1. Causal or Experimental Research
When most people think of scientific experimentation, research on cause and effect is most often brought to mind. Experiments on causal relationships investigate the effect of one or more variables on one or more outcome variables. This type of research also determines if one variable causes another variable to occur or change. An example of this type of research would be changing the amount of a specific treatment and measuring the effect on study participants.
2. Descriptive Research
Descriptive research seeks to depict what already exists in a group or population. An example of this type of research would be an opinion poll to determine which presidential candidate people plan to vote for in the next election. Descriptive studies don’t try to measure the effect of a variable; they seek only to describe it.
3. Relational or Correlational Research
A study that investigates the connection between two or more variables is considered relational research. The variables that are compared are generally already present in the group or population. For example, a study that looks at the proportion of males and females that would purchase either a classical CD or a jazz CD would be studying the relationship between gender and music preference. A theory is a well-established principle that has been developed to explain some aspect of the natural world. A theory arises from repeated observation and testing and incorporates facts, laws, predictions, and tested hypotheses that are widely accepted. A hypothesis is a specific, testable prediction about what you expect to happen in your study. For example, an experiment designed to look at the relationship between study habits and test anxiety might have a hypothesis that states, “We predict that students with better study habits will suffer less test anxiety.” Unless your study is exploratory in nature, your hypothesis should always explain what you expect to happen during the course of your experiment or research. While the terms are sometimes used interchangeably in everyday use, the difference between a theory and a hypothesis is important when studying experimental design.
A theory predicts events in general terms, while a hypothesis makes a specific prediction about a specified set of circumstances.A theory has been extensively tested and is generally accepted, while a hypothesis is a speculative guess that has yet to be tested.
One of the most important distinctions to make when discussing the relationship between variables is the meaning of causation.
A positive correlation is a direct relationship where, as the amount of one variable increases, the amount of a second variable also increases. In a negative correlation, as the amount of one variable goes up, the levels of another variable go down. In both types of correlation, there is no evidence or proof that changes in one variable cause changes in the other variable. A correlation simply indicates that there is a relationship between the two variables.
