Experimental studies are not typically used in educational policy evaluation because it is often difficult to control all of the variables that could affect the outcome of the study.

Data cleaning is the process of removing errors and inconsistencies from the data. This is important because errors and inconsistencies can lead to biased results.

Factor analysis is not typically used in educational policy evaluation because it is not as straightforward to interpret as other data analysis methods.

The purpose of interpretation of results in educational policy evaluation is to draw conclusions about the data, make recommendations for policy changes, and communicate the findings of the study to stakeholders.

The data used in educational policy evaluation is not always accurate and reliable. This can be due to errors in data collection, data entry, or data processing.

Data analysis is important in educational policy evaluation because it helps to identify problems and trends in education, evaluate the effectiveness of educational policies and programs, and make informed decisions about educational policy.

Experimental data is not typically used in educational policy evaluation because it is often difficult to control all of the variables that could affect the outcome of the study.

Descriptive statistics summarize the data, while inferential statistics allow you to make inferences about the population from the sample.

Regression analysis is used in educational policy evaluation to identify the relationship between two or more variables, predict the value of one variable based on the values of other variables, and test hypotheses about the relationship between two or more variables.

Factor analysis is not a type of regression analysis. It is a statistical method used to identify the underlying structure of a set of variables.

Data visualization is important in educational policy evaluation because it helps to make the data more accessible and understandable, identify patterns and trends in the data, and communicate the findings of the study to stakeholders.

Histograms are not a type of data visualization. They are a statistical method used to represent the distribution of data.

Sensitivity analysis is used in educational policy evaluation to assess the impact of changes in the data on the results of the study, identify the most important variables in the study, and test the robustness of the findings of the study.

Factor analysis is not a type of sensitivity analysis. It is a statistical method used to identify the underlying structure of a set of variables.