![]() ![]() Examples might include height, distance or number of items. Quantitative variables are any data sets that involve numbers or amounts. Extraneous variables that might unintentionally influence the outcome include parental support, prior knowledge of a foreign language or socioeconomic status. Take, for example, a study assessing whether private tutoring or online courses are more effective at improving students' Spanish test scores. ![]() These unwanted variables can unintentionally change a study's results or how a researcher interprets those results. Extraneous variablesĮxtraneous variables are factors that affect the dependent variable but that the researcher did not originally consider when designing the experiment. Read more: What Is a Control in an Experiment? (With Definition and Guide) 6. These amounts are always the same so that they do not affect the plants' growth. For example, in an experiment about plant development, control variables might include the amounts of fertilizer and water each plant gets. Researchers might intentionally keep a control variable the same throughout an experiment to prevent bias. Control variablesĬontrol or controlling variables are characteristics that are constant and do not change during a study. That relationship might be weaker in younger individuals and stronger in older individuals. For example, in a study looking at the relationship between economic status (independent variable) and how frequently people get physical exams from a doctor (dependent variable), age is a moderating variable. Moderating variablesĪ moderating or moderator variable changes the relationship between dependent and independent variables by strengthening or weakening the intervening variable's effect. For example, if wealth is the independent variable, and a long life span is a dependent variable, the researcher might hypothesize that access to quality healthcare is the intervening variable that links wealth and life span. They are associations instead of observations. Intervening variablesĪn intervening variable, sometimes called a mediator variable, is a theoretical variable the researcher uses to explain a cause or connection between other study variables-usually dependent and independent ones. When analyzing relationships between study objects, researchers often try to determine what makes the dependent variable change and how. For example, the time you spent studying (dependent) can affect the grade on your test (independent) but the grade on your test does not affect the time you spent studying. Independent variables can influence dependent variables, but dependent variables cannot influence independent variables. A grade on an exam is an example of a dependent variable because it depends on factors such as how much sleep you got and how long you studied. Dependent variablesĪ dependent variable relies on and can be changed by other components. ![]() Related: How To Become a Research Scientist 2. In studies, researchers often try to find out whether an independent variable causes other variables to change and in what way. Independent variables can, however, change other variables. Where someone lives, what they eat or how much they exercise are not going to change their age. Age is an example of an independent variable. Independent variablesĪn independent variable is a singular characteristic that the other variables in your experiment cannot change. Researchers organize variables into a variety of categories, the most common of which include: 1. Related: 6 Types of Research Studies (Advantages and Disadvantages) Types of variables For example, if the variable in an experiment is a person's eye color, its value can change from brown to blue to green from person to person. A variable's value can change between groups or over time. All studies analyze a variable, which can describe a person, place, thing or idea. Variables are things you measure, manipulate and control in statistics and research. In this article, we describe the types of variables and answer some frequently asked questions with regards to variables, experimental design and how to design a study. A strong understanding of variables can lead to more accurate statistical analyses and results. Many types of variables exist, and you must choose the right variable to measure when designing studies, selecting tests and interpreting results. Researchers and statisticians use variables to describe and measure the items, places, people or ideas they're studying. ![]()
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