Univariate, bivariate, and multivariate approaches to analysis are appropriate for different researches. The current paper claims that multivariate analysis is appropriate when it is necessary to examine multi-variables (dependent and independent) to establish their link. Thus, it deals with data sets that have more than one variable. Otherwise, univariate analysis is appropriate.
Research methods that are appropriate for a given study need to fulfil certain requirements to arrive at valid results and recommendations. Cohen and Crabtree (2008) identify such characteristics as credibility and consistency. Others include the deployment of rigorous methods and authentication, precision, and rationality (Rolfe, 2006). Research can be designed as either qualitative or quantitative (Finlay, 2006). This paper discusses the multivariate analysis that is used in quantitative researches.
Multivariate Analysis appropriate for a Quantitative Study
Multivariate analysis implies various approaches and techniques for establishing the link between variables. The affiliation is examined with all the variables in the same time. In a quantitative research, the statistical analysis approach is appropriate where a research examines the connection in data sets that have several forecaster variables (independent variables) and/or various outcome phenomena (independent variables) (Frankfort-Nachmias & Nachmias, 2008). It is most appropriate where one wishes to determine variables that explain a given phenomenon from a range of possible variables.
Multivariate statistical analysis helps in reducing assumptions in studies since a research will assume (hypothesize) that several variables explain a particular phenomenon. For example, instead of assuming that only education levels influence income, research can also hypothesize that gender and ethnicity among other variables influence it simultaneously. Therefore, multivariate analysis yields research outcomes with greater understanding of the phenomena under study.
Univariate and Bivariate Analysis
Univariate statistical techniques aim at establishing the connection between two variables. Thus, they are most appropriate where a quantitative research involves only on dependent and independent variable, for instance, in a study involving the effect of household income levels (independent variable) on expenditure (dependent variable). They are also appropriate in cases involving one-way analysis of variance for different levels of an independent variable (Creswell, 2009). For example, a researcher may be interested in studying the effect of two doses on a prescribed medical condition like stomachache. Thus, while the bivariate analysis focuses on the link and causes, the univariate study does not focus on these areas. It illustrates rather than explicating (Scott, 2011). Bivariate analysis is most appropriate in cases of studies involving paired data sets. An example involves the recording of ages of male and female partners in the same marriage. It is paired in the sense that the ages are taken from the same marriage where one age does not influence the other.
The Appropriateness of Statistical Tests for Future Research
The handout describes various statistical tests, including multiple regression analysis, MANOVA, ANOVA, PA, and Factor Analysis among others. My future research topic is ‘Study of the Relationship between HIV Treatment Compliance and Social Support among African American Women with HIV.’ This research tests the relationships between HIV treatment compliance and the race of HIV positive women where African-American women are the race of interest. Therefore, the listed tests are not appropriate for my future research.
Multiple regression analysis is of great interest in my future research. To help in reducing errors in making assumptions on variables that affect a given phenomenon, it is only appropriate to include many variables that may explain variance in the independent variables. Upon conducting multiple regression analysis, elements that do not explain variance in the independent variable can then be eliminated. Hence, multiple regression analysis can be helpful in my future research by ensuring dependability and reliability of the research findings.
Cohen, D., & Crabtree, B. (2008). Evaluative Criteria for Qualitative Research in Health Care: Controversies and Recommendations. Ann Fam Med., 6(4), 331–339.
Creswell, J. (2009). Research design: Qualitative, quantitative, and mixed methods approach. Thousand Oaks, CA: Sage Publications.
Finlay, L. (2006). Rigor, Ethical Integrity or Artistry” Reflexively Reviewing Criteria For Evaluating Qualitative Research. British Journal of Occupational Therapy, 69(7), 319-326.
Frankfort-Nachmias, C., & Nachmias, D. (2008). Research methods in the social sciences. New York, NY: Worth.
Rolfe, G. (2006). Validity, trustworthiness and rigor: quality and the idea of qualitative research. Journal of Advanced Marketing Research, 53(3), 304-310.
Scott, S. (2011). Research Methodology: Sampling Techniques. Journal of Scientific Research, 2(1), 87-92.