Fishing For Significant Results/error Rate Problem

about a relationship in your observations. You can essentially make two kinds of errors about relationships: conclude that there is no relationship when in fact there is (you

Statistical Conclusion Validity Definition

missed the relationship or didn't see it) conclude that there is a relationship describe the error in the conclusion when in fact there is not (you're seeing things that aren't there!) Most threats to conclusion validity have

Statistical Conclusion Example

to do with the first problem. Why? Maybe it's because it's so hard in most research to find relationships in our data at all that it's not as big or frequent conclusion of statistics project a problem -- we tend to have more problems finding the needle in the haystack than seeing things that aren't there! So, I'll divide the threats by the type of error they are associated with. Finding no relationship when there is one (or, "missing the needle in the haystack") When you're looking for the needle in the haystack you essentially have two basic conclusion of statistics assignment problems: the tiny needle and too much hay. You can view this as a signal-to-noise ratio problem.The "signal" is the needle -- the relationship you are trying to see. The "noise" consists of all of the factors that make it hard to see the relationship. There are several important sources of noise, each of which is a threat to conclusion validity. One important threat is low reliability of measures (see reliability). This can be due to many factors including poor question wording, bad instrument design or layout, illegibility of field notes, and so on. In studies where you are evaluating a program you can introduce noise through poor reliability of treatment implementation. If the program doesn't follow the prescribed procedures or is inconsistently carried out, it will be harder to see relationships between the program and other factors like the outcomes. Noise that is caused by random irrelevancies in the setting can also obscure your ability to see a relationship. In a classroom context, the traffic outside the room, disturbances in the hallway, and countless other irrelevant events can distract the researcher or the parti

- With enlightening examples and illustrations drawn from counseling literature, RESEARCH DESIGN IN COUNSELING fully addresses the common problems that confront counseling researchers. Heppner,

How To Write A Statistical Conclusion

Wampold, and Kivlighan's evenhanded approach maths statistics conclusion provides students with an understanding of the various

A Researcher Can Improve Conclusion Validity By Using

types of...https://books.google.ru/books/about/Research_Design_in_Counseling.html?hl=ru&id=AbYEKcF3jP4C&utm_source=gb-gplus-shareResearch Design in CounselingМоя библиотекаСправкаРасширенный поиск книгПолучить печатную версиюНет электронной версииCengageBrain.comBoleroOzon.ruBooks.ruНайти в библиотекеВсе http://www.socialresearchmethods.net/kb/concthre.php продавцы»Книги в Google PlayВ нашем крупнейшем в мире магазине представлены электронные книги, которые можно читать в браузере, на планшетном ПК, телефоне или специальном устройстве.Перейти в Google Play »Research https://books.google.com/books?id=AbYEKcF3jP4C&pg=PA88&lpg=PA88&dq=fishing+for+significant+results/error+rate+problem&source=bl&ots=Lj6Z93-nb5&sig=ubmwd883UFqvFTB3nClWdNfFJ3o&hl=en&sa=X&ved=0ahUKEwjr2dykvtbPAhUHGR4KHTII Design in CounselingPuncky Paul Heppner, Bruce E. Wampold, Dennis M. Kivlighan, Jr.Cengage Learning, 13 февр. 2007 г. - Всего страниц: 672 1 Отзывhttps://books.google.ru/books/about/Research_Design_in_Counseling.html?hl=ru&id=AbYEKcF3jP4CWith enlightening examples and illustrations drawn from counseling literature, RESEARCH DESIGN IN COUNSELING fully addresses the common problems that confront counseling researchers. Heppner, Wampold, and Kivlighan's evenhanded approach provides students with an understanding of the various types of research, including both quantitative and qualitative approaches. Writing more than just a how-to book, the authors present a compelling rationale for

considered statistically significant. Nevertheless, even if there is only a slight probability that a difference is accidental, it can still be accidental. An accidental difference which is statistically http://www.actualanalysis.com/erate.htm significant is known formally as a Type I error, and less formally as https://www.cramberry.net/sets/74086-research-methods-shadish-et-al-ch-2 a spurious difference. Spurious differences are not likely to be a huge problem if you're only testing one difference, but as the number of differences you're testing increases, the likelihood of detecting a spurious difference grows alarmingly, especially if you're using the most popular significance criterion. For example, if you're assessing ten differences statistical conclusion with a significance criterion of 5% (p < .05), you have a 40% chance of detecting at least one spurious difference. If you assess twenty differences, the probability is 64%. The problem is attenuated considerably if you use the 1% criterion I prefer, but it's still a problem. The equivalent probabilities are 10% in ten comparisons and 18% in twenty. One implication of this problem conclusion of statistics can be seen in the common practice of comparing opinion items individually. For example, people might be asked to rate their agreement with ten statements of opinion before they go into a program, and then to rate it again afterwards. If you compare the ratings of each individual item before and after and find one significant difference, you really cannot accept that as evidence of any change in opinion whatever. If you find two significant differences, you still have little reason to argue for a change in opinion. So what can you do about this problem? If all else fails you can always reduce the significance criterion, and the freeware will work out the value to which to reduce it. The best solution, though, is usually scaling. For example, the ten opinion items may all be intended to measure the same opinion, so scaling will allow you to work out a single attitude score for the ten items (as well as telling you whether you're justified in combining the ratings into a single score). We'll look at how to do that next week. The Error Rate Problem © 1999, John FitzGerald Home page | Decisionmakers'

on (1) empirical findings and (2) consistency of these findings w/ other sources of knowledge and (3) consistency w/ past findings and theories. Validity is tied to the concept of “truth.” Three theories and validity (1) Correspondence theory; (2) Coherence theory; (3) Pragmatism Correspondence theory (of validity) knowledge claim is true if it corresponds to the world. Coherence theory (of validity) Claim is true if it belongs to a coherent set of claims. Pragmatism (related to theory of validity) True if it is useful to believe that claim; useful. Four types of validity Internal, External, Statistical, and Construct Internal validity “Did in fact the exp stimulus make some significant difference in this specific instance? External validity “to what populations, settings, and variables can this effect be generalized?” Also, a type of generalization: generalizing from the sample of persons, settings, times to other populations. Inferences about whether the causal relationship holds over variation in persons, settings, treatment and measurement variables. Statistical validity Appropriate use of statistics to infer whether the presumed ind/dep variables covary. Construct validity inferences about the constructs that research operations represent; extent to which a test measures what it intends to measure; of greatest concern for tests designed to measure abstract concepts such as inteligence, motivation, etc. Threats to validity are a valuable function. What are three critical questions in regard to this? (1) How would the threat apply in the case? (2) Is there evidence that the threat is plausible or just possible? (3) Does the threat operate in the same direction as the observed effect (so it can partially or completely explain the observed finding?) Two types of error that can occur in relationship to validity Type I: incorrectly conclude that cause and effect covary when they do not. (Saying there is a relationship and there isn’t). (alpha level) Type II: incorrectly concluding they do not covary when they do. (Saying there is not a relationship when there is). (beta level). Null Hypothesis Significance Testing Most widely used way of testing cause/effect. It states there is no relationship b/t X & Y. Typically alpha level set at .05. Nine threats to statistical validity 1. Low statistical power 2. Violate assumptions of statistical test 3. Fishing and error rate problem 4. Unreliability of measures 5. Restriction of range 6. Unreliability of treatment implementation 7. Extraneous variance in exp. setting 8. Hetero