Research is a systematic investigation into and study of materials and sources in order to establish facts and reach new conclusions.

Research is important for any innovation, planning and product development. Data Capital International helps clients to come up with viable options for gainful operations.

We work hand-in-hand with our clients to design research options, conduct reconnaissance, develop research tools, collect data, analyze the data and write reports in agreed timelines.

The expert team at Data Capital International will manage your Monitoring and evaluation studies, Market systems assessment, Market surveys, feasibility studies, investment analysis and baseline/mid-line/end-line surveys to inform decision.


Research design refers to the overall strategy that you choose to integrate the different components of the study in a coherent and logical way, thereby, ensuring you will effectively address the research problem; it constitutes the blueprint for the collection, measurement, and analysis of data. Note that your research problem determines the type of design you should use, not the other way around.

 Developing a Research Design

  1. Identify the research problem clearly and justify its selection, particularly in relation to any valid alternative designs that could have been used,
  2. Review and synthesize previously published literature associated with the research problem,
  3. Clearly and explicitly specify hypotheses [i.e., research questions] central to the problem,
  4. Effectively describe the data which will be necessary for an adequate testing of the hypotheses and explain how such data will be obtained, and
  5. Describe the methods of analysis to be applied to the data in determining whether or not the hypotheses are true or false.



There are mainly four types of quantitative research designs: descriptive, correlational, quasi-experimental and experimental. The differences between the four types primarily relate to the degree the researcher designs for control of the variables in the experiment. Following is a brief description of each type of quantitative research design, as well as chart comparing and contrasting the approaches.

Descriptive Design seeks to describe the current status of a variable or phenomenon. The researcher does not begin with a hypothesis, but typically develops one after the data is collected. Data collection is mostly observational in nature.

Correlational Design explores the relationship between variables using statistical analyses. However, it does not look for cause and effect and therefore, is also mostly observational in terms of data collection.

Quasi-Experimental Design (often referred to as Causal-Comparative) seeks to establish a cause-effect relationship between two or more variables. The researcher does not assign groups and does not manipulate the independent variable. Control groups are identified and exposed to the variable. Results are compared with results from groups not exposed to the variable.

Experimental Designs, often called true experimentation, use the scientific method to establish cause-effect relationship among a group of variables in a research study. Researchers make an effort to control for all variables except the one being manipulated (the independent variable). The effects of the independent variable on the dependent variable are collected and analyzed for a relationship.


Research usually involves collecting information usually known as data. The data is collected using well formatted tools. A single set of tools or multiple tools can be used in data collection. What usually matters is whether the tools used can easily capture all the data requirement and particularly quality data. Quite often researchers use questionnaires, observation checklists, recorders and interview guides among others. The major challenge relates to situations where the tools are aligned to the objectives of the study. DataCapital will analyse your research design and develop tools relevant to your study. Teams designing research tools my always have the background knowledge of how data collected using the developed tools should be analysed.


DataCapital has a well trained team of enumerators who have a good experience of the local African setup especially Eastern Uganda. Our team/enumerators have used contemporary gadgets such as TABLETS and GPS during data collection. However depending on a project the key staff recruit and train enumerators with knowledge bias to the research project. We usually use graduates of subject matter to do the enumeration exercises. During data collection, the team leader supervises the process of data editing everyday when the teams have retired from the fields. Those who come with inappropriate information are referred back to those data collection centres or households. The organisation has done data collection for a number of clients including Makerere University Department of agribusiness and natural resources economics, IFPRI, and USAID among others.


Based on the data collected and the tools or gadgets used, data will be transferred to the data analysis package. The common packages used at DataCapital include STATA, SPSS, SAS and R. When questionnaires have been used, there is a task of designing data entry screens, either in the statistical analysis package or using excel. Then after, data is coded and entered questionnaire per questionnaire. It is after data has been entered in the statistical analysis package that analysis will be done. At this stage, data will be analysed according to each objective and consequently questions.


Report writing is very interesting, good writers will first generate the report content framework inform of subsection aligned to the objectives of the study. At this time, the writer considers exhausting the various sections according to the objectives. Remember each objective generates a number of questions or issues to be address, therefore under each objective analysis is done independently from the other to capture right information that answers your research questions. One may say that if you are using hypothesis, your analysis will address hypothesis issues. Otherwise the analysis will address the research question and during report writing you will triangulate literature, field observations and data analysis output among other.