THREE CLAIMS

TABLE 3.3

Examples of Each Type of Claim

Frequency Claims

Frequency claims describe a particular rate or degree of a single variable. In the last example above, “most” refers to a proportion of students (presumably higher than 50%) who could not identify fake news. In the first example, “15%” is the proportion of American adults who smoke. These headlines claim how frequent or common something is. Claims that mention the percentage of a variable, the number of people who engage in some activity, or a certain group’s level on a variable can all be called frequency claims.

Frequency claims are easily identified because they focus on only one variable—such as level of food insecurity, rate of smiling, or amount of texting. In addition, in studies that support frequency claims, the variables are always measured, not manipulated. For example, the researchers measured children’s food insecurity by using a questionnaire or an interview and reported the results.

Some reports give a list of single-variable results, all of which count as frequency claims. Take, for example, the recent report from Gallup stating that 74% of the world smiled yesterday (Gallup.com, 2019). The same report also found that 49% of people said they learned something interesting yesterday. These are two separate frequency claims—they each measured a single variable one at a time. The researchers were not trying to show an association between these single variables, and the report did not claim that the people who learned something interesting were more likely to smile. It simply stated that a certain percentage of the world’s people smiled and a certain percentage learned something interesting.

These headlines are all examples of association claims. An association claim argues that one level of a variable is likely to be associated with a particular level of another variable. Variables that are associated are sometimes said to correlate, or covary, meaning that when one variable changes, the other variable tends to change, too. More simply, they may be said to be related.

Notice that there are two variables in each example above. In the first, the variables are amount of coffee consumption and level of depression: Higher coffee consumption is associated with lower levels of depression (and therefore lower coffee consumption goes with higher levels of depression). In the second example, the variables are the amount of exercise and the income level: More frequent exercise goes with higher levels of income.

An association claim states a relationship between at least two variables. To support an association claim, the researcher usually measures the two variables and determines whether they’re associated. This type of study, in which the variables are measured and the relationship between them is tested, is called a correlational study. Therefore, when you read an association claim, you will usually find a correlational study supporting it.

POSITIVE ASSOCIATION

The headline “New study links exercise to higher pay” is an association in which high goes with high and low goes with low; it’s called a positive association, or positive correlation. Stated another way, high rates of exercise go with higher levels of pay, and low rates of exercise go with lower levels of pay.

One way to represent an association is to use a scatterplot, a graph in which one variable is plotted on the y-axis and the other variable is plotted on the x-axis. Each dot represents one participant in the study, measured on the two variables. Figure 3.3 shows what scatterplots of the associations in three of the example headlines would look like. (Data are fabricated for illustration purposes, and numbers are arbitrary units.) Notice that the dots in Figure 3.3A form a cloud of points, as opposed to a straight line. If you drew a straight line through the center of the cloud of points, however, the line would incline upward; in other words, the mathematical slope of the line would be positive.

More information

Figure 3.3 part A is a scatterplot with amount of weekly exercise on the X axis, from less exercise to more exercise, and income on the Y axis, from lower income to higher income. There is a positive association.; Figure 3.3 part B is a scatterplot with amount of coffee consumption on the X axis, from less coffee to more coffee, and degree of depression on the Y axis, from lower depression to higher depression. There is a negative association between the two.; Figure 3.3 part C is a scatterplot with time of dinner on the X axis, from early to late, and childhood weight on the Y axis, from less obese to more obese. There is zero association between the two variables.

Whereas an association claim merely notes a relationship between two variables, a causal claim goes even further, arguing that one of the variables is responsible for changing the other. Note that each of the causal claims above has two variables, just like association claims: pretending to be Batman and staying on task, intimidating (versus nonthreatening) appearance and head tilt, family meals and eating disorders.

In addition, like association claims, the causal claims above suggest that the two variables in question covary: Reading a story more than once goes with being more likely to believe that story. Eating family meals goes with lower rates of eating disorders. Kids who pretend to be Batman persist longer (than children who don’t), and tilting your head down goes with appearing intimidating. You might also see a causal claim based on a zero association that reports a lack of cause. For example, you might read that vaccines do not cause autism or that being in daycare does not cause behavior problems.

Causal claims, however, go beyond a simple association between the two variables. They use language suggesting that one variable causes the other—verbs such as cause, enhance, affect, decrease, and change. In contrast, association claims use verbs such as link, associate, correlate, predict, tie to, and be at risk for. In Table 3.4, notice the difference between these types of verbs and verb phrases. Causal verbs tend to be more exciting. They are active and forceful, suggesting that one variable comes first in time and acts on the other variable. It’s not surprising, then, that journalists may be tempted to describe family dinners as curbing eating disorders, for example, because it makes a better story than family meals just being associated with eating disorders.

Here’s another important point: A causal claim may contain tentative language—could, may, seem, suggest, sometimes, potentially. If a headline read “Music lessons may enhance IQ,” it would be more tentative, but would still be still considered a causal claim. The verb enhance makes it a causal claim, regardless of any softening or qualifying language.

Advice is also a causal claim; it implies that if you do X, then Y will happen. For example: “Best way to deal with jerks? Give them the cold shoulder.” “Boost your salary by hitting the gym.”

Causal claims are a step above association claims. Because they make a stronger statement, we need to hold them to higher standards. To move from the simple language of association to the language of causality, a study has to satisfy three criteria. First, it must establish that the two variables (the causal variable and the outcome variable) are correlated; the relationship cannot be zero. Second, it must show that the causal variable came first and the outcome variable came later. Third, it must establish that no other explanations exist for the relationship. Therefore, when we encounter a causal claim, we must be sure the study can support it. Later in this chapter, you will learn that only one type of study, an experiment, enables researchers to support a causal claim because it meets all three criteria.

These kinds of headlines, while interesting, are not frequency, association, or causal claims, in which a writer summarizes the results of a poll, survey, or other research study. Anecdotes and interviews are about people’s personal experiences. Such headlines describe a person’s experience with a health problem, raise awareness of rare diseases, or share mental health resources. But they don’t say anything about the frequency of a problem or how it might be solved using research-based evidence.