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Quantitative Methods for Business Decisions

How to Gather Data for Business Research

❶Quantitative Methods American Intercontinental University: This page was last edited on 7 August , at

BREAKING DOWN 'Quantitative Analysis (QA)'

What is 'Quantitative Analysis (QA)'
Use 'quantitative research' in a Sentence
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Qualitative research is explorative or investigative in nature, and its findings are not applicable across the universe. For instance, if a qualitative case study on a store finds that 75 percent of customers are repeat customers, applying the same across all stores requires formulating a hypothesis that says likewise, and undertaking a quantitative study to validate the hypothesis using tools such as questionnaires and a random sample of customers across different stores.

The real life applications of business research suggests that comparing qualitative vs quantitative research, businesses tend to veer towards the quantitative as such data seem more powerful, substantial, and objective. This is a fallacy. A good business research needs to collect both qualitative and quantitative data to gain a proper and in-depth understanding of the subject of research. Consider a hypothetical study on making improvements in the floor design of a supermarket.

This might first require qualitative tools such as deploying focus groups , case studies and observations to determine how customers go around looking for items, congestion at the billing tills at various times, and other characteristic features. To help navigate the heterogeneous landscape of qualitative research, one can further think of qualitative inquiry in terms of 'means' and 'orientation'.

Sociologist Earl Babbie notes that qualitative research is "at once very old and very new. Robert Bogdan in his advanced courses on qualitative research traces the history of the development of the fields, and their particular relevance to disability and including the work of his colleague Robert Edgerton and a founder of participant observation, Howard S.

In the early s, some researchers rejected positivism , the theoretical idea that there is an objective world which we can gather data from and "verify" this data through empiricism. These researchers embraced a qualitative research paradigm , attempting to make qualitative research as "rigorous" as quantitative research and creating myriad methods for qualitative research.

Such developments were necessary as qualitative researchers won national center awards, in collaboration with their research colleagues at other universities and departments; and university administrations funded Ph. Most theoretical constructs involve a process of qualitative analysis and understanding, and construction of these concepts e.

In the s and s, the increasing ubiquity of computers aided in qualitative analyses, several journals with a qualitative focus emerged, and postpositivism gained recognition in the academy. Also, during this time, researchers began to use mixed-method approaches, indicating a shift in thinking of qualitative and quantitative methods as intrinsically incompatible.

However, this history is not apolitical, as this has ushered in a politics of "evidence" e. Qualitative researchers face many choices for techniques to generate data ranging from grounded theory [17] development and practice, narratology , storytelling , transcript poetry , classical ethnography , state or governmental studies , research and service demonstrations , focus groups , case studies , participant observation , qualitative review of statistics in order to predict future happenings, or shadowing , among many others.

Qualitative methods are used in various methodological approaches, such as action research which has sociological basis, or actor-network theory. Other sources include focus groups, observation without a predefined theory like statistical theory in mind for example , reflective field notes, texts, pictures, photographs and other images, interactions and practice captured on audio or video recordings, public e.

To analyse qualitative data, the researcher seeks meaning from all of the data that is available. The data may be categorized and sorted into patterns i. The ways of participating and observing can vary widely from setting to setting as exemplified by Helen Schwartzman's primer on Ethnography in Organizations In participant observation [27] researchers typically become members of a culture, group, or setting, and adopt roles to conform to that setting.

In doing so, the aim is for the researcher to gain a closer insight into the culture's practices, motivations, and emotions. It is argued that the researchers' ability to understand the experiences of the culture may be inhibited if they observe without participating.

The data that is obtained is streamlined texts of thousands of pages in length to a definite theme or pattern, or representation of a theory or systemic issue or approach. This step in a theoretical analysis or data analytic technique is further worked on e. An alternative research hypothesis is generated which finally provides the basis of the research statement for continuing work in the fields. Some distinctive qualitative methods are the use of focus groups and key informant interviews , the latter often identified through sophisticated and sometimes, elitist, snowballing techniques.

The focus group technique e. The research then must be "written up" into a report, book chapter, journal paper, thesis or dissertation, using descriptions, quotes from participants, charts and tables to demonstrate the trustworthiness of the study findings. In qualitative research, the idea of recursivity is expressed in terms of the nature of its research procedures, which may be contrasted with experimental forms of research design.

From the experimental perspective, its major stages of research data collection, data analysis, discussion of the data in context of the literature, and drawing conclusions should be each undertaken once or at most a small number of times in a research study.

In qualitative research however, all of the four stages above may be undertaken repeatedly until one or more specific stopping conditions are met, reflecting a nonstatic attitude to the planning and design of research activities. An example of this dynamicism might be when the qualitative researcher unexpectedly changes their research focus or design midway through a research study, based on their 1st interim data analysis, and then makes further unplanned changes again based on a 2nd interim data analysis; this would be a terrible thing to do from the perspective of an predefined experimental study of the same thing.

Qualitative researchers would argue that their recursivity in developing the relevant evidence and reasoning, enables the researcher to be more open to unexpected results, more open to the potential of building new constructs, and the possibility of integrating them with the explanations developed continuously throughout a study.

Qualitative methods are often part of survey methodology, including telephone surveys and consumer satisfaction surveys. In fields that study households, a much debated topic is whether interviews should be conducted individually or collectively e.

One traditional and specialized form of qualitative research is called cognitive testing or pilot testing which is used in the development of quantitative survey items. Survey items are piloted on study participants to test the reliability and validity of the items. This approach is similar to psychological testing using an intelligence test like the WAIS Wechsler Adult Intelligence Survey in which the interviewer records "qualitative" i. Qualitative research is often useful in a sociological lens.

Although often ignored, qualitative research is of great value to sociological studies that can shed light on the intricacies in the functionality of society and human interaction. There are several different research approaches, or research designs, that qualitative researchers use. As a form of qualitative inquiry, students of interpretive inquiry interpretivists often disagree with the idea of theory-free observation or knowledge. Whilst this crucial philosophical realization is also held by researchers in other fields, interpretivists are often the most aggressive in taking this philosophical realization to its logical conclusions.

For example, an interpretivist researcher might believe in the existence of an objective reality 'out there', but argue that the social and educational reality we act on the basis of never allows a single human subject to directly access the reality 'out there' in reality this is a view shared by constructivist philosophies.

To researchers outside the qualitative research field, the most common analysis of qualitative data is often perceived to be observer impression. That is, expert or bystander observers examine the data, interpret it via forming an impression and report their impression in a structured and sometimes quantitative form. In general, coding refers to the act of associating meaningful ideas with the data of interest. In the context of qualitative research, interpretative aspects of the coding process are often explicitly recognized, articulated, and celebrated; producing specific words or short phrases believed to be useful abstractions over the data.

As an act of sense making, most coding requires the qualitative analyst to read the data and demarcate segments within it, which may be done at multiple and different times throughout the data analysis process.

In contrast with more quantitative forms of coding, mathematical ideas and forms are usually under-developed in a 'pure' qualitative data analysis. When coding is complete, the analyst may prepare reports via a mix of: Some qualitative data that is highly structured e. Quantitative analysis based on codes from statistical theory is typically the capstone analytical step for this type of qualitative data. Contemporary qualitative data analyses are often supported by computer programs termed Computer Assisted Qualitative Data Analysis Software used with or without the detailed hand coding and labeling of the past decades.

These programs do not supplant the interpretive nature of coding, but rather are aimed at enhancing analysts' efficiency at applying, retrieving, and storing the codes generated from reading the data. Many programs enhance efficiency in editing and revision of codes, which allow for more effective work sharing, peer review, recursive examination of data, and analysis of large datasets. A frequent criticism of quantitative coding approaches is against the transformation of qualitative data into predefined nomothetic data structures, underpinned by 'objective properties '; the variety, richness, and individual characteristics of the qualitative data is argued to be largely omitted from such data coding processes, rendering the original collection of qualitative data somewhat pointless.

To defend against the criticism of too much subjective variability in the categories and relationships identified from data, qualitative analysts respond by thoroughly articulating their definitions of codes and linking those codes soundly to the underlying data, thereby preserving some of the richness that might be absent from a mere list of codes, whilst satisfying the need for repeatable procedure held by experimentally oriented researchers.

As defined by Leshan , [39] this is a method of qualitative data analysis where qualitative datasets are analyzed without coding.

A common method here is recursive abstraction, where datasets are summarized; those summaries are therefore furthered into summary and so on. The end result is a more compact summary that would have been difficult to accurately discern without the preceding steps of distillation.

A frequent criticism of recursive abstraction is that the final conclusions are several times removed from the underlying data. While it is true that poor initial summaries will certainly yield an inaccurate final report, qualitative analysts can respond to this criticism.

They do so, like those using coding method, by documenting the reasoning behind each summary step, citing examples from the data where statements were included and where statements were excluded from the intermediate summary. Some data analysis techniques, often referred to as the tedious, hard work of research studies similar to field notes, rely on using computers to scan and reduce large sets of qualitative data.

It's easier with these tools to generalize the results, as well as to study broadly. These research methods produce hard numbers that can be turned into statistics. Financial analysts often use quantitative research to gather information about the performance of stocks or bonds.

Market researchers conduct surveys to learn about the demographics of their customers, including age, gender, education and socioeconomic status. Census is an example of large-scale quantitative data gathering; census-takers survey households and the data are crunched to determine the federal budget.

Because quantitative research methods produce primarily numerical descriptions, they don't yield rich details about behavior, attitudes or emotions. Often, because the research is carried out in sterile or artificial environments such as labs, the results don't accurately reflect real-world situations. The data for a quantitative study are usually gathered in a fairly rigid way and therefore don't inspire discovery.

Structural bias can creep in when questions used in research are standard or tend to reflect the experiences or viewpoints of researchers instead of those of participants. When people need to be observed in their daily routines, for example, quantitative research is not the best way to capture those data.

Quantitative approaches are best when the need is to compare data systematically, such as a comparison between groups or countries. Quantitative research also lends itself to looking at the general features of a population. Before undertaking a study, researchers need to consider their specific goals. If they're primarily interested in generalizing the findings to the larger population, for example, quantitative methods are best.


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Quantitative vs. Qualitative Business Research. There are two major types of data you can collect in market research. Both can be valuable for different purposes. Quantitative research is all about numbers. It uses mathematical analysis and data to shed light on .

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quantitative research. The use of sampling techniques (such as consumer surveys) whose findings may be expressed numerically, and are amenable to mathematical manipulation enabling the researcher to estimate future events or quantities.

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However, such data can highlight potential issues which can be explored in quantitative research. Focus groups and interviews are common methods used to collect qualitative data. This kind of data is often revealing and useful, but it is costly and time-consuming to collect, particularly for a start-up or small business. Quantitative market research tends to be more structured than qualitative research methods due to its statistical nature. Small businesses that are clear on what is quantitative research will obtain an accurate snapshot of their target market by selecting a sizeable sample of respondents and giving them a list of mostly closed questions to answer.

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Jun 26,  · 2 Examples of Quantitative Reasoning for a Business 3 Quantitive Methods in Business Management & Finance 4 Roles Played by the Qualitative & Quantitative Approaches to Managerial Decision Making. Quantitative analysis (QA) is a technique that seeks to understand behavior by using mathematical and statistical modeling, measurement, and research. Quantitative analysts aim to represent a.