Learning Objectives
- Define the concept of a variable, and distinguish between quantitative from categorical variables.
- Explain the difference between a population and a sample.
- Distinguish between experimental and non-experimental research.
- Distinguish between lab studies, field studies, and field experiments.
Identifying and Defining the Variables and Population
Variables and Operational Definitions
Part of generating a hypothesis involves identifying the variables that you want to study and operationally defining those variables so that they can be measured. Research questions in psychology are about variables. A variable is a quantity or quality that varies across people or situations. For example, the height of the students enrolled in a university course is a variable because it varies from student to student. The chosen major of the students is also a variable as long as not everyone in the class has declared the same major. Almost everything in our world varies and as such thinking of examples of constants (things that don’t vary) is far more difficult. A rare example of a constant is the speed of light. Variables can be either quantitative or categorical. A quantitative variable is a quantity, such as height, that is typically measured by assigning a number to each individual. Other examples of quantitative variables include people’s level of talkativeness, how depressed they are, and the number of siblings they have. A categorical variable is a variable defined by its quality; this is why categorical variables are often called qualitative variables. Categorical variables, such as chosen major, are typically measured by assigning a category label to each individual (e.g., Psychology, English, Nursing, etc.). Other examples include people’s nationality, their occupation, and whether they are receiving psychotherapy.
After the researcher generates their hypothesis and selects the variables they want to manipulate and measure, the researcher needs to find ways to actually measure the variables of interest. This requires an operational definition—a description of the variable in terms of precisely how it is to be measured. Many variables that social science researchers are interested in studying cannot be directly observed or measured and this poses a problem because empiricism (observation) is at the heart of the scientific method. Operationally defining a variable involves taking a abstract construct like depression that cannot be directly observed and transforming it into something that can be directly observed and measured. Most variables can be operationally defined in many different ways. For example, depression can be operationally defined as people’s scores on a paper-and-pencil depression scale such as the Beck Depression Inventory, the number of depressive symptoms they are experiencing, or whether they have been diagnosed with major depressive disorder. Researchers are wise to choose an operational definition that has been used extensively in the research literature.
Experimental vs. Non-Experimental Research
The next step a researcher must take is to decide which type of approach they will use to collect the data. As you will learn in your research methods course there are many different approaches to research that can be divided in many different ways. One of the most fundamental distinctions is between experimental and non-experimental research.
Experimental Research
Researchers who want to test hypotheses about causal relationships between variables (i.e., their goal is to explain) need to use an experimental method. This is because the experimental method is the only method that allows us to determine causal relationships. Using the experimental approach, researchers first manipulate one or more variables while attempting to control extraneous variables, and then they measure how the manipulated variables affect participants’ responses.
Independent and Dependent Variables
The terms independent variable and dependent variable are used in the context of experimental research. The independent variable (IV) what the researcher believes is causing changes in the outcome. Technically, the IV is manipulated by the experimenter manipulates, but we often call the variable that we think is the cause the IV whether or not the experimenter had control over it. For example, if we thought that social media made people sadder, then social media would be the IV. If the researcher randomly assigned half of the participants to limit their social media usage and half to use social media normally, then social media would be a true IV. However, often researchers will merely ask participants how often they use social media; this is still often called the IV, even though it should be called a predictor variable. The operational definition of the IV determines whether it is a true IV or a predictor variable.
What do we think that the IV causes? The dependent variable is that outcome variable that we're usually trying to improve. The DV is the variable that the experimenter measures, the presumed effect. In our example of social media and sadness, the DV would be sadness. The operational definition could be self-report 1-5 Likert scale (with 1 being very happy and 5 being very sad), how many times the participant cried that week, or an open-ended self-report question asking about how they feel that participants would complete each time they close a social media app.
Example \(\PageIndex{1}\)
Can blueberries slow down aging? A study indicates that antioxidants found in blueberries may slow down the process of aging. In this study, \(19\)-month-old rats (equivalent to \(60\)-year-old humans) were fed either their standard diet or a diet supplemented by either blueberry, strawberry, or spinach powder. After eight weeks, the rats were given memory and motor skills tests. Although all supplemented rats showed improvement, those supplemented with blueberry powder showed the most notable improvement.
- What is the independent variable?
- What are the dependent variables?
Solution
- IV: dietary supplement: none, blueberry, strawberry, and spinach
- DVs: memory test and motor skills test
One more together, and then one on your own:
Example \(\PageIndex{2}\)
Does beta-carotene protect against cancer? Beta-carotene supplements have been thought to protect against cancer. However, a study published in the Journal of the National Cancer Institute suggests this is false. The study was conducted with 39,000 women aged 45 and up. These women were randomly assigned to receive a beta-carotene supplement or a placebo, and their health was studied over their lifetime. Cancer rates for women taking the betacarotene supplement did not differ systematically from the cancer rates of those women taking the placebo.
- What is the independent variable?
- What is the dependent variable?
Solution
- IV: Supplements: beta-carotene or placebo
- DV: Cancer occurrence
Your turn!
Exercise \(\PageIndex{1}\)
How bright is right? An automobile manufacturer wants to know how bright brake lights should be in order to minimize the time required for the driver of a following car to realize that the car in front is stopping and to hit the brakes.
- What is the independent variable?
- What is the dependent variable?
- Answer
-
- IV: Brightness of brake lights DV:
- Time to hit brakes
If an experiment compares an experimental treatment with a control treatment, then the independent variable (type of treatment) has two levels: experimental and control. If an experiment were comparing five types of diets, then the independent variable (type of diet) would have \(5\) levels. In general, the number of levels of an independent variable is the number of experimental conditions.
Confounds
Extraneous variables are any variable other than the independent variable or the dependent variable. Confounds are a specific type of extraneous variable that systematically varies along with the independent variable, and therefore provides an alternative explanation for the results. When researchers design an experiment they need to ensure that they control for confounds; they need to ensure that extraneous variables don’t become confounding variables because in order to make a causal conclusion they need to make sure alternative explanations for the results have been ruled out.
As an example, if we manipulate the lighting in the room and examine the effects of that manipulation on workers’ productivity, then the lighting conditions (bright lights vs. dim lights) would be considered the independent variable and the workers’ productivity would be considered the dependent variable. If the bright lights are noisy then that noise would be a confound since the noise would be present whenever the lights are bright and the noise would be absent when the lights are dim. If noise is varying systematically with light then we wouldn’t know if a difference in worker productivity across the two lighting conditions is due to noise or light. So confounds are bad, they disrupt our ability to make causal conclusions about the nature of the relationship between variables. However, if there is noise in the room both when the lights are on and when the lights are off then noise is merely an extraneous variable (it is a variable other than the independent or dependent variable) and we don’t worry much about extraneous variables. This is because unless a variable varies systematically with the manipulated independent variable it cannot be a competing explanation for the results.
Non-Experimental Research
Researchers who are interested in describing the current situation in detail or describing or predicting relationships between variables can use non-experimental research. Using the non-experimental approach, the researcher measures variables as they naturally occur, but they do not manipulate them. For instance, if I just measured the number of traffic fatalities in America last year that involved the use of a cell phone but I did not actually manipulate cell phone use then this would be categorized as non-experimental research. Alternatively, if I stood at a busy intersection and recorded drivers’ genders and whether or not they were using a cell phone when they passed through the intersection to see whether men or women are more likely to use a cell phone when driving, then this would be non-experimental research. Qualitative research is a type of non-experimental research. Qualitative research typically involves formulating broad research questions, collecting large amounts of data from a small number of participants, and summarizing the data using nonstatistical techniques.
It is important to point out that non-experimental does not mean nonscientific. Non-experimental research is still scientific research. It can be used to fulfill two of the three goals of science (to describe and to predict). However, unlike with experimental research, we cannot make causal conclusions using this method; we cannot say that one variable causes another variable using this method.
Laboratory vs. Field Research
The next major distinction between research methods is between laboratory and field studies. A laboratory study is research done in a controlled environment, like a laboratory, classroom, or other situation in which the experimenter has control. In contrast, a field study is a study that is conducted in the real-world, in a natural environment. In these field experiments, the experimenter usually does not have full control over the situation.
Laboratory experiments typically have high internal validity. In terms of an experiment, internal validity refers to the degree to which we can confidently infer a causal relationship between variables. When we conduct an experimental study in a laboratory environment we have very high internal validity because we manipulate one variable while controlling all other outside extraneous variables. When we manipulate an independent variable and observe an effect on a dependent variable and we control for everything else so that the only difference between our experimental groups or conditions is the one manipulated variable then we can be quite confident that it is the independent variable that is causing the change in the dependent variable. In contrast, because field studies are conducted in the real-world, the experimenter typically has less control over the environment and potential extraneous variables, and this decreases internal validity, making it less appropriate to arrive at causal conclusions.
But there is typically a trade-off between internal and external validity. External validity refers to the degree to which we can generalize the findings to other circumstances or settings, like the real-world environment. When internal validity is high, external validity tends to be low; and when internal validity is low, external validity tends to be high. So laboratory studies are typically low in external validity, while field studies are typically high in external validity. Since field studies are conducted in the real-world environment it is far more appropriate to generalize the findings to that real-world environment than when the research is conducted in the more artificial sterile laboratory.
Finally, there are field studies which are non-experimental in nature because nothing is manipulated. But there are also field experiments where an independent variable is manipulated in a natural setting and extraneous variables are controlled. Depending on their overall quality and the level of control of extraneous variables, such field experiments can have high external and high internal validity.
