Independent Variable Science: Definition, Explanation And Examples

Sometimes you may hear this variable called the “controlled variable” because it is the one that is changed. Do not confuse it with a control variable, which is a variable that is purposely held constant so that it can’t affect the outcome of the experiment. For example, you want to know if taking your indoor plants outside will make them grow faster than making them stay inside near the window.

  1. If you’re wondering how to do a t test, the easiest way is with statistical software such as Prism or an online t test calculator.
  2. Imagine that you’re conducting an experiment in which you want to see what is the best watering pattern for a particular type of plant.
  3. In many cases, extraneous variables are controlled for by the experimenter.
  4. It also makes it easier for other researchers to replicate a study and check for reliability.
  5. That means restrictive and nonrestrictive clauses are both dependent clauses.

Independent and Dependent Variables

In this example, there is only one independent variable—the watering regularity. All of the other potential variables are kept consistent and unchanged, such as the type of plant, the quality of the soil and even the amount of water administered each day. These represent the third type of variable present in any experiment—the controlled variables.

Qualitative Variable – Types and Examples

A t test tells you if the difference you observe is “surprising” based on the expected difference. When you have a reasonable-sized sample (over 30 or so observations), the t test can still be used, but other tests that use the normal distribution (the z test) can be used in its place. The t test is one of the simplest statistical techniques that is used to evaluate whether there is a statistical difference between the means from up to two different samples. The t test is especially useful when you have a small number of sample observations (under 30 or so), and you want to make conclusions about the larger population. Upon analysis of the results, an alternative hypothesis can be rejected or supported, but it can never be proven to be correct. If we reject the null hypothesis, this doesn’t mean that our alternative hypothesis is correct but does support the alternative/experimental hypothesis.

What is the difference between an extraneous variable and a confounding variable?

Examples of binary independent variables include yes or no questions, such as whether a participant is a smoker or non-smoker. These variables are continuous in nature and can take any value on a continuous scale. Examples of continuous independent variables include age, height, weight, temperature, and blood pressure.

Extraneous Variables in Psychology

It’s important to note that while moderators can have an influence on outcomes, they don’t necessarily cause them; rather they modify or “moderate” existing relationships between other variables. This means that it’s possible for two different groups with similar characteristics, but different levels of moderation, to experience very different results from the same experiment or study design. In such a case, one may find that gender has an influence on how much students’ scores suffer when they’re deprived of sleep. Which specific variables need to be controlled for will vary tremendously depending on the research project at hand, so there’s no generic list of control variables to consult. As a researcher, you’ll need to think carefully about all the factors that could vary within your research context and then consider how you’ll go about controlling them. A good starting point is to look at previous studies similar to yours and pay close attention to which variables they controlled for.

In the world of scientific research, there’s no shortage of variable types, some of which have multiple names and some of which overlap with each other. In this post, we’ve covered some of the popular ones, but remember that this is not an exhaustive list. Variables in psychology are things that can be changed or altered, such as a characteristic or value. Variables are generally used in psychology experiments to determine if changes to one thing result in changes to another.

Essentially, the independent variable is the presumed cause, and the dependent variable is the observed effect. In research, a variable is any characteristic, number, or quantity that can be measured or counted in experimental investigations. One is called the dependent variable, and the other is the independent variable. The independent variable is the presumed cause in an experiment or study, while the dependent variable is the presumed effect or outcome.

Correlation coefficients such as Pearson’s r or Spearman’s rho are used to quantify the strength and direction of the relationship. Nearly 1,000 years later, in the west, a similar concept of labeling unknown and known quantities with letters was introduced. In his equations, he utilized consonants for known quantities, and vowels for unknown quantities. Less than a century later, Rene Descartes instead chose to use a, b and c for known quantities, and x, y and z for unknown quantities. To this day, this is the standard system that remains in use across most of the sciences, including mathematics. It is not intended to provide medical, legal, or any other professional advice.

A clause is a group of words that contains at least one subject and at least one verb, but clauses can be either complete or incomplete sentences, depending on their wording and punctuation. For unpaired (independent) samples, there are multiple options for nonparametric testing. Mann-Whitney is more popular and compares the mean ranks (the ordering of values from smallest to largest) of the two samples. Mann-Whitney is often misrepresented as a comparison of medians, but that’s not always the case. Kolmogorov-Smirnov tests if the overall distributions differ between the two samples.

This is especially useful if the two clauses are contradictory or if the second clause goes in an unexpected direction. Conjunctive adverbs are words like however, although, or consequently, as well as phrases like in the meantime, on the other hand, or as a result. They are very common in writing as a way to improve the flow of reading, but they’re not used as much in speech. Just like with the colon, no extra words or punctuation are needed with the semicolon if the two clauses are obviously related. However, if the relationship between the two clauses is not obvious, you may need to use a conjunctive adverb, a type of transitional expression.

This type of hypothesis is constructed to state the independent variable followed by the predicted impact on the dependent variable. You can also use conjunctive adverbs with a single independent clause or stand-alone sentence as a way to transition from one sentence or paragraph to another. All you need is either a subordinating conjunction (e.g., if, because, before/after, although, once) or a relative pronoun (e.g., where, when, wherever, whenever).

In scientific studies, researchers will typically pay very close attention to the dependent variable (or variables), carefully measuring any changes in response to hypothesised independent variables. This can be tricky in practice, as it’s not always easy to reliably measure specific phenomena or outcomes – or to be certain that the actual cause of the change is in fact the independent variable. These variables are manipulated or controlled by the researcher to observe their effect on the dependent variable. Examples of controlled independent variables include the type of treatment or therapy given, the dosage of a medication, or the amount of exposure to a stimulus. In experiments, even if measured time isn’t the variable, it may relate to duration or intensity. It should be noted that in some experiments there are other variables present apart from the independent and the dependent variables.

The exact formula for any t test can be slightly different, particularly the calculation of the standard error. Not only does it matter whether one or two samples are being compared, the relationship between the samples can make a difference too. Statistical software, such as this paired t test calculator, will simply take a difference between the two values, and then compare that difference to 0.

But in the realm of scientific experiments, variables take on a slightly different (and simpler) role. P values are the probability that you would get data as or more extreme than the observed data given that the null hypothesis is true. It’s a mouthful, and there are a lot of issues to be aware of with P values.

If you take before and after measurements and have more than one treatment (e.g., control vs a treatment diet), then you need ANOVA. An experimental hypothesis predicts what change(s) will occur in the dependent variable when the independent variable is manipulated. Even if the cause-and-effect relationship does exist, the confounding variable still might overestimate or underestimate the impact of the independent variable on the dependent variable.

If any additional controlled variables were changing, it would be impossible to definitively determine the connection between the independent and dependent variables. As we mentioned earlier, one of the major challenges in identifying and measuring causal relationships is that it’s difficult to isolate the impact of variables other than the independent variable. Simply put, there’s always a risk that there are factors beyond the ones you’re specifically looking at that might be impacting the results of your study.

Here are the definitions of independent and dependent variables, examples of each type, and tips for telling them apart and graphing them. If you’re not seeing your research question above, note that t tests are very basic great ways to green your business statistical tools. Many experiments require more sophisticated techniques to evaluate differences. If the variable of interest is a proportion (e.g., 10 of 100 manufactured products were defective), then you’d use z-tests.

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