A guide to correlation coefficients

A correlation coefficient is a number between -1 and 1 that tells you the strength and direction of a relationship between variables.

In other words, it reflects how similar the measurements of two or more variables are across a dataset.

Correlation coefficient value Correlation type Meaning
1 Perfect positive correlation When one variable changes, the other variables change in the same direction.
0 Zero correlation There is no relationship between the variables.
-1 Perfect negative correlation When one variable changes, the other variables change in the opposite direction.

Graphs visualizing perfect positive, zero, and perfect negative correlations

Continue reading: A guide to correlation coefficients

Designing a questionnaire

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.

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Correlation vs causation

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.

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An introduction to correlational research

A correlational research design investigates relationships between variables without the researcher controlling or manipulating any of them.

A correlation reflects the strength and/or direction of the relationship between two (or more) variables. The direction of a correlation can be either positive or negative.

Positive correlation Both variables change in the same direction As height increases, weight also increases
Negative correlation The variables change in opposite directions As coffee consumption increases, tiredness decreases
Zero correlation There is no relationship between the variables Coffee consumption is not correlated with height

Continue reading: An introduction to correlational research

How to write a lab report

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.

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Random vs systematic error

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.

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Explanatory and response variables

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.

Cause (what changes) Effect (what’s measured)
Independent variable Dependent variable
Predictor variable Outcome/criterion variable
Explanatory variable Response variable

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What is a controlled experiment?

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.

Controlling variables can involve:

  • holding variables at a constant or restricted level (e.g., keeping room temperature fixed).
  • measuring variables to statistically control for them in your analyses.
  • balancing variables across your experiment through randomization (e.g., using a random order of tasks).

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Understanding extraneous variables

In an experiment, an extraneous variable is any variable that you’re not investigating that can potentially affect the outcomes of your research study.

If left uncontrolled, extraneous variables can lead to inaccurate conclusions about the relationship between independent and dependent variables.

Research question Extraneous variables
Is memory capacity related to test performance?
  • Test-taking time of day
  • Test anxiety
  • Level of stress
Does sleep deprivation affect driving ability?
  • Road conditions
  • Years of driving experience
  • Noise
Does light exposure improve learning ability in mice?
  • Type of mouse
  • Genetic background
  • Learning environment

Continue reading: Understanding extraneous variables