A ratio scale is a quantitative scale where there is a true zero and equal intervals between neighboring points. Unlike on an interval scale, a zero on a ratio scale means there is a total absence of the variable you are measuring.
Length, area, and population are examples of ratio scales.
Continue reading: What Is Ratio Data? | Examples & Definition
The standard normal distribution, also called the z-distribution, is a special normal distribution where the mean is 0 and the standard deviation is 1.
Any normal distribution can be standardised by converting its values into z-scores. Z-scores tell you how many standard deviations from the mean each value lies.
Converting a normal distribution into a z-distribution allows you to calculate the probability of certain values occurring and to compare different data sets.
Continue reading: The Standard Normal Distribution | Calculator, Examples & Uses
Levels of measurement, also called scales of measurement, tell you how precisely variables are recorded. In scientific research, a variable is anything that can take on different values across your data set (e.g., height or test scores).
There are 4 levels of measurement:
Depending on the level of measurement of the variable, what you can do to analyse your data may be limited. There is a hierarchy in the complexity and precision of the level of measurement, from low (nominal) to high (ratio).
Continue reading: Levels of measurement: Nominal, ordinal, interval, ratio
Observer bias happens when a researcher’s expectations, opinions, or prejudices influence what they perceive or record in a study. It often affects studies where observers are aware of the research aims and hypotheses. Observer bias is also called detection bias.
Observer bias is particularly likely to occur in observational studies. But this type of research bias can also affect other types of research where measurements are taken or recorded manually.
Continue reading: Observer Bias | Definition, Examples, Prevention
Attrition bias is the selective dropout of some participants who systematically differ from those who remain in the study. Almost all longitudinal studies will have some dropout, but the type and scale of the dropout can cause problems.
Attrition is participant dropout over time in research studies. It’s also called subject mortality, but it doesn’t always refer to participants dying!
Attrition bias is especially problematic in randomised controlled trials for medical research.
Continue reading: Attrition Bias | Examples, Explanation, Prevention
In research, demand characteristics are cues that might indicate the study aims to participants. These cues can lead participants to change their behaviors or responses based on what they think the research is about.
Demand characteristics are problematic because they can bias your research findings. They commonly occur in psychology experiments and social sciences studies because these involve human participants.
It’s important to consider potential demand characteristics in your research design and deal with them appropriately to obtain valid results.
Continue reading: Demand Characteristics | Definition, Examples & Control
Ethical considerations in research are a set of principles that guide your research designs and practices. Scientists and researchers must always adhere to a certain code of conduct when collecting data from people.
The goals of human research often include understanding real-life phenomena, studying effective treatments, investigating behaviours, and improving lives in other ways. What you decide to research and how you conduct that research involve key ethical considerations.
These considerations work to:
This article mainly focuses on research ethics in human research, but ethical considerations are also important in animal research.
Continue reading: Ethical Considerations in Research | Types & Examples
In experimental research, random assignment is a way of placing participants from your sample into different treatment groups using randomisation.
With simple random assignment, every member of the sample has a known or equal chance of being placed in a control group or an experimental group. Studies that use simple random assignment are also called completely randomised designs.
Random assignment is a key part of experimental design. It helps you ensure that all groups are comparable at the start of a study: any differences between them are due to random factors.
Continue reading: Random Assignment in Experiments | Introduction & Examples
Data cleaning involves spotting and resolving potential data inconsistencies or errors to improve your data quality. An error is any value (e.g., recorded weight) that doesn’t reflect the true value (e.g., actual weight) of whatever is being measured.
In this process, you review, analyse, detect, modify, or remove ‘dirty’ data to make your dataset ‘clean’. Data cleaning is also called data cleansing or data scrubbing.
Continue reading: Data Cleaning | A Guide with Examples & Steps
Naturalistic observation is a qualitative research method where you record the behaviours of your research subjects in real-world settings. You avoid interfering with or influencing any variables in a naturalistic observation.
You can think of naturalistic observation as ‘people watching’ with a purpose.
Continue reading: Naturalistic Observation | Definition, Guide & Examples