, please cite that as your source, just as you would whenever you are referring to someone else's work.