Exam 21: Statistical Tests and Concepts in Research Analysis

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Reactivism refers to the act of reacting or responding to a situation or event. There are four main types of reactivism:

1. Political reactivism: This type of reactivism involves responding to political events or decisions. It can include protests, advocacy, and other forms of political action in response to government policies or actions.

2. Social reactivism: Social reactivism involves reacting to social issues or injustices. This can include movements for social change, such as the civil rights movement or the feminist movement, as well as individual acts of activism in response to social issues.

3. Environmental reactivism: This type of reactivism involves responding to environmental issues and concerns. It can include actions to protect the environment, such as protests against pollution or advocacy for conservation efforts.

4. Economic reactivism: Economic reactivism involves reacting to economic issues or inequalities. This can include actions to address economic injustices, such as protests against corporate greed or advocacy for fair labor practices.

Overall, reactivism encompasses a wide range of actions and movements aimed at addressing various issues and injustices in society.

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Factor analysis is a statistical method used in research to identify underlying relationships between observed variables. It is often used to explore the structure of a set of variables and to identify underlying factors that may be influencing the observed data.

In factor analysis, the goal is to identify a smaller number of unobserved variables, known as factors, that can explain the patterns of correlations among the observed variables. These factors are used to summarize the information contained in the observed variables and can help researchers understand the underlying structure of the data.

Factor analysis can be used in various fields such as psychology, sociology, economics, and market research to identify underlying dimensions or constructs that may not be directly observable. It can also be used to reduce the dimensionality of a dataset, making it easier to interpret and analyze.

Overall, factor analysis is a valuable tool in research for uncovering the underlying structure of data and identifying the key factors that drive the relationships among observed variables.