Methodology
Extraneous Variables
The major extraneous factors about the given research area are socio-demographic. These variables include age, gender, and race. Moreover, such factors as comorbid conditions, current living situation, substance abuse, educational and marital status, etc., may pose additional biases and should thus be considered.
Since the given research is intended as a randomized control trial, the primary methods of control over these variables may be randomization and regression analysis. In random sampling, extraneous factors affect the results of the evaluation of independent variables in different sample groups. Researchers consider this method of control as foreground in ensuring the balance between experimental groups (Martin & Bridgmon, 2012). However, it may be ineffective in case the sample size is insufficiently large. Statistical control implies the assessment of potential impacts of extraneous variables on dependent variables and their consequent elimination through the correction of their average values in each variant of the independent variable (Martin & Bridgmon, 2012). The given method may ensure a high level of internal validity and, therefore, can ensure credible research findings.
Instruments
In this study, quantitative instruments will be mainly used. Quantitative methodology implies the interpretation of numerical information and the use of statistical analysis. Therefore, for the evaluation of initial data, we will use the SPSS as it provides a large number of test options. For starters, the numbers about participants’ socio-demographic indicators will be assessed (e.g., the average age) using the chi-square test and then the characteristics of different sample groups will be compared. After this, the regression model will be developed applying the variables associated with predictors of readmission and the dichotomous variable as the outcome (yes = 1, no = 0). The model will allow managing differences between the primary and control samples. To examine the suggested model, the likelihood ratio will be evaluated. Researchers suggest using the Hosmer-Lemeshow test as it is one of the most accurate tests for measuring the goodness of regression models (Hosmer, Lemeshow, & Sturdivant, 2013).
Reliable research always shows internal consistency, stability, and equivalence. Thus, the instrument will be evaluated based on these criteria. The first attribute can be measured using item-to-total correlation or split-half reliability, which implies that study results are divided into parts or halves, and correlations are then calculated. Strong correlations will indicate the high reliability of the instrument, and weak correlations will be associated with low reliability (Heale & Twycross, 2015). Stability can be measured using test-retest, i.e., assessment of the same sample several times. And equivalence will be determined by analyzing the level of agreement in more than two researchers’ opinions on the inter-rater reliability of the instrument.
Description of Intervention
The intervention will be based on findings by Taylor et al. (2016) who suggest that hope, willingness, and responsible action is core to recovery. The researchers state that the implementation of brief recovery-focused interviews before the discharge of a patient at high risk of readmission may be effective in reducing the readmission rates. Considering this, the intervention must aim to engage patients in meta-cognition, development of personal responsibility, and willingness to discuss issues with professionals. To achieve this, health providers in a selected setting will conduct 30-minute interviews with study participants included in one of the sample groups before their discharge. During these sessions, practitioners and patients will discuss barriers to an individual commitment to self-care, causes of current admission and possible ways to overcome them in the future, development of the crisis plan, available sources of community support, aftercare methods, and so on. While the sample group that will be exposed to the suggested intervention will also undergo the formal treatment, the control group will only receive the usual care and will not participate in discussions.
The given intervention targets personal motivations, behavior, and attitudes to continual treatment and self-care. It facilitates access to necessary information about aftercare. In this way, it is expected that it will contribute to the reduction of readmission rates.
Data Collection Procedures
The study will employ both qualitative and quantitative data collection methods. The survey (subjective data), questionnaire (objective, socio-demographic data), and interviews will complement each other and will help evaluate the accumulated data from multiple perspectives. Interviews will provide information on the relationship patterns between the studied variables, e.g., practitioners’ attitudes, patients’ self-concepts, and barriers to intervention success (Grenfell & Lebaron, 2014). Qualitative data is interpretable and subjective, whereas, quantitative data is associated with a greater level of precision, accuracy, and objectivity. Quantitative methods (survey and questionnaire) will provide the necessary numerical and statistical information.
The data collection tools will be designed considering the primary ethical principles of research conduct. First of all, it is important to provide complete information about the purpose of the experiment to the participants to get their consent to meet the principle of voluntary participation (Koepsell, 2017). The administration of experiments without participants’ consent may be regarded as a violation of the ethical code.
References
Grenfell, M., & Lebaron, F. (2014). Bourdieu and data analysis: Methodological principles and practice. Bern, Switzerland: Peter Lang AG.
Heale, R., & Twycross, A. (2015). Validity and reliability in quantitative studies. Evidence-Based Nursing, 18(3), 66-7. Web.
Hosmer, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied logistic regression. Hoboken, NJ: John Wiley & Sons.
Koepsell, D. (2017). Scientific integrity and research ethics: An approach from the ethos of science. Cham, Switzerland: Springer.
Martin, W., & Bridgmon, K. (2012). Quantitative and statistical research methods : From hypothesis to results. Hoboken, NJ: John Wiley & Sons.
Taylor, C., Holsinger, B., Flanagan, J., Ayers, A., Hutchison, S., Terhorst, L., &… Hutchison, S. L. (2016). Effectiveness of a Brief Care Management Intervention for Reducing Psychiatric Hospitalization Readmissions. Journal of Behavioral Health Services & Research, 43(2), 262-271. Web.