Chapter 4: Measurement and Units of Analysis Show While it is very common to hear the terms independent and dependent variable, extraneous variables are less common, which is surprising because an extraneous variable can destroy the integrity of a research study that claims to show a cause and effect relationship. An extraneous variable is a variable that may compete with the independent variable in explaining the outcome. Remember this, if you are ever interested in identifying cause and effect relationships you must always determine whether there are any extraneous variables you need to worry about. If an extraneous variable really is the reason for an outcome (rather than the IV) then we sometimes like to call it a confounding variable because it has confused or confounded the relationship we are interested in. (see example below)
Example Suppose we want to determine the effectiveness of new course curriculum for an online research methods class. We want to test how effective the new course curriculum is on student learning, compared to the old course curriculum. We are unable to use random assignment to equate our groups. Instead, we ask one of the college´s most experienced online teachers to use the new online curriculum with one class of online students and the old curriculum with the other class of online students. Imagine that the students taking the new curriculum course (the experimental group) got higher grades than the control group (the old curriculum). Do you see any problems with claiming that the reason for the difference between the two groups is because of the new curriculum? The problem is that there are alternative explanations. First, perhaps the difference is because the group of students in the new curriculum course were more experienced students, both in terms of age and where they were in their studies (more third year students than first year students). Perhaps the old curriculum class had a higher percentage of students for whom English is not their first language and they struggled with some of the material because of language barriers, which had nothing to do with then old curriculum. In other words, we have a problem, in that there could be alternative explanations for our findings. These alternative explanations are called extraneous variables and they can occur when we do not have random assignation. Indeed, it is very possible that the difference we saw between the two groups was due to other variables (i.e. experience level of students, English language proficiency), rather than the IV (new versus old curriculum). It is important to note that researchers can and should attempt to control for extraneous variables, as much as possible. This can be done in two ways. The first is by employing standardized procedures. This means that the researcher attempts to ensure that all aspects of the experiment are the same, with the exception of the independent variable. For example, the researchers would use the same method for recruiting participants and they would conduct the experiment in the same setting. They would ensure that they give the same explanation to the participants at the beginning of the study and any feedback at the end of the study in exactly the same way. Any rewards for participation would be offered for all participants in the same manner. They could also ensure that the experiment occurs on the same day of the week (or month), or at the same time of day, and that the lab is kept at a constant temperature, a constant level of brightness, and a constant level of noise (Explore Psychology, 2019). The second way that a researcher in an experiment can control for extraneous variables is to employ random assignation to reduce the likelihood that characteristics specific to some of the participants have influenced the independent variable. Random assignment means that every person chosen for an experiment has an equal chance of being assigned to either the test group of the control group (Explore Psychology, 2019). Chapter 6 provides more detail on random assignment, and explains the difference between a test group and a control group.
Extraneous variables are factors that may confound a researcher's ability to demonstrate causation. Here is a partial list of extraneous variables marketing researchers confront: 1. History: History refers to events that are external to the experiment. These events occur at the same time as the experiment. History makes it more difficult for marketing researchers to get a "clean read" from their test market because the change in the dependent variable may be due to historical events and not the study's independent variables. The longer the experiment the greater the probability that history will impact the research. Here is an example of this phenomenon from my own career. During my first month as an advertising executive, I travelled to Green Bay, WI with my client from Clairol. Clairol was test marketing a new shampoo that was to compete directly with a shampoo from Proctor & Gamble[i]. We made this trip to visit retailers. Our goal was to check shelf placement, in-store displays, and the availability of our "saleable sample." Clairol was using this saleable sample—a 1 oz. bottle of the shampoo selling at 39¢—to gain trial; which is to say, get shampoo users to try the product. Upon visiting the first store, we immediately noticed that P&G priced a 6 oz. bottle of their brand at 39¢. As a consequence, Clairol's saleable sample tactic failed to achieve the hoped form levels of trial. 2. Maturation: Maturation refers to changes that occur to the test subjects during a test market that are not related to the test market. Maturation effects the test market. The target markets preferences may change because of maturation factors—changes in test subjects' demographics, psychographics, usage behaviors rather than the test variables. The longer the test market, the more likely it will suffer from maturation. Imagine a two-year experiment conducted among teenagers for an Acne remedy. The normal aging of test subjects is a maturation effect, which could severely limit researchers' attempt to make sound conclusions from their findings. 3. Testing Effects: Being part of an experiment changes people and could confound the results. The mere fact of being observed can cause people to change their attitudes and behavior. Advertisers frequently use "pre-post" persuasion tests to measure the effectiveness of advertising. But, such tests can have a testing effect. The fact that people are asked to discuss their purchase intent before seeing an advertisement may influence their perception of the advertisement. 4. Mortality: Mortality refers to the loss of test subjects over time. This is an especially serious problem with longitudinal tests that measure test variables over a long period of time. If a researcher is going to get a read of consumers' attitudes over several time periods, the impact of people dropping out of the study can undermine the validity of the study. 5. Selection Bias: Selection bias is an extraneous variable that undermines an experiment's validity. Selection bias occurs when the test group or control group is significantly different from the population in purports to represent. Let's go back to Clairol's test market in Green Bay. Why Green Bay? Well, when conducting field experiments, marketing researchers look for small, relatively isolated markets, to represent the United States. The goal is to find markets that are "Little USAs." Of course, there are no perfect test markets that give a 100 percent accurate portrayal of the USA. These extraneous variables are often called confounding variables as they undermine, or confound, the market researcher's ability to draw clear conclusions from an experiment. When conducting an experiment, researchers attempt to control the influence of extraneous variables. Here are some of the techniques they use:
[i] At that time Clairol was own by Bristol-Myers. In 2001 Proctor & Gamble acquired Clairol from Bristol-Myers Squibb. toc | return to top | previous page | next page In an experiment, an extraneous variable is any variable that you’re not investigating that can potentially affect the outcomes of your research study. If left uncontrolled, extraneous variables can lead to inaccurate conclusions about the relationship between independent and dependent variables.
Extraneous variables can threaten the internal validity of your study by providing alternative explanations for your results. In an experiment, you manipulate an independent variable to study its effects on a dependent variable. Example: Experimental studyIn a study on mental performance, you test whether wearing a white lab coat, your independent variable, improves scientific reasoning, your dependent variable.You recruit students from a university to participate in the study. You manipulate the independent variable by splitting participants into two groups:
All participants are given a scientific knowledge quiz, and their scores are compared between groups. When extraneous variables are uncontrolled, it’s hard to determine the exact effects of the independent variable on the dependent variable, because the effects of extraneous variables may mask them. Uncontrolled extraneous variables can also make it seem as though there is a true effect of the independent variable in an experiment when there’s actually none. Example: Extraneous variablesIn your experiment, these extraneous variables can affect the science knowledge scores:
If these variables systematically differ between the groups, you can’t be sure whether your results come from your independent variable manipulation or from the extraneous variables. Controlling extraneous variables is an important aspect of experimental design. When you control an extraneous variable, you turn it into a control variable. A confounding variable is a type of extraneous variable that is associated with both the independent and dependent variables.
In a conceptual framework diagram, you can draw an arrow from a confounder to the independent variable as well as to the dependent variable. You can draw an arrow from extraneous variables to a dependent variable.  People who work in labs would regularly wear lab coats and may have higher scientific knowledge in general. Therefore, it’s unlikely that your manipulation will increase scientific reasoning abilities for these participants. Variables that only impact on scientific reasoning are extraneous variables. These include participants’ interests in science and undergraduate majors. While interest in science may affect scientific reasoning ability, it’s not necessarily related to wearing a lab coat. Types and controls of extraneous variablesDemand characteristicsDemand characteristics are cues that encourage participants to conform to researchers’ behavioral expectations. Sometimes, participants can infer the intentions behind a research study from the materials or experimental settings, and use these hints to act in ways that are consistent with study hypotheses. These demand characteristics can bias the study outcomes and reduce the external validity, or generalizability, of the results. Example: Demand characteristicsResearch participants in the experimental group easily draw links between the lab setting, being asked to wear lab coats, and the questions on their scientific knowledge.They work harder to do well on the quiz by paying more attention to the questions. You can avoid demand characteristics by making it difficult for participants to guess the aim of your study. Ask participants to perform unrelated filler tasks or fill out plausibly relevant surveys to lead them away from the true nature of the study. Experimenter effectsExperimenter effects are unintentional actions by researchers that can influence study outcomes. There are two main types of experimenter effects:
Participants wearing the non-lab coats are not encouraged to perform well on the quiz. Therefore, they don’t work as hard on their responses. To avoid experimenter effects, you can implement masking (blinding) to hide the condition assignment from participants and experimenters. In a double-blind study, researchers won’t be able to bias participants towards acting in expected ways or selectively interpret results to suit their hypotheses. Situational variablesSituational variables, such as lighting or temperature, can alter participants’ behaviors in study environments. These factors are sources of random error or random variation in your measurements. To understand the true relationship between independent and dependent variables, you’ll need to reduce or eliminate the effect of situational factors on your study outcomes. Example: Situational variableTo perform your experiment, you use the lab rooms on campus. They are only available either early in the morning or late in the day. Because time of day may affect test performance, it’s an extraneous variable.To avoid situational variables from influencing study outcomes, it’s best to hold variables constant throughout the study or statistically account for them in your analyses. Participant variablesA participant variable is any characteristic or aspect of a participant’s background that could affect study results, even though it’s not the focus of an experiment. Participant variables can include sex, gender identity, age, educational attainment, marital status, religious affiliation, etc. Since these individual differences between participants may lead to different outcomes, it’s important to measure and analyze these variables. Example: Participant variablesEducational background and undergraduate majors are important participant variables for your study on scientific reasoning. Participants with strong educational backgrounds in STEM subjects are likely to perform better than others.To control participant variables, you should aim to use random assignment to divide your sample into control and experimental groups. Random assignment makes your groups comparable by evenly distributing participant characteristics between them.
An extraneous variable is any variable that you’re not investigating that can potentially affect the dependent variable of your research study. A confounding variable is a type of extraneous variable that not only affects the dependent variable, but is also related to the independent variable.
There are 4 main types of extraneous variables:
|
Which are the ways through which a researcher attempts to control extraneous variables in a field experiment?
Related Posts
Copyright © 2024 themosti Inc.
