An Introduction to Psychology:
The Science of Mind and Behavior

What is the Best Way to Test Theories?

In Section 1-1, you learned that scientific psychologists assume the truth of the brain-mind theory, which states that human cognitions, emotions, and behaviors are caused most directly by activity in the nervous system. In Section 1-2, you learned that scientific theories must be supported by evidence. This evidence is obtained by:

• deriving predictions from a theory;
• making observations that either confirm or disconfirm these predictions.

Good theories are ones that lead to many confirmed predictions and few disconfirmed predictions. The brain-mind theory generally has been confirmed by a plethora[∂] of observations over the last two centuries. In fact, the brain-mind theory is so well supported that we now consider it to be a fact: a true theory. In other words, patterns of electrical activity in the central nervous system (CNS) — which consists of the brain and spinal cord — and in the peripheral nervous system (PNS) — which consists of the nerve pathways leading to and from the CNS — are the most direct causes of our cognitions, emotions, and behaviors.

There are several research methods commonly used to test predictions derived from scientific theories: case studies, correlational studies, and experimental studies. In everyday life, on the other hand, we often evaluate and justify our beliefs by pointing to personal experiences (either our own or those of others). In this section, we will start with a discussion of personal experience and then begin to learn about the research methods used in psychology, especially the strengths and weaknesses of each.

Personal Experience: Perception
My personal experience tells me that the moon is largest just after it rises above the eastern horizon or just before it sets below the western horizon. My personal experience also tells me that the moon is smallest when it is in the night sky directly above my head. It is obvious to me that this is true: I have seen it "with my own eyes." Based on these observations, I might have concluded that the moon grows larger or is closer to the earth when it is near the horizon. But what my personal experience tells me is true actually is false. The moon is neither larger nor closer to the earth when it is near the horizon. The apparent changes in size are part of an optical illusion that is called the moon illusion.

For most of us, our personal experiences seem to be the most compelling type of evidence on which to base our beliefs. If we have experienced something, it seems obvious to us that it must be true. But, as the example of the moon illusion demonstrates, compelling personal experiences may cause us to believe something that is not true. This is because our perceptions of the world are the end-product of a sequence of mental processes in which some information is filtered out and other information is added in. The sensory receptors on the periphery of our bodies (for example, the visual receptors in the retina of our eyes, or the touch receptors in our skin) are activated by a small range of stimuli[∂] (for example, the visual receptors are activated by light waves within a very small range of frequencies), which means that most stimuli in the world are not sensed. The sensory receptors then activate nerve pathways that eventually enter the CNS. The spinal cord and brain process this information, which involves the addition of new information to "fill in the gaps." The addition of new information is essential for completing and, therefore, creating meaningful perceptions. For example, we do not perceive a "hole" in our visual field where the optic nerve exists our eye — a point at which, because there are no visual receptors, there exists a "blind spot" in our visual field (see Figure 1). The brain processes constructing visual perceptions "fill in" this hole by adding information.

Figure 1. The Optic Nerve Exits the Eye Causing a "Blind Spot."
(Illustration by Mark Erickson at http://www.stlukeseye.com/anatomy/OpticNerve.asp)

In this way, the perceptions we construct often closely reflect reality; but they also include inaccuracies that, at times, cause us to misperceive reality, as in the case of optical illusions. Because a personal experience consists of a group of perceptions combined with memories, emotions, thoughts, and so on, it is highly subjective[∂]. For example, if I take a pill that I believe is going to cure my headache, and I perceive that the pain stops, I probably will conclude that the pill cured my headache. My perceptions (pain followed by the sensations associated with taking the pill) combined with my beliefs about the pill and the resulting expectations I have regarding its effectiveness will, when followed by pain reduction, cause me to conclude from my personal experience that the pill cures headaches.

Hundreds of people over a distance of several hundred miles looked up at the night sky and saw a set of five to seven lights in a V-formation moving slowly and noiselessly from north to south. Almost two hours later, nine balls of bright light appeared in the sky south of the city of Phoenix and seemed to hover there for several minutes until they slowly disappeared.

On September 11th, 2002, one year after the 9/11 terrorist attacks on the World Trade Center in New York City, the winning lottery number in the evening drawing of the New York State Lottery was "911." Many people concluded that this unusual conjunction of events — the first anniversary of the attacks (Event 1) and the drawing of 911 in the lottery (Event 2) — must be meaningful. They argued that such a meaningful conjunction was highly unlikely to have occurred by chance. (see Paulos, 2002)

Drawing a royal flush in poker, getting heads five times in a row, winning the lottery — all these events may seem incredibly unlikely in any instance. But they're virtually certain to happen sometime to someone. With enough chances for something to happen, it will happen. (Schick & Vaughn, 2002, p. 55)

Suppose on a given day you were involved in 100 distinct events.... You watch television, talk on the telephone, meet people, negotiate the route to work or to the store, do household chores, take in information while reading, complete complex tasks at work, and so on. All these events contain several components that are separately memorable. One hundred, then, is probably on the low side, but we will stick with it.... How many possible different pairs of events are there in the 100 events of your typical day? Using a simple formula to obtain the number of combinations, we calculate that there are 4,950 different pairings of events possible in your typical day. This is true 365 days a year. (Stanovich, 2004, p. 1??)

Case Studies
In Section 1-1, you learned about Greg, who suffered extensive damage to his brain because of a tumor (Sacks, 1995). Greg represents a case study, which in clinical areas (such as professional psychology) refers to an intensive examination of a single individual who exhibits physical and/or mental abnormalities. In case studies, we make detailed observations of a number of physical and mental factors that we think may be related to the abnormalities. In Greg’s case study, the factor that was thought to cause his physical and mental abnormalities was the extensive brain damage. Thus, detailed observations of this damage in terms of its location and extent were made. Case studies, therefore, include much information of potential relevance to explaining abnormalities — information from medical histories, neurological tests, psychological tests, childhood experiences, and so on. The most important strength of case studies is the large amount of potentially useful information collected.

Clinical researchers collecting information for case studies are searching for a factor or factors (causal variables) responsible for the development of abnormalities. Their ultimate goal is to distinguish the effects of one or more of these factors from the possible effects of extraneous variables, which is any factor, other than the one being investigated, that possibly may cause changes in the phenomenon of interest. For example, although neurologists immediately would have suspected brain damage after observing Greg's physical and mental changes, the members of Greg’s religious sect believed that these changes were caused by his spiritual "enlightenment," which the neurologists would have considered to be an extraneous variable:

[Greg] complained that his vision was growing dim, but this was interpreted, by his swami[∂] and others, in a spiritual way: he was “an illuminate,” they told him, it was the “inner light” growing. Greg had worried at first about his eyesight, but was reassured by the swami’s spiritual explanation. His sight grew still dimmer, but he offered no further complaints. And indeed, he seemed to be becoming more spiritual by the day — an amazing new serenity had taken hold of him. He no longer showed his previous impatience or appetites [desires], and he was sometimes found in a sort of a daze, with a strange ... smile on his face. It is beatitude, said his swami — he is becoming a saint. (Sacks, 1995, pp. 43-44)

Before concluding that Greg's brain damage caused the development of his physical and mental abnormalities, some might think it important to rule out the effects of his religious practices.

Those performing case-study research may be able to rule out some extraneous variables by comparing and contrasting case studies of people who exhibit similar problems. For example, let's say that researchers compared and contrasted the case reports of people with extensive damage to the same areas of the brain, and who also exhibited similar physical and mental abnormalities. If the researchers found that only Greg’s case involved unusual religious practices, they then could rule out any influence of Greg's "enlightenment" on the development of his abnormalities. Furthermore, if it could be shown that all these cases shared only one obvious characteristic — extensive damage to particular parts of the brain — the theory that this damage caused the development of the physical and mental abnormalities would be supported. Nevertheless, it is possible that there exist other unconsidered extraneous variables, unconsidered either because they were not salient[∂] or because of experimenter (clinician) bias[∂]. This points to one major weakness of case studies: we cannot, with sufficient certainty, rule out the effects of all extraneous variables (especially those of which we are not aware).

When an extraneous variable affects research results in a systematic[∂] way, we say that there exists a confound, a concept which refers to the the intertwining effects of the factor under investigation and an extraneous variable. For example, let's say that we have bred two strains of mice to differ with respect to a gene thought to influence the development of addictive behavior: one strain has an abnormal gene variant thought to increase addictive behavior and the other strain has the normal gene variant. In order to test whether or not the abnormal gene variant actually increases addictive behavior, let's say that we inject individuals from each strain daily with a highly addictive drug and then, after several weeks of this regimen[∂], give all subjects free access to a lever that delivers a dose of the drug. In this case, the measure of addictive behavior is the average frequency with which individuals from each strain press the lever. If all extraneous variables are controlled, this should provide a direct measure of the influence of the gene variants on addictive behavior. But let's say that one strain has been raised under different conditions and fed different types of food than the other. These extraneous factors would be "confounded with" the gene difference, thereby making it impossible to interpret any average difference in lever pressing found between the two strains.

The major goal of controlled research[∂] is to eliminate or to take into account the effects of confounds in order to determine the causal effects of the factor under investigation. Case studies do not allow researchers to accomplish this goal.

Study Questions

10. How would you define, in your own words, a case study?
11. When are case studies most likely to be performed in psychology?
12. What is a strength of case studies?
13. How would you define "extraneous variable" in your own words?
14. What is an example of an extraneous variable not mentioned in the text?
15. How can case-study research be used to rule out the effects of extraneous variables?
16. What is a major weakness of case studies?
17. How would you define a "confound" in your own words?
18. What is an example of a confound not mentioned in the text?
19. What is the major goal of controlled research?

Correlational Studies
In performing case-study research, researchers try to find associations between two or more variables (such as number of pounds and number of inches) used to measure concepts (such as weight and height). For example, the comparison of the case studies of a number of people such as Greg has demonstrated that there is an association between damage to particular brain structures and particular physical & mental abnormalities. From the results of such comparisons of case studies, researchers may predict that a similar association between normal activity in these brain structures and normal mental and physical functioning exists in the general population. Because case-study research involves only a small number of cases, however, it is not possible to establish that the predicted association is a common one (that is, that it is true of most individuals).

We can establish that there exists such a general relationship only by examining a large number of individuals. This goal can be accomplished through correlational studies — a type of study in which two (or more) variables are directly measured and compared in a large group of individuals. The results of a correlational study allow us to determine whether or not the variables “go together” — that is, whether or not they change together, on average. If two variables change together in the same direction, such as is true for height and weight (taller people tend to be heavier, on average, and vice versa), we say that the variables are positively correlated. If two variables change together in the opposite direction, such as alcohol intake and driving ability (the more alcohol one drinks, the less able one is to drive, on average, and vice versa), we say that the variables are negatively correlated. The major strength of correlational studies is that they allow us to quickly discover general relationships among variables.

Let's look at an example of a correlational study. Deady and Smith (2006) calculated, in 679 women between the ages of 20 and 29 years, correlations between their height and three other variables:

1. maternal personality (importance of having children, desire to have children);
2. reproductive ambition (ideal number of children, ideal age to have first child);
3. career orientation (importance of having a career, career competitiveness.

The researchers found a positive correlation between height and career orientation: on average, the taller a woman was, the stronger was her career orientation. They found a negative correlation between height and maternal personality: on average, the taller a woman was, the less of a maternal personality she had. Finally, they found a negative correlation between height and reproductive ambition: the taller a woman was, the weaker was her reproductive ambition. From these results, can we conclude that height causes women to have less reproductive ambition, a reduced maternal personality, and a greater career orientation? Can we conclude that little girls who, when thinking about what they would like when they grow up, express the desire to have many children and seem less interested in having a career, do not grow as tall as do little girls with the opposite goals? What precisely can we conclude from these correlations?

When interpreting the results of correlational studies, it is important to remember two limitations of correlational data:

1. Regardless of the degree to which two variables are correlated, the relationship will not be observed in all individuals. For instance, there is a correlation between how much nutritious food is eaten during childhood and how tall one is in adulthood. Nevertheless, there are people who ate very well as children who are very short, and others who had poor nutrition who are very tall. In a similar way, not all short women want to raise a large family and forgo a career; and not all tall women want to have one child and become CEO of a Fortune-500 company.
2. Regardless of the degree to which two variables are correlated, the association will not tell us the cause of the correlation. For instance, there is a correlation between where students sit in class and their grades: those who sit in front tend to get higher grades than those who sit in back. But this does not mean that, if a student is not doing well on tests, the instructor should tell her to sit in front so that she can improve her test grades. In a similar way, it would not make sense for a mother to give her daughter growth-hormone injections in order to make the daughter more career-oriented.

When we find a correlation between two variables, such as where students sit in a classroom and course grades, there is no way we can tell from the correlation alone what is causing the two variables to be correlated. Why? Because when two variables, A and B, are correlated, there are three possible causal explanations:

• Changes in Variable A may be causing changes in Variable B.
• Changes in Variable B may be causing changes in Variable A.
• Changes in Variable C — an unmeasured third variable — may be causing changes in Variable A and in Variable B.

In other words, correlational studies have two major problems that make it impossible to infer anything about the cause of a correlation based on the correlation alone:

• The Directionality Problem. This refers to the possibility that Variable A is affecting Variable B, or that Variable B is affecting Variable A.
• The Third-Variable Problem. This refers to the possibility that there is an unmeasured variable, C, that is affecting both Variable A and Variable B.

These two problems are illustrated in Figure 2.

Figure 2. The Directionality and Third-Variable Problems in Correlational Studies.

Let's examine these problems by looking at some examples. There is a correlation between the kind of car one owns and whether or not one has cancer: people who own sports cars are less likely, on average, to have cancer than people who own other types of car. What is causing this correlation? The directionality problem suggests two possibilities:

• Owning a sports car causes people to be less likely to develop cancer. Perhaps they feel better about themselves and this positive attitude results in less cancer.
• Developing cancer causes people to choose to own automobiles other than sports cars. Perhaps their cancer treatments cost so much that they cannot afford sports cars.

The third-variable problem suggests another possibility:

• An extraneous variable affects both the kind of car one owns and one’s risk of having cancer. Perhaps people who are older are less likely to buy sports cars and more likely to have cancer.

All we can conclude from the negative correlation between whether or not one owns a sports car and whether or not one has been diagnosed with cancer is that the two variables are associated in the general population: as one variables increases, the other variable decreases.

Let's look at another example. It has been found that the existence of gum disease (Variable A) in a pregnant mother is negatively correlated with the birth weight (Variable B) of her baby. In other words, pregnant mothers with gum disease tend to give birth to low-weight babies. What is causing this correlation? The directionality problem suggests two possibilities:

• Gum disease in pregnant mothers causes abnormal fetal development, which leads to lower birth weights. Perhaps the mother's immune system is using too much "energy" to fight off the disease — energy that then is not available to the developing fetus.
• Abnormal fetal development causes gum disease in pregnant mothers. Perhaps the abnormal development is sapping the mother’s strength, thereby leaving her more vulnerable to various diseases.

The third-variable problem suggests another possibility:

• An extraneous variable causes the development of maternal gum disease and abnormal fetal development. Perhaps impoverished pregnant women are less likely to have adequate health care, which leaves both the women and their developing fetuses more vulnerable to disease.

Thus, the major weakness of correlational studies is that the directionality and third-variable problems do not allow researchers to infer cause-and-effect relationships from correlational data .

Study Questions

20. How are correlational studies similar to case studies?
21. How do correlational studies differ from case studies?
22. What is an example of a positive correlation not mentioned in the text?
23. What is an example of a negative correlation not mentioned in the text?
24. What is the major strength of correlational studies?
25. What are two limitations of correlational data that affect how we interpret correlations?
26. How would you define the directionality problem in your own words?
27. What is an example of the directionality problem not mentioned in the text?
28. How would you define the third-variable problem in your own words?
29. What is an example of the third-variable problem not mentioned in the text?
30. What is the major weakness of correlational studies?

What Is A Cause?

Oliver Sacks (1974) presented the case of a woman who, over the period of a few days, developed some very unusual symptoms:

Christina was a strapping young woman of twenty-seven, given to hockey and riding, self-assured, robust, in body and mind.... She had an active, full life — had scarcely known a day's illness. Somewhat to her surprise, after an attack of abdominal pain, she was found to have gallstones, and removal of her gallbladder was advised. She was admitted to hospital three days before the operation date, and placed on an antibiotic [to prevent bacterial infection]....

The day before surgery Christina ... had a disturbing dream of peculiar intensity. She was swaying wildly, in her dream, very unsteady on her feet, could hardly feel the ground beneath her, could hardly feel anything in her hands, found them flailing to and fro, kept dropping whatever she picked up. She was ... so distressed [by the dream] that we requested an opinion from the psychiatrist. "Pre-operative anxiety," he said. "Quite natural, we see it all the time."

But later that day the dream came true. Christina did find herself very unsteady on her feet, with awkward flailing movements, and dropping things from her hands. The psychiatrist was again called.... "Anxiety hysteria," he now snapped, in a dismissive tone. "typical conversion symptoms [conversion disorder is a mental disorder in which, it is claimed, anxiety due to unconscious psychological conflicts is transformed into sensory or movement problems]....

By the day of surgery Christina was still worse. Standing was impossible — unless she looked down at her feet. She could hold nothing in her hands, and they 'wandered' — unless she kept an eye on them. When she reached out for something, or tried to feed herself, her hands would miss, or overshoot wildly, as if some essential control or coordination was gone. She could scarcely even sit up — her body 'gave way'. Her face was oddly expressionless and slack, her jaw fell open, even her vocal posture was gone. "Something awful's happened," she mouthed, in a ghostly flat voice. "I can't feel my body. I feel weird — disembodied." (Sacks, 1974, pp. 44-45)

What caused this woman to develop the inability to feel her body? Was the psychiatrist correct: was the problem caused by anxiety and her maladaptive psychological attempts to deal with it? If so, what precisely does this mean? How and under what conditions can anxiety cause a physical problem? Perhaps the cause involved some other factor. For example, perhaps it had something to do with the antibiotics the woman was taking. But, if so, why did the antibiotics affect her in this way? Why do antibiotics not do this to other people who take them? Finally, she could have been affected by a factor unrelated to her planned surgery — perhaps a virus that, simply by coincidence, infected her at this time. But again, why would a viral infection affect her in such an unusual way? Perhaps the best explanation might be to suppose that several factors working together led to the development of this woman's strange symptoms. In this case, could we call each of the individual factors a "cause" given that none of them acting alone could produce the symptoms?

It seems that, as we speculate more and more about possible causes of this woman's feeling of disembodiment, we are led to more and more questions about what it means to say that something is a cause. For example, if it were found that the antibiotics had caused her problems, would you conclude that antibiotics cause people to feel as if they are disembodied? Why or why not? Most of you probably would answer "no" and then argue that, because antibiotics don't generally cause such symptoms in people, it would not make sense to say that "antibiotics cause people to feel disembodied." On the other hand, you might conclude that antibiotics caused this particular woman's symptoms; and perhaps could even cause them in others who are overly sensitive to their effects. But, in this case, if a factor influences the development of a disorder only in a very small number of people, what does it mean to refer to that factor as a "cause"?

Of course, at this point, you may be stating (perhaps with some annoyance), "it's obvious what a cause is! It's like when one car hits another and causes a dent: the dent is caused by the first car hitting the second car. It's easy! A cause is a force that changes something else." Although this definition makes some sense when we are talking about the causes of large-scale events in the physical world, the issue quickly becomes more complicated when we consider other kinds of phenomena, such as the case study above. This is especially true when we consider the causes of nonphysical events such as thoughts and emotions. Thus, let's examine some ideas that may help us to better understand what is meant by the word "cause" in psychology.

Sufficient Conditions
In everyday life, most of us think of a cause as something that, if it is present, then the effect will follow inevitably. For example, if I press the power button on the remote control, the television will turn on; and we would expect that, any time the power button is pushed, the television inevitably will light up. As another example, you might state that a fire was caused by throwing a lit match into a wastebasket full of paper. By saying this, you are implying that, anytime a lit match is thrown into a wastebasket, a fire will develop. These examples suggest that, in everyday life, we tend to think of a cause in terms of a "sufficient condition." A sufficient condition is one that, if present, will always lead to the development of the event. In other words, "if I do this, this other thing is going to happen, no doubt about it."

What we often fail to consider, however, is that sufficient conditions tend to be very complex. For example, pressing the power button will not turn the television set on unless some other things also are true:

• the batteries in the remote control are charged
• the remote control is within a certain distance of the television
• the remote control is pointed in the general direction of the television
• the television is in working order

There are still other factors that must be present if pressing the power button is going to turn on the television. Thus, as you can see, a sufficient condition for turning on a television actually involves the co-occurrence of a number of individual factors. In a similar way, a lit match thrown into a wastebasket full of paper will not be sufficient to cause a fire if the paper is wet. The general problem that these examples point to is this: our notion of cause in everyday life is too simplistic. That is, we tend to ignore many of the additional factors that also must occur if one particular factor is to act as a cause of something else. Each factor contributes to the outcome, but none alone is sufficient for the effect to occur.

Although you might think that the ultimate goal of research in psychology is to discover sufficient conditions for causing specific changes in mental and behavioral phenomena, this is not always, and probably not even often, the case. Individual researchers typically have much more modest goals for their work.

Necessary Conditions
Some of the factors included in the set making up a sufficient condition may be thought of as "necessary conditions." A necessary condition is one that, if it does not occur, then the event cannot occur. For example, in order for a fire to develop by throwing a lit match into a wastebasket full of paper, there must exist an adequate supply of oxygen in the space around the wastebasket. Because a fire cannot develop in the absence of oxygen, oxygen is a necessary — but not a sufficient — condition for a fire. To take another example, one must obtain a lottery ticket if one hopes to win the lottery. Thus, obtaining a lottery ticket is a necessary condition for winning the lottery, but it definitely is not a sufficient condition.

Again, although you might think that psychologists are trying to determine the necessary conditions for the occurrence of mental and behavioral phenomena, this is not often the case. Instead, psychologists try to find individual factors that tend to lead (that is, to lead on average) to changes in mental and behavioral phenomena. In other words, they try to find factors that increase or decrease the likelihood that a particular mental or behavioral event will occur.

The behaviors and mental events studied by psychologists also tend to be caused by a large number of interacting factors. We need to perform investigations of a number of these factors and their interactions before we can develop a comprehensive theory of a psychological phenomenon. For example, our present understanding of schizophrenia suggests that the disease is caused by many interacting factors, such as abnormal gene variants, viral infections, abnormal biochemical activity in the brain, psychosocial stress, and environmental toxins. Brown and Ghiselli (1955) stated that such theories are the norm in psychology:

The most consistent characteristic of human behavior is the complexity of its determinants[∂]. We must think of every behavior sequence as stemming from many determinants. Some of these initiate their influence at the time of the conception of the individual, others come into play just prior to the occurrence of the observed behavior, while numerous others arise in between these two extremes on the time axis. Behavioral processes do not function independently but interact with each other in many intricate and complex ways. Combinations of determinants act over various intervals: sometimes forming genetic series occupying hours, days, months, or years; at other times acting for merely a second or as a simultaneous flash occurrence." (pp. 70-71)

We refer to this as multifactorial causation (see Section 1-2), which means that a phenomenon is determined by many interacting factors. As Brown and Ghiselli suggested, the many factors that determine a phenomenon are thought to occur over the lifetimes of individuals. In the case of schizophrenia, there are distal factors (those that occurred some time ago) — such as viral infections during fetal development — and proximal factors (those that occurred recently) — such as stressful events (see Figure 3). The distal and proximal factors tend to interact in complex ways that can be difficult to investigate.

Insert Fig 3 Here
Figure 3. Distal and Proximal Factors Involved in the
Multifactorial Causation of a Phenemenon.

Let's look at an example. Why does smoking only increase a person's chances of developing lung cancer? Why does smoking not guarantee that a person will develop lung cancer? It probably occurred to you that there are other factors that increase or decrease the harmful effects of smoking. For example, genes, diet, amount of exercise, stress levels, amount of alcohol consumed, and so on, are factors that may interact with smoking in determining who will and who will not develop lung cancer.

Why can nonsmokers develop lung cancer? Again, other factors, such as genes, diet, stress levels, etc., are thought to influence the development of lung cancer. Each factor, when looked at individually, increases the chance of developing lung cancer, but probably none alone is sufficient to cause lung cancer. It seems very likely that the causes of lung cancer are many and that their interactions are complex.

Study Questions

31. How would you define a sufficient condition in your own words?
32. What is an example of a sufficient condition not mentioned in the text?
33. How would you define a necessary condition in your own words?
34. What is an example of a necessary conditioned not mentioned in the text?
35. When developing theories, are researchers trying to find necessary conditions? Are they trying to find sufficient conditions?
36. How do researchers define the word "cause"?
37. What is an example of a psychological cause that you have learned in this course?
38. How would you define the concept of "multifactorial causation" in your own words?
39. What is an example of multifactorial causation from your everyday experience?
40. How would you define a distal factor in your own words?
41. What is an example of a distal factor?
42. How would you define a proximal factor in your own words?
43. What is an example of a proximal factor?