What are some best practices and tips for conducting quasi-experimental design in education?

1 Define your research question and hypothesis

Before you start your quasi-experimental design, you need to have a clear and specific research question and hypothesis. Your research question should state what you want to investigate, and your hypothesis should state what you expect to find. For example, your research question could be: How does a flipped classroom model affect student achievement in math? And your hypothesis could be: Students in the flipped classroom model will have higher math scores than students in the traditional model.

2 Choose an appropriate design and comparison group

There are different types of quasi-experimental designs, such as pretest-posttest, posttest-only, interrupted time series, and nonequivalent control group. Each design has its own strengths and weaknesses, depending on the research question, the availability of data, and the validity threats. You should choose a design that best suits your purpose and context. You also need to select a comparison group that is as similar as possible to the treatment group, in terms of relevant variables, such as demographics, prior achievement, and motivation. You can use matching, stratification, or propensity score methods to create a comparison group.

3 Control for confounding factors and biases

One of the main challenges of quasi-experimental design is that there may be confounding factors or biases that affect the outcomes, and make it difficult to attribute the effects to the intervention. Confounding factors are variables that are related to both the treatment and the outcome, and may cause spurious associations. Biases are systematic errors that may arise from the design, the measurement, or the analysis of the data. You should try to control for confounding factors and biases by using techniques such as covariate adjustment, difference-in-differences, regression discontinuity, or instrumental variables.

4 Collect and analyze valid and reliable data

Another important aspect of quasi-experimental design is to collect and analyze data that are valid and reliable. Validity refers to the extent to which the data measure what they are intended to measure, and reliability refers to the consistency and accuracy of the data. You should use valid and reliable instruments and methods to collect data, such as standardized tests, surveys, or observations. You should also use appropriate statistical techniques to analyze data, such as descriptive statistics, inferential statistics, or causal inference methods.

5 Report and interpret your results and limitations

Finally, you should report and interpret your results and limitations of your quasi-experimental design. You should present your results in a clear and concise way, using tables, graphs, or figures. You should also interpret your results in relation to your research question and hypothesis, and explain the possible mechanisms and implications of your findings. You should also acknowledge the limitations of your quasi-experimental design, such as the threats to internal and external validity, the generalizability of the results, and the alternative explanations or confounding factors.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?