Week 11_Analyzing data_day 2

Week 11_Analyzing - Now that you have Now that you have data what do you do with it Week 11 On the On the menu today… Review from Monday More on

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Unformatted text preview: Now that you have Now that you have data, what do you do with it? Week 11 On the On the menu today… Review from Monday More on how we interpret what we’ve found in research What we look for in doing What we look for in doing analyses We look for patterns—do they reflect what our RQs and hypotheses posited we would, based on theory and on prior work? What presentation of the results best reflects the reality of our findings? Ex: percentages—15% of people in Asia have Internet access; but that number constitutes 33% of the global population that is online. How can we compare our findings to others’ results? Review: Review: Measures of Central Tendency Measures that describe the center point of a distribution of quantitative data. Mean: a.k.a., “average” Median: a.k.a., “middle point” (used mostly when the mean is affected by extreme scores) Mode: a.k.a., most frequently occurring (mostly for nominal data) Review: Measures of Dispersion Review: Measures that report how far a set of scores are spread around the center point of the data and across the distribution. #1: Range: distance between highest and lowest scores in a distribution Ex: 6, 2, 1, 3, 5, 9, 8, 9, 11 Highest number? Lowest number? Distance between them? Measures of Dispersion Measures of Dispersion #2: Standard Deviation (SD): It’s a complicated concept, but the easiest way to think of it is as the "mean of the mean.” Standard deviation measures how much ­ on average ­ individual scores vary (or deviate) from the mean score for the group. In other words, the standard deviation is useful because it shows us how many subjects in the group score within a certain range of variation from the average for the entire group. Knowing this can help us make decisions about how to interpret our results. Say these were your midterm Say these were your midterm scores: # students What would each of these distributions tell me about the exam I gave you? # students 0 100% 0 100% What could each of these standard What could each of these standard deviations tell us? Low SD= Low dispersion High SD= High dispersion Normal distribution: Where the SD tells you much more about the distribution of variance distribution 68% 2.5 % --3SD 3 2.5 % 95.4 % -2sSD -2 -1s -1SD 0 0 1s 1SD 22s SD 3s 3SD How do we understand the How do we understand the significance of what we’ve found? Statistical significance as a way to understand Statistical significance as a way to understand if our results are due to chance (null hypothesis); or actually due to our manipulation of the DV (research hypothesis) Statistical significance vs. social significance Correlation vs. causation Real world example: Do we have ESP? The situation: The situation: Dr. Bem (Cornel Univ.) tested a classic memory study in reverse. The original study: participants study 48 words and then divide half those words into categories. They are more likely to remember the subset than those they didn’t practice. Dr. Bem’s version: Dr. Bem’s version: Gave 100 college students a memory test before they did the categorizing — and found they were significantly more likely to remember words that they practiced later. Conclusion: “The results show that practicing a set of words after the recall test does, in fact, reach back in time to facilitate the recall of those words.” Proof #2? Proof #2? Subjects chose one of two curtains on a computer screen—one hid a photograph, the other had just a blank screen. After students picked a curtain, a software program randomly posted a picture behind of the curtains. Finding: students beat chance, by 53 percent to 50 percent—when the photos being posted were erotic (did not do better than chance on other photo types). Bem’s conclusion: “Subjects could sense the Real world example: Do we have ESP? All four reviewers said the paper met the journal’s standards, even though “there was no mechanism by which we could understand the results.” Many experts say that is precisely the problem. Claims that defy almost every law of science are by definition extraordinary and thus require extraordinary evidence. Neglecting to account for this — as conventional social science analyses do — makes many findings look far more significant than they really What does this reveal about the review What does this reveal about the review process? Dr. Bem’s reputation may have played a role in the paper’s acceptance. Peer review is anonymous—but all four reviewers were social psychologists, and would have known whose work they were checking. None were topflight statisticians. One journal editor said, “The problem was that this paper was treated like any other, and it wasn’t.” The null hypothesis here is ESP does not exist. Refusing to give that hypothesis weight makes no sense, experts say; if ESP exists, why aren’t people getting rich off it? What efforts have been made at What efforts have been made at self­correction? Unusual move by the journal—the article was published along with other articles that claim to rebut the findings. So far, at least three efforts to replicate the experiments have failed. But more are in the works, Dr. Bem said, adding, “I have received hundreds of requests for the materials” to conduct studies. Alternative explanation? “He’s got a great sense of humor,” said Dr. Hyman, of Univ. Oregon. “I wouldn’t rule out that this is an elaborate joke.” Summary: Why interpretation Summary matters Interpretations can vary, and are result from decisions made during the research procedure and analysis Honest assessments of a study’s limitations Need for replication Findings have consequences Instrumental and symbolic value Vested interests in outcomes? Consequences for policy, ethics, public perception, and future research ...
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This note was uploaded on 02/14/2012 for the course COMM 300 taught by Professor Yanovitsky during the Spring '08 term at Rutgers.

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