Published on
July 12, 2021
by
Pritha Bhandari.
Revised on
June 22, 2023.
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 sources and interpret scientific research.
Published on
July 7, 2021
by
Pritha Bhandari.
Revised on
June 22, 2023.
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
Published on
May 20, 2021
by
Pritha Bhandari.
Revised on
July 23, 2023.
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.
Published on
May 7, 2021
by
Pritha Bhandari.
Revised on
June 22, 2023.
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. Gone unnoticed, these errors can lead to research biases like omitted variable bias or information bias.
Published on
April 19, 2021
by
Pritha Bhandari.
Revised on
June 22, 2023.
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.
Published on
April 19, 2021
by
Pritha Bhandari.
Revised on
June 22, 2023.
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.
Published on
April 2, 2021
by
Pritha Bhandari.
Revised on
June 22, 2023.
In an experiment, an extraneous variable is any variable that you’re not investigating that can potentially affect the outcomes of your research study.
Published on
April 1, 2021
by
Pritha Bhandari.
Revised on
January 17, 2024.
The APA Publication Manual is commonly used for reporting research results in the social and natural sciences. This article walks you through APA Style standards for reporting statistics in academic writing.
Published on
March 29, 2021
by
Pritha Bhandari.
Revised on
June 22, 2023.
In experiments, a different independent variable treatment or manipulation is used in each condition to assess whether there is a cause-and-effect relationship with a dependent variable.
In a within-subjects design, or a within-groups design, all participants take part in every condition. It’s the opposite of a between-subjects design, where each participant experiences only one condition.
A within-subjects design is also called a dependent groups or repeated measures design because researchers compare related measures from the same participants between different conditions.
All longitudinal studies use within-subjects designs to assess changes within the same individuals over time.
Published on
March 12, 2021
by
Pritha Bhandari.
Revised on
June 22, 2023.
In experiments, you test the effect of an independent variable by creating conditions where different treatments (e.g., a placebo pill vs a new medication) are applied.
In a between-subjects design, also called a between-groups design, every participant experiences only one condition, and you compare group differences between participants in various conditions. It’s the opposite of a within-subjects design, where every participant experiences every condition.
A between-subjects design is also called an independent measures or independent-groups design because researchers compare unrelated measurements taken from separate groups.
