Quantitative research

Quantitative research relies much on assumptions from the positivist approach to science. Basically quantitative research involves: a language of variables, hypotheses, units of analysis, and causal explanation.

High quality quantitative research depends on a well defined and focused research question.

Variable is the concept that varies. The language of quantitative research is the language of variables and relationships among variables.

Record we learn there are two types of concepts: those that refer to a fixed phenomenon and those that vary in quantity, intensity, or amount (eg, amount of education). The second type of concept and measures of concepts are variables.

Commonly, there are three types of variables: independent variables (cause variables), dependent variable (effect variables) and the intervening variable, ie it comes between the independent and dependent variables and shows the link or mechanism between them.

Hypothesis and causality

A hypothesis is a proposition to be tested or a tentative statement of a relationship between two or more variables. It is kind of guess what social world looks like.

A causal hypothesis has the five characters:

o It has at least two variables
o It expresses a cause and effect relationship between variables
o It can predict a future event
o It is logically linked to a research question
o It is falsifiable, ie. It is capable of being tested against empirical evidence and shown to be true of false.

Unit and level of analysis

The unit of analysis refers to the type of unit a researcher uses when measuring variables, commonly individuals, groups, organization, social category or social institution.

The level of analysis is the level of social reality to which theoretical explanations refer. The social reality varies from individual to a group, from micro to macro level. To be clear the level of analysis helps researcher to delimit the kinds of assumptions, concepts and theories that a researcher uses.

Testing causal relationship

The researchers avoid using term proved when testing hypotheses. In the language of science, knowledge is tentative, and creating knowledge is an ongoing process that avoids premature closure. Scientists do not say they have proven a hypothesis or the causal relationship it represents. The best they can say is that overwhelming evidences, or all studies to date, support or are consistent with the hypothesis. Scientists do not close off the possibility of discovering new evidence that might contradict past finding.

The positive or confirming evidence for a hypothesis is less critical because alternative hypotheses may make the same predication. Negative and disconfirming evidence shows that predications are wrong. Negative evidence is more significant because the hypothesis is tarnished or soiled if the evidence fails to support it.

Spuriousness occurs when two variables are associated but are not causally related because there is actually an unseen third factor that is the real cause.

In summary, quantitative research design uses a deductive logic, begins with a general topic, narrow it down to research questions and hypothesis, and finally tests hypothesis against empirical evidence.