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.

Operationalization example
The concept of social anxiety can’t be directly measured, but it can be operationalized in many different ways. For example:

  • self-rating scores on a social anxiety scale
  • number of recent behavioral incidents of avoidance of crowded places
  • intensity of physical anxiety symptoms in social situations

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Face Validity | Guide with 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.

Types of measurement validity
Face validity is one of four types of measurement validity. The other three are:

  • Construct validity: Does the test measure the concept that it’s intended to measure?
  • Content validity: Is the test fully representative of what it aims to measure?
  • Criterion validity: Do the results accurately measure the concrete outcome they are designed to measure?

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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.

Types of measurement validity
Construct validity is one of four types of measurement validity. The other three are:

  • Content validity: Is the test fully representative of what it aims to measure?
  • Face validity: Does the content of the test appear to be suitable to its aims?
  • Criterion validity: Do the results accurately measure the concrete outcome they are designed to measure?

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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.

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. In this case, the researcher observes the participant in a controlled environment (e.g., a lab). The observer controls most variables and makes sure participants are observed structurally (e.g., by coding certain behaviors).

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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.
Example: Independent and dependent variables
You design a study to test whether changes in room temperature have an effect on math test scores.

Your independent variable is the temperature of the room. You vary the room temperature by making it cooler for half the participants, and warmer for the other half.

Your dependent variable is math test scores. You measure the math skills of all participants using a standardized test and check whether they differ based on room temperature.

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An easy introduction to deductive reasoning

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.

Deductive-reasoning

Note: Deductive reasoning is often confused with inductive reasoning. However, in inductive reasoning, you draw conclusions by going from the specific to the general.

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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.

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.

Examples: Triangulation in different types of research
  • Qualitative research: You conduct in-depth interviews with different groups of stakeholders, such as parents, teachers, and children.
  • Quantitative research: You run an eye-tracking experiment and involve three researchers in analyzing the data.
  • Mixed methods research: You conduct a quantitative survey, followed by a few (qualitative) structured interviews.

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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 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.

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