Understanding Business Research Terms and Concepts Number

UnderstandingBusiness Research Terms and Concepts

Number:

UnderstandingBusiness Research Terms and Concepts

Quantitativedata are concerned with testing hypotheses collected, expressed andanalyzed in the form of numbers. It can then be presented aspercentages, averages and in tables or graphs. Quantitative approachallows collection of large amounts of data. This essay expounds onthe data collection and statistical methods in relation toquantitative data. When conducting business search, it becomesimperative for the researcher to understand he terms used. Businessresearch is any research conducted that relates to business. Themethods applied in conducting research vary with topic, as well asthe size and nature of the research and the business. This paperlooks into quantitative data collection instruments and methods ofsampling well as comparing descriptivestatistical method and another using an inferential method.

DataCollection

Experimentstest dependent and independent variables in order to determine theexistence of a cause-effect relationship. Experiments are one of theoldest instruments used to collect quantitative data. This is becauseof the accuracy in portions required when carrying out experiments.The data is also collected in tables and graphs, hence emphasizing onthe numerical nature of the quantitative methods. However,experiments can be time consuming and may require many resources(Byrne, 2002).

Quantitativedata can also be collected by using interviews which may be donethrough the telephone, computers or face-to-face. Interviews are morestructured for quantitative data in order to ensure numeracy.Face-to-face interviews yield the highest response rate whiletelephone interviews have the least response rate. However,interviews can be inefficient when the participants involved fail todisclose all the information (Byrne, 2002).

Questionnairesare also used in collection of quantitative data. They are howeverdeveloped specifically with regard to the numerical nature ofquantitative data. This is achieved through the use of scaledquestionnaires, and standardized tests based on psychometricproperties (Byrne, 2002). The validity of the responses inquestionnaires is questionable. In addition mailed questionnaireshave very low response rates.

SamplingMethods

Randomsampling is a statistical method in which everyone in the populationhas an equal chance of being included in the sample. The inclusivityin random sampling makes this method very effective because of thewide scope covered. This method also eliminates sampling bias,although it requires many resources to succeed. Stratified samplingselects samples based on the target population to get arepresentation from population proportions. This makes this methodhighly representative as far as the target population is concerned.However, establishing the specific variables can be time consumingand difficult to achieve. Systematic sampling uses a sequence inselecting the participants in a population proportion. This isachieved by dividing the population (N) by the number of samples (n)desired, and then using the result to develop a sequence of sampling.This method is representative, though time consuming.

Descriptiveand Inferential statistical methods

Inbusiness, research is carried out in order to clarify a problem andto find a sustainable solution. This begins with the identificationof the problem followed by strategizing on how to carry out theresearch best. In so doing, a business problem may be researchedusing the descriptive or the inferential statistical methods. Thedescriptive statistical method describes the main features of thequantitative data, giving a summarized analysis. On the other hand,inferential statistical methods give a generalized analysis of apopulation based on the given sample. The inferential method thusallows the usage of probability theory in its analysis. Therefore, abusiness chooses the most suitable method based on the problem(Anderson, Sweeney, &amp Williams, 2010).

Businessesin trying to identify their financial standing, in terms of stock,profits, costs and revenue, may prefer to use the differentialstatistical method. This is because the financial problem is specificto that business. A look into the previous financial statements maybe useful in revealing the patterns of performance. The differencesover time may be utilized to predict future business performance(Anderson, Sweeney, &amp Williams, 2010). In developing an effectivemarketing strategy, a business may opt to take on inferentialresearch. This is especially useful when the business wants todiversify its market domain. The research may be based on markettrends, consumer preferences and tastes, and opinions on pricing. Theinferential statistical method is important in this case becauseconstraints make it impossible to carry out research on the wholepopulation, hence the necessity to have a deductive conclusion(Anderson, Sweeney, &amp Williams, 2010).

Descriptivestatistical methods are more efficient as they are able to give moreaccurate information, which is based on a small sample. This meansthat the analysis of a descriptive research may prove useful,especially when a single entity is involved and does not requireforeign data. However, since descriptive statistical method seeks tofind information that is limited in scope, the feedback may not beaccurate. For example, in a bid to find out in-depth feedback on aproduct a researcher may ask personal questions in interviews and theparticipants may give false answers so as to protect their privacy.

Onthe other hand, inferential or deductive statistical methods enableanalysis of phenomena which may not be possibly researched due tolimited resources including time, finances and mobility. Therefore,this method is an essential tool for population researches which drawfundamental conclusions. This aspect of probability subsequentlyreduces the efficiency of this method as the conclusions drawn arenot uniformly distributed throughout the population. To counter this,inferential methods employ confidence levels, which give thepercentage of accuracy of the results (Byrne, 2002).

Thesimilarities between descriptive and inferential statistical methodsmake it possible for them to be used in combination. The fact thatthey use similar tools for research is essential in enabling theircombined use. Combining both the descriptive and inferentialstatistical methods yields more validity to the findings. Thestarting point for each method is based on a sample statistic. Thedescriptive method can therefore be assigned to different samples andthe findings of each sample compiled in the inferential method tomake a conclusion of the population. In other words, the descriptivemethod is considered a sub-set of the inferential method when thesetwo are used in combination.

Inconclusion, statistical research is essential in any field in orderto understand the underlying implications, which are essential in theend. Both the descriptive and inferential statistical methods areimportant in carrying out specific research based on the hypothesis.Therefore, for a reliable research outcome, both methods should beemployed in order to achieve comprehensive results.

References

Anderson,D., Sweeney, D., &amp Williams, T. (2010). Statisticsfor Business and Economics.

Pearson.

Byrne,D. (2002). InterpretingQuantitative Data.SAGE Publications Limited.