VARIABLES A variable, as the word implies, is something that varies, so it must have at least two levels, or values. A constant is something that could potentially vary but that has only one level in the study in question Measured and Manipulated Variables A measured variable(dependent variable) is one whose levels are simply observed and recorded. Some variables, such as height and IQ, are measured using familiar tools (a ruler, a test). Other variables, such as gender and hair color, are also said to be “measured.” To measure variables such as depression and stress, researchers devise a special set of questions to represent the various levels. In each case, measuring a variable is a matter of recording an observation, a statement, or a value as it occurs naturally. a manipulated variable(independant variable) is a variable a researcher controls, usually by assigning study participants to the different levels of that variable Some variables cannot be manipulated—they can only be measured. Age can’t be manipulated because researchers can’t assign people to be older or younger; they can only measure what age people already are. Some variables, however, can be either manipulated or measured, depending on the goals of a study. If childhood music lessons were your variable of interest, you could measure whether children already taking music lessons, or you could manipulate this variable if you assigned some children to take music lessons and others to take something else (such as drama lessons). From Conceptual Variable to Operational Definition Each variable in a study can be referred to in two ways. ● ● When researchers are discussing their theories and when journalists write about their research, they use more abstract names, called constructs or conceptual variables. When testing hypotheses with empirical research, they create operational definitions of variables, also known as operational variables, or operationalizations. To operationalize a concept of interest means to turn it into a measured or manipulated variable. For example, a researcher’s interest in the construct “coffee consumption” could be operationalized as a structured question in which people tell an interviewer how often they drink coffee. Alternatively, the same construct might be operationalized by having people use an app in which they record everything they eat or drink for a period of time Sometimes this operationalization step is simple and obvious. For example, a researcher interested in a conceptual variable such as “weight gain” in laboratory rats would probably just weigh them. A researcher who is interested in the conceptual variable “income” might operationalize this variable by asking each person about their total income last year. Other times, the concepts researchers wish to study are harder to operationalize because they are difficult to see, touch, or feel. Examples are personality traits, states such as “argumentativeness,” and behavior THREE CLAIMS A claim is an argument someone is trying to make. Researchers make claims about theories based on data. Journalists make claims when they report on studies they read in empirical journals frequency claims - describe a particular rate or degree of a single variable. These headlines claim how frequent or common something is In the last example above, “most” refers to a proportion of students (presumably higher than 50%) who could not identify fake news. association claims -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 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. NEGATIVE ASSOCIATION The study behind the claim “Coffee drinking linked to less depression in women” obtained a negative association. In a negative association (or negative correlation), high goes with low and low goes with high. In other words, high rates of coffee go with less depression, and low rates of coffee go with more depression. ZERO ASSOCIATION The study behind the headline “A late dinner is not linked to childhood obesity, study shows” is an example of a zero association, or no association between the variables (zero correlation). In a scatterplot, both early and late levels of dinnertime are associated with all levels of obesity MAKING PREDICTIONS BASED ON ASSOCIATIONS Some association claims are useful because they help us make predictions. How much pay does this group of people get? How much coffee does that person drink? With a positive or negative association, if we know the level of one variable, we can more accurately guess, or predict, the level of the other variable. the word predict, as used here, does not necessarily mean predicting into the future. It means predicting in a mathematical sense—using the association to make our estimates more accurate Are these predictions going to be perfect? No—they will usually be off by a certain margin. The stronger the relationship between the two variables, the more accurate our prediction will be. The weaker the relationship between the two variables, the less accurate our prediction will be Both positive and negative associations can help us make predictions, but zero associations cannot. Causal Claims 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. each of the causal claims above has two variables, just like association claims: 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. 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.” To move from the simple language of association to the language of causality, a study has to satisfy three criteria. 1) it must establish that the two variables (the causal variable and the outcome variable) are correlated; the relationship cannot be zero. 2) it must show that the causal variable came first and the outcome variable came later 3) 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. INTERROGATING THE THREE CLAIMS USING THE FOUR BIG VALIDITIES Validity refers to the appropriateness of a conclusion or decision and in general, a valid claim is reasonable, accurate, and justifiable. In psychological research, however, we do not say a claim is simply “valid.” Instead, psychologists specify which of the validities they are applying Interrogating Frequency Claims To evaluate frequency claims we use a) Construct Validity b) external validity c) Statistical Validity CONSTRUCT VALIDITY OF FREQUENCY CLAIMS Construct validity refers to how well a conceptual variable is operationalized. When you ask how well a study measured or manipulated a variable, you are interrogating the construct validity—be it smiling, smoking, texting, gender identity, food insecurity, or knowing when news is fake. For example, when evaluating the construct validity of a frequency claim, the question is how well the researchers measured their variable of interest. Consider this claim: “39% of teens text while driving.” There are several ways to measure this variable, though some are better than others. You could ask teenagers to tell you on an online survey how often they engage in text messaging while they’re behind the wheel. You could stand near an intersection and record the behaviors of teenage drivers. You could even use cell phone records to see if a text was sent at the same time a person was known to be driving. You would expect the study behind this claim to use an accurate measure of texting among teenagers, and observing behavior is probably a better way than casually asking, “Have you ever texted while driving?” To ensure construct validity, researchers must establish that each variable has been measured reliably (meaning the measure yields similar scores on repeated testings) and that different levels of a variable accurately correspond to true differences in, say, texting or happiness EXTERNAL VALIDITY OF FREQUENCY CLAIMS The next important questions to ask about frequency claims concern generalizability: How did the researchers choose the study’s participants, and how well do those participants represent the intended population? Such questions address the study’s external validity—how well the results of a study generalize to, or represent, people or contexts besides those in the original study STATISTICAL VALIDITY OF FREQUENCY CLAIMS Researchers use statistics to analyze their data. Statistical validity, also called statistical conclusion validity, is the extent to which a study’s statistical conclusions are precise, reasonable and replicable. How well do the numbers support the claim? To understand statistical validity, it helps to know that the value we get from a single study is not an objective truth. Instead, it’s an estimate of that value in some population. For example, for the report claiming that “39% of teenagers text while driving,” researchers interviewed a sample of about 9,000 teen drivers to estimate the behavior of the population of all U.S. teenage drivers To evaluate statistical validity, we start with the point estimate. In a frequency claim, the point estimate is usually a percentage. Next we ask about the precision of that estimate. For a frequency claim precision is captured by the confidence interval, or margin of error of the estimate. The confidence interval is a range designed to include the true population value a high proportion of the time. In the report about how many teenagers text while driving, the 39.2% point estimate was accompanied by the confidence interval, 37.0–41.4. This means that the true number of teens who text while driving may be as low as 37% or as high as 41.4%, An analogy for the confidence interval is a contractor who estimates your home repair will cost “between $1,000 and $1,500.” The repair will probably cost something in that range (but might cost less or cost more). Finally, statistical validity improves with multiple estimates. Researchers ideally conduct studies more than once and then consider the results of all investigations of the same topic. Combining many estimates is better than using a single one Interrogating Association Claims CONSTRUCT VALIDITY OF ASSOCIATION CLAIMS ● ● ● To support an association claim, a researcher measures two variables, so you have to assess the construct validity of each variable. For the headline “Study links coffee consumption to lower depression in women,” you should ask how well the researchers measured coffee consumption and how well they measured depression. The first variable, coffee consumption, could be measured by asking people to document their food and drink intake every day for some period of time. The second variable, depression, could be measured using a series of questions developed by clinical psychologists that ask about depression symptoms In any study, measuring variables is a fundamental strength or weakness— and construct validity questions assess how well such measurements were conducted. If you conclude that one of the variables was measured poorly, you would not be able to trust the study’s conclusions. However, if you conclude that the construct validity of both variables was excellent, you can have more confidence in the association claim being reported EXTERNAL VALIDITY OF ASSOCIATION CLAIMS ● ● ● You might also interrogate the external validity of an association claim by asking whether it can generalize to other populations, as well as to other contexts, times, or places. For example, the association between coffee consumption and depression came from a study of women. Will the association generalize to men? You can also evaluate generalizability to other contexts by asking, for example, whether the link between coffee consumption and depression might be generalizable to other forms of caffeine (such as tea or cola). STATISTICAL VALIDITY OF ASSOCIATION CLAIM When applied to an association claim, statistical validity considers how strong the estimated association is and how precise that estimate is, and it considers other estimates of the same association Aspects of statistical validity 1) Strength- : How strong is the estimated association? Some associations—such as the association between education and income—are quite strong. People with bachelor’s degrees usually earn much more money than those with high school degrees—about 66% more income over a 40-year career 2) Precision- We can also ask about the precision of the estimated association. (e.g., 9% higher income for frequent exercisers) The estimate of an association can be accompanied by a confidence interval, just as it can for frequency claims. We might read for example, that frequent exercisers earn “between 6% and 12%” more income than others. The confidence interval is designed to capture the true relationship between exercising and income in a high proportion of cases. Studies with smaller samples have wider, less precise intervals that reflect uncertainty (e.g., 1% to 25%). Studies with larger samples have narrower, more precise intervals 3) Number of times a study has been conducted Finally, we ask whether the study has been conducted more than once, because multiple estimates of the association are better than one. Interrogating Causal Claims When you interrogate such a claim, your first step will be to make sure it is backed up by research that fulfills the three criteria for causation: 1) Covariance, the extent to which two variables are observed to go together, is established by the results of a study. It is the first criterion a study must satisfy in order to establish a causal claim 2) temporal precedence means that the method was designed so that the causal variable clearly comes first in time, before the effect variable. To make the claim “Pretending to be Batman helps kids stay on task,” a study must show that “pretending to be Batman” came first and staying on task came later. Although this statement might seem obvious, it is not always so. 3) Internal Validity or the third-variable criterion, refers to a study’s ability to eliminate alternative explanations for the association. For example, to say “Pretending to be Batman helps kids stay on task” is to claim that pretending to be a hardworking hero like Batman causes increased persistence. But an alternative explanation could be that older kids are more likely to pretend to be hardworking heroes and are better able to stay on task. In other words, there could be an internal validity problem. The maturity of the child, not pretending to be Batman, leads children to persist longer. EXPERIMENTS CAN SUPPORT CAUSAL CLAIMS Usually, to support a causal claim, researchers must conduct a well designed experiment, in which one variable is manipulated and the other is measured. Experiments are considered the gold standard of psychological research because of their potential to support causal claims. I When psychologists conduct an experiment, they manipulate the variable they think is the cause and measure the variable they think is the effect (or outcome). In the context of an experiment, the manipulated variable is called the independent variable and the measured variable is called the dependent variable (Basic) A Study’s Method Can Establish Temporal Precedence and Internal Validity. Why does the method of manipulating one variable and measuring the other help scientists make causal claims? For one thing, manipulating the independent variable—the causal variable—ensures that it comes first. In addition, when researchers manipulate a variable, they have the potential to control for alternative explanations; that is, they can ensure internal validity Random assignment ensures to take away individual differences by randomly assigning participants into the group OTHER VALIDITIES TO INTERROGATE IN CAUSAL CLAIMS 1) External Validity of Causal Claims. We could ask, in addition, about external validity. The study tested 6-year-old children from Minneapolis, Minnesota. Can this sample generalize to children from other states or other countries? Would it generalize to younger kids? What about generalization to other situations—could the results generalize to other targets, such as pretending to be an admired teacher? 2) Statistical Validity of Causal Claims. We can also interrogate statistical validity. We can also ask whether a study has been repeated—whether we can consider estimates from multiple studies over time Which of the four validities is the most important? It depends. When researchers plan studies, they usually find it impossible to conduct a study that satisfies all four validities at once. Depending on their goals, sometimes researchers place a lower priority on certain validities. They decide where their emphasis lies.
