Pritha Bhandari

Construct validity is about how well a test measures the concept it was designed to evaluate. It’s crucial to establishing the overall validity of a method.

Assessing construct validity is especially important when you’re researching something that can’t be measured or observed directly, such as intelligence, self-confidence, or happiness. You need multiple observable or measurable indicators to measure those constructs.

Naturalistic observation is a qualitative research method where you record the behaviors 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.

Note: Naturalistic observation is one of the research methods that can be used for an observational study design. Another common type of observation is the controlled observation.

Deductive reasoning is a logical approach where you progress from general ideas to specific conclusions. It’s often contrasted with inductive reasoning, where you start with specific observations and form general conclusions.

Deductive reasoning is also called deductive logic or top-down reasoning.

Triangulation in research means using multiple datasets, methods, theories and/or investigators to address a research question. It’s a research strategy that can help you enhance the validity and credibility of your findings.

Triangulation is mainly used in qualitative research, but it’s also commonly applied in quantitative research. If you decide on mixed methods research, you’ll always use methodological triangulation.

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 or ascertainment bias.

Observer bias is particularly likely to occur in observational studies. But it can also affect other types of research where measurements are taken or recorded manually.

Missing data, or missing values, occur when you don’t have data stored for certain variables or participants. Data can go missing due to incomplete data entry, equipment malfunctions, lost files, and many other reasons.

In any dataset, there are usually some missing data. In quantitative research, missing values appear as blank cells in your spreadsheet.

The geometric mean is an average that multiplies all values and finds a root of the number. For a dataset with n numbers, you find the nth root of their product. You can use this descriptive statistic to summarize your data.

The geometric mean is an alternative to the arithmetic mean, which is often referred to simply as “the mean.” While the arithmetic mean is based on adding values, the geometric mean multiplies values.

Data cleansing 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, analyze, detect, modify, or remove “dirty” data to make your dataset “clean.” Data cleansing is also called data cleaning or data scrubbing.

Attrition is participant dropout over time in research studies. It’s also called subject mortality, but it doesn’t always refer to participants dying!

Almost all longitudinal studies will have some dropout, but the type and scale of the dropout can cause problems. Attrition bias is the selective dropout of some participants who systematically differ from those who remain in the study.

Attrition bias is especially problematic in randomized controlled trials for medical research.