Basic Concepts of Quantitative Research

For quantitative research you want to measure the relationship between two things and explain the reason for the connection.

A. In quantitative research you do not attempt to influence any variables.
B. You only observe (and/or record) them to measure the effects of variables.

1. For example: What is the relationship between blood pressure and cholesterol?
2. Variables are things that we can measure, control or manipulate in research.
a. Independent Variables-those variables that are manipulated
b. Dependent Variables-you measure the effects of your manipulation on the
dependent variables. Depended variables depend on what someone will do or
will not do to create (measure) a certain result.
c.These terms are also used in studies where we do not literally manipulate
independent variables but only assign subject to experimental groups based on
some pre-existing properties of the subjects. For example, if in an experiemtn,
males are compared with females regarding their white cell count, gender could be called the independent varibable and cell count the dependent variable.


II In order for your research to have value it must be able to predict that a similar occurance will happen in some predictable way.
A. In other words we are not so interested in what is going on in your research samples.
B. We are more interested in a theory as to how your work can predict results on general populations.

III Statistical Significance-In normal English, "significant" means important, while in Statistics "significant" means probably true (not due to chance).
A. Significance is a statistical term that tells how sure you are that a difference or relationship exists.
B. Some researchers use the word "significant" to describe a difference or relationship
that may be strategically important to a client (regardless of any statistical tests).

IV Population vs. samples
The basic idea of statistics is simple: you want to extrapolate from the data you have collected to make general conclusions. There is a large population of data out there, and you have randomly sampled parts of it. You analyze your sample to make inferences about the population. Consider several situations:
Quality control : Sample: The items you tested. Population: The entire batch of items produced.
Political polls: Sample: The ones you polled. Population: All voters.

Clinical studies: Sample: Subset of patients who attended Tuesday morning clinic in August Population: All similar patients.

Laboratory research: Sample: The data you actually collected Population: All the data you could have collected if you had repeated the experiment many times the same way