{"id":558,"date":"2018-08-15T12:01:30","date_gmt":"2018-08-15T16:01:30","guid":{"rendered":"http:\/\/pressbooks.library.upei.ca\/upeiintropsychology\/?post_type=chapter&p=558"},"modified":"2020-08-17T10:54:23","modified_gmt":"2020-08-17T14:54:23","slug":"chapter-3-experimental-research","status":"publish","type":"chapter","link":"https:\/\/pressbooks.library.upei.ca\/upeiintropsychology\/chapter\/chapter-3-experimental-research\/","title":{"raw":"Experimental Research","rendered":"Experimental Research"},"content":{"raw":"

If somebody gave you $20 that absolutely had to be spent today, how would you choose to spend it? Would you spend it on an item you\u2019ve been eyeing for weeks, or would you donate the money to charity? Which option do you think would bring you the most happiness? If you\u2019re like most people, you\u2019d choose to spend the money on yourself (duh, right?). Our intuition is that we\u2019d be happier if we spent the money on ourselves.<\/p>\r\n

Knowing that our intuition can sometimes be wrong, Professor Elizabeth Dunn (2008<\/a>) at the University of British Columbia set out to conduct an experiment on spending and happiness. She gave each of the participants in her experiment $20 and then told them they had to spend\u00a0the money by the end of the day. Some of the participants were told they must spend the money on themselves, and some were told they must spend the money on others (either charity or a gift for someone). At the end of the day she measured participants\u2019 levels of happiness using a self-report questionnaire. (But wait, how do you measure something like happiness when you can\u2019t really see it? Psychologists measure many abstract concepts, such as happiness and intelligence, by beginning with operational<\/strong><\/a> definitions<\/strong> <\/a>of the concepts. See the Noba modules on Intelligence [http:\/\/n<\/strong><\/a>oba.to\/ncb2h79v<\/strong>] and Happiness [http\u00ad<\/strong>:\/<\/strong>\/noba.to\/qnw7g32t<\/strong>], respectively, for more information on specific measurement strategies.)<\/p>\r\n \r\n\r\n[caption id=\"\" align=\"alignnone\" width=\"532\"]\"image\" At the Corner Perk Cafe customers routinely pay for the drinks of strangers. Is this the way to get the most happiness out of a cup of coffee? Elizabeth Dunn's research shows that spending money on others may affect our happiness differently than spending money on ourselves. [Image: The Island Packet, https:\/\/goo.gl\/DMxA5n][\/caption]\r\n

In an experiment, researchers manipulate, or cause changes, in the independent<\/strong> variable<\/strong><\/a>, and observe or measure any impact of those changes in the dependent<\/strong> variable<\/strong><\/a>. The independent variable is the one under the experimenter\u2019s control, or the variable that is intentionally altered between groups. In the case of Dunn\u2019s experiment, the independent variable was whether participants spent the money on themselves or on others. The dependent variable is the variable that is not manipulated at all, or the one where the effect happens. One way to help remember this is that the dependent variable \u201cdepends\u201d on what happens to the independent variable. In our example, the participants\u2019 happiness (the dependent variable in this experiment) depends on how the participants spend their money (the independent variable). Thus, any observed changes or group differences in happiness can be attributed to whom the money was spent on. What Dunn and her colleagues found was that, after all the spending had been done, the people who had spent the money on others were happier than those who had spent the money on themselves. In other words, spending on others causes us to be happier than spending on ourselves. Do you find this surprising?<\/p>\r\n

But wait! Doesn\u2019t happiness depend on a lot of different factors\u2014for instance, a person\u2019s upbringing or life circumstances? What if some people had happy childhoods and that\u2019s why they\u2019re happier? Or what if some people dropped their toast that morning and it fell jam-side down and ruined their whole day? It is correct to recognize that these factors and many more can\u00a0easily affect a person\u2019s level of happiness. So how can we accurately conclude that spending money on others causes happiness, as in the case of Dunn\u2019s experiment?<\/p>\r\n

The most important thing about experiments is random<\/strong> assignment<\/strong><\/a>. Participants don\u2019t get to pick which condition they are in (e.g., participants didn\u2019t choose whether they were supposed to spend the money on themselves versus others). The experimenter assigns them to a particular condition based on the flip of a coin or the roll of a die or any other random method. Why do researchers do this? With Dunn\u2019s study, there is the obvious reason: you can imagine which condition most people would choose to be in, if given the choice. But another equally important reason is that random assignment makes it so the groups, on average, are similar on all characteristics except what the experimenter manipulates.<\/p>\r\n

By randomly assigning people to conditions (self-spending versus other-spending), some people with happy childhoods should end up in each condition. Likewise, some people who had dropped their toast that morning (or experienced some other disappointment) should end up in each condition. As a result, the distribution of all these factors will generally be consistent across the two groups, and this means that on average the two groups will be relatively equivalent on all these factors. Random assignment is critical to experimentation because if the only difference between the two groups is the independent variable, we can infer that the independent variable is the cause of any observable difference (e.g., in the amount of happiness they feel at the end of the day).<\/p>\r\n

Here\u2019s another example of the importance of random assignment: Let\u2019s say your class is going to form two basketball teams, and you get to be the captain of one team. The class is to be divided evenly between the two teams. If you get to pick the players for your team first, whom will you pick? You\u2019ll probably pick the tallest members of the class or the most athletic. You probably won\u2019t pick the short, uncoordinated people, unless there are no other options. As a result, your team will be taller and more athletic than the other team. But what if we want the teams to be fair? How can we do this when we have people of varying height and ability? All we have to do is randomly assign players to the two teams. Most likely, some tall and some short people will end up on your team, and some tall and some short people will end up on the other team. The average height of the teams will be approximately the same. That is the power of random assignment!<\/p>\r\n\r\n

Other considerations<\/h2>\r\n

In addition to using random assignment, you should avoid introducing confounds into your experiments. Confounds<\/strong> <\/a>are things that could undermine your ability to draw causal inferences. For example, if you wanted to test if a new happy pill will make people happier, you could randomly assign participants to take the happy pill or not (the independent variable) and compare these two groups on their self-reported happiness (the dependent variable). However, if some participants know they are getting the happy pill, they might develop expectations that influence their self-reported happiness. This is sometimes known as a placebo<\/strong><\/a> <\/a>effect<\/strong><\/a>. Sometimes a person just knowing that he or she is receiving special treatment or something new is enough to actually cause changes in behavior or perception: In other words, even if the participants in the happy pill condition were to report being happier, we wouldn\u2019t know if the pill was actually making them happier or if it was the placebo effect\u2014an example of a confound.<\/p>\r\n

A related idea is participant<\/strong> demand<\/strong><\/a>. This occurs when participants try to behave in a way they think the experimenter wants them to behave. Placebo effects and participant demand often occur unintentionally. Even experimenter<\/strong> expectations<\/strong> <\/a>can influence the outcome of a study. For example, if the experimenter knows who took the happy pill and who did not, and the dependent variable is the experimenter\u2019s observations of people\u2019s happiness, then the experimenter might perceive improvements in the happy pill group that are not really there.<\/p>\r\n

One way to prevent these confounds from affecting the results of a study is to use a double- blind procedure. In a double-blind procedure, neither the participant nor the experimenter knows which condition the participant is in. For example, when participants are given the happy pill or the fake pill, they don\u2019t know which one they are receiving. This way the participants shouldn\u2019t experience the placebo effect, and will be unable to behave as the researcher expects (participant demand). Likewise, the researcher doesn\u2019t know which pill each participant is taking (at least in the beginning\u2014later, the researcher will get the results for data-analysis purposes), which means the researcher\u2019s expectations can\u2019t influence his or her observations. Therefore, because both parties are \u201cblind\u201d to the condition, neither will be able to behave in a way that introduces a confound. At the end of the day, the only difference between groups will be which pills the participants received, allowing the researcher to determine if the happy pill actually caused people to be happier.<\/p>\r\n

<\/p>","rendered":"

If somebody gave you $20 that absolutely had to be spent today, how would you choose to spend it? Would you spend it on an item you\u2019ve been eyeing for weeks, or would you donate the money to charity? Which option do you think would bring you the most happiness? If you\u2019re like most people, you\u2019d choose to spend the money on yourself (duh, right?). Our intuition is that we\u2019d be happier if we spent the money on ourselves.<\/p>\n

Knowing that our intuition can sometimes be wrong, Professor Elizabeth Dunn (2008<\/a>) at the University of British Columbia set out to conduct an experiment on spending and happiness. She gave each of the participants in her experiment $20 and then told them they had to spend\u00a0the money by the end of the day. Some of the participants were told they must spend the money on themselves, and some were told they must spend the money on others (either charity or a gift for someone). At the end of the day she measured participants\u2019 levels of happiness using a self-report questionnaire. (But wait, how do you measure something like happiness when you can\u2019t really see it? Psychologists measure many abstract concepts, such as happiness and intelligence, by beginning with operational<\/strong><\/a> definitions<\/strong> <\/a>of the concepts. See the Noba modules on Intelligence [http:\/\/n<\/strong><\/a>oba.to\/ncb2h79v<\/strong>] and Happiness [http\u00ad<\/strong>:\/<\/strong>\/noba.to\/qnw7g32t<\/strong>], respectively, for more information on specific measurement strategies.)<\/p>\n

 <\/p>\n

\"image\"
At the Corner Perk Cafe customers routinely pay for the drinks of strangers. Is this the way to get the most happiness out of a cup of coffee? Elizabeth Dunn’s research shows that spending money on others may affect our happiness differently than spending money on ourselves. [Image: The Island Packet, https:\/\/goo.gl\/DMxA5n]<\/figcaption><\/figure>\n

In an experiment, researchers manipulate, or cause changes, in the independent<\/strong> variable<\/strong><\/a>, and observe or measure any impact of those changes in the dependent<\/strong> variable<\/strong><\/a>. The independent variable is the one under the experimenter\u2019s control, or the variable that is intentionally altered between groups. In the case of Dunn\u2019s experiment, the independent variable was whether participants spent the money on themselves or on others. The dependent variable is the variable that is not manipulated at all, or the one where the effect happens. One way to help remember this is that the dependent variable \u201cdepends\u201d on what happens to the independent variable. In our example, the participants\u2019 happiness (the dependent variable in this experiment) depends on how the participants spend their money (the independent variable). Thus, any observed changes or group differences in happiness can be attributed to whom the money was spent on. What Dunn and her colleagues found was that, after all the spending had been done, the people who had spent the money on others were happier than those who had spent the money on themselves. In other words, spending on others causes us to be happier than spending on ourselves. Do you find this surprising?<\/p>\n

But wait! Doesn\u2019t happiness depend on a lot of different factors\u2014for instance, a person\u2019s upbringing or life circumstances? What if some people had happy childhoods and that\u2019s why they\u2019re happier? Or what if some people dropped their toast that morning and it fell jam-side down and ruined their whole day? It is correct to recognize that these factors and many more can\u00a0easily affect a person\u2019s level of happiness. So how can we accurately conclude that spending money on others causes happiness, as in the case of Dunn\u2019s experiment?<\/p>\n

The most important thing about experiments is random<\/strong> assignment<\/strong><\/a>. Participants don\u2019t get to pick which condition they are in (e.g., participants didn\u2019t choose whether they were supposed to spend the money on themselves versus others). The experimenter assigns them to a particular condition based on the flip of a coin or the roll of a die or any other random method. Why do researchers do this? With Dunn\u2019s study, there is the obvious reason: you can imagine which condition most people would choose to be in, if given the choice. But another equally important reason is that random assignment makes it so the groups, on average, are similar on all characteristics except what the experimenter manipulates.<\/p>\n

By randomly assigning people to conditions (self-spending versus other-spending), some people with happy childhoods should end up in each condition. Likewise, some people who had dropped their toast that morning (or experienced some other disappointment) should end up in each condition. As a result, the distribution of all these factors will generally be consistent across the two groups, and this means that on average the two groups will be relatively equivalent on all these factors. Random assignment is critical to experimentation because if the only difference between the two groups is the independent variable, we can infer that the independent variable is the cause of any observable difference (e.g., in the amount of happiness they feel at the end of the day).<\/p>\n

Here\u2019s another example of the importance of random assignment: Let\u2019s say your class is going to form two basketball teams, and you get to be the captain of one team. The class is to be divided evenly between the two teams. If you get to pick the players for your team first, whom will you pick? You\u2019ll probably pick the tallest members of the class or the most athletic. You probably won\u2019t pick the short, uncoordinated people, unless there are no other options. As a result, your team will be taller and more athletic than the other team. But what if we want the teams to be fair? How can we do this when we have people of varying height and ability? All we have to do is randomly assign players to the two teams. Most likely, some tall and some short people will end up on your team, and some tall and some short people will end up on the other team. The average height of the teams will be approximately the same. That is the power of random assignment!<\/p>\n

Other considerations<\/h2>\n

In addition to using random assignment, you should avoid introducing confounds into your experiments. Confounds<\/strong> <\/a>are things that could undermine your ability to draw causal inferences. For example, if you wanted to test if a new happy pill will make people happier, you could randomly assign participants to take the happy pill or not (the independent variable) and compare these two groups on their self-reported happiness (the dependent variable). However, if some participants know they are getting the happy pill, they might develop expectations that influence their self-reported happiness. This is sometimes known as a placebo<\/strong><\/a> <\/a>effect<\/strong><\/a>. Sometimes a person just knowing that he or she is receiving special treatment or something new is enough to actually cause changes in behavior or perception: In other words, even if the participants in the happy pill condition were to report being happier, we wouldn\u2019t know if the pill was actually making them happier or if it was the placebo effect\u2014an example of a confound.<\/p>\n

A related idea is participant<\/strong> demand<\/strong><\/a>. This occurs when participants try to behave in a way they think the experimenter wants them to behave. Placebo effects and participant demand often occur unintentionally. Even experimenter<\/strong> expectations<\/strong> <\/a>can influence the outcome of a study. For example, if the experimenter knows who took the happy pill and who did not, and the dependent variable is the experimenter\u2019s observations of people\u2019s happiness, then the experimenter might perceive improvements in the happy pill group that are not really there.<\/p>\n

One way to prevent these confounds from affecting the results of a study is to use a double- blind procedure. In a double-blind procedure, neither the participant nor the experimenter knows which condition the participant is in. For example, when participants are given the happy pill or the fake pill, they don\u2019t know which one they are receiving. This way the participants shouldn\u2019t experience the placebo effect, and will be unable to behave as the researcher expects (participant demand). Likewise, the researcher doesn\u2019t know which pill each participant is taking (at least in the beginning\u2014later, the researcher will get the results for data-analysis purposes), which means the researcher\u2019s expectations can\u2019t influence his or her observations. Therefore, because both parties are \u201cblind\u201d to the condition, neither will be able to behave in a way that introduces a confound. At the end of the day, the only difference between groups will be which pills the participants received, allowing the researcher to determine if the happy pill actually caused people to be happier.<\/p>\n

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