Introduction
There are two main types of research design, quantitative and qualitative research designs. In the best-case scenario, quantitative and qualitative methodologies should work in tandem. The qualitative technique occurs at the beginning of a study to explore values that are to be measured in the following quantitative phase (Schmitz, 2012). In this way, the former helps to improve the usefulness and efficacy of the latter. Researchers select either of the two designs based on the study aim, objectives, nature of topic and research questions, and data collection and processing (Susan et al., 2001). The table below shows a comparison of the two techniques and is based on Antwi and Hamza (2015):
Examples of Quantitative Data Sets Used in Business Research
In business, quantitative research is concerned with the measurement of a market or population. It is most applicable in cases where stable and representative measurements of the market are needed. Stability is essential both in the case that study is conducted on a repetitive basis and in the instance where the objective is to detect changes over time. On the other hand, representativeness is crucial in instances, which market research is applied to aid in decision-making. There are several examples of quantitative data sets used in business research, and they comprise market segmentation, customer satisfaction, and advertising effectiveness data. Market segmentation data is that which entails the type and number of consumer groups present with regards to them sharing similarities in product preferences and characteristics (B2B International, n.d.). The results obtained from a marketing segmentation analysis are primary in the development of a customized marketing mix.
Conversely, customer satisfaction data is defined as demographic, behavioral, and personal information regarding clients that is collected by companies. It can also be adjusted to give a representation of customer satisfaction levels and loyalty overtime or on various aspects of the company’s products and services (B2B International, n.d.). Finally, advertising effectiveness data is concerned with the effect of a marketing campaign on advertising awareness or brand association (B2B International, n.d.). It comprises of both collecting the pre- and post-advertisement awareness data to measure effectiveness.
Examples of Quantitative Data Collection Tools and Data Analysis Tools Used in Business Research
Quantitative data collection is dependent on sampling and structured data collection tools that facilitate the acquisition of information required in preordained brackets. Relevant data is collected in several ways, and they are interviews, surveys, and experiments. It is essential to note that the first three tools embrace the use of close-ended questions. These methods can either be used independently or in combination to enhance the advantages and mitigate the disadvantage of individual techniques. Interviews are the most popular quantitative data collection tool. There are two types of surveys, namely, personal and telephone interviews (Brannen, 2017). In the first type, the interviewer and participant are in one physical location. On the other hand, telephone interviews are described as dialogues between an interviewer and the respondent over the phone through a structured questionnaire. It can be used to measure the level of customer satisfaction with a purchased product or the effectiveness of promotional campaigns. Mail-out surveys are characterized to have a low response rate, concerning the number of mail-outs.
Depending on the numerical data that has been collected during research, the descriptive or inferential methods can be used for analysis. Descriptive analytical methods are those that summarize data in the form of charts and tables, however, they do not attempt to conclude from the samples in which the sample was taken (Bhattacherjee, 2012). Examples include the line, bar graphs, and measures of central tendency. Conversely, inferential statistics, hypothesis testing, and estimation statistics make it easy to conclude (Bhattacherjee, 2012). Thus, it entails concepts, such as the ANOVA, Chi-Squared, T-test, and regression, among others.
Examples of Qualitative Data Collection Tools and Data Analysis Tools Used in Business Research
Different methods are used to collect data in qualitative research, however, the most popular include interviews, focus groups, and document analyses. By combining two or more methods, the credibility of a study is enhanced. Interviews are further divided into structured, semi-structured, and unstructured (Brannen, 2017). Structured interviews are characterized to have a limited participant response; hence, they are less time-consuming and easy to administer. On the other hand, unstructured interviews do not mirror any predetermined concepts and are conducted with a minimal organization; hence, they are time-consuming. Third, for semi-structured interviews, apart from the questions targeting the already defined areas, it allows the interviewer and participant to diverge and pursue an idea in detail. Interviews aim to explore opinions, beliefs, or experiences on particular matters. Conversely, focus groups share some standard features with unstructured interviews (Brannen, 2017). It is defined as a group dialogue on a specific topic identified for study purposes. It is usually steered, observed, and recorded by the researcher. The objective of this collection tool is to obtain information on collective views (Brannen, 2017). Lastly, document analysis is centered on existing resources, such as scholarly articles, government reports, and books, among others.
There are different techniques for analyzing qualitative data; therefore, the selection of a particular technique relies on the research question, the research’s theoretical foundation, and the appropriateness of the technique. The three main analytical techniques include content and narrative analysis and grounded theory (Brannen, 2017). Content analysis is utilized in the evaluation of documented information, such as media, texts, or media. On the other hand, narrative analysis is employed in the examination of information from several sources, be it from interviews, surveys, or direct observations. It is focused on the experiences and stories shared by participants used to answer the study items. Finally, the grounded theory infers to the process of utilizing qualitative data to explain the cause of a phenomenon. It achieves so by factoring an array of similar cases in different settings and using the information to formulate causal explanations. It is essential to note that the investigators can modify or develop new explanations as they continue to analyze more cases until an explanation is identified, which aligns with all.
Pros and Cons of Mixed Methods
In business research, it is highly likely to find studies employing an amalgamation of both quantitative and qualitative strategies. This is what is referred to as mixed research. The mixed method primarily assumes that it is capable of addressing some research questions more comprehensively as compared to using the quantitative or qualitative options independently. As a result, it is summed up to have the ability to harness the individual strengths and offset the weakness of each approach (Brannen, 2017). In addition, data triangulation facilitates the performance of in-depth research; hence, a more meaningful interpretation of the data phenomenon under analysis.
Despite its considerable strengths, the mixed method design also has its drawbacks. First, combining the methodologies is regarded by some researchers as problematic based on the perspective that they belong in separate paradigms. Furthermore, mixing two methods in a single study is time-consuming, and requires researchers who are experienced and skilled in using both methods. Third, attaining a perfect integration of dissimilar data types can be challenging. It is hence vital to reflect on the findings of a study and ascertain whether they have been enhanced by the amalgamation of the different data types. Lastly, the presentation of the findings of the mixed methods is considered as a disadvantage as hinders its application. Consequently, some researchers opt to present quantitative and qualitative data distinctly based on the target audience (Brannen, 2017). Moreover, they may choose not to focus on some interpretations and conclusions.
Characteristics of Qualitative Research
Qualitative data is more challenging to define; however, stress is placed on “understanding” instead of simple measurement. For instance, quantitative research may identify which of two separate adverts is more recalled; nevertheless, it is also essential to consider how one element works as an advert and the reason behind its higher efficiency. This is where qualitative research comes in. Hence, it can be generally said to be dealing with questions such as “Why,” “Would,” and “How.” Qualitative research is often conducted among small samples; thus, the results are not necessarily statistically valid. Regardless, such data can highlight potential issues that can be analyzed in quantitative research (Tetnowski, 2015). Second, it requires intense researcher involvement; hence, researchers are forced to explain clearly the purpose of the investigation all through the entire study in an attempt to eliminate prejudice (reflexivity and flexibility).
Third, it embraces an emergent design; therefore, the initial plan for the research should be flexible. This can lead to different methods being used for research, and in some cases, the research problem can be altered, thus, resulting in an entirely new study (Tetnowski, 2015). The fourth characteristic is that it is holistic as it constitutes a broader picture of the issue under study – the researcher concentrates on varying perspectives and identifies the varied factors involved. An ongoing data analysis also characterizes it since the examination of qualitative data does not occur at the end of the research.
References
Antwi, S., & Hamza, K. (2015). Qualitative and quantitative research paradigms in business research: A philosophical reflection. European Journal of Business Management, 7(3), 217-225. Web.
Bhattacherjee, A. (2012). Social science research: Principles, methods and practices. Textbook Collection. Web.
Brannen, J. (2017). Mixed methods: Qualitative and quantitative research. Taylor and Francis.
B2B International. (n.d.). What is the difference between qualitative and quantitative research? Web.
Schmitz, A. (2012). Principles of sociological inquiry: Qualitative and quantitative methods. Saylor Academy. Web.
Susan, A. M., Gibson, C. B., & Mohrman, M., Jr. (2001). Doing research that is useful to practice: A model and empirical exploration. Academy of Management Journal, 44(2), 357-375. Web.
Tetnowski, J. (2015). Qualitative case study research design. Perspectives on Fluency and Disorders, 25(1), 39-45. Web.