The purpose of this module is to introduce dependent and independent variables and how to identify them. This unit will also discuss various types of sampling procedures and their uses.

Learning Objectives

• Define and identify the dependent and independent variables in an experiment
• Compare and contrast probability and nonprobability sampling and give examples of each

Variables

It is very important when designing a research study that the researcher fully understands the variables in the study for the results to be valid and have meaning. A variable is something in the study that can be changed such as a characteristic or a value.

The most common types of variables, especially when study cause and effect relationships, are the independent and dependent variables. The independent variable is the one that can be controlled or manipulated. The dependent variable is the one that is measured by the experimenter. For example, if a researcher wanted to study the effects of fertilizer on plant growth, the independent variable would be the amount of fertilizer and the dependent variable would be the height of the plant. It is very important that the research can identify these variables in a study and describe how these variables are measured and defined. For practice in identifying these variables, this YouTube video below defines dependent and independent variables and provides numerous examples and opportunities to practice identifying each variable in sample research problems.  http://www.youtube.com/watch?v=pabthvd9r4c 

The following YouTube video provides additional and more detailed information regarding other types of variables used in research studies. This 20-minute video contains a Research Methods lecture that uses examples in psychology to discuss types of variables.

Sampling

Researchers cannot study every member of a population or group. Rather, they collect data from a subset of that population or group which is called a sample. The data collected and observations made are used to make inferences about the entire population or group with the idea that the sample will be representative of the larger population.

There are two main approaches to sampling - probability sampling and nonprobability sampling. Probability sampling is when all members of a population or elements of a group have the same opportunity and chance of being included in the study. Following are the three most common types of probability sampling:

• Simple random sample - all members of a population or elements have an equal chance of being selected and sampling is done is a single staged time frame where each member is selected individually.
• Systematic samples - taken by starting with a randomly selected member of population or element and then selecting every nth member of the population. For example - selecting ever 50^th name on a list of community members.
• Cluster samples - Starts with first selecting groups of elements or members of populations in clusters (schools, people living on a particular block, etc..) and then selecting individuals from each cluster.

 Nonprobability sampling occurs when members of the study or elements of a group are selected based on availability (perhaps they volunteered) or on the researcher's judgment that they will be representative. This is often called convenience sampling because researchers use whatever individuals are available.

 For the results to be valid, other factors that must be considered in sampling are the response rate, sampling size and sampling error. See the Resources on this page and in the Suggested Reading for additional information.

The following slide show defines terminology related to sampling techniques for both quantitative and qualitative studies. The presentation lists and explains the different types of sampling techniques for both probability and nonprobability sampling.

Sampling in qualitative and quantitative method from Jinnah University For Women