Research design is a complete plan for data collection in an empirical research project
Research design is a complete plan for data collection in an empirical research project. It elaborates the procedure for obtaining required information; that is, how research participants are sampled, information collection, measurement and how data is analysed. It also outlines the instruments to be used in the investigation and how those instruments will be used in the research process. Research design answers the questions such as: What is the research about? Why is it being done? Where is it going to be done? What type of data will be required? What sample technique will be used? What time will it be done? What data collection technique will be used? How will the collected data be analysed? How will the report be written? Research design therefore aims to design a study that will successfully validate the research hypothesis.
Research designs can be classified into two categories:
This classification depends on the goal of scientific research. Positivist research designs are meant for theory testing, while interpretive designs are meant for theory building. Positivist designs seek generalized patterns based on an objective view of reality, while interpretive designs seek subjective interpretations of social phenomena from the perspectives of the subjects involved.
Positivist research designs include laboratory experiments, field experiments, field surveys, secondary data analysis, and case research. On the other hand, interpretive research designs include case research, phenomenology, and ethnography.
However, some designs can fall in both categories, for example case research; which can be used for theory building or theory testing, though not at the same time. Not all techniques are suited for all kinds of scientific research. Some techniques such as focus groups are best suited for exploratory research, others such as ethnography are best for descriptive research, and still others such as laboratory experiments are ideal for explanatory research. Following are brief descriptions of some of these designs.
1. Experimental studies
This research design is intended to test a cause-effect relationship or hypotheses in a controlled environment. The cause is separated from the effect in time; the cause is administered to the group, treatment group whereas the control group remains without cause. Observation is then made to see the effects of the cause on the treatment group.
This design can be ideal testing the efficacy of a newly invented drug in treating or curing certain ailment. Here, a laboratory experiment will be designed to test the efficacy of the drug in test. First, a random sample of people afflicted with the disease will be assigned randomly to either treatment or control groups. The drug is then administered to those in treatment group, while those in control group are not (the people in control group are usually given a placebo, a sugar pill with no medicinal value).
In experimental design, if subjects are assigned between groups (treatment and control) randomly then the design is known as true experimental design. Contrary to this, the design becomes quasi-experimental.
Experimental studies can be carried out in a laboratory at university or artificial lab; this is known as laboratory experiment. In a scenario where experiment is carried in a field context where the subject being studied is, we call this a field experiment.
In laboratory experiments, the researcher can isolate the variables of interest and control those that are extraneous. This makes the inferences or conclusions from laboratory experiments much stronger in terms of its internal validity. On the other hand field experiments are stronger in external validity. The data from experimental studies are usually analysed using quantitative statistical techniques.
Experimental studies is mainly applicable in evaluating therapeutic interventions by randomised controlled trials, for example, treatment of rheumatoid arthritis using two different set of treatments.
2. Field Surveys
Field surveys are non-experimental designs that measure variables and test their effects using statistical methods without manipulating independent variables. In this research design, the phenomenon under study is usually not reproducible in a laboratory. The researcher attempts to capture snapshots of practices, beliefs, or situations from a random sample of subjects in field settings through a survey questionnaire or through a structured interview (face-to-face, office or home interview, telephone interview) but the latter is less frequently used.
There are two kinds of field survey designs, that is, cross-sectional field surveys and longitudinal field surveys. In cross-sectional field surveys, independent and dependent variables are measured at the same point in time (e.g., using a single questionnaire), while in longitudinal field surveys, dependent variables are measured at a later point in time than the independent variables.
The merits of field surveys are their external validity (since data is collected in field settings), their ability to capture and control for a large number of variables, and their ability to study a problem from multiple perspectives or using multiple theories. However, because of their non-temporal nature, internal validity (cause-effect relationships) are difficult to infer, and surveys may be subject to respondent biases (e.g., subjects may provide a “socially desirable” response rather than their true response) which further hurts internal validity.
This type of research design can be used to compare competing products in a market and thus the producers can see ways of making their product popular than the competitor.
3. Case research
Case research design is a thorough investigation of a problem in one or more real-life situations (case sites) over a long period of time. Data may be collected using a combination of interviews, personal observations, and internal or external documents. Case studies can be positivist in nature (for hypotheses testing) or interpretive (for theory building). The strength of this research method is its ability to unearth a wide variety of social, cultural, and political factors potentially related to the phenomenon of interest that may not be known in prior to the study.
Analysis tends to be qualitative in nature, but heavily contextualized and nuanced. However, interpretation of findings may depend on the observational and integrative ability of the researcher, lack of control may make it difficult to establish causality, and findings from a single case site may not be readily generalized to other case sites. Generalizability can be improved by replicating and comparing the analysis in other case sites in a multiple case design.
4. Focus group research
This is a type of research that involves bringing in a small group of subjects (typically 6 to 10 people) at one location; usually in an informal setting, and having them discuss a phenomenon of interest for a period of 1.5 to 2 hours. The discussion is conducted by a facilitator, who sets the agenda and poses an initial set of questions for participants, makes sure that ideas and experiences of all participants are represented, and attempts to build a holistic understanding of the problem situation based on participants’ comments and experiences.
Internal validity cannot be established due to lack of controls and the findings may not be generalised to other settings because of small sample size. Hence, focus groups are not generally used for explanatory or descriptive research, but are more suited for exploratory research.
5. Action research
Action research is of the assumption that complex social phenomena are best understood by introducing interventions into those phenomena and observing the effects of those actions. Here, the researcher is usually a consultant or an organizational member embedded within a social context such as an organisation, who initiates an action such as new organisational procedures or new technologies, in response to a real problem such a dip in profitability or operational bottlenecks. The researcher’s choice of actions must be based on theory, which should explain why and how such actions may cause the desired change. The researcher then observes the results of that action, modifying it as necessary, while simultaneously learning from the action and generating theoretical insights about the target problem and interventions. The initial theory is validated by the extent to which the chosen action successfully solves the target problem. Simultaneous problem solving and insight generation is the central feature that distinguishes action research from all other research methods, and hence, action research is an excellent method for bridging research and practice.
This method is also suited for studying unique social problems that cannot be replicated outside that context, but it is also subject to researcher bias and subjectivity, and the generalizability of findings is often restricted to the context where the study was conducted.
This is an interpretive research design inspired by anthropology that asserts that research phenomenon must be studied within the context of its culture. The researcher is deeply immersed in a certain culture over an extended period of time (for instance 8 months to 2 years). During the time, he or she engages, observes, and records the daily life of the studied culture, and theorizes about the evolution and behaviors in that culture. The researcher collects information via observation and interaction, with participants in that culture. He or she then field notes which he or she uses for data analysis. In reporting, the researcher narrates his or her experience in details.
Ethnography generates a deep understanding of responded in its context as well as giving research unbiased response. However, it consumes a lot of time and resources to carry out such investigation. Besides, the findings from ethnography are specific to a given culture and may not be applicable in others.
7. Secondary data analysis
This is an analysis of data that has previously been collected and tabulated by other sources. The data being used in this design include those from government agencies such as employment statistics. In Kenya, such data may be obtained from Kenya Bureau of Statistics. Other data sources for this kind of research design include: development statistics by country from the United Nations Development Program, data collected by other researchers, or publicly available third-party data, such as financial data from stock markets. This research design doesn’t include collecting data as part of researcher’s job.
Secondary data analysis may be an effective means of research where primary data collection is too costly or infeasible, and secondary data is available at a level of analysis suitable for answering the researcher’s questions. The limitations of this design are that the data might not have been collected in a systematic or scientific manner and hence unsuitable for scientific research, since the data was collected for a presumably different purpose, they may not adequately address the research questions of interest to the researcher, and interval validity is problematic if the temporal precedence between cause and effect is unclear.
An example of published secondary data analysis is ”Scalar Timing Theory” in an article in the Journal of Mathematical Psychology (Gibbon, 1971).