write a response to this discussion post, use references for support

Zordia Zordia : May 28 1:07pm | Last reply May 30 8:44pm Week 1 Discussion Post: A brief description of the design and where it is most appropriately used Correlation & Causation Correlation helps to identify and measure an existing relationship between two variables and causation explains how one variable may have caused a change in another variable (Coursera Staff, 2024). When the value of one variable goes up and the other variable does well this is considered a positive correlation and when the value of one variable goes up but the other one goes down this is considered a negative correlation (Coursera Staff, 2024). Causation happens when one variable directly impacts s or causes changes to occur in another (Coursera Staff, 2024). Correlation does not always imply causation (Coursera Staff, 2024). This type of design in best utilized with two or more variables The assumptions for the design Before conducting a Pearson correlation test the following assumptions should be checked for: are both variables utilizing a continuous scale? Can a linear relationship between the two variables be seen visually in a scatterplot? Are there any spurious outliers? And are the variables close to being normally distributed? (Warner, 2021). The strengths and weaknesses of the design and the threats to internal and external validity Strengths include; Research can be conducted in public and private settings. Data can be collected through surveys, observation and archival. Research is conducted in real life situations and is more applicable to everyday life. Due to the amount of data accumulated during this research new opportunities can be created. The researcher could discover new correlations during the study. Results can be easy to classify (Gaille, 2020). Weaknesses include; Although correlation can discover relationships it does not help to explain why or how they occurred. This design cannot inform the responsible variable influence. This method can be time consuming. Unique outcomes can interfere with the study. Time and money spent on study does not always generate useful data (Gaille, 2020). Analyzation Correlation and causation has some strengths and weaknesses that need to be considered when thinking about the appropriateness of this design for your research study. This design will work out great for my research because | want to know more about the relationship between training adequacies for family preservation service providers working with substance using families and stress and burnout. | suppose it depends on the time, knowledge and motivation to pursue the research. What also needs to be taken into consideration when using this design in being open to various outcomes without bias. References Coursera Staff, (2024). Correlation ws. Causation: What's the difference? Coursera.org