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Statistical Methods in Business Research Research Paper


Introduction

Available scholarship demonstrates that statistics and research methods play a significant role in today’s business environment as they assist in minimizing uncertainty and leading to more effective planning and decision making processes (Burns & Burns, 2009).

Accordingly, aspects related to statistical analysis and research design oblige a good foundation in statistical concepts, particularly in studies that prefer to utilize quantitative or mixed methods (Bezzina & Saunders, 2014). This paper reviews two articles using different statistical analysis methods with the view to understanding business research.

Article Summaries

The study titled “Choice of Consumer Research Methods in the Front End of New Product Development” used an internet-based questionnaire to collect data from 88 respondents who were purposively sampled based on their knowledge of consumer research methods used by their respective companies (Creusen, Hultink, & Eling, 2013).

Most of the items in the questionnaire used a 5-point Lickert-type scale, and data were analyzed using a descriptive statistical method involving the tabulation of frequency distributions and the development of simple bar charts to interpret findings.

The study titled “Measuring the Role and Value of a Corporate Brand on Customers’ Perception of Products and Services” utilized a survey approach (questionnaire) to collect data from 108 respondents who were randomly sampled from a population of customers using products and services of the food industries in Bu-Ali industrial park (Miraftabzadeh, Ahanger, Khadivi, Jahanikia, & Yousefi, 2015).

The study employed inferential statistics (goodness of fit test, Pearson correlation coefficient test, and Friedman ranking test) to not only analyze data from the field but also to test various hypotheses relating to the impact of brand equity on customers’ perception of using products and services.

Strengths and Weaknesses of Sampling Approaches

Purposive sampling technique is preferred in business research as it is less costly to conduct and uses little time; however, it is subjective and does not allow for generalizations.

On the other hand, simple random sampling is preferred as it is easily understood by researchers and has the capacity to project results. However, some of its major weaknesses include (1) cost concerns, (2) lack of capacity to provide any assurance of representativeness, and (3) difficulties in constructing a sampling frame (Sekaran & Bougie, 2013).

Instruments & Designs

The summarized articles used a questionnaire instrument and were quantitative in approach. In business contexts, questionnaires are mostly used when the researcher is interested in collecting primary marketing research data from customers in the field. The quantitative research approach is mostly used in situations where the researcher is interested in generating numbers (statistics) to aid in the planning and decision-making processes.

In design, the study by Creusen et al. (2013) used a descriptive research design while that of Miraftabzadeh et al. (2015) used a correlational research design.

A descriptive research design is mostly used when the researcher is interested in acquiring a lot of information through the description, while a correlational research design is mostly used in situations requiring the examination of specific relationships between and among variables (Sekaran & Bougie, 2013).

Strengths and Weaknesses of Statistical Approaches

The strengths of descriptive statistics include:

  1. clarity in describing large volumes of data,
  2. lack of uncertainties in the tabulated values,
  3. ease of use, particularly when examining the tendencies, spread, ordinariness, and reliability of a data set.

The weaknesses include a lack of generalizations and capacity for misuse or misinterpretation, particularly when the samples are small. The strengths of inferential statistics include the capacity to:

  1. etermine or predict causality,
  2. generalize findings to the larger population,
  3. assess the strength of the relationship between the independent and dependent variables,
  4. evaluate the relative impact of various phenomena of interest.

The weaknesses include various measurement errors as well as problems of validity and reliability of findings (Burns & Burns, 2009; Sekaran & Bougie, 2013).

Combining the Two Approaches

Drawing from the above elaboration, it is clear that the two statistical methods can be used to complement each other in a single research study. Descriptive statistics can be used to describe large sets of data entailing the demographic characteristics of respondents, including variables such as age, occupation, purchases made, and salary range, among others.

On the other hand, inferential statistics can be used to establish relationships between variables with the view to helping the researcher understand the most important variables or sets of behaviors in the business context.

Appropriate Methods

Purposive sampling technique, quantitative research approach, and inferential statistics are most appropriate in their own business and functional area. Purposive sampling enables the researcher to sample respondents who are knowledgeable in the area of study and hence, more likely to add important insights and rich contextual information to the study (Sekaran & Bougie, 2013).

Similarly, a quantitative research approach using a survey (questionnaire) to collect data is objective, and the numerical findings are of immense importance in planning and decision making. Lastly, the most important variables or sets of consumer behaviors in today’s business environment can only be known using inferential statistics as this method has the capability to establish causality and test the strength of relationships.

Conclusion

This paper has reviewed two articles with the view to getting a proper understanding of business research. Overall, it can be concluded that students and business researchers require a good understanding of these methods and techniques to avoid making mistakes, which may emanate from statistical misconceptions.

References

Bezzina, F., & Saunders, M. (2014). The pervasiveness and implications among academics with a special interest in business research methods. Electronic Journal of Business Research Methods, 12(2), 29-40.

Burns, R. P., & Burns, R. (2009). Business research methods and statistics using SPSS. Thousand Oaks, CA: Sage Publications Ltd.

Creusen, M., Hultink, E. J., & Eling, K. (2013). Choice of consumer research methods in the front end of new product development. International Journal of Market Research, 55(1), 81-104.

Miraftabzadeh, S. M., Ahanger, N., Khadivi, A. M., Jahanikia, A. H., & Yousefi, V. (2015). Measuring the role and value of a corporate brand on customers’ perception of products and services. International Journal of Academic Research, 7(1), 302-306.

Sekaran, U., & Bougie, R. (2013). Research methods for business: A skill-building approach (6th ed.). West Sussex: John Wiley & Sons.

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IvyPanda. 2020. "Statistical Methods in Business Research." May 5, 2020. https://ivypanda.com/essays/statistical-methods-in-business-research/.

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IvyPanda. (2020) 'Statistical Methods in Business Research'. 5 May.

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