Experiments and Small-N Designs

Quasi-Experiments and small-N designs - Researchers do not have full control. First, select a dependent and independent variable. Study participants that were exposed to each level of the IV May not be able to randomly assign. Assigned by teachers, political regulations, or nature. Also, own choice. 2 examples of Independent groups Quasi-experiments - - Use of independent group design These types of quasi experimental designs are typically called non-equivalent control group design. Not equivalent control group designs have at least one treatment group and one comparison group but unlike in a true experiment, participants have not been randomly assigned to the two groups. In the example called the psychological effects of walking by a church, it was an example of a non-equivalent control group post test only design because the participants were not randomly assigned to groups, and were tested only after exposure to one level of the independent variable or the other. - In the example of the psychological effects of cosmetic surgery, it is an example of not equivalent Control Group pretest Posttest Design because the participants were not randomly assigned to groups, and were tested both before and after some intervention. - Quasi-experiments can also include repeated measures. Participants experience all levels of the IV. Since this is a quasi experimental design it takes a different nature than a true experiment so the researcher would rely on an already scheduled event, a policy or regulation, or a chance occurrence that manipulates the IV. The judicial decision making study is an example of an interrupted time series design, a quasi experimental study that measures participants repeatedly on a dependent variable in this example, parole decision making, before during and after the interruption caused by some events such as food break In the example of assessing the impact of the healthcare laws the reason it was a quasiexperimental study was because there is a lack of full experimenter control. The design used in assessing the impact of healthcare laws is called a non-equivalent control group interrupted time series design. This type of design combines two of the previous designs the equivalent control group design in the end interrupted time series design. In this example the independent variable was studied us both a repeated measures Variable and a independent groups variable. In both cases however the researcher did not have experimental control over either independent variable. Repeated measures variable is also called interrupted time series Independent groups variable is also called non equivalent control group - - - Internal Validity in Quasi-Experiments So even though all of these examples look like they are a true experiment because they make causal claims and there is an IV and a DV the only problem is is the experimenter lacks complete control. - - - - - So by the experimenter giving up complete control means that they are also giving up internal validity so they have less of the ability to draw Causal conclusions from the results. If that happens why would we think that a quasi-experiment sports cause a clean well that depends on two things, it’s design and it’s results. To interrogate internal validity, you asphalt alternative explanations for observed pattern of results. The internal validity threats for an experiment discussed in chapter 10 and 11 also apply to quasi experiments.. So the best way for a researcher to make up for the lack of control of the independent variable they would have to choose better designs and they can use a pattern of results to rule out some internal validity threats. Selection Threats When we look at selection of folks we must remember that they are only relevant for independent group designs not for repeated measure designs. Selection threat to internal validity of flies when the kinds of participants at one level of the IV or systematically different from those out at the other level. So a good example of selection threats would be in the case of the religion example. More people who are more spiritual may choose to walk the path in front of a church versus regular folk. A selection threat can also apply to the cosmetic surgery example because more people who are financially well may end up going through with the procedure more than people who would like to go through the procedure but are not financially well off. Wait at some researchers control for selection of fax is by using a wait list design, in which all the participants plan to receive the treatment there randomly aside to do so at different times. An important thing to note is that the waitlist design, unlike the original, is a true experiment because it ensures that all the same kinds of people are in each group. Design Confounds In certain quasi-experiments, design countdowns can be a problem. We remember that in a design confound, some outside variable accidentally and systematically varies with the levels of the targeted IV. Maturation Threat Maturation threats occur when, in an experimental or quasi experimental design with a pretest and post test, and observe change could’ve emerge more or less spontaneously overtime. - The cosmetic surgery said you had a pretest and post Post test, so it was potentially susceptible to a maturation threat because the participants had surgery did move over in time and selfesteem, life satisfaction and general attractiveness. You might ask whether the surgery was the reason or whether it was maturation. History Threat This is a threat that occurs when external, historical event happens for everyone and a study at the same time as a treatment. But the history threat, it is unclear whether the outcome is caused by the treatment or by the external events or factor. - In the Massachusetts health insurance study, residence ability to afford medical care between 2007 and 2009 may have improved, for instants, not because of the state law but because of an economic up turn or a presidential election. As you might imagine, history that’s gonna be especially relevant when a quasi-experiment relies on external factors to manipulate it’s IV variable. However because this particular study included a comparison group, the researchers could roll out most history threats. Economic conditions and political climate would likely have affected all neighbouring streets, but according to the study results, only Massachusetts showed a decline in the variable affording care. - In a selection history threat, the historical event systematically effects participants only in the treatment group or early in the comparison group, not both. When quasi-experiments include a comparison group, history threats to internal validity can usually be ruled out. Second, well good economic conditions provide an alternative exclamation for increased life satisfaction, they are unlikely to explain an increase in other dependent variables, such as self-esteem and general attractiveness. Regression to the Mean Regression to the main occurs when an extreme finding is caused by the combination of random factors that are unlikely to happen in the same combination again, so the extreme find and gets less extreme overtime. - Remember that regression to the mean can threaten internal validity only for pretest Posttest Design ‘s, and even then, only one a group is selected because of its initially very high or very low scores. The initial group means are extreme, in part, because of a combination of random factors that will not happen the same way twice. - The study designs described in this chapter are generally not susceptible to regression to the meat. Either was the results do not fit the regression pattern, or the researchers do not use a pretest Posttest designs. Recall as well that true experiments use random assignment to place participants in the groups, a practice that illuminates regression to the meet is an internal validity threat. Attrition Threat In designs with pretest and post test, attrition occurs when people drop out of a study overtime. Attrition becomes an internal validity threat one systematic kinds of people drop out of the study. Systematic people would be people who have the biggest influence on the study. Testing and Instrumentation Threats Whatever researchers measure participants more than once, they need to be concerned about testing threats to internal validity. A testing threat is the kind of order a fact in which participants tend to change as a result of having being tested before. Repeating testing might cause people to improve, regardless of the treatment they receive. Repeating test my also cause performance to decline because of fatigue or boredom. - Instrumentation can be an internal validity throughout when participants are tested or observe twice. A measurement instrument could be changed over repeated uses and this change will threaten internal validity. If a study uses two versions of a test with different standards or if I study uses coders you changed your standards overtime, then participants might appear to change, when in reality there is no change between one observation in the next. Observer bias, Demand characteristics, and placebo effects Three final THreats to Internal and Validity are related to human subjectivity. Observer bias, in addition to being a threat to construct validity, can also threat threaten internal validity when the experimenters expectations influence their interpretation of the results. Of the threats include demand characteristics, when participants guess with the study is about and change their behaviour in the expected direction, and placebo effect, when participants improve but only because they believe they are receiving the effective treatment. Fortunately, these three threats are easy to interrogate. For observer bias you simply ask who measured the behaviors. Was the design blind or masked or double blind? For experimental demand, you can think about whether the participants were able to detect the study schools and respond accordingly. For placebo effect, you can ask whether design of a study included it comparison group that received an inert, or placebo, treatment. Balancing priorities in Quasi-Experiments - - Real world opportunities External validity, this is because real world settings of many closet experiments can enhance the external validity because of the likelihood that the patterns of Zirbel generalized other circumstances and other individuals. Ethics because concern are another reason researchers might choose a quasi experimental design. Many questions of interest to researchers would be unethical to study in a true experiment. It would not be ethical to assign elective cosmetic surgery to people who don’t want it or to withhold insurance benefits from residents of randomly assign states, but quasiexperiments can be an ethical option for studying these interesting questions. Construct validity and statistical validity in quasi-experiments - For construct validity you would interrogate have successfully the Study manipulated or measured it’s variables. Usually, closet experiment so excellent construct validity for the independent variable. Are Quasi-experiments the same as correlational studies? - - - Some closet experiment seems similar in design to correlational studies. When a quasi experiment use as an independent groups design, that is, when it compares two groups without using random assignment, the groups in the study can look similar to a categorical variables in correlational studies. Quasi experiments Correlational designs also have similar internal validity concerns: just as you might ask about third variables or unmeasured lurking Variables in correlational Study, you might ask about selection effects in a quasi experimental study. Quasi- experiments there is more meddling than correlational. The best way to decide whether something is quasi experimental or correlational you should apply what you know about the threats to internal validity and evaluate and Causal claims. These critical thinking tools up like regardless of how you classify the studies design. CHECK YOUR UNDERSTANDING 1. How is a nonequivalent control group design different from a true independentgroups experiment? 2. How are interrupted time-series designs and nonequivalent control group interrupted timeseries designs different from true within-groups experiments? 3. Describe why both the design and the results of a study are important for assessing a quasi -experiment’s internal validity. 4. What are three reasons a researcher might conduct a quasiexperiment, rather than a true experiment, to study a research question? Small N Designs – Studying only a few individuals Sometimes researchers conduct experiments and studies let’s just a few participants. As discussed in earlier chapters, having a very large sample such as a large N is not always necessary.‘ For the purpose of external validity, how a sample of selected is more important than the sample size. When a researcher expect the large effect size, a small sample can be sufficient to detect it, meeting that statistical Validity is not necessarily undermined by a small sample. But researchers use a small and design, instead of gathering a little information from a larger sample, they obtain a lot of information from just a few cases. They may even restrictor study to a single animal or one person, using a single and design. Large and designs and small and designs differ in to keyways keyways. Balancing Priorities in Case Studies - Experimental Control Studying Special Cases – Small N studies Disadvantages of Small-N Studies Participants in small N studies may not represent the general population very well. Behaviour Change Studies in Applied Settings: 3 Small N designs Stable baseline design - This is a study which a practitioner researcher observes behaviour for an extended baseline. Before beginning a treatment or other intervention. If behaviour during the baseline is stable, the researcher is more certain of the treatment effectiveness. One example comes from a study of memory technique known as expanded rehearsal, which was used with a real Alzheimer’s patient. Multiple baseline designs - And this design Researcher stagger their introduction of an intervention across a variety of individuals, times, or situations to rule out alternative explanations. Reversal designs – As in the other 2 small N designs, A researcher observes a problem behaviour both with and without treatment, but take the treatment away for a while to see whether the problem behaviour returns. So the time that it takes some thing like the treat in a way it would be considered the reversal. And if the behaviour returns that means it reverses. Evaluating the 4 validities in Small N Designs - Internal of Validity of these small designs are usually high. Well it’s hard to criticize the external validity of a small study because one person doesn’t really represent the population of interest. And in these statements usually the causal statement applies only one person therefore it is still useful. - When you interrogate the small designs you should also evaluate the construct validity. Researchers wanna be sure that their measurements are reliable and valid. I but one researchers are recording the distance a child gets to a dog or whether a person is ruminating, they should use multiple observers and check for interrater reliability, in case one observer is biased or the behaviour is difficult to identify.. 1. 2. 3. 4. Stable, multiple and reversal Within groups designs Look Because it is still important even if it only applies to one person. ‘’