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# 5 Essential Terms to Know Before Carrying Out Data Analysis

Understanding the results or findings of a research is a vital part of knowing whether a research study provides a compelling case for changing practice. To understand research result, it is important to understand how studies are designed and how data are analysed.

Before carrying out your data analysis it is important to know following essential terms:

1) Population:

A population is the set of all possible cases of interest.
In determining the population of interest, we usually specify the point in time that defines the population and also specify the geographic region over which the population spread, if relevant.

Eg: Am I interested in my currently enrolled science students, or those who also completed the course last year?

2) Sample:

A sample is a set of cases that does not include every member of the population.
Or

A subset of the population is called as a sample.

Eg: It may be too costly or time-consuming to include every student in the study instead choose only those students in a class whose name begins with ‘A’ and thus be only working with a sample.

3) Variable:

A variable is a quality or condition that can differ from one case to another.

The opposite notion to a variable is a constant, which is simply a condition or quality that does not vary among cases. Most research is devoted to understanding variables – whether a variable takes on certain traits for some cases and different traits for other cases.

4) Research Questions:

A research question states the aim of a research project in terms of cases of interest and the variable upon which these are thought to differ.
One needs a clear research question in mind before undertaking statistical analysis to avoid the situation where huge amounts of data are gathered unnecessarily and do not lead to any meaningful results.

Eg: What is the age distribution of the student in my science class?

5) Conceptual Definition:

The conceptual definition of a variable uses literal terms to specify the qualities of a variable.
A conceptual definition is much like a dictionary definition: it provides a working definition of the variable so that we have a general sense of what it ‘means’.

Eg: A researcher might define ‘health’ conceptually as ‘an individual’s state of well- being’.

It is clear, though, that if the researcher now instructs any other researchers to go out and measure people’s ‘state of well-being’, they would leave scratching their heads.