What are the most common statistical analyses for evaluating program implementation?

1 Descriptive statistics

Descriptive statistics are the simplest and most widely used type of statistical analysis. They summarize and display the basic characteristics of your data, such as the mean, median, mode, standard deviation, frequency, and percentage. Descriptive statistics can help you understand the distribution, variability, and central tendency of your data, and identify any outliers or errors. You can use descriptive statistics to describe the characteristics of your program participants, the inputs and outputs of your program activities, and the baseline and follow-up values of your program outcomes.

2 Inferential statistics

Inferential statistics are more advanced and complex than descriptive statistics. They allow you to test hypotheses and make generalizations about your data based on a sample. Inferential statistics can help you determine the significance, effect size, and confidence level of your results, and compare the differences or relationships between groups or variables. You can use inferential statistics to assess the impact of your program interventions, the factors that influence your program outcomes, and the similarities and differences between your program subgroups.

3 Regression analysis

Regression analysis is a type of inferential statistics that examines the relationship between one or more independent variables (predictors) and one dependent variable (outcome). Regression analysis can help you estimate how much your outcome variable changes when you change one or more predictor variables, while controlling for other variables. You can use regression analysis to measure the contribution of your program inputs or activities to your program outcomes, the interaction effects of your program components, and the moderation effects of your program context.

4 ANOVA and ANCOVA

ANOVA (analysis of variance) and ANCOVA (analysis of covariance) are types of inferential statistics that compare the means of two or more groups on one outcome variable. ANOVA can help you test whether there is a significant difference between the groups, while ANCOVA can help you adjust for the effects of other variables (covariates) that may influence the outcome. You can use ANOVA and ANCOVA to compare the effects of different program interventions, the performance of different program sites, and the outcomes of different program segments.

5 Chi-square test

Chi-square test is a type of inferential statistics that tests the association between two categorical variables. Chi-square test can help you determine whether the observed frequencies of the variables are different from the expected frequencies, based on the null hypothesis of no association. You can use chi-square test to examine the relationship between your program participation and your program outcomes, the distribution of your program characteristics across categories, and the agreement between your program ratings and your program standards.

6 T-test and Z-test

T-test and Z-test are types of inferential statistics that compare the means of two groups on one outcome variable. T-test can help you test whether there is a significant difference between the means of two independent or paired samples, while Z-test can help you test whether the mean of one sample is significantly different from a population mean or a specified value. You can use T-test and Z-test to compare the changes in your program outcomes before and after your program implementation, the differences in your program outcomes between two program groups, and the deviation of your program outcomes from a benchmark or a goal.