Big things come in small packages – which work the same for business research papers. What makes a good paper does not necessarily have to be an overwhelmingly exemplary presentation. Logically, what catches the attention of the reader to keep on reading such is the value of its contents, most especially, the credibility of the research. No matter how wonderful the presentation of the data may seem to be, but the content has been poorly resourced out, the overall material is of no use in the end.
Therefore, it is clear that the type of research conducted should be appropriate to the subject in discussion, wherein the data is able to be best measured and consequently, the conclusion of the research is rational and plausible. Moving forward, the various types of research design will be detailed further to elaborate each of the type’s advantages and disadvantages. There are three (3) types of research design being utilized in different areas of study – Field Study, Field Experiment and Survey.
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Field Study is where the researcher visits a site, observes and asks questions, but does not alter anything. It is similar to a naturalist observing wildlife without doing anything and maintaining the balance of the habitat (Borgatti, 1997). What makes this method very reliable is that there is a relatively large amount of factual data from the natural surroundings. Moreover, the variables collected are retrieved simultaneously from the area in question, thus, the consistency of the samples are not highly affected.
Another advantage of Field Studies is that the researcher tends to have less impact on the subject data, as compared to other schemes (McCarthy, 1998), thereby arriving to a stronger conclusion, all derived from the variables natural state. It should also be noted however, that the drawback of not having to manipulate the variables on hand may also mean the inability to infer causality amongst the variables (McCarthy, 1998). This is due to the fact that field studies normally deduce conclusions from simply what is given by the environment – less thought-processing therefore is involved in terms of creating hypothesis remarks.
The second type, Field Experiment, is where the subject is observed in a natural setting rather than in a contrived laboratory environment. However, like natural experiments, field experiments suffer from the possibility of contamination that may affect the experimental conditions in question. There is less control on the side of the researcher and the difficulty of penetrating a business or industrial setting for this purpose may also pose as a problem (McCarthy, 1998).
Finally, the third scheme, Survey - method of gathering information by determining a set of pre-formulated questions in a structured questionnaire to a select group of individuals drawn to represent a defined population (Borgatti, 1997). What makes studies drawn through surveys reliable is because results of surveys often yield new hypotheses from the answers garnered (McCarthy, 1998). In addition, surveys have the flexibility to be in various forms – questionnaire, interviews, observations, etc.
It should be considered however when making conclusions, that survey results may not entirely reflect high return rates thus, threatening the representation of responses, resulting to biases. Understanding the types of research design is not complete without being able to interpret your gathered data qualitatively and quantitatively. Depending on the need of the subject matter, it is vital that the data is analyzed and measured accordingly. Four (4) types of measurement scales can be used to aid one’s business research in making them more compelling, and they are as follows: (a) nominal; (b) ordinal; (c) interval; and (d) ratio.
Nominal scale, the lowest level of measurement, involves either verbal or numerical labels for any given category (Gavin, 1996). Variables here are placed within a mutually exclusive and exhaustive category, such as “male” or “female” categories denoting sex or gender as the nominal scale (Sable Tutorial, 1999). Another example can be the numbers of the baseball players found at the back of their jerseys. The numbers in this example are used to identify the players and one will notice that no two players are alike. Each variable (number per player) is different and can only be assigned to one person.
Ordinal scale is similar to that of nominal scales but differ as the former has the property of order or ranking (Gavin, 1996). Ordinal scales, however, does not denote any size or quantity of the variable as it merely permits the classification of categories be identified alongside with the hierarchy of interests and its frequency. The ordinal scale is a much stronger form of measurement than the nominal scale because the variables are ordered, ranked and can be subjected to non-parametric tests as the results will be more sensible (Gavin, 1996).
An example of ordinal scale is the winning set of players in a marathon game – the 1st -, 2nd - and 3rd-placers. There is indeed a ranking but the scale does not provide information about the absolute differences between 1st and 2nd or between 2nd and 3rd. We can also take as an example of ordinal scales the rankings of four restaurants in study. They can be assigned with numbers that will represent the scale being the most-liked up to the least-liked numerical ranking (Gavin, 1996).
The consistency should be maintained as to which number represents a certain definition so as to keep the ordinal scale in place. Next is the third measurement scale, Interval scale. Interval scales provide us with still more quantitative data. In an interval scale, variables are measured against a plane where the distance between numbers or units is equal over all levels of the scale (Sable Tutorial, 1999). To cite an example, the Fahrenheit scale of measurement in a thermometer shows that the distance between 10 degrees and 30 degrees is the same as the distance between 50 degrees and 70 degrees.
The year dates in most calendars can also be assigned and measured against an interval scale. Note that in this particular scale of measurement, there is no fixed zero that means a zero quantity of the aspect of interest. Finally, the most powerful form of measurement scale is the Ratio scale. Ratio scale differs only from the interval scale given the former’s true zero point. The zero point allows further more interpretations and more valid assumptions and conclusions.
Time is a very certain example of ratio scale as not only can we say the difference between two hours and ten hours, but we can also say that ten hours is five times as long as two hours – ratio in comparison (Sable Tutorial, 1999). Other social variables of ratio measure include age, length of residence in a given place and in a particular period of time. These measurement scales are chronologically arranged from the least to the most powerful form. In the end regardless of which scale is used it is crucial that the instrument should be practical, valid and reliable (Gavin, 1996).
Research methods are basically used to avoid or at least lessen biases. A multidisciplinary team approach is best in conducting research work, armed with proper research techniques and valid statistical method of scales will equate to a non-refutable conclusion. At the end of the day, one should be smart enough to acknowledge the fact that whatever scheme of research or measurement is chosen, nothing will replace one’s sound judgment and keen consideration of underlying assumptions in one’s data analysis. References McCarthy, P. (1998). Steps in the research process. Retrieved on July 18, 2007 from www.
mtsu. edu/ Sable Tutorial. (1999). Measurement scales in social science research. Retrieved on July 18, 2007 from Virginia Tech in www. simon. cs. vt. edu Borgatti, S. (1997). Research glossary. Retrieved on July 18, 2007 from www. analytictech. com Gavin, T. (1996). Research forum-methodology-measurements, Part 1: Principles & theory. Retrieved on July
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