Understanding research philosophy and research design

By Abhinash on November 28, 2013

Research philosophy is the nature of the approach considered for the project. It tests the reality quotient followed by validity, reliability and generality. These paradigms can be studied in detail as under:

Nature of research

There are three paradigms which are of primary importance in the process of research analysis:

  1. Realism, interpretivism and
  2. Positivism.


Positivism is associated with natural sciences where a positivist hypothesis has the notion of a single concrete reality which is unchangeable and can define relationships that apply at all times. They are generally associated with quantitative studies that result in statistical analysis. Positivism can be of the following types:

  • Logical positivism
  • Social positivism and
  • Critical positivism.


If positivism is considered to lie at one end of the spectrum of paradigms and interpretivism towards the other end, then realism bridges these perceived extremes, overlapping each. It understands that social structure has some form of independent existence, but it also argues, unlike positivism that social structures themselves are the product of social relationships and behaviour.


The Interpretivist researcher assumes that the world is just as people assume it to be. Therefore the aim of Interpretivism research is for the researcher to “uncover the socially constructed meaning as it is understood by an individual or group of individuals”.

Research design

There are several types of research designs. A researcher selects a particular type of research based on the nature of his/her study. Each type of research has its advantages and disadvantages.

Descriptive designs

These are designs that describe phenomena in order to answer a research question. They are mainly used in the case of quantitative research, but may also be sometimes used in a qualitative study. Quantitative descriptive designs produce very weak ‘evidence’ in comparison to experiments but are useful in knowing in-depth information about the subject matter. They can be of many types, shown as under:

  • Descriptive research.
  • Survey.
  • Naturalistic observation.
  • Case study.
  • Cor-relational studies:
    • Case-control study.
    • Cross-sectional study.
    • Longitudinal study.
    • Cohort study.
    • Observational study.
    • Semi-experimental designs.
    • Quasi-experimental design.
    • Field experiment.
    • Twin studies.
    • Experimental designs.
    • Double-blind experiment.
    • True experimental design.

Simple experimental techniques

They measure known variables in a population. Various methods can be used to collect descriptive data. They may make use of the following techniques:

  • Control group.
  • Pretest & post-test design.
  • Randomized controlled trials.
  • Randomization.
  • Between subjects design.
  • Within-subject design.

Complex experimental designs

Complex experimental designs can be divided into the following categories:

  • Matched subjects design.
  • Solomon four-group design.
  • Repeated measures design.
  • Factorial design.
  • Bayesian probability.
  • Counterbalanced measures design.

I am currently working as a Research Associate. My work is centered on Macroeconomics with modern econometric approach. Broadly, the methodological research focuses on Panel data and Times series data analysis for causal inference and prediction. I also served as a reviewer to Journals of Taylor & Francis Group, Emerald, Sage.