Qualitative data refers to non-numeric data that relies on characterization when designing an analysis method. According to the National Institutes of Health (2020), analyzing qualitative data relies on different procedures that seek to provide an understanding of collected data through explanation and interpretation of the investigated situations. Social sciences and other natural setting studies always rely on qualitative research methodologies in offering insights to the audiences. The study on perceptions on the end of life care carried out among relatives of terminally ill patients qualifies for qualitative research analysis since the study is based on the collection of views and experiences of the participating population. The setting of the research would emphasize the need to explain the results based on the feedback obtained from the participating cohorts. The study analysis would rely on the dimensions of qualitative data collection that include contextual understanding, the interaction of participants, and the flexibility of the respondents’ feedback. The choice of methodology for the study pre-determined the nature of analytic tools that can be employed to analyze the data collected from the study surveys. The paper herein discusses the Analysis of Variance (ANOVA) as the appropriate analytic methodology to determine the questionnaire outcomes with a focus on the variable and the hypothesis forming part of the research questions.
Variables
Variables refer to the measurable characterizations or trait within a study and are regarded as the determinants of the study outcome. Variables can either be dependent or independent, depending on the ability to be manipulated to affect the study outcomes (Mena et al., 2017). Carrying out a study on ‘The perception of relatives of terminally ill patients on end of life care,’ offers several variables that can be relied upon to provide a feedback mechanism for the study. Among the key variables that the research should consider manipulating include level of education, religious/faith affiliation, marital status and income, location of the end-of-life care, gender, and age (Gallagher & Krawczyk, 2013). The mentioned factors always have a significant influence on the perceptions and attitudes of an individual, making them critical components to consider when developing qualitative survey questions or an interview questionnaire. The variables are largely social and, as such, have a significant influence on the type of data analysis method employed to report on the study findings.
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Null Hypothesis and Research Hypothesis
Research always bases on the-need-to-know or the urgency of offering a solution to an existing problem. A research hypothesis, therefore, offers a proposed study of a testable proposition depending on the identifiable variables. Key aspects of the research hypothesis are always considered among the presumable differences in the representative population sampled for the study. The relationship between the variables in a study hence informs the researchers’ choice of hypothesis to test in their statistical examination (Mena et al., 2017). An example of a research hypothesis would be that “The relationship of the respondent with the patient influenced their perception on the end of life care.”
Null Hypothesis
The null hypothesis refers to a statistical reference to two examinable variables in the study. The purpose of the Null hypothesis is to help the researcher discredit a claim between variables relationships by providing statistical significance. In this study, the null hypothesis would be that,
H O : Relationship with the patient has no influence on the respondents’ perception on end of life care. The alternative hypothesis therefore would be that;
H A : Relationship with the patient influences the respondents’ perception on end of life care.
Both the null and research hypotheses help in forming research questions and objectives. They provide the researchers with a definitive scope of the study, allowing them to choose the methodology for data collection, analysis, and presentation. Further, the hypothesis allows the researcher to outline their expected results at the proposal stages of the study.
Analysis Conducted (ANOVA)
Statistical data analysis takes different forms depending on the nature of data to be analyzed or the type of study, either qualitative or quantitative. The study of perception on the end of life care as qualitative research would need the analysis of multiple groups of data, including differences in age, gender, educational background, and beliefs. The complexity of such data would require that the research employs a data analysis method that factors the differential data collected from questionnaires.
Analysis of Variance (ANOVA) as a statistical tool of analysis helps in determining statistical significance in different independent variables, normally three or more variables. The effective use of ANOVA as a statistical tool for analysis is preferred in group data analysis to compare means between groups or variables. The comparison of the means in the different groups of data indicates as to whether there exists a significant statistical difference. In the test for a null hypothesis, ANOVA allows the researcher to employ the equation:
Employing one way ANOVA is considered omnibus and offers no definite ascription to the group of data that has a higher significance and difference between the groups or variables examined in the study.
Why ANOVA is Appropriate
The study on perception on the end of life care, being qualitative research requires an analysis that differentiates the statistical difference between the different groups of data from the feedback from the respondents. As such, using ANOVA in analyzing such groups of data varied upon the different factors that affect perception including relationships, level of education, gender, age, and income allows researchers the compare the statistical significance between the data groups (Mena et al., 2017). ANOVA test allows for comparison of data, and from their significance difference, the study null hypothesis is tested as a proof or claim. Further, ANOVA grants different researcher modes of analyzing their data. Depending on the type and size of the data in terms of groups, one could choose to use one-way, two-way, or N-way ANOVA test depending on the number of independent variables in the study.
In the case of One-way ANOVA test, the study offers a statistical test for a study with one independent variable. The two-way ANOVA test is employed, where there are two independent variables. The test offers an analysis of the interaction between then different variables as independent variables (Mena et al., 2017). The variation in the interactions can further be extrapolated and provide a different dimension in the interaction between the independent variables with dependent variables (Alase, 2017). In the case of the study, level of education, and the relationship between the respondent to the patient may influence their perception on the end of life care. However, religious beliefs and the facility from where the care is offered may alter their perception where the difference in perception may be greater in males than females. N-Way ANOVA provides a broader spectrum of analysis, considering that the researcher can compare and test the independent variable of a value higher than two. The researcher using N-Way ANOVA where ‘n’ is the number of independent variables can compare a larger amount of data from groups more than two. For example, being the perception can be termed to be different across age groups, racial lines, country, and income lines.
The significance level: The Expected Results/Rejection and Acceptance of results
The significance level in ANOVA is determined through the calculation of the F-ratio as associated with the probability value p-value. The significance test where the association of p-value to F-value is to the value.05 is used in testing the null hypothesis. If p>.05 then the null hypothesis is rejected, and alternative hypothesis is confirmed (Mena et al., 2017). The rejection of null hypothesis, therefore, infers that the means between the groups are not different. ANOVA, therefore, has a significance level of .05 to help in null hypothesis testing.
References
Alase, A. (2017). The interpretative phenomenological analysis (IPA): A guide to a good qualitative research approach. International Journal of Education and Literacy Studies , 5 (2), 9-19.
Gallagher, R., & Krawczyk, M. (2013). Family Members' Perceptions Of End-Of-Life Care Across Diverse Locations Of Care. BMC Palliative Care , 12 (1), 25.
Mena, B., José, M., Alarcón, R., Arnau Gras, J., Bono Cabré, R., & Bendayan, R. (2017). Non-normal data: Is ANOVA still a valid option?. Psicothema, 29 ( 4):552-557 .
National Institutes of Health. (2020). Definition of Human Subjects Research. Retrieved 8 April 2020 from https://grants.nih.gov/policy/humansubjects/research.htm
