In multistage sampling, or multistage cluster sampling, you draw a sample from a population using smaller and smaller groups (units) at each stage. It’s often used to collect data from a large, geographically spread group of people in national surveys.
A questionnaire is a list of questions or items used to gather data from respondents about their attitudes, experiences, or opinions. Questionnaires can be used to collect quantitative and/or qualitative information.
Questionnaires are commonly used in market research as well as in the social and health sciences. For example, a company may ask for feedback about a recent customer service experience, or psychology researchers may investigate health risk perceptions using questionnaires.
Correlation means there is a statistical association between variables. Causation means that a change in one variable causes a change in another variable.
In research, you might have come across the phrase “correlation doesn’t imply causation.” Correlation and causation are two related ideas, but understanding their differences will help you critically evaluate and interpret scientific research.
A lab report conveys the aim, methods, results, and conclusions of a scientific experiment.
The main purpose of a lab report is to demonstrate your understanding of the scientific method by performing and evaluating a hands-on lab experiment. This type of assignment is usually shorter than a research paper.
Lab reports are commonly used in science, technology, engineering, and mathematics (STEM) fields. This article focuses on how to structure and write a lab report.
In scientific research, measurement error is the difference between an observed value and the true value of something. It’s also called observation error or experimental error.
There are two main types of measurement error:
Random error is a chance difference between the observed and true values of something (e.g., a researcher misreading a weighing scale records an incorrect measurement).
Systematic error is a consistent or proportional difference between the observed and true values of something (e.g., a miscalibrated scale consistently registers weights as higher than they actually are).
By recognizing the sources of error, you can reduce their impacts and record accurate and precise measurements.
In research, you often investigate causal relationships between variables using experiments or observations. For example, you might test whether caffeine improves speed by providing participants with different doses of caffeine and then comparing their reaction times.
An explanatory variable is what you manipulate or observe changes in (e.g., caffeine dose), while a response variable is what changes as a result (e.g., reaction times).
The words “explanatory variable” and “response variable” are often interchangeable with other terms used in research.
In experiments, researchers manipulate independent variables to test their effects on dependent variables. In a controlled experiment, all variables other than the independent variable are controlled or held constant so they don’t influence the dependent variable.