PSYC 300, Take-Home Exam
PSYC 300, Take-Home Exam
CSU - Dominguez hills
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Date Created: 11/16/15
TITLE: YOUR NAME: SCHOOL: [Type text] Essay Question #1: What is an extraneous variable? Give an example of a study in which there is an extraneous variable, including how you would modify the study to solve the problem. Extraneous variables are variables other than the independent variable that may bear any effect on the behavior of the subject being studied. This only affects the people in the experiment, not the place the experiment is taking place in. Some examples are gender, ethnicity, social class, genetics, intelligence, age. A variable is extraneous only when it can be assumed to influence the dependent variable. It introduces noise but doesn't systematically bias the results. Extraneous variables are often classified into three types: 1. Subject variables, which are the characteristics of the individuals being studied that might affect their actions. These variables include age, gender, health status, mood, background, etc. 2. Experimental variables are characteristics of the persons conducting the experiment which might influence how a person behaves. Gender, the presence of racial discrimination, language, or other factors may qualify as such variables. 3. Situational variables are features of the environment in which the study or research was conducted, which have a bearing on the outcome of the experiment in a negative way. Included are [Type text] the air temperature, level of activity, lighting, and the time of day. Extraneous Variables are undesirable variables that influence the relationship between the variables that an experimenter is examining. Another way to think of this, is that these are variables the influence the outcome of an experiment, though they are not the variables that are actually of interest. These variables are undesirable because they add error to an experiment. A major goal in research design is to decrease or control the influence of extraneous variables as much as possible. Say you wanted to work out how clever a fish species were in finding food depending on how long since they had eaten. But if their ability to find food also depended on the temperature of the water and you were not able to either control or measure accurately the temperature of the water. Then the temperature could be described as an extraneous variable. But then you can create a situation where the temperature has been kept constant and then you can carry out the same experiment, which will surely give you results as you have worked on Extraneous Variable. Or simply if we take live example everyone of us like to sleep a lot and if given opportunity we will sleep at least 14 hours a day. Now when we sleep after a late night party and our body is very relaxed in that bed. Every chemistry is so perfect [Type text] in giving so the sleep that we want and we want it more. Now at 7:00 AM in the morning you hear a huge crowd cheering on the road and then follows the voice of your mother who has already got up an hour or more before you and has come to wake you up out of the bed. Now can that be called as Extraneous Variable? Yes anything which affects your daily dose of action which you have to experiment is extraneous variable. Now may be in this simple example, closing the window and talking to your mother and taking her permission to sleep for some more time will surely work out. ESSAY QUESTION N0: 2 Explain the concept of power and how to increase power. The power of a statistical test is the probability that the test will reject the null hypothesis when the null hypothesis is actually false (i.e. the probability of not committing a Type II error, or making a false negative decision The power of a statistical procedure can be thought of as the probability that the procedure will detect a true difference of a specified type. As in talking about p-values and confidence levels, the reference category for "probability" is the sample. So spelling this out in detail: [Type text] Power is the probability that a randomly chosen sample satisfying the model assumptions will detect a difference of the specified type when the procedure is applied, if the specified difference does indeed occur in the population being studied. Note also that power is a conditional probability: the probability of detecting a difference, if indeed the difference does exist. In many real-life situations, there are reasonable conditions that we would be interested in being able to detect, and others that would not make a practical difference. Examples: • If you can only measure the response to within 0.1 units, it doesn't really make sense to worry about falsely rejecting a null hypothesis for a mean when the actual value of the mean is within less than 0.1 units of the value specified in the null hypothesis. • Some differences are of no practical importance -- for example, a medical treatment that extends life by 10 minutes is probably not worth it. In cases such as these, neglecting power could result in one or more of the following: • Doing much more work than necessary • Obtaining results which are meaningless, • Obtaining results that don't answer the question of interest. Elaboration [Type text] For a confidence interval procedure, power can be defined as the probability1 that the procedure will produce an interval with a half-width of at least a specified amount2. As with Type II error, we need to think of power in terms of power against a specific alternative rather than against a general alternative. Common Mistakes involving Power:- Rejecting a null hypothesis without considering practical significance. Accepting a null hypothesis when a result is not statistically significant, without taking power into account. Being convinced by a research study with low power. Neglecting to do a power analysis/sample size calculation before collecting data Neglecting to take multiple inference into account when calculating power. Using standardized effect sizes rather than considering the particulars of the question being studied. Confusing retrospective power and prospective power. Essay Question #3 (37.5 points possible): Why are college students so often participants in [Type text] psychological research? What are some drawbacks to using college students as participants, according to Sears (1986)? A common setting for psychological research is a university campus, with college students serving as the participants. Ethical issues have been raised about how coercive recruitment procedures or course requirements could appear to be to the students participating. College students tend to have less crystallized attitudes, social relationships, and identities. They tend to be a select group of high-cognitive functioning, high- conformity individuals who would conceivably respond differently to laboratory tasks than members of other populations. As a result, social psychological research has portrayed human nature as cognitively-driven, quite unstable, and easily prone to external influence. Now when a survey was conducted for the same, they observed that students did it for such as receiving extra credit and monetary compensation as well as fulfilling a course requirement, they generally did not object to the procedures. Sears argued that characteristics of college students could very well bias the general conclusion of the social phenomena. College students are more able cognitively, more susceptible to social influence and more likely to change their attitudes on issues. It is a good opportunity for students to earn some extra money in some instances which can bring them a few more lunch boxes. This surely is an additional factor when researchers announce that they will be giving some bonus in additional to the money. [Type text] As there are different ways of giving bonus like free credits. Students surely do it for the work experience that they get, the deep reason behind that can be explained, students take admission in the college and they have some or many expectations out of themselves. Usually they do daydream or visualize what they might be doing or want to do. So when such an opportunity of working for a research or being a part of research makes them feel included in the college activity. If they are given small amount of work they are satisfied and they do it very enthusiastically. For some students it can be a plain reason to just interacting with all other students or rather they might get an opportunity to talk to the group of girls or boys they have always been wanting to. Some of them think it is their moral duty to participate in to everything that is being organized in the campus. Some think it will be an additional factor in their portfolio or biodata. Or some of them plainly take part in it because their friends have suggested or forced to do. It has been found out that some students do have mentality of going through the researches that have been carried out in the college as just part of experience of their lives. Taking part in research allows the undergraduate participant to see the importance of clear instructions and appropriate debrief, and possibly to experience the pressures of social desirability (e.g. ‘Can I really admit that?) and demand characteristics (e.g. ‘I know what she wants me to say now’). Each of these insights. Students come to know about the research designs and purposes behind carrying out the researches. [Type text] These are some of the reasons college students are so often participants in the psychological researches. What are the disadvantages in taking students as participants in the research. Students trust less and resiprocate less than the general populations, thus students behavior is closer to equillibrium and these differences can be entirely experienced by demografic differences. So as it is said the data varies research to research and students to students. So the definite behavioral pattern can not be expected through studners. Also if the students are forced to do the same they might collectively ruin the whole research effectively giving only waste of time and money at the end of the day. Most of the times seasoned participants become more likely to become aware of a reinforcement contingency in a verbal conditioning experiment and perform differently as a consequence, yet reporting fewer attempts to determine what the experiments were about. Distinguish between a Type I and a Type II error. How can a researcher minimize the probability of making a Type I error? How can a researcher minimize the probability of making a Type II error? A type I error, also known as an error of the first kind, is the wrong decision that is made when a test rejects a true null hypothesis (H0). A type I error may be compared with a so called false positive in other test situations. Type I error can be viewed as the error of excessive credulity. In terms of folk tales, an investigator may be "crying wolf" (raising a false alarm) without a wolf in sight (H0: no wolf). [Type text] The rate of the type I error is called the size of the test and denoted by the Greek letter (alpha). It usually equals the significance level of a test. In the case of a simple null hypothesis is the probability of a type I error. If the null hypothesis is composite, is the maximum (supremum) of the possible probabilities of a type I error. A type II error, also known as an error of the second kind, is the wrong decision that is made when a test accepts a false null hypothesis. A type II error may be compared with a so-called false negative in other test situations. Type II error can be viewed as the error of excessive skepticism. In terms of folk tales, an investigator may fail to see the wolf ("failing to raise an alarm"; see Aesop's story of The Boy Who Cried Wolf). Again, H0: no wolf. The rate of the type II error is denoted by the Greek letter (beta) and related to the power of a test (which equals ). What we actually call type I or type II error depends directly on the null hypothesis. Negation of the null hypothesis causes type I and type II errors to switch roles. Note: "The alternate hypothesis" in the definition of Type II error may refer to the alternate hypothesis in a hypothesis test, or it may refer to a "specific" alternate hypothesis. In practice, people often work with Type II error relative to a specific alternate hypothesis. In this situation, the probability of Type II error relative to the specific alternate hypothesis is often called β. In other words, β is the probability of making the wrong decision when the specific alternate hypothesis is true. [Type text] A type I error is one that looks at information that should not substantially change one's prior estimate of probability, but does. A type II error is that one looks at information which should change one's estimate, but does not. (Though the null hypothesis is not quite the same thing as one's prior estimate, it is, rather, one's pro forma prior estimate.) Hypothesis testing is the art of testing whether a variation between two sample distributions can be explained by chance or not. In many practical applications type I errors are more delicate than type II errors. In these cases, care is usually focused on minimizing the occurrence of this statistical error. Suppose, the probability for a type I error is 1% , then there is a 1% chance that the observed variation is not true. Common Mistakes: Neglecting to think adequately about possible consequences of Type I and Type II errors (and deciding acceptable levels of Type I and II errors based on these consequences) before conducting a study and analyzing data. • Sometimes there may be serious consequences of each alternative, so some compromises or weighing priorities may be necessary. The trial analogy illustrates this well: Which is better or worse, imprisoning an innocent person or letting a guilty person go free?6 This is a value judgment; value judgments are often involved in deciding on significance levels. Trying to avoid the issue by always choosing the same significance level is itself a value judgment. • This is an instance of the common mistake of expecting too much certainty. • This is why replicating experiments (i.e., repeating the experiment with another sample) is [Type text] important. The more experiments that give the same result, the stronger the evidence. • There is also the possibility that the sample is biased or the method of analysis was inappropriate; either of these could lead to a misleading result. References:- Essay Question #1 (37.5 points possible): What is an extraneous variable? Give an example of a study in which there is an extraneous variable, including how you would modify the study to solve the problem. http://en.wikipedia.org/wiki/Extraneous_variable http://answers.yahoo.com/question/index? qid=20060920114809AAku0iK http://answers.yahoo.com/question/index? qid=20110625070828AAh3H7e http://answers.yahoo.com/question/index? qid=20111012073023AAVnKCX http://answers.yahoo.com/question/index? qid=20110601083750AABsyjW http://www.researchmethodsinpsychology.com/wiki/index.php? title=Section_2.5:_Extraneous_variables http://www.alleydog.com/glossary/definition.php? term=Extraneous%20Variable http://www.le.ac.uk/psychology/amc/lepsrese.html Essay Question #2 (37.5 points possible): Explain the concept of power and how to increase power. The power of a statistical test is the probability that the test will reject the null hypothesis when the null hypothesis is actually false (i.e. the probability of not committing a Type II error, or making a false negative decision). http://www.ma.utexas.edu/users/mks/statmistakes/FactorsInfluen cingPower.html http://www.ma.utexas.edu/users/mks/statmistakes/PowerMistakes .html [Type text] Essay Question #3 (37.5 points possible): Why are college students so often participants in psychological research? What are some drawbacks to using college students as participants, according to Sears (1986)? http://books.google.co.in/books? id=eNsVUGTMcDoC&pg=PA186&lpg=PA186&dq=What+are+so me+drawbacks+to+using+college+students+as+participants, +according+to+Sears+(1986)&source=bl&ots=rnQAlQ2q1l&sig =T7P9t28qz_gOPQWKEaA4ODBKCnQ&hl=en&sa=X&ei=YqBRT7D HHdCqrAfY3OXeDQ&ved=0CDAQ6AEwAg#v=onepage&q=What %20are%20some%20drawbacks%20to%20using%20college %20students%20as%20participants%2C%20according%20to %20Sears%20(1986)&f=false http://www.tandfonline.com/doi/abs/10.1207/s15328023top1502_ 2 http://psychcentral.com/blog/archives/2010/08/26/psychology- secrets-most-psychology-studies-are-college-student-biased/ http://answers.yahoo.com/question/index? qid=20110901174842AAJiMxS http://answers.yahoo.com/question/index? qid=20100220132658AAYa6sB http://answers.yahoo.com/question/index? qid=20110308155126AAdzCJp http://findarticles.com/p/articles/mi_6894/is_1_10/ai_n28518817/ Essay Question #4 (37.5 points possible): Distinguish between a Type I and a Type II error. How can a researcher minimize the probability of making a Type I error? How can a researcher minimize the probability of making a Type II error? http://www.psychwiki.com/wiki/What_is_the_difference_between_ a_type_I_and_type_II_error%3F http://www.ma.utexas.edu/users/mks/statmistakes/errortypes.ht ml http://www.alleydog.com/glossary/definition.php?term=Type%20II %20Error http://en.wikipedia.org/wiki/Type_I_and_type_II_errors http://www.talkstats.com/showthread.php/5630-Probability-of-a- Type-II-error [Type text]
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