(1) The greatest advantage of quantitative method over other methods is its general acceptance by others as being rational, logical, planned and systematic. The findings are regarded as credible.
(2) It is regarded as being straight –forward and provides facts.
(3) It employs very large samples designed to reflect and be representative of the population being studied.
(4) It enables geographically or immobile people to be surveyed either by the use of postal survey, telephone or internet.
(5) It makes it possible for computers and other new technology to be used.
(6) It makes it possible for data to be re-examined, audited and re- analysed or used for other purposes.
(7) It enables research projects to be carried out by terms in which specialist talents can be properly exploited and work sub- contracted to agencies.
(8) It is suitable for the development or grand, meta and micro theory by testing logical hypothesis.
(9) It can be used to identity clusters of relatively small-scale phenomena and to analyse these statistically to identify whether
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particular concentrations may be attributed to chance or potentially localised causes.
(10) Quantitative method can be used to mine and analyse existing data banks.
(11) Most concepts are capable of being represented by measurable indicators.
3.7.1 Criticisms against Quantitative Research
The following are the criticisms against quantitative research;
(1) The underlying doctrine of positivism is contestable in its application to social world.
(2) It is too detached, remote and clinical to really understand and explore the complex social and political world.
(3) Its use by the social sciences does not meet the high standards of the natural science in which it reputation and claims lie.
(4) Quantitative research methods in politics relies on the ability to express concepts as measurable indicators.
(5) The reliance on observation limits the range and depth of observable and measurable indicators.
SELF-ASSESSMENT EXERCISE
Describe two categories of statistical analysis?
4.0 CONCLUSION
In this unit, we have discussed two categories of statistical data analysis descriptive and causal studies. Descriptive statistics resolve complexity by summarising and compressing data to identify their essential characteristics to create a brief but relatively accurate impression to the observer. Examples of simple statistical measures used by researchers to describe are: arithmetic mean, median, mode, range, variance and standard deviation.
On the other hand, the concept of causality (cause and effect) is the basis of explanatory theory. Causality is central to all the main approaches used in politics where causes are sought to explain effects. Examples of statistical data analysis techniques are: t-test, Z-test, Chi-test, product- moment correlation coefficient, Regression analysis, Multi- Linear Regression analysis and ANOVA. However, our concern in causal analysis is with how one variable affects, or is responsible for, changes in another. The stricter interpretation of causation, found in experimentation, is that some external factor produces a change in the
dependent variable. In political research, we often find that the cause- effect relationship is less explicit. We are more interested in understanding, explaining, predicting and controlling relations between variables than we are discerning causes.
5.0 SUMMARY
Quantitative research is formalised studies that include descriptive and causal studies. They are studies with substantial structure, specific hypotheses to be tested or research questions to be answered.
Descriptive studies are those we use to describe phenomena associated with a subject population or to estimate proportions of the population that have certain characteristics. Causal studies seek to discover the effect that a variable(s) has on another (or others) or why certain outcomes are obtained. The concept of causality is grounded in the logic of hypothesis testing, which in turn, produces inductive conclusions.
Such conclusions are probabilistic and thus can never be demonstrated with certainty. It is difficult to know all the relevant information necessary to prove causal linkages beyond doubt. However, the relationships that occur between two variables may be symmetrical, reciprocal or asymmetrical. Of greatest interest to the researcher is the asymmetrical relationships – that is, postulating that changes in one variable (the independent variable) and responsible for changes in another variable (the dependent variable). We, therefore, test causal hypotheses by seeking to do three things:
measure the covariation among variables
determine the time-order relationship among variables and
ensure that other factors do not confound the explanatory relationships.
In any case, causality is central to all the main approaches used in political research where causes are sought to explain (Pierce, 2009:30).
6.0 TUTOR-MARKED ASSIGNMENT
1. Describe any statistical tools used in descriptive studies?
2. What do you understand by causal studies?
7.0 REFERENCES/FURTHER READING
Burnham, P., Gilland, K., Grant, W., & Layton-Henry, Z. (2004).
Research Methods in Politics. New York: PALGRAVE Macmillan.
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Burdens, K.S. & Abott, B.B. (1988). Research Design and Methods. A Process Approach. California: Mayfield Publishing.
Eneanya, A.N. (2012). Research Method in Political Science and Public Administration. Lagos: University of Lagos Press Ltd.
Glaser, B. G. & Strauss, A. L. (1995). The Discovery of Grounded Theory: Strategies for Qualitative Research. New York: Aldine de Gruyter.
Pierce, R. (2008). Research Methods in Politics: A Practical Guide.
London: SAGE Publication Ltd.
Ihenacho, E.A. (2004). Basic Steps for Quality Research Project.
Lagos: Noble-Alpha International.