Web Activity - Research in Human Science
Focus: What about qualitative research?
Is it any more or less reliable than the experimental model?
How is qualitative datum different and how can we use it most effectively?
To complete this assignment successfully, you should:
Education makes frequent changes. Many of the reforms are based on ideas with little factual basis for implementation. Others are based on thoughts generated by previous best practice or research. If your school wishes to make changes, it is critical to be able to gather and analyze the kind of data that drives the new ideas, and approach the implementation, whether it is a plan for a single youngster, or a whole program, with a clear idea of what to expect, what has worked in the past, and what barriers others encountered. Sometimes research will give us that solid foundation. Sometimes the results are not so clear.
During the past twenty years, education of early adolescents moved from the Jr. High model to the middle school model. Is that change a good one for students? You probably have an opinion, based on your own experiences - probably some feelings about personal experiences as a student, and if you are currently teaching adolescents, you have additional data to consider. Are those feelings, gathered from personal experiences valid research? Are they as valid as findings from an experimental study? Can we use changes in achievement scores to direct our decisions, inform us of the best plan?
We like to believe that using experimental methods of research will give us the best answer about what works and does not work in education. We also have a sense that once we have used the scientific method, we have a truth we can count on.
It is often a surprise to graduate students, or those who become familiar with research, to find out that the scientific method is actually a set of hypotheses - null hypotheses, and scientists are using a form of falsification research to look for facts that will hold up under scrutiny, and that scientific facts are really hard to come by - while theories are everywhere -- and exactly what they sound like - suppositions. Theories are hypotheses that we develop and then try to disprove. If we can disprove them even once, they are no longer useful and quietly fade away.
In addition, experimental research is best done in a laboratory setting where variables can truly be controlled and some factors can be held constant. The tests for significance, for accurately assessing findings and determining the validity of the outcomes of tests are based on things that are nearly impossible to get or control when working with people.
It is difficult to get a random sample. To get a real random sample, people would automatically agree to be a part of experiments when assigned to groups, would follow through on all instructions, keep careful data and do what they were told -- as well as not doing what was forbidden [This is not a good description of the people I know - in fact, I doubt if I could get a random sample just using the four girls in my house]
It is ethically questionable practice to withhold a treatment that looks promising and give one group a treatment that improves their health and another a placebo for the sake of science. There has been quite an uproar about the choices made in research on syphilis. It is just as challenging to do something noxious. Dr. Watson's research with little Albert and white furry objects received so much negative attention that it is still famous - or infamous, half a century later.
It is also possible to manipulate data - to repress it, or use it in a spurious fashion. For instance, one could make the case that orange juice is a lethal beverage -- all who drank it in 1845 are now dead. Goldfish were given an opportunity to swim in it --- ooops! They died almost immediately.
Sometimes we have partial answers because we carefully choose a sample: All students went to an exclusive girl's school, or use an available sample - the schools that agreed to try this free computer program. Or actually falsify data. Like using a very small sample, -- two out of three parents [because the first two agreed, and the next twenty did not, so if we stop at three, most people agreed].
We may only report early trends that look promising,We can also skew findings by only reporting on one geographic setting or economic sector. For instance, we continue to say that the economy is booming - and it is in some parts of the country, but not in the rust belt. If we report on the growth in California and Arizona we get a very different picture than reporting the growth in Minnesota and South Dakota. We can report increases in scores by reporting on schools that come from a university town, or make a program look unsuccessful by reporting test results for a group of students who are just learning to speak English and just recently arrived in this country from a war torn situation.
It is also easy to have good intentions and miss important factors. Much of our current medical research is based on findings with a sample composed of males. It seems that there was an assumption that finding out about medicinal effects in a group of men would universally apply to everyone.
We have a large body of research that was done on university students. It is widely quoted, but it is a very skewed sample, inadvertently gathered with a coercive technique - "want to pass Psycho 101? Sign up for an experiment.
One more thing -- we love to give something a number - and when we do that, we can quantify it -- and when we can quantify it, we can say - SEE There? She is the tallest -- he is the smartest, she got the best grade. And we can do that. We can measure and find the tallest, the strongest, the fastest, the best score on a test. But it may not help us as much as we want it to. Is the kid with the highest score the smartest? Is it the kid who studied the most? Is it evidence that a reform is effective? Hmmmmm.
Nominal is a naming scale - male or female, age, weight, racial or ethnic background. It is like an on or off switch. It really does not have much use as a number -- instead it is more or a namer - nominal = naming. If you need to put data in a computer, you may arbitrarily decide that male =1 and female = 2. You may decide that Caucasian = 1, Hispanic =2, Native American = 3, Asian decent = 4. Another example might be going to the drive through hamburger place. Ordering a number 2 does not mean you get second best, it means getting the burger with bacon and cheese. When we are using a nominal scare these numbers look like numbers but they have no real numeric value. Because values on a nominal scale represent names or labels rather than values, they are not useful for doing numeric operations.
Ordinal is a ranking scale - first second, third, last. It is a way of ordering people or ideas. It has a lot of usefulness when we are doing research with people because we can make comparisons or compete between two things. He is oldest. She got the highest score. Jan is the tallest. It is useful, allows us to make comparisons but it isn't real good information.
He is oldest - of the triplets; of the octogenarians. She got the highest score by one point - and no one passed the test. Oh, by the way, half the class got one point less than she, and no one seems to have the concepts of working problems suing binomials. Jan is the tallest - and he is ten feet tall; tallest in the fifth grade; taller than the teacher, and the only male.
We can make a comparison, rank things, put them in order, but we have no way of reporting how close the next two in the rank are. So, ordinal data is useful and it is often used when comparing things like achievement and IQ. After all, we do not really know that an IQ of 100 is the same distance from 99 as it is from 101. We don't really know if an IQ of 69 is the same distance away from 70 as 144 is from 145.
We can have people stand in order of height and there can be just centimeters separating the tallest from the next to the next - or there may be three feet difference between numbers four and five and half an inch between numbers one, two, and three. The heights may have been done very accurately, or just estimated, yet there is no way to tell this once a ranking is done.
Some IQ tests are accurate within a few points, and more accurate for some ages and some areas of measuring IQ than others. Some are not very good tests at all - perhaps accurate within 10 points. Some children are not willing to work on a test one time and a lot more willing to give their best one on one with an examiner that gains their trust and cooperation.
So --- ordinal data can be useful, especially for putting people in some sequence, on a continuum with higher and lower values, but it is important to remember that the magnitude of difference has not been established.
Interval may also be referred to as equal interval. In this numbering scale, the numbers are equal distances apart. A rule is a good example. Each number on the scale is exactly the same distance apart. Now, when we measure those students we talked about earlier, we can use a number that gives us definition. The three boys are 48 inches, 56 and 108 inches tall, respectively. Those triplets are the same age. They were born on the same day, and four minutes separated them, so in all they are less than fifteen minutes younger or older.
An interesting thing about interval data - and most of us used this scale all the way through school, is that it does not have an absolute zero - a relative zero or arbitrary zero, yes, but not an absolute zero. That means that we are free to add and subtract these numbers, but we are not really supposed to use them to multiply or divide - that is, to make ratio comparisons. Here is an example: My daughter wears a size three in young ladies, and I wear a size six. Are my feet twice as long as hers? What this supports is the importance of recognizing what kinds of numbers we are using, and what the data are. We can give numbers to occurrences and we can measure things but doing so does not promise us "truth" and it actually may make us more responsible to understand what numbers mean rather than assuming that a number is enough.
Ratio scales are interval scales too, but with the difference of having an absolute zero. We seldom find absolute zero outside of a laboratory setting. One of the easy examples of this is measuring temperatures. To have absolute zero, all atoms must stop moving. That is zero on a Kelvin scale. Of course absolute zero doesn't happen in the real world. Even rocks, which we see as relatively stable, still have atoms moving. Even in the Arctic, where water is frozen solid for miles, the molecules are still moving. So, potentially, this scale is very powerful and we use numbers as though they were ratio numbers all the time, but of course they are not - and so we do not get the absolute ratio data we act like we have.
All in all we love the idea that we can know the truth and the truth will make us free.
And we love to make believe that we can use numbers in an infallible way
and know who belongs in special education, in the gifted program,
who deserves an A, who doesn't understand the material and gets a C ,
who studied and who didn't crack the book
Assignment One: What do you think? Write a sixty second essay discussing new things you just realized, or countering the previous argument. You may want to talk about the idea of numbers having different importance and value, depending on what scale is being used. If that is new information, discuss what you believe about the reading or feelings you have as a result of learning this new concept.
Possibly this information about the scientific method and number scales is something you have understood well and have a good idea or example of how to present the material more effectively. You may want that to be the substance of a quickie essay. When you are finished, email it to the instructor. Be certain to keep track of the points. [25 points for each essay].
Assignment Two: Qualitative Methods What about qualitative research? Is it any more or less reliable than the experimental model? How is qualitative data different from quantitative data and how can we use it most effectively?
To begin to answer those questions, surf the web and come up with a definition or set of practices that describe each of the following types of qualitative research. Then give an example of when it might be most appropriate to use each of these methods to determine something about the quality of an educational program or reform, or to better understand human nature and the human condition. [This activity is worth 150 points]
Visit a course module about Qualitative Research