Research Communication Strategies
Please ensure that the Discussion includes more than 400 words with scholarly articles (include at least two scholarly references), and the plagiarism level must remain below 20%.
You must pick only ONE of the topics mentioned below for your discussion post this week:
• Understanding Pearson chi-square test
• Examine tools used to evaluate significance
• Explain how to disseminate research findings
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Research Communication Strategies
Understanding the Pearson Chi-Square Test
The Pearson Chi-Square test is one of the most commonly used statistical tests in categorical data analysis. It allows researchers to examine whether there is a statistically significant association between two or more categorical variables. This makes it particularly useful in fields like healthcare, education, and the social sciences.
Purpose and Hypotheses
The primary goal of the Pearson Chi-Square test is to test for independence or association between variables. The null hypothesis (H₀) assumes that the variables are independent (no relationship), while the alternative hypothesis (H₁) asserts that a significant relationship exists.
Assumptions and Requirements
The Pearson Chi-Square test comes with key assumptions:
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Independence of observations: Each subject or case must only appear in one category.
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Expected frequency count: Each cell in the contingency table should generally have an expected value of at least 5 to maintain validity (McHugh, 2013).
Violating these assumptions can lead to misleading conclusions. If the assumptions are not met, alternative tests like Fisher’s Exact Test may be more appropriate.
Applications in Research
This test is especially helpful in healthcare research to identify patterns and relationships among patient demographics, disease states, and treatment responses.
Interpreting Results
A p-value less than 0.05 typically indicates a statistically significant relationship, suggesting the variables are not independent. However, the test does not indicate how strong the relationship is. That’s why researchers may also calculate effect sizes using tools like Cramér’s V or Phi coefficient,
