Published on
May 6, 2022
by
Pritha Bhandari.
Revised on
October 10, 2022.
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 exampleThe 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
Published on
February 24, 2022
by
Pritha Bhandari.
Revised on
August 31, 2022.
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 validityFace 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?
Published on
February 17, 2022
by
Pritha Bhandari.
Revised on
September 15, 2022.
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 validityConstruct 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?
Published on
February 10, 2022
by
Pritha Bhandari.
Revised on
September 14, 2022.
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).
Published on
February 3, 2022
by
Pritha Bhandari.
Revised on
October 17, 2022.
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 variablesYou 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.
Published on
January 20, 2022
by
Pritha Bhandari.
Revised on
July 21, 2022.
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.
Note: Deductive reasoning is often confused with inductive reasoning. However, in inductive reasoning, you draw conclusions by going from the specific to the general.
Published on
January 12, 2022
by
Pritha Bhandari.
Revised on
October 10, 2022.
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.
Published on
January 3, 2022
by
Pritha Bhandari.
Revised on
October 10, 2022.
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.
Published on
December 8, 2021
by
Pritha Bhandari.
Revised on
October 17, 2022.
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.
Published on
December 8, 2021
by
Pritha Bhandari.
Revised on
October 10, 2022.
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.
