Research Methods - Ch. 10
What are the three causal criteria for an experiment?
1. Covariance. Do the results show that the causal variable is related to the effect variable? Are distinct levels of the independent variable associated with different levels of the dependent variable? 2. Temporal precedence. Does the study design ensure that the causal variable comes before the outcome variable in time? 3. Internal validity. Does the study design rule out alternative explanations for the results?
What are two ways in which experiments establish covariance?
1. has a comparison group/condition; this offers evidence for covariance by answering "Compared to What." 2. the Outcome, when looking at the outcome of the variables if there is NO difference in measurement, then there is NO correlation, thus NO covariance
what is a causal claim
A CAUSAL CLAIM IS the boldest kind of claim a scientist can make. A causal claim replaces verb phrases such as related to, is associated with, or linked to with powerful verbs such as makes, influences, or affects.
Distinguish between a control group and a treatment group, with a suitable example.
A control group is a level of an independent variable that is intended to represent "no treatment" or a neutral condition. Example: In the van Kleef et al. study (2012), one control variable was the quality of the food: It was always the same kind of pasta. treatment groups: the other level(s) of the independent variable in the study other than the control group
Explain how design confounds are a threat to internal validity and how to control for these using suitable examples.
A design confound is an experimenter's mistake in designing the independent variable; it is a second variable that happens to vary systematically along with the intended independent variable and therefore is an alternative explanation for the results. ex: When seeing if the bowl size had an effect and the experimenter used DIFFERENT TYPES of pasta in the bowls. The different types of pasta would be a Design Confound
What is necessary to have an experiment?
A manipulated variable and a measured variable. Manipulate one and measure the other.
Distinguish between manipulated and measured variables using suitable examples.
A manipulated variable is a variable that is controlled, such as when the researchers assign participants to a particular level (value) of the variable. For example, Mueller and Oppenheimer (2014) manipulated notetaking by flipping a coin to determine whether a person would take notes with a laptop or in longhand. (In other words, the participants did not get to choose which form they would use.) Measured variables take the form of records of behavior or attitudes, such as self-reports, behavioral observations, or physiological measures (see Chapter 5). After an experimental situation is set up, the researchers simply record what happens. Example: In their first study, Mueller and Oppenheimer measured student performance on the essay questions. After manipulating the notetaking method, they watched and recorded—that is, they measured—how well people answered the factual and conceptual questions. The van Kleef team manipulated the serving bowl size, and then measured two variables: how much pasta people took and how much they ate.
Give an example of levels/conditions of an independent variable.
Example 1: Advertising Spend Suppose a marketer conducts an experiment in which he spends three different amounts of money (low, medium, high) on TV advertising to see how it affects the sales of a certain product. In this experiment, we have the following variables: Independent Variable: Advertising Spend 3 Levels: Low Medium High Dependent Variable: Total sales of the product Example 2: Placebo vs. Medication Suppose a doctor wants to know if a certain medication reduces blood pressure in patients. He recruits a simple random sample of 100 patients and randomly assigns 50 to use a pill that contains the real medication and 50 to use a pill that is actually just a placebo. In this experiment, we have the following variables: Independent Variable: Type of Medication 2 Levels: True medication pill Placebo pill Dependent Variable: Overall change in blood pressure
Experiments can have more than one dependent variable and independent variable. Give an example of a study with more than one dependent variable.
For example, the notetaking study had two dependent variables: performance on factual questions and performance on conceptual questions. Similarly, the pasta bowl study's dependent variables were the grams of pasta taken from the bowl and the calories of pasta consumed. When the dependent variables are measured on different scales (e.g., grams and calories), they are usually presented on separate graphs
Why is a comparison group important in experiments?
Gives us covariance criterion. An experiment, in contrast to our experience, provides the comparison group you need. Therefore, an experiment is a better source of information than your own experience because an experiment allows you to ask and answer: Compared to what?
Explain how selection effects are a threat to internal validity and how to control for these using suitable examples. Be sure to explain random assignment and matched groups.
In an experiment, when the kinds of participants in one level of the independent variable are systematically different from those in the other, selection effects can result. They can also happen when the experimenters let participants choose (select) which group they want to be in. A selection effect may result if the experimenters assign one type of person (e.g., all the women, or all who sign up early in the semester) to one condition, and another type of person (e.g., all the men, or all those who wait until later in the semester) to another condition autism example: parents that chose to be in the new intensive therapy were probably more focused and worked harder to achieve the desired result. The motivation of the parents becomes a possible confound researchers should use random assignment
Explain (with an example) what is meant by the following statement: All experiments need a comparison group but the comparison group does not need to be a control group.
Not every experiment has—or needs—a control group, and often, a clear control group does not even exist. Use a carefully designed comparison group instead. The Mueller and Oppenheimer notetaking study (2014) had two comparison groups—laptop and longhand—but neither was a control group, in the sense that neither of them clearly established a "no notetaking" condition. All experiments need a comparison group so the researchers can compare one condition to another, but the comparison group does not need to be a control group
Explain why not every potentially problematic extraneous variable is a confound using suitable examples and the terms "systematic variability" and "unsystematic variability."
Not every potentially problematic variable is a confound. Consider the example of the pasta bowl experimenters. It might be the case that some of the research assistants were generous and welcoming, and others were reserved. The attitude of the research assistants is a problem for internal validity only if it shows systematic variability with the independent variable. Did the generous assistants work only with the large-bowl group and the reserved ones only with the medium-bowl group? Then it would be a design confound. However, if the research assistants' demeanor showed unsystematic variability (random or haphazard) across both groups, then their attitude would not be a confound.
Why are control variables essential in experiments?
They allow researchers to separate one potential cause from another and thus eliminate alternative explanations for results. Control variables are therefore important for establishing internal validity
What is a placebo group?
When the control group is exposed to an inert treatment such as a sugar pill, it is called a placebo group, or a placebo control group
What is a confound? Describe the potential confounds in the pasta study.
confound: several possible alternative explanations. Confound = confused about what is causing a change in DV in the study Type of Pasta served or the generosity of the servers could be possible CONFOUNDS in the amount of pasta consumption other than the bowl size
Define an experiment.
experiment specifically means that the researchers manipulated at least one variable and measured another
__________ are the only way to investigate causal issues.
experiments
When researchers graph their results, the ________ is almost always on the x-axis, and the _____________ is almost always on the y-axis.
independent variable; dependent variable
Define a control variable. Using either the notetaking study or the pasta study, identify the variables that the experimenters controlled for (note that these are also called extraneous variables).
researchers need to make sure they are varying only one thing at a time—the potential causal force or proposed "active ingredient" (e.g., only the form of notetaking, or only the size of the serving bowl). Therefore, besides the independent variable, researchers also control potential third variables (or nuisance variables) in their studies by holding all other factors constant between the levels of the independent variable. Control variables are not really variables at all because they do not vary; experimenters keep the levels the same for all participants Note taking example: manipulated the method people used to take notes, but they held constant a number of other potential variables: People in both groups watched lectures in the same room and had the same experimenter. They watched the same videos and answered the same questions about them, and so on. Any variable that an experimenter holds constant on purpose is called a control variable Pasta example: one control variable was the quality of the food: It was always the same kind of pasta. The researchers also controlled the size of the serving spoon and the size of the plates (each participant served pasta onto a 9-inch plate).
Distinguish between independent and dependent variables using suitable examples.
the manipulated (causal) variable is the independent variable The name comes from the fact that the researcher has some "independence" in assigning people to different levels of this variable The independent variable in the van Kleef study was serving bowl size, which had two conditions: medium and large The measured variable is the dependent variable, or outcome variable How a participant acts on the measured variable depends on the level of the independent variable. Researchers have less control over the dependent variable; they manipulate the independent variable and then watch what happens to people's self-reports, behaviors, or physiological responses. A dependent variable is not the same as its levels, either. The dependent variable in the van Kleef study was the amount of pasta eaten (not "200 calories").
What is covariance?
there is a relationship between the two variables