The point that fascinated me the most is, the use of scales is to present information numerically to facilitate the interpretation of data either as analysis, or in presenting the findings of research. The purpose is though apparently very simple but the real part depends on the type of scale chosen to represent it. Nominal, ordinal, interval and the ration, these four types of scales are the real storyteller that how the data will be selected and interpreted. All these kinds of scales are different in strength, weakness, and limitation from one another and taking a proper scale will have a major effect on the final layout. For instance, if a researcher intends to collect information to find the ratio of people living in an area, in accordance with the age, the first step will be picking up a scale because the whole research is dependent on it and it will decide that which manner will be most appropriate to collect and express the information. The right scale will be the one, which plots out all the information according to the ratio of people from all the age groups.
Question:
The use of different scales for different types of surveys is an important attribute of good research methods. The choice of the scale type and complexity determines the level of information, and depth of the information available to the researchers for analysis. However, the choice of scale types is in many cases left at the discretion of the research team. Choice of one research tool might hinder the representation of certain aspects of the investigation that would have been possible by the use of a different scale. For instance, if the researcher performed a survey and interviews to collect the data for his proposed hypothesis, how do the researchers determine the best scale to use under these circumstances to represent the results collected from both tools, avoiding choice of scales presenting the least effort in their use, or obscuring important aspects of the investigation deliberately?
Interesting Point:
Another interesting point learnt in the chapters is the importance of precision in analyzing data and getting maximum relevant information from the obtained samples. The relevance depends on both, the size and precision equally, but the challenge is that they both are inversely proportional to each other i.e. larger sample size gives out less précised results. Now here all depends on the researcher’s skills that how he would determine and plan his design. The ideal situation is to use largest population to get more variety in samples. If there is a lot of variation and it is difficult to get accurate results then the decision should be made while keeping few the variables under consideration. First is to decide the margins of allowable errors i.e. the precision of collected data. For example, if 50 people will be interviewed, how many would probably provide irrelevant information. Second variable is also linked with that how much chance of error will be tenable and will not affect the results. Third is to predict the range of variability in the population i.e. to what extent it exist in the people who would be used for sampling. In addition, the last is to determine the cost and benefits of increasing the sample size that, to what extent this would give the best output results and how much precision would be sacrificed.
Question
The application of precision and size both is crucial part for ensuring that the research results are being portrayed in the most representative and useful manner in accordance with the research design. Neglecting either of them would result into representing flaws and hence misinterpretation of data. However, in case where the sample size cannot be increased further because of certain reasons like geographical boundaries, lack of resources or some other theoretical factors ,how would the researcher decide between the two aspects to gain the finding which would sufficiently trustworthy for the research?
Interesting Point:
I believe sampling is another basic factor while performing the research. Before designing the overall layout, the initiation step is to plan how the sampling would be done, because the whole work is based on it. The point that fascinated me is related to the purposive sampling, in which the sampling is restricted to specific types of people who can provide the relevant information. As sub-type, the judgment sampling is more preferred for this category, in which the data is collected through gathering information based on the general experiences of the desired population, but according to my analysis, the quota sampling is easier and less time consuming. While doing a comparison study, for instance the work attitude of blue-collar workers in an organization in comparison with the white- collar workers, and their ratio in organization is 60% and 40% respectively. Therefore, if 30 people would be interviewed to find the answer to the research question, then a quota of 18 blue-collar workers and 12 white-collar workers will form the sample. However, this will not be enough to get the general idea of completely targeted population but the convenience of time, cost, and effort makes it attractive tool for some researchers. This systematic sampling ensures that all the sub groups in the population are being equally represented and are selected non-randomly.
Question
Choosing a correct sampling method is equally important for research, as the precision and representing the data in best way. The nature of the population and the inclusivity of the required results are also some of the variables that influence the choice of the sampling methods to apply. While performing the research, there is many qualitative data, which cannot be expressed statistically. Researchers have to make the design while keeping certain parameters under consideration to minimize the effect of those qualitative factors on the overall the analysis. Now it depends on their experience and commitment to the work that what method they choose for sampling and to what extent they overcome those factors. For instance, if a research has to be done on low and poor class population living in an area, which is highly threatened by the hazardous viral diseases, and there is a high risk for researcher, of being infected. Apparently, he went there for sampling and processed out his finding, but while keeping those environmental factors in mind, to what extent the results would be reliable and justified? How it could be ensured that sampling methods are not being abused or the results are not based on biased observation and analysis?
