Question

When conducting research to answer a question or learn about a concept or phenomenon, observations about the characteristics or behaviors of a sample are often used to help researchers learn about a larger population. Statistics allow us to examine relationships between variables in the sample as a group, eliminating the need to consider each participant’s characteristics individually. This week, it was presented that researchers use a correlational design when they examine relationships between variables without actively manipulating any variables. Conversely, an experimental design is used when researchers manipulate a variable to see how another variable changes as a result.

a) What is the purpose of using observations about the characteristics or behaviors of a sample in research?
a) To create statistical models
b) To understand the larger population
c) To manipulate variables
d) To eliminate participant characteristics individually

Answer 1

The purpose of using observations about a sample in research is to understand the larger population, create statistical models, and detect patterns that can provide insights about the wider group without the need to study every individual. Observational studies offer insight into relationships between variables, while experimental designs can establish cause and effect.

Step-by-step explanation:

The purpose of using observations about the characteristics or behaviors of a sample in research is to gain insights into a larger population. By studying a subset of individuals, researchers can create models that describe patterns or relationships within that sample which, if the sampling method is appropriate, can be generalized to a wider group. In this way, researchers can avoid the impracticality and ethical concerns of studying every individual in a population. Observational studies, such as surveys or archival research, reflect the recognition of patterns in data without actively manipulating variables. However, they cannot establish cause and effect, which is the domain of experimental designs.

One classic experimental example involves the manipulation of an independent variable to observe changes in a dependent variable. The example provided about aspirin use and the risk of heart attacks in a controlled study is a case in point. In this study design, researchers can manipulate the independent variable (aspirin intake) and measure the dependent variable (occurrence of heart attacks), enabling them to draw conclusions about the causal relationship between the two.

It is vital for researchers to choose a method that matches their research question. Where experimental studies are not feasible, observational studies or surveys can effectively illuminate relationships between variables, although they are limited in making causal inferences.