Factor analysis is a statistical method used to show the relationship existing between different variables of interacting research phenomenon. It analyzes dependent variables by indirect use of independent variables (factors) that influence them. Factor analysis is applied in numerous fields dealing with various policies and scientific concerns. First and foremost, factor analysis is used in data reduction (parsimony). This involves reduction of the number of variables to be used based on their correlation. The variables are reduced to their specific common patterns that replace the overall variables without losing much of the information. Next, factor analysis is used in data classification/description. Variables involved are grouped into various descriptive categories on the basis of their similar features or behaviors. Another application of factor analysis is scaling. Factor analysis is used to develop scales upon which variables can be compared or rated. Furthermore, factor analysis is used in data transformation. Factor analysis largely transforms data to be compatible with other techniques. In addition to this, factor analysis is employed in testing of hypothesis. Lastly, factor analysis is used in structuring of data, exploring of unknown data as well as mapping of data (Shi, 2008).
As far as multilevel analysis is concerned, it is also a statistical method used to analyze hierarchical and non- hierarchical nested data such as clustered data. Multilevel data is mostly prevalent in social, behavioral and biomedical sciences respectively. Multilevel analysis has many applications in research. These include prediction, data reduction, causal inference and so forth. Prediction is amongst one of the most important application of multilevel analysis. Additionally, prediction is a significant aspect of research. Likewise factor analysis, multilevel analysis is widely used when data reduction is required. Finally, multilevel analysis is best suited when interpretation of causal inference for the observational data is necessary (Shi, 2008).
Health care research targets various audiences. These include students both at undergraduate and graduate level as well as general readers seeking information regarding the field together with its practices. Moreover, this research is most significant and beneficial to health care practitioners with the urge to stay informed about the updates and changes in this field. Further, organization administrators, directors and departmental leaders are also targets of health care research. This is crucial for them to create or expand health care services and programs for their staff or students. In the above mentioned health care research audiences, communication is essential and crucial. However, different communication techniques are employed for the respective audiences. First of all, health communication should be clear prior to the designing, implementation or reporting of the research results. It is also necessary for it to be reliable and evidence-based. Communication can take several forms of interaction; intrapersonal, interpersonal, group and community dynamics. Intrapersonal communication relates to how information is processed on individualistic influence whereas interpersonal relates to influence of two people in processing information. Health care research commonly employs interpersonal communication. The main aim of communication is to promote change in individuals and larger groups at large. In regards to the individual scenario, two communication intervention forms are employed. First, informed consent for making decisions. Here, an individual is given information to enable him/her make health decisions. Secondly, persuasion oriented communication, aimed at convincing people to change. Relative to the group scenario, advocacy intervention is used. This involves change of laws or policies at particular levels like safety and working conditions improvement in workplace (Shi, 2008).
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