Quantitative Research Methods Quotes

In business, qualitative measurements and quantitative measurements are equally important.

Finally, another large-scale study [of false rape allegations] was conducted in Australia, with the 850 rapes reported to the Victoria police between 2000 and 2003 (Heenan & Murray, 2006). Using both quantitative and qualitative methods, the researchers examined 812 cases with sufficient information to make an appropriate determination, and found that only 2.1% of these were classified as false reports. All of these complainants were then charged or threatened with charges for filing a false police report." Lonsway, K. A., Archambault, J., & Lisak, D. (2009). False reports: Moving beyond the issue to successfully investigate and prosecute non-stranger sexual assault. The Voice, 3(1), 1-11.

[…] a student in our class asked disdainfully why quantitative methodologists do not openly criticize qualitative methods. He scoffed, 'They don't even mention it. But in courses in qualitative methods, quantitative methods always come up.' […] I pointed out that the lack of critical remarks and the absence of any mention of qualitative research in 'methods' courses indicate the hegemony of the quantitative approach. Were not his statistics professors making a strong statement about the place of qualitative methods by omitting them entirely? Qualitative researchers, then, have to legitimate their perspective to students in order to break the methodological silence coming from the other side.

I am surprised at how often researchers fail to draft a title early in the development of their projects. In my opinion, the working or draft title becomes a major road sign in research—a tangible idea that the researcher can keep refocusing on and changing as the project goes on (see Glesne & Peshkin, 1992).

Good, sound research projects begin with straightforward, uncomplicated thoughts that are easy to read and understand.

Whether it’s anthropology or sociology or geography, social scientists are often asked – no, required – early in their careers, to choose between humanistic and scientific approaches to the subject matter of their discipline and between collecting and analyzing qualitative or quantitative data. Even worse, they are taught to equate science with quantitative data and quantitative analysis and humanism with qualitative data and qualitative analysis. This denies the grand tradition of qualitative approaches in all of science, from astronomy to zoology. When Galileo first trained his then-brand-new telescope on the moon, he noticed what he called lighter and darker areas. The large dark spots had, Galileo said, been seen from time immemorial and so he said, “These I shall call the ‘large’ or ‘ancient’ spots.” He also wrote that the moon was “not smooth, uniform, and precisely spherical” as commonly believed, but “uneven, rough, and full of cavities and prominences,” much like the Earth. No more qualitative description was ever penned

There is a perhaps understandable reluctance to come to grips scientifically with the problem of race differences in intelligence—to come to grips with it, that is to say, in the same way the scientists would approach the investigation of any other phenomenon. This reluctance is manifested in a variety of ‘symptoms’ found in most writings and discussions of the psychology of race differences. These symptoms include a tendency to remain on the remotest fringes of the subject, to sidestep central questions, and to blur the issues and tolerate a degree of vagueness in definitions, concepts and inferences that would be unseemly in any other realm of scientific discourse. Many writers express an unwarranted degree of skepticism about reasonably well-established quantitative methods and measurements. They deny or belittle facts already generally accepted—accepted, that is, when brought to bear on inferences outside the realm of race differences—and they demand practically impossible criteria of certainty before even seriously proposing or investigating genetic hypotheses, as contrasted with with extremely uncritical attitudes towards purely environmental hypotheses. There is often a failure to distinguish clearly between scientifically answerable aspects of the question and the moral, political and social policy issues; there is tendency to beat dead horses and set up straw men on what is represented, or misrepresented I should say, as the genetic side of the argument. We see appeals to the notion that the topic is either too unimportant to be worthy of scientific curiosity, or is too complex, or too difficult, or that it will be forever impossible for any kind of research to be feasible, or that answers to key questions are fundamentally ‘unknowable’ in any scientifically accepted sense. Finally, we often see complete denial of intelligence and race as realities, or as quantifiable attributes, or as variables capable of being related to one another. In short, there is an altogether ostrich-like dismissal of the subject.

And while the details of how it is implemented vary somewhat from company to company, the core elements of the method are: the creation of a cross-functional team, or a set of teams that break down the traditional silos of marketing and product development and combine talents; the use of qualitative research and quantitative data analysis to gain deep insights into user behavior and preferences; and the rapid generation and testing of ideas, and the use of rigorous metrics to evaluate—and then act on—those results.

One type of nonexperimental quantitative research is causal-comparative research in which the investigator compares two or more groups in terms of a cause (or independent variable) that has already happened.

book Research Design: Qualitative and Quantitative Approaches (J. W. Cresswell, 1994), or his work Designing and Conducting Mixed Methods Research co-authored with Ann Carroll Klassen, Vicki Plano Clark, and Katherine Clegg Smith (2007), or his short paper focusing on health research specifically (J. Cresswell, Klassen, Plano Clark, & Clegg Smith, 2011).

Figure 2: Empirical and Perception-based Qualitative and Quantitative Research

Mixed methods research is a research design with philosophical assumptions as well as methods of inquiry. As a methodology, it involves philosophical assumptions that guide the direction of the collection and analysis of data and the mixture of qualitative and quantitative approaches in many phases of the research process (J. W. Cresswell & Plano Clark, 2007, p. 5).

Quantitative researchers focus more on scale and causation, and like to have replicability and precise measurement. This differs significantly from qualitative researchers, who concern themselves with describing richness of context, the nature of change, and having empathy for participants.

unconsciously held objectivist ontology. If they stop focusing solely on scale and causation, and focus instead on coherence and customers, they begin to feel better equipped to make decisions about their products or services. However, most people are simply unaware that there is a qualitative, inductive logic that is just as legitimate as a quantitative, deductive method.

to focus on scale and causation. Designers develop an agile mind to easily flip between divergence and convergence. Likewise, researchers must develop this same agility to flip between qualitative and quantitative. Innovation expert and management theorist Roger Martin called this the “opposable mind,” (Martin, 2007) and noted it is abductive logic (not deductive or inductive) that characterizes

explanatory concepts that can later be tested quantitatively

The typical practical reasons why you might choose to mix methods include: 1. Time does not permit in-depth qualitative research 2. Time does not permit in-depth quantitative research 3. Negotiating access to participants is challenging 4. Finding a large data set is challenging

quantitative research will measure pervasiveness of things we already know, and qualitative research will uncover things we don’t know much about.

This book is primarily concerned with what I will argue is the intellectual core of any understanding of the digital humanities: the use of computational methods in humanities research and scholarship; more specifically, the use of sophisticated quantitative methods for text and data mining.

Within the practice of contemporary geography, many traditional components such as maps are still important, though satellite remote sensing, sometimes known as Earth Observation (EO), Geographical Information Systems (GIS), and other powerful quantitative methods have been added to traditional fieldwork and the comparative method. Established core concepts of space and place have been transformed, in human geography at least, by modern social and cultural theory. The need to understand the biophysical and human environments of people and their interactions is becoming increasingly urgent as issues of sustainability and the protection and preservation of planet Earth become imperative. As integration within geography as a whole has weakened, both physical and human geography have become more specialized and have adopted different approaches to many of their research problems. Most importantly, physical geography is asserting its scientific credentials, while human geography emphasizes critical theory, values, and ethics.

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The ‘quantitative revolution’ in geography required the discipline to adopt an explicitly scientific approach, including numerical and statistical methods, and mathematical modelling, so ‘numeracy’ became another necessary skill. Its immediate impact was greatest on human geography as physical geographers were already using these methods. A new lexicon encompassing the language of statistics and its array of techniques entered geography as a whole. Terms such as random sampling, correlation, regression, tests of statistical significance, probability, multivariate analysis, and simulation became part both of research and undergraduate teaching. Correlation and regression are procedures to measure the strength and form, respectively, of the relationships between two or more sets of variables. Significance tests measure the confidence that can be placed in those relationships. Multivariate methods enable the analysis of many variables or factors simultaneously – an appropriate approach for many complex geographical data sets. Simulation is often linked to probability and is a set of techniques capable of extrapolating or projecting future trends.

The person must utilize adequate quantitative and/or qualitative reasoning in order to solve both ill and well defined problems for his or her assigned tasks.

the core elements of the method are: the creation of a cross-functional team, or a set of teams that break down the traditional silos of marketing and product development and combine talents; the use of qualitative research and quantitative data analysis to gain deep insights into user behavior and preferences; and the rapid generation and testing of ideas, and the use of rigorous metrics to evaluate—and then act on—those results.

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