The research approach is a plan and procedure consisting of the steps from general hypotheses to detailed methods of data collection, analysis and interpretation. The research approach is essentially divided into two categories: the data collection approach and the data analysis or reasoning approach.
Types of research approach to data collection
It places great emphasis on the methods used to collect or generate data. However, it places less emphasis on analysis techniques for the interpretation of data. In addition, the inductive approach primarily uses a detailed reading of secondary data to derive concepts, topics, and models. Therefore, it is widely used to analyze qualitative data. Start with the selection of the study area and build a theory. The inductive approach includes:
- Combining varied secondary data into a brief summary.
- Creation of clear links between research objectives and the results of raw data. Also, make those links clear to others and how those links will fulfill the goal of the research.
- Develop a theory based on the experiences and processes revealed by textual data (Jebreen 2012).
- Choosing an inductive approach through thematic analysis (a “data-driven” approach) to the study determines that the goal of the study is to gain an understanding of a phenomenon. It does not focus on testing the hypothesis.
Thematic analysis can realistically present the experiences, meaning and reality of the participants. It can also be used to examine the impact of those experiences, events and realities operating in society.
The approach taken by qualitative researchers tends to be inductive, meaning they develop a theory or look for a pattern of meaning from the data they have collected. This involves a move from the specific to the general and is sometimes called a bottom-up approach. However, most research projects also involve a certain degree of deductive reasoning (see the section on quantitative research for details).
Qualitative researchers do not base their research on predetermined hypotheses. However, they clearly identify a problem or topic they want to explore and can be guided by a theoretical lens, a kind of general theory that provides a framework for their research.
The approach to data collection and analysis is methodical, but allows for greater flexibility than in quantitative research. Data are collected in text form from observation and interaction with participants, for example through participant observation, in-depth interviews and focus groups. They are not converted to numerical form and are not statistically analyzed.
Data collection can be carried out in several phases and not once and for all. Researchers can even adapt the process halfway through, deciding to address additional issues or removing questions that are not appropriate based on what they learn during the process. In some cases, researchers will interview or observe a certain number of people. In other cases, the process of data collection and analysis may continue until researchers consider that no new issues arise.
Researchers will tend to use methods that give participants a certain degree of freedom and allow for spontaneity rather than forcing them to select from a set of predetermined responses (none of which might be appropriate or accurately describe the participant’s thoughts, feelings, attitudes, or behaviors) and to try to create the right atmosphere to allow people to express themselves. This may mean adopting a less formal and less rigid approach than that used in quantitative research.
It is believed that people are constantly trying to attribute meaning to their experience. Therefore, it would not make sense to limit the study to the researcher’s view or understanding of the situation and expect to learn something new about the participants’ experience. As a result, the methods used can be more open, less narrow and more exploratory (especially when very little is known about a particular topic). Researchers are free to go beyond the initial answer given by the participant and ask why, how, in what way, etc. In this way, subsequent questions can be adapted to the answers that have just been given.
Number of Participants
Qualitative research usually has a smaller number of participants. This may be because the methods used, such as in-depth interviews, require a lot of time and work, but also because it does not take a large number of people for statistical analysis or to make generalizations from the results.
The objectives of both types of research and their underlying philosophical assumptions are simply different. However, as discussed in the section “philosophies that guide research”, this does not mean that the two approaches cannot be used in the same study.
Quantitative research often involves the use of statistical analysis to establish the connection between what is known and what can be learned through research. Consequently, the analysis of data with quantitative strategies requires an understanding of the relationships between variables through descriptive or inferential statistics. Descriptive statistics help to make inferences about populations and to estimate parameters (Trochim 2000).
Inferential statistics are based on descriptive statistics and assumptions that generalize the population from a selected sample (Trochim 2000). Quantitative data require statistical analysis to test hypotheses. The deductive approach is the most widely used, as it allows research to reason from the generic to the specific. In addition, the deduction from general perspectives leads the researcher to develop a theoretical framework (hypothesis) and to test it to reach a specific conclusion.
The deductive approach to analysis or reasoning consists of the following steps:
- Exploration of theories.
- Development of a theoretical framework or hypothesis.
- Observation by means of statistical tests of the hypotheses.
- Confirmation of a specific conclusion logically drawn from the premises (Soiferman 2010).
However, it seems that choosing one research approach over another greatly limits the scope of the study. As Creswell and Clark (2011) observed, a single approach cannot answer all the questions that may arise in the course of researching a topic. To facilitate a more complete study, researchers should have access to all available research tools. Therefore, the dichotomy needs to be reconsidered and researchers need to master both types of approaches. When selecting the research approach, the objective and problem of the research must be taken into account.
Researchers have one or more hypotheses. These are the questions they want to address that include predictions about the possible relationships between the things they want to investigate (variables). To find answers to these questions, researchers will also have at their knowledge of various tools and materials (e.g. paper or computer tests, observation checklists, etc.) and a clearly defined action plan.
Data are collected by various means following a strict procedure and prepared for statistical analysis. Today, this is done with the help of sophisticated statistical software packages. The analysis allows researchers to determine the extent to which there is a relationship between two or more variables. It may be a simple association (for example, people who exercise daily have lower blood pressure) or a causal relationship (for example, daily exercise actually leads to lower blood pressure). Statistical analysis allows researchers to discover complex causal relationships and determine the extent to which one variable influences another.
The results of the statistical analyses are presented in the journals in a standard way, the final result being a P-value. For people unfamiliar with the jargon of scientific research, the discussion sections at the end of peer-reviewed journal articles often describe the results of the study and explain the implications of the findings in simple terms.
Researchers are very careful to prevent their own presence, behavior, or attitude from affecting outcomes (e.g., changing the situation studied or making participants behave differently). They also critically examine their methods and conclusions for any possible bias.
Researchers do their best to make sure they actually measure what they claim to measure. For example, if the study is about whether background music has a positive impact on nursing home residents’ concerns, researchers need to be clear about what kind of music to include, the volume of music, what they understand by restlessness, how to measure the restlessness, and what is considered a positive impact. All this must be considered, prepared and controlled in advance.
External factors that may affect the results must also be monitored. In the example above, it would be important to ensure that the introduction of the music was not accompanied by other changes (for example, that the person bringing the CD player chats with the residents after the music session), as it could be the other factor that produced the results (i.e. social contact and not music). Some potential contributing factors cannot always be ruled out, but researchers must recognize them.
The main emphasis of quantitative research is deductive reasoning, which tends to go from the general to the specific. It is sometimes called a top-down approach. The validity of conclusions depends on whether one or more premises (statements, results or preconditions) are valid. Aristotle’s famous example of deductive reasoning was: All men are mortal àSócrates is a man à Socrates is mortal. If the premises of an argument are inaccurate, then the argument is inaccurate. This type of reasoning is also often associated with the fictional character Sherlock Holmes. However, most studies also include an element of inductive reasoning at some stage of the research (see the section on qualitative research for details).
Researchers rarely have access to all members of a given group (e.g. all people with dementia, carers or healthcare professionals). However, they are often interested in being able to make inferences from their study of these larger groups. For this reason, it is important that the people involved in the study are a representative sample of the larger population/group.
However, the degree to which generalizations can be made depends to some extent on the number of people participating in the study, how they were selected, and whether they are representative of the larger group. For example, generalizations about psychiatrists should be based on a study involving psychiatrists and not one based on psychology students.
In most cases, random samples are preferred (so that each potential participant has the same chance of participating), but sometimes researchers may want to make sure to include a certain number of people with specific characteristics and this would not be possible using random sampling methods. The generalization of the results is not limited to groups of people, but also to situations. The results of a laboratory experiment are supposed to reflect the real-life situation that the study aims to clarify.
When examining the results, the P-value is important. P stands for probability. It measures the probability that a certain observed finding or difference is caused by chance. The P-value is between 0 and 1. The closer the result is to 0, the less likely it is that the observed difference is random. And if the closer the result is to 1, the greater the probability that the finding is random (random variation) and that there are no differences between the groups/variables.
Pragmatic approach to research (mixed methods)
The pragmatic approach of science is to use the method that seems most appropriate for the research problem and not to get entangled in philosophical debates about what the best approach is. Therefore, pragmatic researchers are granted the freedom to use any of the methods, techniques and procedures typically associated with quantitative or qualitative research. They recognize that each method has its limitations and that different approaches can be complementary.
They can also use different techniques at the same time or one after the other. For example, they can start with face-to-face interviews with multiple people or have a discussion group and then use the results to build a questionnaire to measure attitudes in a large-scale sample for statistical analysis.
Depending on the measures that have been used, the data collected are analysed appropriately. However, it is sometimes possible to transform qualitative data into quantitative data and vice versa, although the transformation of quantitative data into qualitative data is not very common.
The possibility of mixing different approaches has the advantage of allowing triangulation. Triangulation is a common feature of mixed-method studies. It involves, for example, the use of:
- Various data sources (data triangulation)
- Several different researchers (triangulation of researchers)
- Multiple perspectives for interpreting the results (triangulation of the theory)
- Multiple methods for studying a research problem (methodological triangulation)
Some studies use both qualitative and quantitative methods simultaneously. In others, one approach is used first and then the next, and the second part of the study can expand on the results of the first. For example, a qualitative study that includes in-depth interviews or discussion in focus groups can be used to obtain information that will then be used to contribute to the development of an experimental measure or attitude scale, the results of which will be analyzed statistically.
Defensive/participatory (emancipatory) research approach
To some extent, researchers who adopt a advocacy/engagement approach consider that the research approaches described so far do not respond to the needs or situation of people from marginalized or vulnerable groups. As its goal is to bring about a positive change in the lives of research subjects, its approach is sometimes described as emancipatory. It is not a neutral position. Researchers are likely to have a political agenda and try to give a voice to the groups they study. As they want their research to translate directly or indirectly into some kind of reform, it is important that they involve the group studied in the research, preferably at all stages, to avoid being further marginalised.
Researchers may adopt a less neutral position than is usually required in scientific research. This may involve interacting informally or even living among research participants (who are sometimes referred to as co-researchers in recognition that the study is not simply about them, but also about them).
Presentation of the Results
Research results can be presented in more personal terms, often using the exact words of research participants. Although this type of research could be criticized for not being objective, it must be borne in mind that for some groups of people or for certain situations, it is necessary, since otherwise you would not be able to access the thoughts, feelings or behaviors of the different members of the group or fully understand them.
Vulnerable groups are rarely in a position of power within society. For this reason, researchers are sometimes members of the group they study or have something in common with the members of the group.
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