ࡱ>  QMbjbjww 4QE----Ly-$/666K^zЫo-6Ra0j"866 : Research Methods From: http://wikinotes.wikidot.com/psychology:myersap-1e-02 Introduction People turn to psychology in many ways and for many different reasons. The trick, though, is to distinguish fact from opinion. Many people and world leaders believe in their instincts and go by their gut feeling. Many times our instincts are wrong, however. People tend to repeatedly over-estimate their ability to go by gut feelings. Did we know it all along? Hindsight bias People often havehindsight biaslooking back in time makes an event seem as though it were inevitable to happen. This is like saying, I knew it all along, or, Hindsight is 20-20. Aftersomething happens, it just seems so obvious. Whats more, people tend to believe that they actually saw it coming. Hindsight bias seems to be common sense. But, it goes both ways. People can have common sense in opposite or conflicting directions. Thus, theres a problem. Psychological studies often oppose common sense and our body determines much about what we do and feel. Overconfidence People are often overconfident.Overconfidenceoccurswhen we are more confident that we know something than we are correct. An example study had people unscramble jumbled words. Once they saw the word unscrambled, hindsight bias made them think they knew it all along and that the task was easy. People predicted theyd unscramble the word in a few seconds.In actuality, it was minutesoverconfidence. The scientific attitude The main principle of science is to let the facts speak for themselves. There are 3 underlying parts to science Curiosity you need to really want to find the truth. Skepticism scientists dont take people merely at their claims, scientists seek factual proof. Humility a scientist has to be able to admit when he or she is proven wrong by the facts. Critical thinking Scientists always ask questions, the top questions being, What evidence do you have to support that? or Are there any possible alternative explanations? The scientific method The basic steps of thescientific methodare: Hypothesis a statement predicts the outcome. Procedure decide on a research method and a procedure to carry it out. Observation recording the results by letting the results speak for themselves. Conclusion measuring the observed results against the hypothesis prediction. Report findings by reporting results to others, they can replicate the experiment if desired. Atheoryis an explanation that organizes observations and tries to predict outcomes. Ahypothesisis a prediction that can be tested. Experimenter bias can be a problem when testing a hypothesis. If not careful, they can see what they expect to see. To cut down on bias, anoperational definitionis formed. An operational definition has two parts: (1)a precise statement of the experimenters procedures and conceptsand (2)something that is measured numerically. We need numbers because otherwise theres no way to be sure about things. This is the subjective vs. objective problem. Subjectivityisa judgment based on or including a persons opinion or emotions A rose is a beautiful symbol of love. Objectivityisa judgment that has had opinion or emotion stripped away from it. A rose is a plant, normally 6 to 24 inches tall. It often has a thorny stem, serrated leaves, and a large-petal flower that issues a scent. Suppose someone was to say, Person A is happier than person B, I can just tell (subjective statement). But its better to say, Person A smiled 5.8 times more per hour than person B and, according to our definition that smiling is a sign of happiness, person A is therefore happier than B. A good operational definitionshould be detailed enough to enable other scientists to replicate the experimentif they wished. This way, other scientists can replicate the experiment and either support the hypothesis or debunk it. The 3 main types ofmethods of researchused by psychologists are: Description Case study Acase studyisa thorough study of one person in hopes of learning about people in general. Think of a manila folder stuffed with everything about a person letters written, photos, researchers notes, interviews, 3rd grade test scores, etc. A problem with case studies is that of numbers studying onlyoneperson then applying the results toallpeople is risky. The person may not exemplify all people. Case studies can lead psychologists to other, more reliable, types of studies. Survey Asurveyasks questions and deals with many more people (cases), but in much less depth. Surveys can easily gather info from a large number of people. They can easilyquantifydata (turn something into numbers) whereas a case study may have trouble doing this. This is critical to meet the operational definition. Surveys often have problems Wording theresults of the survey can be dramatically different depending on the wording of the survey and/or the question order. Here, the survey itself is changing its own results. Random sampling surveys must be from arepresentative sampleof whatever group theyre trying to represent. To get a representative sample (where the small group truly represents the whole group), the survey-takers must come from a random sample.A random sample is where every person in the group has the same chance of being selected for the survey. Getting a random sample, is trickier than it sounds, but there are two main ways to do it: (1) the bigger the number in the original group of people the better, and (2) use a computer or machine to do the picking. Non-examples: standing on the corner asking people to do the survey is not random (some people wont walk by), giving everyone a questionnaire is not random (some will/wont return it). A representative sample can be very accurate at predicting the entire population. A non-representative sample can be very wrong. So, how to tell if we have a representative sample?Random sampling! Naturalistic observation Anaturalistic observationiswatching a person or animal behave in its normal surroundings. Think of a person squatting in a jungle taking notes on gorillas. The idea is to be as little intrusive as possible so the subject will behave normally, then record only what is observed. The explanation, the why? must come from another source. Correlation When two things are related or they go together, they are said to correlate. Statisticians use acorrelational coefficientto measure how closely two things go together (or not). This coefficient is usually seen in two ways: Numerically The correlation coefficient is on a scale from 1.0 down to -1.0 A coefficient of say 0.95 is very high (its close to 1.0) and would look like the bottom-left to top-right graph. A coefficient of say -0.87 is very high as an inverse relationship (its close to -1.0) and would look like the top-left to bottom-right graph. A coefficient of 0 is exactly no correlation at all (random pattern of dots). Inscatterplots Graphs with the two things on the X and Y axes and dots scattered throughout the graph. The tighter the dots line up from bottom-left to top-right, the higher the two things have a positive correlation. Like this: In real life, things usually dont correlate that highly. The dots may line up much more generally. Like this scatterplot that would have a correlation of about 0.6 or so: If the dots line up the other way (top-left to bottom-right), they have a highinverse correlationornegative correlation. Be aware, just because itsnegative, its still a high correlation if its close to -1.0. Like this: If the dots are splotched everywhere in no pattern, there is a very low correlation or none at all. Like this: Correlation and causation Correlations have a trap suppose A and B correlate very highly.Just because the two things correlate, its incorrect to say AcausesB. Amightcause B, but we cant prove that just from a correlation. There might be a third or fourth factor involved that is the cause. Traps like these are reported all the time. For example, you may hear a report that says, "People who eat French fries at least twice a week are 3 times more likely to become obese." It's tempting to say, "Eating fries causes obesity," but, it's incorrect to say that. It's correct to say, "Eating fries 3 times a week and obesity have a positive correlation" (the two go together). Only an experiment shows causation because it isolates one variable to be tested. An example: a persons shoe size and their vocabulary correlate very highly the larger the shoe size, the greater number of words the person knows. Why is this? Does a larger footcausea person to know more words? Of course not, there is another factor involved age or time. Illusory correlations People often see correlations that arenotthere. If two things happen that seem out-of-the-ordinary, we link them together. Such as an astrology prediction that seems to come true. However, we ignore them when they dont pan out. The result is that we remember the times things work and forget the times they dont. Then we wrongly conclude they actually correlate. Experimentation The gold-standard research method is the experiment. All of the other methods have their pluses, butexperiments alone show cause-and-effect. Parts of a good experiment Random selection The participants come from a large population and are randomly selected to be involved. Random assignment Once chosen to be in the study, participants are randomly assigned to either the control or experimental group. Double-blind procedure A technique where the participantsandresearchers dont know which group theyre in and/or the hypothesis being tested. Having participants and researchers "blind" is to cut down on any bias. Placebo effect People are often given a placebo (a fake drug thats just a sugar pill) in double-blind experiments. Though fake, theythinkits real and have real positive benefits. Independent variable(IV) The IV is what the experimenter manipulates. Thisis theonlything different between the experimental and control groups. Dependent variable(DV) The DV is what the IV supposedly affects. The DV is what is measured. Confounding variables These are other factors that might make the experiment go wrong. Specifically, these are factors that might affect the DV. We want an experiment where only the IV affects the DV. Otherwise, we dont know what caused the effect. Our results are said to be confounded. Random selection, random assignment, and good procedures filter out confounding variables. A simple example experiment Suppose a student wants to measure reaction time differences between students chewing sugar-free gum and chewing gum with sugar. From a list of every student in her school, the experimenter has a computer generate a list of 50 people. They all agree to be in the study. A coin flip determines if they will be in the sugar-free or sugar group. Theyre not told which group theyre in. With their hand flat on a table, the students chew gum, then when a light comes on, they smack a switch. The reaction time is measured. The parts of this experiment would be Random selection The experimenters started with a large list and had a computer randomly generate 50 names. To be even better, the experimenters would use an evenlargerlist, say of every student in the county. Random assignment The coin flip is random assignment, this will cancel out confounding variables. IV Type of gum, sugar or sugar-free (this is whats different between the groups, its what the experimenters manipulated). DV The reaction time (this is what is measured; its what the type of gum supposedly affects). Summary and strengths/weaknesses of methods of research (see chart at the bottom of this page). Describing data When reviewing data, its important to look at things carefully and critically. Things like the scale of graphs can influence how theyre perceived. Measures of central tendency Central tendencyrefers to the center of a bunch of numbers. There are three usual measurements of central tendency: Mode The number which occurs most frequently. Mean The average. Median The middle number, once theyve all been listed from lowest to highest. This is also the 50th percentile (50% are lower and 50% are higher). Measures of variation Range Is the distance between the lowest and highest numbers in a group. Standard deviation Ameasurement of how much the numbers vary from the mean(average). Just realize that if the numbers are all pretty close, the standard deviation will be low. It the numbers have a wide range, the standard deviation will be high. Normal curve Also called a bell curve, is a graph that often occurs in nature with things like height and intelligence scores on tests. Look at this graph of IQ scores.We see the median IQ is 100 (also the 50th percentile is 100). The break-point scores are the standard deviations. So, at -1 standard deviation a person scored at 85. Notice the percentages. 68.2% of people score within 1 standard deviation (between 85 and 115). And almost 96% score within 2 standard deviations (between 70 and 130). These percentages are amazingly stable in nature. Making inferences This will be covered later (Unit 11: Testing and Individual Differences), but it should be mentioned here. There are two pillars of measurement: Validity Atest or bit of research measures what its supposed to measure. If you took a test called The Geography Assessment but the questions were all algebra word problems, your score wouldnt reflect your geography knowledge at all. The test would not be valid. ReliabilityThe test yields the same results over and over. If you took a U.S. Literature test and scored a 97. Then took the U.S. Literature test twice more and scored a 57 and a 83, youd say the results are not very reliable. But if you scored a 97, then a 94, then a 95, the test would be pretty reliable. Ways to make tests as reliable as possible: Representative sampling This is done with random selection of participants. Low variability Results with low ranges and low standard deviations are more reliable than high ones. More is better The more numbers we have, the better the results. Statistical significance Theobserved difference between two numbers is not due to chance. Usuallystatistical significance is measured by a "p-value" and goes by the 5% rule. A p-value is normally shown like 0.04 (or 4%), for example. Scientists will saythe numbers are statisticallysignificantif there is less than a 5% chance that they were caused by chance(in other words, the p-value is 0.05 or lower). For example, in an experiment, we're at least 95% certain that the Dependent Variables numbers were caused by Independent Variable. Psychology applied Research in a laboratory can test principles that can be applied to everyday life. Culture comes into play during research. Due to cultural differences, a study in one culture may or may not be able to be applied to another culture. In some ways, humanity is united in certain ways across cultures. For instance, smiles and frowns are universal. Gender differences, and similarities, are real as well. Ethics in research Scientists often study animals for many reasons such as Their biological systems are often simplified versions of humans'. Their life-cycles are often short so its faster to study life spans and generations. Its not ethical or moral to do certain things to humans. Animal research Using animals in research is sometimes controversial. The arguments center on the morality of using animals to hopefully better humankind (and animalkind). Over time, weve developed an unofficial ranking of animals: (1) humans, (2) primates and household pets, (3) mammals, (4) birds, fish, reptiles, (5) insects. Human research When using humans in research there are ethical stop-signs. It would be wrong to electrically shock people in a study. But would it be okay tofakea shock? To put people through stress? (The general consent is, "No, thats not ethical"). TheAPA(American Psychological Association) suggest two things: Informed consent This means participants (1) know whats going on and (2) they give their permission to be in the study. Debriefingof participants When the studys finished, researchers and participants go back over the whole thing. Values still work their way into studies. For instance, our values impact the decision ofwhatto study (and what not to study). And values affect the way we word things, like asking if you think a person is cheap (negative connotation) or penny-wise (positive connotation). Type of ResearchPurposeStrengthsWeaknessesCase studyTo gather information.Inexpensive. Requires only one participant. Can often be done when ethical factors wont allow experiments.Individual cases can be misleading. Doesnt show causation.SurveyTo gather information.Inexpensive. Can gather lots of info fast. Can often be done when ethical factors wont allow experiments.Wording and personal bias can severely alter results. Doesnt show causation.Naturalistic observationTo gather information.Inexpensive. Can often be done when ethical factors wont allow experiments.Individual cases can be misleading. Doesnt show causation.CorrelationTo find out if two things go together, if they co-relate.Can handle large numbers of people/data. Can often be done when ethical factors wont allow experiments.Doesnt show causation.ExperimentTo find cause-and-effect.Shows cause-and-effect.More costly. Ethical factors may make experiments impractical. 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