Every research process follows certain steps. The first step involves identification of a problem that needs to be investigated (Cronin, Coughlan, & Smith, 2014). A research problem is a clearly and explicitly stated expression about a troubling question that is found in scholarly literature or in real practice that needs to be well understood through investigation. After the research problem has been identified, the next step involves a thorough search of the literature for information related to the problem identified. Literature reviews familiarize the researcher with the currently available knowledge on the topic, aids in the identification of any existing gaps in the literature, and helps in the formulation of research questions (Cronin, Coughlan, & Smith, 2014).
After reviewing the literature, the researcher is supposed to devise research question(s) or hypotheses. Information found in the literature review are useful in the formulation of interrogative statements that explicitly identifies what the researcher aims to achieve in the research. The research question is important in the identification of the methodological approaches for the study. Hypotheses are same as research questions but not interrogative (Cronin, Coughlan, & Smith, 2014).
A literature review is followed by research methodology. The method chosen should easily help in answering the research questions or the hypotheses. Quantitative studies employ positivist or post-positivist paradigm associated methods while qualitative studies involve the use of constructivist or advocacy paradigm related methods. Once a methodological approach for a study has been selected, the researcher identifies the population and sample for the study. A population is all the individuals who are potential participants in the study while a sample is a subset of the population recruited to participate in the study (Cronin, Coughlan, & Smith, 2014).
Next, planning method of data collection precedes identification of the population and the sample. Data collection approaches are influenced by methodological approaches of the study. When collecting data from participants, the researcher must adhere to ethical principles guiding research. The last step of the research involves analysis of data. There is two types of analysis in research: descriptive and inferential statistical analysis. The choice of the analysis depends on the research question or hypothesis that needs to be addressed.
Hypothesis vs. Research Question
There exist many differences between a hypothesis and a research question. The first difference is about their uses. A hypothesis is used if there is a lot of readily available knowledge about a subject or a research topic that allows the researcher to predict the results of his or her study. On the other hand, a research question is used to explore and investigate a topic or a subject that does not have a significant body of knowledge and requires a researcher to collect data and conduct an analysis before reaching any conclusions. Therefore, a hypothesis is predictive in nature whereas a research question is inquisitive in nature.
The second difference between a hypothesis and a research question is its constituents. A complete hypothesis has variables, population, and the hypothesized association between these variables while a good research question is comprised of the purpose or aim, the population, and the variables. Third, a hypothesis is usually used in quantitative studies which seek to answer open-ended questions. Conversely, a research question is often used in both qualitative and quantitative studies. Lastly, data collection and analysis in studies containing hypotheses is used to support or reject hypotheses thus enabling the investigator to arrive at a conclusion. On the contrary, data collection and analysis in studies involving research questions are only used to make conclusions at the end of the study.
Types of Research Methodologies
Research methodologies are based on positivist or post-positivist paradigms and interpretvist or constructivist paradigms. Examples of methodologies that fall under positivist or post-positivist paradigms include quasi-experimental, experimental, and correlational research methods. In experimental method, the researcher manipulates the independent variable and observes its effects on the dependent variable. In this method, participants are randomly assigned to treatment and control groups. Non-experimental method, on the other hand, is similar to experimental method but lacks random assignment. Lastly, correlational research method is used to describe the existing relationships between variables, and to establish the relationship between the predictor variable and the outcome variable. This method does not include manipulation of the independent variable (Brink, Walt, & Rensburg, 2006).
Phenomenology, grounded theory, and ethnography methods fall under interpretvist or constructivist paradigms. Phenomenological methods explore the lived experience of people in a particular phenomenon (Taylor, 2006) while grounded theory seeks to systematically generate a theory from empirically gathered and analyzed data (Swanson & Holton, 2005). Lastly, ethnography explores the social relationships across many groups of individuals and also in their cultural setting (Cronin, Coughlan, & Smith, 2014).
Types of Data Collection Tools Used in Health Care Research
In health care research, data is gathered using both qualitative and quantitative data collection tools. In quantitative studies, questionnaires are the widely used tools. Questionnaires can be structured or semi-structured. Structured questionnaires have fixed questions with precoded response choices while semi-structured questionnaire (Ebrahim & Bowling, 2005) while semi-structured questionnaire contains a combination of open-ended and close-ended questions. On the other hand, qualitative studies usually use interview method. An interview is a method of data collection whereby a researcher asks the subjects purposeful questions with the aim of exploring a research problem (Taylor, 2006). Examples of interview methods include a structured and unstructured interview.
Types of Sampling Methodologies
Sampling methodologies can be categorized into two types: probability sampling methods and non-probability sampling methods. In probability sampling, there is a random selection of the participants leading to the formation of a random sample (Chatburn, 2010). Examples include simple, stratified, cluster, systematic, and sampling techniques. Simple sampling is where every individual in a population has the same and independent chance of being picked. This sampling method is achieved by numbering every individual and then a sample is selected using a table of random numbers. In stratified sampling, the population is divided into strata, and a sample is chosen by selecting a specific number of units from each stratum. Cluster sampling, on the other hand, involves partitioning of a population into groups, known as clusters. This is then followed by a sampling of a few clusters. Lastly, in systematic sampling, individuals are selected at regular intervals from a sampling frame (Goyal, 2010).
Non-probability sampling is comprised of methods in which participants do not have equal chances of being chosen for participation. These methods do not involve random selection. Examples of non-probability sampling techniques include convenience sampling, quota sampling, purposive sampling, and snowball sampling. In convenience sampling, the researcher gathers data from the readily available group or individuals who are easy to reach. As for quota sampling, the researcher intentionally selects the proportions of the participants for various subgroups. Turning to purposive sampling, the researcher purposely selects participants who he or she judges to be knowledgeable on the issues being studied (Polit & Beck, 2008). Lastly, in snowball sampling, the researcher ask each participant to identify another person who might be willing to do the study (Maltby, Williams, Mcgarry, & Day, 2014).
Types of Statistical Analysis
Statistical analysis is broadly divided into two: descriptive and inferential analysis. Both descriptive and inferential statistics use a similar set of data. Descriptive statistics uses this set of data to describe data in a meaningful manner, so that patterns may arise from this data. This type of analysis does not enable the researcher to make conclusions. Examples of descriptive analysis include range, quartiles, variance, standard deviation, mean, mode, and median (Howlett, Rogo, & Shelton, 2013). Conversely, inferential statistics involves the uses a set of data to make generalizations about a population. This type of statistics is usually used to answer research hypotheses. Examples of inferential statistics include chi-square, t-test, ANOVA, MANOVA, and correlation (Howlett, Rogo, & Shelton, 2013).
Brink, H., Walt, C. V. der, & Rensburg, G. V. (2006). Fundamentals of Research Methodology for Health Care Professionals. Juta and Company Ltd.
Chatburn, R. L. (2010). Handbook for Health Care Research. Jones & Bartlett Learning.
Cronin, P., Coughlan, M., & Smith, V. (2014). Understanding Nursing and Healthcare Research. SAGE.
Ebrahim, S., & Bowling, A. (2005). Handbook of Health Research Methods: Investigation, Measurement and Analysis. McGraw-Hill Education (UK).
Goyal, R. C. (2010). Research Methodology for Health Professionals. Jaypee Brothers Publishers.
Howlett, B., Rogo, E., & Shelton, T. G. (2013). Evidence Based Practice for Health Professionals. Jones & Bartlett Publishers.
Maltby, J., Williams, G., Mcgarry, J., & Day, L. (2014). Research Methods for Nursing and Healthcare. Routledge.
Polit, D. F., & Beck, C. T. (2008). Nursing Research: Generating and Assessing Evidence for Nursing Practice. Lippincott Williams & Wilkins.
Swanson, R. A., & Holton, E. F. (2005). Research in Organizations: Foundations and Methods in Inquiry. Berrett-Koehler Publishers.
Taylor, B. J. (2006). Research in Nursing and Health Care: Evidence for Practice. Cengage Learning Australia.
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