Why do researchers employ the double-blind procedure?
108056
Double blinding a study helps to determine the actual effect of the drug, or whatever is being tested.  If neither the participant or the investigator knows which treatment the participant is receiving, there will be less interference in the data due to placebo effects etc.\n\nThe investigator may treat the participant differently if he knows which treatment the participant is receiving.  Likewise, if the participant knows they are getting the active treatment, they may psychologically feel it is working better.
676990
Mainly that it is more focused on benefiting the company (though many companies fund pure research in their labs) and that the company will put a bit of effort into getting patent protecting which may delay publication (although a lot of universities are also doing the same).\n\nThe people who do the research though tend to be just as competant and honest as those in universities or other government labs so there isn't really that much difference.
136183
Good research which can survive the scrutiny of knowledgeable peers should be published. Why the scrutiny? SO that we do not get bogged down in mountains of poorly designed experiments, questionable data and misguided conclusions.  Of course, there is the chance that the reviewing peers may be personally overly critical of the work of a commpeting colleague, but good research will still win through. I, myself, had a reviewer sit on one of my manuscripts for 6 months until I compained to the publisher that unless a response came soon I would go to another peer-reviewed journal. Do not feel that the critical review is a personal attack. It is just how we keep the structure and integrity of science intact.
251100
Yes!\n\nExperimental designs are efficient strategies for designing experiments using a minimum number of runs while enabling a maximum number of factors or parameters to be tested simultaneously. They also facilitate the statistical data analysis phase and the interpretation of results.\nCovers fundamental statistical methods for the Design of Experiments (DOE): accounting for various sources of variability, tools for minimizing and controlling variability, the comparison of two groups, the comparison of more than two groups, the Student's t-test, the Analysis of Variance (ANOVA), and the techniques of multiple comparisons for locating statistical differences.\n\n\nOne Way Analysis of Variance\n\nThe one way analysis of variance allows us to compare several groups of observations, all of which are independent but possibly with a different mean for each group. A test of great importance is whether or not all the means are equal.\n\n*********************\n\nThe observations all arise from one of several different groups (or have been exposed to one of several different treatments in an experiment). We are classifying 'one-way' according to the group or treatment.\n\n\nBasically ANOVA is just one tool to use, not unlike a t-test and design of experiments we are concerned with the analysis of data generated from an experiment. It is wise to take time and effort to organise the experiment properly to ensure that the right type of data, and enough of it, is available to answer the questions of interest as clearly and efficiently as possible. \n\nThe specific questions that the experiment is intended to answer must be clearly identified before carrying out the experiment. We should also attempt to identify known or expected sources of variability in the experimental units since one of the main aims of a designed experiment is to reduce the effect of these sources of variability on the answers to questions of interest. That is, we design the experiment in order to improve the precision of our answers.\n\nTreatment\n\nIn experiments, a treatment is something that researchers administer to experimantal units . For example, a corn field is divided into four, each part is 'treated' with a different fertiliser to see which produces the most corn; a teacher practices different teaching methods on different groups in her class to see which yields the best results; a doctor treats a patient with a skin condition with different creams to see which is most effective.\n\n*********************\n\nTreatments are administered to experimental units by 'level', where level implies amount or magnitude. For example, if the experimental units were given 5mg, 10mg, 15mg of a medication, those amounts would be three levels of the treatment. 'Level' is also used for categorical variables, such as Drugs A, B, and C, where the three are different kinds of drug, not different amounts of the same thing.\n\nFactor\n\nA factor of an experiment is a controlled independent variable; a variable whose levels are set by the experimenter.\n\n*********************\n\nA factor is a general type or category of treatments. Different treatments constitute different levels of a factor. For example, three different groups of runners are subjected to different training methods. The runners are the experimental units, the training methods, the treatments, where the three types of training methods constitute three levels of the factor 'type of training'.
802189
I think you mean double blind experiment. This is the test where both the person who administering the test and the person under the test do not know the answer.
623017
With the stuff they're experimenting with nowadays, I wouldn't be surprised.  For example, they are studying some reptiles that can regrow their body parts in an attempt to find a way for humans to regrow their organs.  I'm not a scientist, but this may be able to reanimate the body given the right conditions.
422801
That's not just in nursing, it's in any medical-related field.  First of all, personal experience is what's relied upon most in medicine - if something worked in the past, let's try it again!  It can be difficult to apply research findings (that apply to a population or the 'average' person to an individual.  That said, it is amazing how many clear-cut research findings are ignored (eg look at the # of post-heart attack patients not on a beta-blocker and/or an ACE inhibitor.)  I know in at least pharmacy curricula there's a shift toward emphasizing evidence-based medicine and how to apply and evaluate research data.  So maybe there's a change-a-comin'.
429143
There is no clinical, double-blind, peer reviewed reseach to suggest it really works so I would say don't use it!
299127
You need to be more specific.\n\nHowever:\nThere are secondary variables - unintentional variations other than those variable you are testing.  The way to avoid this, such as noise, is to either find a way to eliminate it or make it uniform.\n\nA large sample size will make an experiment more accurate.\n\nDouble-blind studies (one where the research assistant administering the test does not know what is being tested for and therefor cannot influence the results) are good.\n\nIn any case, I cannot really answer further unless I know more about the study.
29764
None really - science is a methodology.  The scientists might have faith in all sorts of things -but science itself does not.
1016200
Why not just do the experiment and find out for yourself?  It is the best way to learn and retain information.  Doing is better than seeing is better than hearing for information retention.
966754
If it were just two independent and one dependent it would be classic ANOVA.  MANOVA is traditionally used for multiple dependent variables.  Imagine that you were ran the ANOVA for the entire sample, then run separate ANOVAs for each of the following:  above age above edu, above age below edu, below age above edu, and below age below edu.  In the separate runs the data would not be symmetrical (unless the age and edu distributions are completely homogenous) so comparing the relationships would be useless.  The same applies to if you ran an ANOVA for above age all edu, because if age and edu were at all correlated, the mean edu would differ from the full sample and you couldn't compare the two.  In SAS there are corrections for skewed data which you could feasibly use.  MANOVA is probably an option too, if you use some sort of repeated measures MANOVA (where one set of independents measures the dependent variable is the other set is age and edu (dichotomous) and they measure the dependent variables of the relationships of the first study) .
1045520
A good researcher is never satisfied by one or two sources.They always dig a little deeper to find the answers.They also can take the research and convey it in terms that a common person can understand.If you can no longer ask the question"what if"then you have the answer and no longer need to do research.
579180
if you're talking about in statistics, a convenience sample would be asking the first 100 people who walk past you on the street the same question. it tends to create biased results.
238657
Usually theories and models help to define the way that you design experiments.  So for example, if you start with the theoretical framework of the big bang, then there are things that you can assume about what the appropriate baseline red-shift of an object should be.  Deviations from this baseline can tell you information about the objects relative motion to you or internal motion in the object.\n\nMost research builds on previous theories and ideas to push the envelope to new ideas.  Using an established theoretical framework is just part of good science.  Now an important additional note is that sometimes your experiement will bring the theoretical framework into question, because the results will violate what you'd expect given the parameters of the framework.  In this case, it is also good science to question and revise your framework.
212255
The scientific method creates objectivity. It is very difficult for a person with an idea in his head to discard it when facts indicate they are wrong.
652749
using the scientific method of forming a hypothesis, testing the hypothesis and interpreting the results