Research Methods: Mental Health

Research Methods: Mental Health

Research topic: What is the impact of proper discharge arrangements on mental health outcomes?

Dependent variable

The dependent variable refers to the variable that is reliant on changes in the independent variable (William, 2007). In this study, the researcher is interested in measuring the dependent variable, mental health outcomes measured among all patients using a standardized test.

Independent variable

The independent variable refers to the variable that is manipulated in an experimental study to identify its effects (William, 2007). In this study, the independent variable is discharge arrangements, which vary between groups.  

Null hypothesis: Increased adoption of appropriate discharge arrangements for mentally ill patients has no impact on mental health outcomes.

Research  hypothesis: Increased adoption of appropriate discharge arrangements for mentally ill patients will improve mental health outcomes.

Type of analysis to be conducted: Experimental design

Explanation 

An experimental design is the most appropriate design for this research since it is used to explore causal relationships between two variables using regression. Also, experimental design methodically involves creating a set of procedures to test a hypothesis. This requires the researcher to create a controlled experiment whereby they precisely manipulate the independent variable to measure the depend on the variable. The experimental design is appropriate for this research since the researcher has identified the study size, control group, and treatment group (Emmert-Streib & Dehmer, 2019).

Significance level

The significance level is the probability of rejecting a null hypothesis when it is true. For example, a significance level of 0.05 indicates a 5% risk of acknowledging that a difference exists while there is no actual difference (Schwab, 2006).  

Results

There are several ways to determine whether the researcher will accept or reject any hypothesis. In addition to the significance level, the P-value is used to draw the line for statistical significance on the graph. These two tools will be used to test the null and alternative hypotheses as follows. The researcher defines the significance level before embarking on his experimental research. For instance, a significance level of 0.05 highlights a 5% risk of acknowledging that a difference exists while there is no actual difference. Similarly, lower significance levels highlight that the researcher requires stronger evidence before rejecting the null hypothesis (Freeman et al., 2007). The p-Value is defined as the measure of probability when the null hypothesis is rejected when the null hypothesis is true. To determine whether a null hypothesis is rejected or not rejected, the p-value and significance level are compared. If the P-value is less than the significance level, then the researcher has sufficient evidence to reject the null hypothesis. Likewise, if the p-value is greater than the significance level, the researcher concluded that there is no significant evidence to reject the null hypothesis. While the null hypothesis is not rejected, it is neither accepted. In practice, when data is used to calculate a test statistic, the T function of the data assumes that the null hypothesis is true. The researcher sets a condition for T, which occurs with a small significance level. When the data is collected, the T value is calculated. The null hypothesis is rejected if the condition is not met (Freeman et al., 2007).

The number of mentally ill patients returned to the hospital for the same medical condition because of their undiagnosed mental illness.

68% of the participants out of a sample size of 200 were readmitted back to the hospital because of complications of mental illness. Thus, 136 patients were readmitted. 

References

Emmert-Streib, F., & Dehmer, M. (2019). Understanding statistical hypothesis testing: The logic of statistical inference. Machine Learning and Knowledge Extraction, 1(3), 945-962.

Freeman, M., deMarrais, K.,  Preissle, J.,  Roulston, K., &  St Pierre, E. A. (2007). Standards of evidence in qualitative research:  An incitement to discourse. Educational Researcher, 36(1), 25-32. doi: 10.3102/0013189X06298009.

Schwab, D. P. (2006). Research Methods for Organizational Studies. Organizational Research Methods, 9(4), 572–574. https://doi.org/10.1177/1094428106290197

Williams, C. (2007). Research methods. Journal of Business & Economics Research (JBER), 5(3).