Question

(a) differentiate between parametric and non parametric statistical data.give one example each (b) explain the differences between scientific research and common sense; the four ways of attaining knowledge (c) what do you consider the factors that hinder research efforts in West Africa. (d) enumerate the advantage and disadvantage of secondary data

Answer 1

a)

The fundamental differences between parametric and nonparametric test are discussed in the following points:

1. A statistical test, in which specific assumptions are made about the population parameter is known as the parametric test. A statistical test used in the case of non-metric independent variables is called nonparametric test.
2. In the parametric test, the test statistic is based on distribution. On the other hand, the test statistic is arbitrary in the case of the nonparametric test.
3. In the parametric test, it is assumed that the measurement of variables of interest is done on interval or ratio level. As opposed to the nonparametric test, wherein the variable of interest are measured on nominal or ordinal scale.
4. In general, the measure of central tendency in the parametric test is mean, while in the case of the nonparametric test is median.
5. In the parametric test, there is complete information about the population. Conversely, in the nonparametric test, there is no information about the population.
6. The applicability of parametric test is for variables only, whereas nonparametric test applies to both variables and attributes.
7. For measuring the degree of association between two quantitative variables, Pearsons coefficient of correlation is used in the parametric test, while spearmans rank correlation is used in the nonparametric test.

for example

ANOVA is a parametric test

whereas Kruskal–Wallis test   is non-parametric

b)

The goals of science and commonsense are different. Commonsense is mainly concerned with immediate action in context; science is mainly concerned with achieving some understanding which - to some extent - is independent of persons and context, and in this interest may eschew the need for guiding immediate action.

Science has developed an extensive tool-kit of theoretical models, investigated in great detail, so that its imaginative resources are very finely structured and elaborated. It has generated a variety of new (and some not widely shared) ways of being rational. 'Logic' has a special role in science here, in the transactional domain where consequences of imaginings are followed through. Commonsense relies more on the broad brush of basic dimensions of how things can possibly be. Its rationality boils down to what makes sense.

Science relies more on extensive collaborative and competitive work towards unarguable agreement. Commonsense is certainly collaborative (even collusive), but when differences arise, agreements to differ are common. In the commonsense world, persons think as they do; in the scientific world, knowledge is what it currently is.

In the interests of knowledge, science tries to go behind things as they seem. To detect, control and understand the behaviour of entities, it creates artificial events (experiments) so as to isolate the effects of various entities. For this reason, experiments are, from the everyday point of view, thoroughly impractical. They work only in contrived circumstances. Commonsense is more concerned with coping with things as they are, in all their awkward combinations.

Out of all this, science has created a large ontological zoo of entities, many as real as any stone, but never before thought of, and quite beyond the ken of everyday commonsense. Science, unlike commonsense, is in a way never satisfied. New entities, once made real and serving in an imaginative world to create histories which explain certain phenomena, become themselves phenomena to be explained by going one layer deeper.

c)

This question is not clear, please post it again with background.

d)

Secondary data is available from other sources and may already have been used in previous research, making it easier to carry out further research. It is time-saving and cost-efficient: the data was collected by someone other than the researcher. Administrative data and census data may cover both larger and much smaller samples of the population in detail. Information collected by the government will also cover parts of the population that may be less likely to respond to the census (in countries where this is optional).

A clear benefit of using secondary data is that much of the background work needed has already been carried out, such as literature reviews or case studies. The data may have been used in published texts and statistics elsewhere, and the data could already be promoted in the media or bring in useful personal contacts. Secondary data generally have a pre-established degree of validity and reliability which need not be re-examined by the researcher who is re-using such data. Secondary data is key in the concept of data enrichment, which is where datasets from secondary sources are connected to a research dataset to improve its precision by adding key attributes and values

Secondary data can provide a baseline for primary research to compare the collected primary data results to and it can also be helpful in research design.

However, secondary data can present problems, too. The data may be out of date or inaccurate. If using data collected for different research purposes, it may not cover those samples of the population researchers want to examine, or not in sufficient detail.^[1] Administrative data, which is not originally collected for research, may not be available in the usual research formats or may be difficult to get access to.