1. Variables and Samples

2. Variables Variables Independent variable (IV) – the predictor variable
Dependent variable (DV) – the outcome variable
Researchers manipulate and control the independent variable to study its effect on the dependent variable.
Variables are operationalized to make the terms used in a study as explicit as possible.
Operationalization is the process of strictly defining variables into measurable factors. The process defines fuzzy concepts and allows them to be measured, empirically and quantitatively.
Example: An individual’s comfort zone can be operationalized by taking precise measurements of when their physiological signs begin to demonstrate that they are experiencing discomfort.

3. Variables
Example: By recording the number and type of questions asked by students we can measure their confusion with new subjects.
The subjects are being manipulated therefore this is the independent variable.
The dependent variable is represented by the variable in which the IV affects. In this case, the dependent variables are the number and type of questions asked by students as well as their measured level of confusion.

4. Variables
Continuous variable – Values that can be expressed even in decimal points. Example: age, height, weight.
Non-continuous variable – Values that can be expressed only in integers. Also known as a discrete variable. Example: number of students in a class, number of children in a family.
Extraneous variable – An independent variable, which affects the dependent variable, but is not directly related to the purpose of the study. This influence on the DV is known as the experimental error.

5. Samples
All the items considered in any field of inquiry constitutes a “universe” or population.
Study of the entire population is known as a census study.
Given that census studies are near impossible to carry out, we instead select a few items from a given population, a sample.
Samples are said to be heterogeneous when they vary and homogeneous when they do not.

6. Samples
Random sampling – Each item in the population has an equally probable chance of being selected.
Non-random sampling – All items do not have an equally probable chance of being selected. Selection depends upon the convenience and judgment of the researcher.

7. Samples Characteristics of good samples:
Random sample
Representative of the population and problems being studied
Free of bias
Bias can come in the form of a weight-control medication study selecting only people without long-term weight issues to participate.
Demographic bias occurs when identifiable groups of people in the population are underrepresented.
A larger sample is always better
Adhering to these guidelines facilitates the generalization of results to a wider population.

8. Samples
Example: A researcher plans to evaluate high school students' reactions to a new policy on closed campus stations. Located near the office of the dean, the researcher interviews every fourth student who visits the dean. The researcher eventually secures data from 100 interviews and publishes the findings as the, "Reactions of high school students to a new policy for a closed campus". What may be wrong with this approach?
In this case, the sample is biased toward students that visit the dean. This sampling is not representative of the whole student population.

9. Applying your Knowledge
Natural Selection Simulation
Now that you have learned about variables and samples, it is your turn to be the scientist and manipulate the mutation level of the organisms in the natural selection simulation to see how it influences your sample.