Plight of Ovarian Cancer Sufferers

Business Research Terms and Concepts

KNOWING THE DIFFERENCES

Understanding Research Terms and Concepts

Quantitative Research Methods and Instruments

Quantitative research tests hypotheses from theories or approximates the magnitude of a particular phenomenon (Eau Claire, 2014). Participants or volunteers are assigned at random to different aspects or derive data from them to control their influence on a dependent variable. Probability sampling may be used if the intent is to generalize (Eau Claire).

Quantitative data collection methods utilize and depend on random sampling and structured data collection instruments (Euau Claire, 2014). These methods and their instruments are suited to different anticipated types of responses. Their findings are easy to determine, summarize, analyze, compare and generalize. The most typical methods are experiments or clinical trials, observation and recording of a specific event, securing data from an entity's management information systems, and surveys with closed-ended questions. Surveys are conducted through interviews or questionnaires as instruments (Eau Claire).

Interviews may be face-to-face, by telephone or by computer-assisted personal interviewing (Eau Claire, 2014). Face-to-face interviews enable the researcher to personally connect with the participants and thus yield the highest level of response. Researchers are also able to clarify uncertain matters or follow up on some question or detail. These interviews are, however, impractical when the population sample is too large. Telephone interviews are faster and more inexpensive. They also provide quick and easy access to participants with telephones. They provide more reliable responses than those derived from mailed interview questions. But these responses are not dependable in that they exclude random participants without telephones who are necessary to the research. And computer-assisted personal interviews are conducted by entering responses directly into a laptop or hand-held computer's database. This type saves times and eliminates the need to carry bulky questionnaires. But they are expensive and require computer and typing skills (Eau Claire).

Questionnaires are either the paper-and-pen type or web-based (Eau Claire, 2014). The paper-and-pen type is cheaper and time-saving to the researcher. Respondents or participants also tend to be truthful with their answers because the questionnaires are anonymous. But the downside is that the majority of respondents do not send back these questionnaires. The result thus is not representative of the sample size. Web-based questionnaires are conducted through emails, which bring the respondents to a website where they are asked to fill in the questionnaire. Like the telephone survey, however, the results are not representative of the sample as the responses of those without computers are excluded (Eau Claire).

Two Articles

The article, entitled "Starbucks, Bank One, and Visa Launch Starbucks Duetto," discusses the use of the descriptive statistical method in managing the business problem of Starbucks (Schindler, 2006). The article presents a concrete concept, planning and mechanics, a clear timetable, accomplishments and projections. Results of quantitative studies lend support to the statement of accomplishments and projections (Schinder).

On the other hand, the article entitled, "Mastering Teacher Leadership," uses the inferential statistical method (Schindler & Cooper, 2001). It uses a multi-stage communication study on teachers at the Wittenberg University Department of Education in determining the viability of a certain course for a certain sample population. This course is the Master of Education program and the sample population consists of Ohio-certified teachers within the school districts in the county (Schindler & Cooper).

Comparison and Contrast

Descriptive Statistical Method

This method brings out particular data features through the mean and standard deviations with exact figures or measurements (Laerd, 2013; Brown, 2010). It analyzes the data in order to describe, demonstrate or summarize them in establishing patterns. It is limited to the description of the data. The importance of this method lies in the necessity of the very description and interpretation of raw data, especially if they are voluminous. The method presents these raw data in a meaningful way to those who need them. From the description, researchers can interpret them in a simple, clear and meaningful way (Laerd, Brown).

The two general types of statistics used by this method are measures of central tendency and measures of spread (Laerd, 2013; Brown, 2010). Measures of central tendency refer to the central position of a frequency distribution of the data. And measures of spread summarize the data through the available statistics, such as range, quartiles, absolute deviation, variance and standard deviation. Summarizing data combines tabulations, graphs and statistical commentaries. This method, however, cannot make generalizations beyond what the data represent or indicate (Laerd, Brown).

Inferential Statistical Method

This method, like the earlier one, uses the same calculations, such as the mean and standard deviations (Laerd, 2013; Brown, 2010). It also starts with a sample but, unlike the descriptive statistical method, it leads to a generalization of the sample. Moreover and unlike the earlier method, the information it brings out is not in numbers but in parameters. These parameters, however, represent a range of potential numbers. This method because meaningful in a research when the researcher cannot capture the entire population needed. It makes use of samples to represent the larger population. This method allows the use of these samples in establishing generalizations from the samples. But these samples must represent the population. The process is called sampling and the methods involved are the estimation of parameters and testing of statistical hypotheses (Laerd, Brown).

Strengths

The descriptive statistical method provides clear and certain meaning and interpretation of even large volumes of data (Taylor, 2015). In contrast, the inferential statistical method does what the descriptive statistical method cannot. It can generalize using samples, which represent the population that cannot be accessed (Taylor).

Weaknesses

The descriptive statistical method allows a summary and conclusion only on the sample respondents or objects measured (Taylor, 2015). It cannot extend to those, which have not been measured. It cannot make inferences (Taylor). The inferential statistical method, on the other hand, has two major limitations or weaknesses. The first is the uncertainty of the values of data, which have not been measured. That value is based only on inference. The other limitation is related to the first. Some inferential tests require educated guesses, which always have room for error that may have adverse effects on the results of the research (Taylor).