## How to Calculate Variance | Calculator, Analysis & Examples

The variance is a measure of variability. It is calculated by taking the average of squared deviations from the mean.

Variance tells you the degree of spread in your data set. The more spread the data, the larger the variance is in relation to the mean.

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## Operationalization | A Guide with Examples, Pros & Cons

Operationalization means turning abstract concepts into measurable observations. Although some concepts, like height or age, are easily measured, others, like spirituality or anxiety, are not.

Through operationalization, you can systematically collect data on processes and phenomena that aren’t directly observable.

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## What Is Face Validity? | Guide, Definition & Examples

Face validity is about whether a test appears to measure what it’s supposed to measure. This type of validity is concerned with whether a measure seems relevant and appropriate for what it’s assessing on the surface.

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## Construct Validity | Definition, Types, & Examples

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 or run the risk of introducing research bias into your work.

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## Naturalistic Observation | Definition, Guide & Examples

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.

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## Independent vs. Dependent Variables | Definition & Examples

In research, variables are any characteristics that can take on different values, such as height, age, temperature, or test scores.

Researchers often manipulate or measure independent and dependent variables in studies to test cause-and-effect relationships.

• The independent variable is the cause. Its value is independent of other variables in your study.
• The dependent variable is the effect. Its value depends on changes in the independent variable.

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## What Is Deductive Reasoning? | Explanation & Examples

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.

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## Inductive Reasoning | Types, Examples, Explanation

Inductive reasoning is a method of drawing conclusions by going from the specific to the general. It’s usually contrasted with deductive reasoning, where you go from general information to specific conclusions.

Inductive reasoning is also called inductive logic or bottom-up reasoning.

Note
Inductive reasoning is often confused with deductive reasoning. However, in deductive reasoning, you make inferences by going from general premises to specific conclusions.

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## Triangulation in Research | Guide, Types, Examples

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 and mitigate the presence of any research biases in your work.

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.

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## Observer Bias | Definition, Examples, Prevention

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 it can also affect other types of research where measurements are taken or recorded manually.

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