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Follow all directions Resources Required Text Malec, T. & Newman, M. (2013). Research methods: Building a knowledge base. San Diego, CA: Bridgepoint Education, Inc. ISBN-13: 9781621785743, ISBN-10: 1621785742. Chapter 5: Experimental Designs – Determining Cause-and-Effect Relationships Required References Explorable (2010). Experimental research. Available at  https://explorable.com/experimental-research (Links to an external site.) Onwuegbuzie, A. & Leech, N. L. (2005). On becoming a pragmatic researcher: The importance of combining quantitative and qualitative research methodologies. International Journal of Social Research Methodology, 8(5), 375-387. doi: 10.1080/13645570500402447 Svensson, C. (2014). Qualitative methodology in unfamiliar cultures: Relational and ethical aspects of fieldwork in Malaysia. London: SAGE Publications Ltd doi: 10.4135/978144627305014533923 Trochim, W. M. K. (2006). Research methods: Knowledge base. Available at  http://www.socialresearchmethods.net/kb/ (Links to an external site.) Tsene, L. (2016). Qualitative multi-method research: Media social responsibility. London: SAGE Publications Ltd. doi: 10.4135/978144627305015595393 Week 4 - Discussion 1 Quantitative Research Designs A research design is like a blueprint for conducting research. It guides the researcher in determining when and how often the data will be collected, what data will be collected and from whom, and how the collected data will be analyzed. While there are several research designs available, the most common designs associated with quantitative research include true experiments, quasi- experiments, pre-experiments and correlations. Instructions: First, select one quasi-experimental design and develop an example of a study that would require that design. Identify the independent and dependent variables, and discuss the necessary conditions required for that design. Then, answer the following questions: · How could you change this study to make it a true experiment? · What would be the advantages of using a true experimental design over a quasi-experimental design? · In what situations might a quasi-experimental design be preferred over a true experimental design? Week 4 - Discussion 2 Experimental Validity Experimental validity refers to the manner that variables influence the results of the research and the generalizability of the results to the population at large. The two types of validity that are relevant to experimental designs include internal validity and external validity. Instructions: Using a topic of interest to yourself, briefly describe a proposed research study you would like to conduct. · Provide a detailed discussion regarding some of the potential threats that could occur to the internal validity of your study. · Examine how these threats could reduce the validity of your study and possibly make the study invalid. · What are some ways you could increase the internal validity? · What is the importance of external validity for your study? · Is internal validity or external validity more important for your study? · What do you find most difficult about the idea of validity? · What aspects of evaluating it or integrating it into research desin are the most challenging and why? · What questions do you still have about experimental validity after this exercise? Researchers at the University of Minnesota work with twins in order to Creatas Images/Thinkstock Chapter 5 Experimental Designs—Determining Cause-and-Effect Rela�onships Cosmo Condina/Stone/Ge�y Images Chapter Contents Experiment Terminology (h�p://content.thuzelearning.com/books/Malec.5743.13.1/sec�ons/sec5.1#sec5.1) Key Features of Experiments (h�p://content.thuzelearning.com/books/Malec.5743.13.1/sec�ons/sec5.2#sec5.2) Experimental Validity (h�p://content.thuzelearning.com/books/Malec.5743.13.1/sec�ons/sec5.3#sec5.3) Experimental Designs (h�p://content.thuzelearning.com/books/Malec.5743.13.1/sec�ons/sec5.4#sec5.4) Analyzing Experiments (h�p://content.thuzelearning.com/books/Malec.5743.13.1/sec�ons/sec5.5#sec5.5) Wrap-Up: Avoiding Error (h�p://content.thuzelearning.com/books/Malec.5743.13.1/sec�ons/sec5.6#sec5.6) Cri�quing a Quan�ta�ve Study (h�p://content.thuzelearning.com/books/Malec.5743.13.1/sec�ons/sec5.7#sec5.7) Mixed Methods Research Designs (h�p://content.thuzelearning.com/books/Malec.5743.13.1/sec�ons/sec5.8#sec5.8) One of the oldest debates within psychology concerns the rela�ve contribu�ons that biology and the environment make in shaping our thoughts, feelings, and behaviors. Do we become who we are because it is hard-wired into our DNA or in response to early experiences? Do people take on their parents’ personality quirks because they carry their parents’ genes or because they grew up in their parents’ homes? There are, in fact, several ways to address these types of ques�ons. In fact, a consor�um of researchers at the University of Minnesota has spent the past 2 decades comparing pairs of iden�cal and fraternal twins to tease apart the contribu�ons of genes and environment. You can read more at the research group’s website, Minnesota Center for Twin and Family Research, h�p://mc�r.psych.umn.edu/ (h�p://mc�r.psych.umn.edu/) . An alterna�ve to using twin pairs to separate gene�c and environmental influence is through the use of experimental designs, which have the primary goal of explaining the causes of behavior. Recall from Chapter 2 (Sec�on 2.1 (h�p://content.thuzelearning.com/books/Malec.5743.13.1/sec�ons/sec2.1#sec2.1) , Overview of Research Designs) that experiments can speak to cause and effect because the experimenter has control over the environment and is able to manipulate variables. One par�cularly ingenious example comes from the laboratory of Michael Meaney, a professor of psychiatry and neurology at McGill University, using female rats as experimental subjects (Francis, Dioro, Liu, & Meaney, 1999). Meaney’s research revealed that the paren�ng ability of female rats can be reliably classified based on how a�en�ve they are to their rat pups, as well as how much �me they spend grooming the pups. The ques�on tackled in this study was whether these behaviors were learned from the rats’ own mothers or transmi�ed gene�cally. To answer this ques�on experimentally, Meaney and colleagues had to think very carefully about the comparisons they wanted to make. It would have been https://content.ashford.edu/books/Malec.5743.13.1/sections/sec5.1#sec5.1 https://content.ashford.edu/books/Malec.5743.13.1/sections/sec5.2#sec5.2 https://content.ashford.edu/books/Malec.5743.13.1/sections/sec5.3#sec5.3 https://content.ashford.edu/books/Malec.5743.13.1/sections/sec5.4#sec5.4 https://content.ashford.edu/books/Malec.5743.13.1/sections/sec5.5#sec5.5 https://content.ashford.edu/books/Malec.5743.13.1/sections/sec5.6#sec5.6 https://content.ashford.edu/books/Malec.5743.13.1/sections/sec5.7#sec5.7 https://content.ashford.edu/books/Malec.5743.13.1/sections/sec5.8#sec5.8 http://mctfr.psych.umn.edu/ https://content.ashford.edu/books/Malec.5743.13.1/sections/sec2.1#sec2.1 study the impact of gene�cs versus upbringing on personality traits. insufficient to simply compare the offspring of good and bad mothers—this approach could not dis�nguish between gene�c and environmental pathways. Instead, Meaney decided to use a technique called cross-fostering, or switching rat pups from one mother to another as soon as they were born. This resulted in four combina�ons of rats: (1) those born to ina�en�ve mothers but raised by a�en�ve ones, (2) those born to a�en�ve mothers but raised by ina�en�ve ones, (3) those born and raised by a�en�ve mothers, and (4) those born and raised by ina�en�ve mothers. Meaney then tested the rat pups several months later and observed the way they behaved with their own offspring. The setup of this experiment allowed Meaney to make clear comparisons between the influence of birth mothers and the rearing process. At the end of the study, the conclusion was crystal clear: Maternal behavior is all about the environment. Those rat pups that ul�mately grew up to be ina�en�ve mothers were those who had been raised by ina�en�ve mothers. This final chapter is dedicated to experimental designs, in which the primary goal is to explain behavior. Experimental designs rank highest on the con�nuum of control (see Figure 5.1) because the experimenter can manipulate variables, minimize extraneous variables, and assign par�cipants to condi�ons. The chapter begins with an overview of the key features of experiments and then covers the importance of both internal and external validity of experiments. From there, the discussion moves to the process of designing and analyzing experiments and a summary of strategies for minimizing error in experiments. It concludes with guidelines for cri�quing a quan�ta�ve study. Figure 5.1: Experimental designs on the continuum of control A variable is any factor that has more than one value, such as height. Flying Colours Ltd/Photodisc/Thinkstock 5.1 Experiment Terminology Before we dive into the details, it is important to cover the terminology that we will use to describe different aspects of experimental designs. Much of this will be familiar from previous chapters, with a few new addi�ons. First, let’s review the basics. Recall that a variable is any factor that has more than one value. For example, height is a variable because people can be short, tall, or anywhere in between. Depression is a variable because people can experience a wide range of symptoms, from mild to severe. The independent variable (IV) is the variable that is manipulated by the experimenter in order to test hypotheses about cause. The dependent variable (DV) is the variable that is measured by the experimenter in order to assess the effects of the independent variable. For example, in an experiment tes�ng the hypothesis that fear causes prejudice, fear would be the independent variable and prejudice would be the dependent variable. To keep these terms straight, it is helpful to think of the main goal of experimental designs. That is, we test hypotheses about cause by manipula�ng an independent variable and then looking for changes in a dependent variable. Thus, our independent variable causes changes in the dependent variable; for example, fear is hypothesized to cause changes in prejudice. Any manipula�on of independent variables results in two or more versions of the variable. One common way to describe the versions of the independent variable is in terms of different groups, or condi�ons. The most basic experiments have two condi�ons: The experimental condi�on receives treatment designed to test the hypothesis, while the control condi�on does not receive this treatment. In our fear and prejudice example, the par�cipants who make up the experimental condi�on would be made to feel afraid, while the par�cipants who make up the control condi�on would not. This setup allows us to test whether introducing fear to one group of par�cipants leads them to express more prejudice than the other group of par�cipants, who are not made fearful. Another common way to describe these versions is in terms of levels of the independent variable. Levels describe the specific set of circumstances created by manipula�ng a variable. For example, in the fear and prejudice experiment, the variable of fear would have two levels—afraid and not afraid. There are countless ways to introduce fear into the experiment. One op�on would be to adopt the technique used by the Stanford social psychologist Stanley Schachter (1959), who led par�cipants to believe they would be exposed to a series of painful electric shocks. In Schachter’s study, the painful shocks never happened, but they did induce a fearful state as people an�cipated them. So those at the “afraid” level of the independent variable might be told to expect these shocks, while those at the “not afraid” level of the independent variable would not be given this expecta�on. At this stage, it may seem odd to have two sets of vocabulary terms—“levels” and “condi�ons”—for the same concept. However, there is a subtle difference in how these terms are used once we get into advanced experimental designs. As the designs become more complex, it is o�en necessary to expand independent variables to include several groups and mul�ple variables. Once this happens, we will need different terminology to dis�nguish between the versions of one variable and the combina�ons of mul�ple variables. We will return to this complexity later in the chapter, in Sec�on 5.4, Experimental Designs. Medical studies o�en use invasive manipula�on to change internal, physiological processes. iStockphoto/Thinkstock 5.2 Key Features of Experiments The overview of research designs (Chapter 2, Sec�on 2.1 (h�p://content.thuzelearning.com/books/Malec.5743.13.1/sec�ons/sec2.1#sec2.1) ) described the overall process of experiments in the following way: A researcher controls the environment as much as possible so that all par�cipants have the same experience. He or she then manipulates, or changes, one key variable, and then measures the outcomes in another key variable. In this sec�on, we will examine this process of control in more detail. Experiments can be dis�nguished from all other designs by three key features: manipula�ng variables, controlling the environment, and assigning people to groups comprising experimental and control condi�ons. Manipulating Variables The most crucial element of an experiment is that the researcher must manipulate, or change, some key variable. To study the effects of hunger, for example, a researcher could manipulate the amount of food given to the par�cipants. Or, to study the effects of temperature, the experimenter could raise and lower the temperature of the thermostat in the laboratory. Because these factors are under the researchers’ direct control, they can feel more confident that changing them contributes to changes in the dependent variables. In Chapter 2 we discussed the main shortcoming of correla�onal research: These designs do not allow us to make causal statements. As you’ll recall from that chapter (as well as from Chapter 4), correla�onal research is designed to predict one variable from another. One of the examples in Chapter 2 concerned the correla�on between income levels and happiness, with the goal of trying to predict happiness levels based on knowing people’s income level. If we measure these as they occur in the real world, we cannot say for sure which variable causes the other. However, we could se�le this ques�on rela�vely quickly with the right experiment. Let’s say we bring two groups into the laboratory, give one group $100 and a second group nothing. If the first group were happier at the end of the study, this would support the idea that money really does buy happiness. Of course, this is a rather simplis�c look at the connec�on between money and happiness, but because it manipulated levels of money, this study would bring us closer to making causal statements about the effects of money. To manipulate variables, it is necessary to have at least two versions of the variable. That is, to study the effects of money, we need a comparison group that does not receive money. To study the effects of hunger, we would need both a hungry and a not-hungry group. Having two versions of the variable dis�nguishes experimental designs from the structured observa�ons discussed in Chapter 3 (Observa�onal Research), in which all par�cipants received the same set of condi�ons in the laboratory. Even the most basic experiment must have two sets of condi�ons, which are o�en an experimental group and a control group. But, as we will see later in this chapter, experiments can become much more complex. You might have one experimental group and two control groups, or five degrees of food depriva�on, ranging from 0 to 12 hours without food. Your decisions about the number and nature of these groups will depend on considera�on of both your hypotheses and previous literature. When it comes to the manipula�on of variables, there are three op�ons available. First, environmental manipula�ons involve changing some aspect of the se�ng. Environmental manipula�ons are perhaps the most common in psychology studies, and they include everything from varying the temperature to varying the amount of money people receive. The key is to change the way different groups of people experience their �me in the laboratory—it is either hot or cold, and they either receive or don’t receive $100. Second, instruc�onal manipula�ons involve changing the way a task is described in order to change par�cipants’ mind-sets. For example, you could give all par�cipants the same math test but describe it as an intelligence test for one group and a problem-solving task for another. Because an intelligence test is thought to have implica�ons for life success, you might expect par�cipants in this group to be more nervous about their scores. Finally, an invasive manipula�on involves taking measures to change internal, physiological processes and is usually conducted in medical se�ngs. For example, studies of new drugs involve administering the drug to volunteers to determine whether it has an effect on some physical or psychological symptom. Or, for example, studies of cardiovascular health o�en involve having par�cipants run on a treadmill to measure how the heart func�ons under stress. Finally, there is one qualifica�on to the rule that we must manipulate a variable. In many experiments, researchers divide up par�cipants based on an inherent difference (e.g., gender) or personality measures (e.g., self-esteem or neuro�cism) that capture stable individual characteris�cs among people. The idea behind these personality measures is that someone scoring high on a measure of neuro�cism (for example) would be expected to be more neuro�c across situa�ons than someone scoring lower on the measure. Using this technique allows us to compare how, for example, men and women, or people with high and low self-esteem, respond to manipula�ons. When existent differences are used in an experimental context, they are referred to as quasi-independent variables—“quasi,” or “nearly,” because they are being measured, not manipulated, by the experimenter, and thus do not meet the criteria for a regular independent variable. Because these variables are not manipulated, an experimenter cannot make causal statements about them. In order for a study to count as an experiment, these quasi-independent variables would have to be combined with a true independent variable. This could be as simple as comparing how men and women respond to a new an�depressant drug—gender would be quasi- independent while drug type would be a true independent variable. Controlling the Environment The second important element of experimental designs is that the researcher has a high degree of control over the environment. In addi�on to manipula�ng variables, a researcher conduc�ng an experiment ensures that the other aspects of the environment are the same for all par�cipants. For instance, if you were interested in the effects of temperature on people’s mood, you could manipulate temperature levels in the laboratory so that some people experienced warmer temperatures and other people cooler temperatures. But it would be equally important to make sure that other poten�al https://content.ashford.edu/books/Malec.5743.13.1/sections/sec2.1#sec2.1 The 25 par�cipants in our sample consist of a mix of happy and sad people. The goal of random assignment is to have these differences distributed equally across the experimental condi�ons. Thus, the two groups on the right each consist of six happy and six sad people, and our random assignment was successful. influences on mood were the same for both groups. That is, you would want to make sure that the “warm” and “cool” groups were tested in the same room, around the same �me of day, and by the same experimenter. The overall goal, then, is to control extraneous variables, or variables that add noise to your hypothesis test. In essence, the more you are able to control extraneous variables, the more confidence you can have in the results of your hypothesis test. As we will discuss in the sec�on “Experimental Validity,” the impact of extraneous variables can vary in a study. Let’s say we conduct the study on temperature and mood, and all of our par�cipants are in a windowless room with a flickering fluorescent light. This would likely have an influence on mood—making everyone a li�le bit grumpy—but cause few problems for our hypothesis test because it would affect everyone equally. Table 5.1 shows hypothe�cal data from two varia�ons of this study, using a 10-point scale to measure mood ra�ngs. In the top row, par�cipants were in a well-lit room; we can see that par�cipants in the cooler room reported being in a be�er mood (i.e., an 8 versus a 5). In the bo�om row, all par�cipants were in the windowless room with flickering lights. These numbers suggest that people were s�ll in a be�er mood in the cooler room (5) than a warm room (2), but the flickering fluorescent light had a constant dampening effect on everyone’s mood. Table 5.1: Influence of an extraneous variable Cool Room Warm Room Varia�on 1: Well-Lit 8 5 Varia�on 2: Flickering Fluorescent 5 2 Assigning People to Conditions The third key feature of experimental designs is that the researcher can assign people to receive different condi�ons, or versions, of the independent variable. This is an important piece of the experimental process: The experimenter not only controls the op�ons—warm versus cool room; $100 versus no money, etc.—he or she also gets to control which par�cipants get each op�on. Whereas a correla�onal design might assess the rela�onship between current mood and choosing the warm room, an experimental design will have some par�cipants assigned to the warm room and then measure the effects on their mood. In other words, an experimenter is able to make causal statements because that person causes things to happen. The most common, and most preferable, way to assign people to condi�ons is through a process called random assignment. An experimenter who uses random assignment makes a separate decision for each par�cipant as to which group he or she will be assigned to before the par�cipant arrives. As the term implies, this decision is made randomly—by flipping a coin, using a random number table (for an example, click here (h�p://sta�rek.com/tables/random.aspx) ), drawing numbers out of an envelope, or some other random process. The overall goal is to try to balance out existent differences among people, as illustrated in Figure 5.2. So, for example, some people might generally be more comfortable in warm rooms, while others might be more comfortable in cold rooms. If each person who shows up for the study has an equal chance of being in either group, then the groups in the sample should reflect the same distribu�on of differences as the popula�on. Figure 5.2: Random assignment Another significant advantage of forming groups through random assignment is that it helps to avoid bias in the selec�on and assignment of subjects. For example, it would be a bad idea to assign people to groups based on a first impression of them because par�cipants might be placed in the cold room if they arrived at the laboratory dressed in warm clothing. Experimenters who make decisions about condi�on assignments ahead of �me can be more confident that the independent variable is responsible for changes in the dependent variable. It is worth highligh�ng the difference here between random selec�on and random assignment (discussed in Chapter 4, Sec�on 4.3 (h�p://content.thuzelearning.com/books/Malec.5743.13.1/sec�ons/sec4.3#sec4.3) , Sampling From the Popula�on). Random selec�on means that the sample of par�cipants is chosen at random from the popula�on, as with the probability sampling methods discussed in Chapter 4. However, most psychology experiments use a convenience sample of individuals who volunteer to complete the study. This means that the sample is o�en far from fully random. However, a researcher can s�ll make sure that the group assignments are random so that each condi�on contains an equal representa�on of the sample. http://stattrek.com/tables/random.aspx https://content.ashford.edu/books/Malec.5743.13.1/sections/sec4.3#sec4.3 The 20 par�cipants in our sample represent a mix of very high, average, and very low intelligence test scores (measured 1–100). The goal of matched random assignment is to ensure that this varia�on is distributed equally across the two condi�ons. The experimenter would first rank par�cipants by intelligence test scores (top box) and then distribute these par�cipants alternately between the condi�ons. The result is that both groups (lower boxes) contain a good mix of high, average, and low scores. In some cases—most notably, when samples are small—random assignment may not be sufficient to balance an important characteris�c that might affect the results of a par�cular study. Imagine conduc�ng a study that compared two strategies for teaching students complex math skills. In this example, it would be especially important to make sure that both groups contained a mix of individuals with, say, average and above-average intelligence. For this reason, it would be necessary to take extra steps to ensure that intelligence was equally distributed among the groups, which can be accomplished with a varia�on on random assignment called matched random assignment. This requires the experimenter to obtain scores on an important matching variable (in this case, intelligence), rank par�cipants based on the matching variable, and then randomly assign people to condi�ons. Figure 5.3 shows how this process would unfold in our math skills study. First, par�cipants are given an IQ test to measure exis�ng differences in intelligence. Second, the experimenter ranks par�cipants based on these scores, from highest to lowest. Third, the experimenter would move down this list in order and randomly assign each par�cipant to one of the condi�ons. This process s�ll contains an element of random assignment, but adding the extra step of rank ordering ensures a more balanced distribu�on of intelligence test scores across the condi�ons. Figure 5.3 Research: Making an Impact The Stanford Prison Experiment The landmark 1971 Stanford Prison Experiment had an extremely widespread impact on mul�ple fields and real-life se�ngs such as prison reform, the ethics of human research, and terrorism and torture tac�cs (Clements, 1999; Haney & Zimbardo, 1998). The study, conducted by Phillip Zimbardo and his colleagues at Stanford University, placed volunteer par�cipants in a simulated prison environment and randomly assigned them to play the roles of “guards” and “prisoners.” These 24 par�cipants had been selected based on their personality traits that marked them as “good apples” and who had no previous evidence of an�social behavior (Haney, Banks, & Zimbardo, 1973). Nevertheless, the simulated prison quickly took on the characteris�cs of a real prison, with simulated situa�ons of dominance, dehumaniza�on, severe psychological distress, and the unexpected phenomena of social control, obedience, and effects of power (Haney, 2002; Banuazizi & Movahedi, 1975). The prisoners were subjected to humilia�on similar to what has been seen in prison scandals such as Abu Ghraib: nakedness, sexual humilia�on, verbal torment, chains, and bags over prisoners’ heads. Although planned to run for 2 weeks, the experiment was stopped a�er only 6 days due to the severe psychological harm it was causing the prisoners and the unexpected …