HOW TO CHECk YOUR HOMOGENEOUS SAMPLE?
Take from : https://imadeputrawan.wordpress.com/statistic/
Sample could be said homogeneous when it has a characteristic that it was derive from the representative population. However, it is hard for any researchers when he/she is assigned to determine population which actually it is abstract. Population related to data not number of person involved in research chosen to be respondents.
When someone put hypothesis to be verified, means that this hypothesis is a population. Researchers do only recognize a number of list of names with a specific amount called sampling frame. From this sampling frame we are allowed to select part of them as a representative of the whole population.
Fortunately, we are allowed to take part of the population which is called sample. Imagined when we do our research by taking all population, we are not able to verify scientifically unless we take part of them randomly, we call it sample. The question, then arise, is it going to be the same characteristic belong to the population comparing to what we have selected, briefly is it homogeneous sample. If the answer yes it is not homogeneous, so this sample could be said was not derived from the same population. If this is a case, it will influence our hypothesis testing which probably come up with the failure in rejecting the null hypothesis due to high variances reflected by our sample.
Another point indicated also by the low of level of representativeness of our sample from our population.
Some of our colleges sometime told to the students during the examination by saying that the sample used was not homogeneous because it consists of several level of employee rank or position. Logically, this is not the way we look at the homogeneous sample since the indicator is simply the rank position of our sample.
When our unit of analysis, let’s say school principals, we do not care about whether they come from public or private school, as long as they are school principal we can not say it is heterogeneous.
Statistically, when we do our analysis by applying regression and correlation, needless to say, sample as we take school principal as an example, it is good for being analyzed if it is still heterogeneous, because variances plays important role in resulting our correlation coefficient.
Finally, we are very glad because there is a formula for finding out whether our sample homogeneous or not. It’s formula as follows
Stand Error (SE)= standard deviation devided by root square of sample size
If SE is higher than 2 or above 3, we could interpret that sample is too heterogeneous, therefor we still have chance to take number of sample and the more size the sample is taken, the lower SE would be. This is because the size of sample will affect the diminishing of our SE. The lowest SE means the more homogeneous is our sample would be.
It is efficient way because the researchers would not spend a lot of time and energy to take a useless sample size when finally the result would be not homogeneous.
Have a nice time to try, good luck
Kemendiknas, PAUD, Monday, 18 October 2010
Sample could be said homogeneous when it has a characteristic that it was derive from the representative population. However, it is hard for any researchers when he/she is assigned to determine population which actually it is abstract. Population related to data not number of person involved in research chosen to be respondents.
When someone put hypothesis to be verified, means that this hypothesis is a population. Researchers do only recognize a number of list of names with a specific amount called sampling frame. From this sampling frame we are allowed to select part of them as a representative of the whole population.
Fortunately, we are allowed to take part of the population which is called sample. Imagined when we do our research by taking all population, we are not able to verify scientifically unless we take part of them randomly, we call it sample. The question, then arise, is it going to be the same characteristic belong to the population comparing to what we have selected, briefly is it homogeneous sample. If the answer yes it is not homogeneous, so this sample could be said was not derived from the same population. If this is a case, it will influence our hypothesis testing which probably come up with the failure in rejecting the null hypothesis due to high variances reflected by our sample.
Another point indicated also by the low of level of representativeness of our sample from our population.
Some of our colleges sometime told to the students during the examination by saying that the sample used was not homogeneous because it consists of several level of employee rank or position. Logically, this is not the way we look at the homogeneous sample since the indicator is simply the rank position of our sample.
When our unit of analysis, let’s say school principals, we do not care about whether they come from public or private school, as long as they are school principal we can not say it is heterogeneous.
Statistically, when we do our analysis by applying regression and correlation, needless to say, sample as we take school principal as an example, it is good for being analyzed if it is still heterogeneous, because variances plays important role in resulting our correlation coefficient.
Finally, we are very glad because there is a formula for finding out whether our sample homogeneous or not. It’s formula as follows
Stand Error (SE)= standard deviation devided by root square of sample size
If SE is higher than 2 or above 3, we could interpret that sample is too heterogeneous, therefor we still have chance to take number of sample and the more size the sample is taken, the lower SE would be. This is because the size of sample will affect the diminishing of our SE. The lowest SE means the more homogeneous is our sample would be.
It is efficient way because the researchers would not spend a lot of time and energy to take a useless sample size when finally the result would be not homogeneous.
Have a nice time to try, good luck
Kemendiknas, PAUD, Monday, 18 October 2010
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