User:GCooper316/sandbox/Approaches to Knowledge/2020-21/Seminar group 10/Evidence

Introduction
The emergence of the social sciences at the beginning of the 19th century was followed by many criticisms, especially on the fact that they were given the name of science. Indeed, according to some, the disciplines of the social sciences do not respect the criteria of the scientific method established by Karl Popper which must be unbiased, falsifiable and repeatable. Thus, the fact that this field is considered unscientific implies that the information collected was not accurate. However, the question stands whether the same methods could be used in the natural and social sciences, knowing that the fields of the research differ -it is easier to do experiments on a living cell than on human behaviour-, which resulted in a broader definition of science. . Methods implemented in these numerous disciplines to collect evidence will be explored.

Nature of economics
Economics is a social science and academic discipline that studies the production and consumption of goods and services between different parties. The subject is primarily concerned with the problem of scarcity, in that there are unlimited wants but limited resources, and how to allocate resources under this condition. Economic theories and models are central to the study of economics, allowing economists to break down complex real-life situations into simplified versions to conduct analysis and make predictions.

How evidence is collected and quantified for economics
At a fundamental level, economists break down different issues using economic models. These models are based on certain assumptions, and constructed from various forms of evidence which combine with hypotheses. This results in the formation of empirical models and or theories which are then applied to other situations. The successful application of these theories can then, in turn, allow analysis to be conducted and further economic evidence to be drawn. From the evidence, conclusions can then be made. The remainder of this section is as follows: firstly we analyse how economic evidence is collected to construct models, then we examine how models are applied to create new forms of economic evidence.

For an example of how economic theory is formed, one can look to the book “Growing Public: Social Spending and Economic Growth since the Eighteenth Century” which analyzes data regarding the growth of states for countries that enjoyed massive economic growth following the industrial revolution. The book takes into account several factors behind growth while acknowledging the fact that countries will have inherent differences in their developments. Through this analysis, the book ultimately proposes the theory that instead of social spending acting as an inhibitor to economic growth, as is often portrayed in the media and by politicians, it instead is instrumental in achieving economic growth. From this example, we can see how economic theories are constructed from economic evidence.

For an example of how economic theory is then applied to further generate economic evidence, one can then look to the concept of full employment. Full employment refers to when there is an efficient use of all resources in labour. Economist John Keynes asserted a theory that full employment was something to be achieved through government intervention, justifying different policies as a means to ensure labour is allocated efficiently. His theory on full employment was widely applied, with an example being Australia's policies in the 25 years after World War II. A white paper on full employment based upon Keynes’ theories was drafted and adopted in the Australian government. This adoption of Keynesian theories in policymaking is credited for a ‘golden age’ for the Australian economy at the time. The economy experienced high growth rates, with unemployment at a low as well. This example was then used to generate further empirical evidence for economists, with a model of the Australian economy being constructed afterwards revealing the in-depth effects of and factors behind the government intervention for Australia. Notably, it was discovered shifts in indirect tax were more effective than that of direct taxes, Kriesler P, Nevile J. Keynesianism in Australia. History of Economics Review. 2018;69(1):44-61. which holds implications for the controversial issue of tax policy today. Overall, from Australia's example, one can see how economic models are used to generate further economic evidence and models.

Economic models based on empirical evidence as not entirely objective
As mentioned previously, economic models and theories require the breaking down of real-life situations into simplified elements, meaning that assumptions have to be made. An example is an assumption that people will always act rationally, using rational calculations to make choices. Another common assumption is that all choices have a cost: the result of the scarcity condition. This means that under economics, whenever people make a choice, they must give up some of their resources. For example, if someone gives up a job to study for a degree, they are giving up the potential income earned otherwise.

While these assumptions are key to creating theories, some assumptions may lead to the omission of important factors which damage the quality of economic analysis conducted on the premise of the theory in question. Take the example of the 2008 financial crisis, prior to which different iterations of the Dynamic Stochastic General Equilibrium model were used prominently in macroeconomics. However, leading up to the 2008 crisis, many models operated on the assumption that financial frictions did not exist, or that banks and financial organisations did not play important economic roles. This, in turn, contributed to the financial crisis as a devastating economic situation that was ultimately unexpected for many economists. From this example, it is clear that before relying too much on a model, economists must take into account its limitations, as well as the information that a simplified model has left out even if the model is based upon empirical evidence.

Introduction to Sociology
Sociology — being a branch of social sciences that deals with human societies, their institutions, and their interactions — is often considered to be in the grey area between Humanities and Sciences. This is reflected in the ways it acquires knowledge and evidence for its theories. Since the disciplinary divide corresponds to qualitative versus quantitative evidence, sociologists often face difficulties trying to fit the standards of the science community while carrying out complex research tasks with qualitative analyses. Social science has from its beginnings experienced confrontations between qualitative and quantitative paradigm, and which one is favoured has mostly depended on the funding agency of the research at hand. In recent years, sociology has faced increasing pressure to produce 'scientific-based' and 'evidence-based research' as quantitative evidence is preferred by government agencies. However, I believe neither qualitative nor quantitative evidence can be fully comprehensive when discussing such complex issues as sociologists do.

Advantages of Quantitative Evidence in Sociological Research
Sociologists carrying out quantitative research use specific methods of data collection and hypothesis testing, for instance: surveys, secondary data analysis, and statistical analysis. This way, the evidence found can be tested, checked, and perhaps even repeated. For example, voting behaviour is an area of high interest in political sociology. Quantitative evidence on voter participation, the correlation between income, age and political affiliation, and so on allows sociologists to present findings in a straightforward, less-open-to-error manner as well as to establish patterns and perhaps make predictions.

Advantages of Qualitative Evidence in Sociological Research
Qualitative data on the other hand allows for more insights, unconstructed interviews, it is more respondent-led, therefore avoiding the imposition problem and is useful for more sensitive topics. Sticking with the example of voter behaviour, insight into opinion of leaders' debates, their experience with voter registration, as well as their political socialisation by their families can give a more precise and well-rounded understanding of the voter's preferences.

Introduction to psychology
Psychology is a relatively new discipline. It is interested in the study of mental processes and behaviour. Psychology had to face many debates. First of all, the one questioning it as a science because as we mentioned in the introduction, it does not apply the traditional scientific method, but also through its lack of rigour and its inability to predict or reproduce events. It has divided many psychologies who took different approaches, behaviourists like Skinner, who participated in the acceptance of psychology as a science, and humanists like Maslow who thinks that psychology cannot be a science because the behaviours of each one cannot be standardized. However, in modern psychology, a scientific approach has opted. Psychologists have accentuated their research towards a more quantitative procedure. Thus, the first Experimental Psychology Laboratory opened in 1879. But, over time, this approach was considered too reductive to explain human behaviour and it is with the contribution of the qualitative method that research has progressed.

Advantages of Quantitative Evidence in Psychology Research
To obtain quantitative evidence, researchers start from a hypothesis or theory and then test it on many individuals through laboratory experiments or observations. The data is then aggregated and analyzed using statistics. This will make it possible to study the relationships and sort the information obtained to be able to generate a conclusion that can be applied to a larger number of individuals and therefore support or not the theory put into practice. This research method is often praised because it is objective and involves a mathematical analysis generating strong evidence.

Advantages of Qualitative Evidence in Psychology Research
Qualitative research in psychology is more interested in the explanation of behaviours in a certain context. To achieve this, researchers will go directly to seek their information in the field. Data collection is done through interviews, focus groups, case studies, etc. Qualitative research takes into account the variation in human behaviour and thus makes it possible to collect new information on the links, causes, and operations of a certain phenomenon. By not wanting to standardize a type of conduct and allowing the diversity of humankind to appear in their data, qualitative research enables us to have a better understanding of reality.

Combining Qualitative and Quantitative analysis
Therefore, critiques quantitative research faced is that it only allows the testing of preexisting hypotheses and simplifies human behaviour by drawing generalized conclusions, who are sometimes partial. These "flaws" can be avoided if we combine quantitative approach with a qualitative viewpoint. One way of doing it is called the mixed-methods research. This method aims to use qualitative research to find new postulate and quantitative research to experiment with them. Another method that has been used is called triangulation: we study a subject or a context using a quantitative and qualitative approach, and at the end, contrast the observation in both to see the similarity and convergence to see if we can come into a coherent conclusion. These methods can help to gain stronger evidence.

Between Two Sciences: Evidence in Neuropsychology
Neuropsychology is a sub-discipline of psychology, it creates a bond between the social and the natural sciences by studying the relation between the way our brain works and the way we behave.

The methodology of Neuropsychological Evaluation
Neuropsychological assessment is a vital procedure that antecedes neurorehabilitation. It includes both qualitative and quantitative analysis when examining patients’ cognitive deficit; a combination of these methods can contribute to further analytical development of medical remedy.

Qualitative Evidence in Neuropsychology
The qualitative evaluation includes analysis of remarkable neuropsychological syndromes, which are matched up with the intricate regions of the brain and its deficit. By observing symptoms with its feasible origins, data from the qualitative evaluation can organize the overall structure of the analysis and data elucidation. An example of qualitative evaluation is the neuropsychological assessment conducted by A.R. Luria. This assessment included controlling tests, client interviews, language and speech, memory, and stimulating. A 3-point range (from 0 to 3) was used to assess the performance on each test in order to organize the pattern and data which can be used for further explanation.

Quantitative Evidence in Neuropsychology
The quantitative assessment further classifies and ranges the syndromes depending on the degree of the aliment and dynamics of its change. This type of evaluation is called psychometric analysis. It basically illustrates the repetitious patterns of cognitive deficits, thus determining the range and category of brain disorder based on outlined elements from qualitative research; it funnels down the understanding of specific neurological lesions. Referring back to A.R. Luria assessment, the symptoms observed from the qualitative research were then specifically analysed and data were ranged depending on the degree of intensity and clusters of syndromes.

Combining Qualitative and Quantitative Analysis
To sum up, a qualitative method evaluates the overall structure of the deficit while a quantitative approach determines its extension and prevalence. Factors picked from a qualitative evaluation are then related to the specific spot in the brain and their functioning. For example, in a quantitative approach, psychometric tests identify data of a particular cognitive dysfunction. If this is done without classifying the status of each factor, it impedes the identification of the overall structure of the deficit, which is the goal of qualitative analysis. Therefore, neuropsychological evaluation should necessarily consider both neuropsychological syndromal methods(Qualitative Evidence) and psychometric analysis(Quantitative Evidence). The same method is done with the neuropsychological assessment of A.R. Luria. Results show that the combinations of syndromes were in the same track as the evaluation process used both qualitative approach (factor analysis) and quantitative approach (its magnitude). The combination of these two approaches leads to further progress in terms of treatment and rehabilitation.

Introduction to cultural anthropology
Cultural anthropology is a stream of anthropology that deals with the comparative analysis of culture using methods such as ethnography, linguistics, and folklore. Within the discipline, an anthropologist uses a wide variety of research methods to collect evidence such as participant observation, ethnography, interviews, and even historiography.

Qualitative Methods in Anthropology
Participant observation, in particular, is one of the more prevalent qualitative methods in collecting evidence as it allows researchers to participate and observe in the activities of the people studied under a natural setting over a long period of time. Some advantages in collecting evidence through this method is that they provide a means for researchers to understand the nonverbal communication of feelings, the interactions within the participants studied, and how it is conveyed. However, the qualitative research method also has its fair share of drawbacks such as its interpretive nature. This lack of objectivity may consequently cause ethical and methodological implications to arise. As participant observation needs the anthropologist to stay within the community for a few months, the data collected may not be as accurate as the presence of the anthropologist themselves could have already caused a shift in the behaviour of the people studied. Moreover, the method’s lack of objectivity could be further demonstrated through the different backgrounds and existing preconceptions of the anthropologist himself which would produce different comparative analyses of the culture and people studied. While an anthropologist may be able to confront the issue of subjectivity through a reflection of their positionality, little to no other methods have been used to resolve this issue.

As a result, we see that this lack of objectivity within-participant observation and other qualitative methods may also expose the reasons for the lack of quantitative research present in cultural anthropology, its aversion to positivism and how it has fostered the flames of contentious discourse within the discipline as a whole.

Implications within the discipline
Scholars within the discipline have argued that the lack of quantitative methods to produce evidence was due to the inherently untestable nature of cultural anthropology itself and that concepts of empiricism and positivism could not be applied effectively due to the uncontrollable nature of its socio-political implications. Consequently, this means that the causal relationship between these concepts is essentially non-operational and cannot be quantified. Anthropologists such as Durkheim and Radcliffe-Brown who argued for a more positivist approach in the discipline were frequently picked on - even in death. For instance, Rivera remarks while other streams of anthropology such as physical anthropology employ an extensive amount of quantitative models and methods “for the purpose of reaching an evolutionary understanding of our species”, cultural anthropologists reject the notion of evolutionism on the grounds of ethical ethnocentrism. This argument has been one of the central topics of debates on whether or not the discipline of anthropology should be viewed as a science whose long-simmering tensions has boiled over through events such as the controversy and subsequent revision in the American Anthropological Association’s Long Range Plan after it omitted the word 'science' in its statement and how “tensions surfaced” in the association's annual meetings

Interdisciplinary Evidence in the COVID-19 Pandemic
On the 31st of December 2019, a cluster of pneumonia cases emerged in Wuhan City, Hubei Province of China. Soon after, it was deduced that a novel coronavirus, named COVID-19, was the cause of this phenomena. COVID-19 exceeded expectations in terms of how quickly it spread, and on the 11th of March 2020, the World Health Organisation declared COVID-19 as a pandemic. Since then, the world has been battling the brutal pandemic through the efforts of several disciplines. As a result of this interdisciplinary approach, various forms of evidence play a significant role in the pandemic.

How disciplines present evidence and their significance in the pandemic
With lots of information from the work of many disciplines contributing to the aid of the pandemic, here are a few major examples:

Clinical and laboratory medicine
Since the beginning, clinical and laboratory medicine practitioners have been at the forefront of the pandemic. As part of COVID-19 testing, clinicians directly collect specimen from a suspected patient and laboratorians process the specimen to determine if the patient is tested positive or not. There are two types of COVID-19 tests: PCR testing and antibody testing. Both tests produce qualitative results, whereby PCR testing utilises Real-time RT-PCR for the qualitative detection of genetic material from the virus within the specimen, and antibody tests can detect the presence of antibodies to the virus in the patient’s blood. Despite the ease and large volume in which evidence can be obtained at, issues such as false-positive or false-negative results in the RT-PCR tests can arise from contamination at various points of the process. Mishaps like these could affect pandemic procedures: causing errors in contract tracing programmes, fearmongering, or generating public overconfidence.

Epidemiology
Epidemiologists work to consistently present evidence to the public, as a form of communication on the progress of the pandemic. Epidemiologic evidence, such as the number of confirmed cases, number of recovered patients, death rate, etc. is readily accessible and available in various forms as well - be it on a regional, national, or international scale. Much of the aforementioned epidemiologic evidence is quantitative and acquired from clinics and labs. Further quantitative epidemiology work has also been done - mathematical epidemiological models have been implemented to predict the outcome of non-pharmaceutical interventions, influencing policy-making. Yet, models are usually based on assumptions, and sometimes fail to represent social implications properly. This produces flawed quantitative evidence, however, awareness of such weaknesses can lead improvements in the models.

Psychosociology
A large number of psychosociology research and efforts have been made towards the COVID-19 pandemic, with articles largely focused on mental health and how different aspects of the pandemic have affected it. According to an umbrella review of the literature regarding the effects of quarantine and isolation due to the pandemic on mental health, evidence for such research was obtained through various ways - cohort studies, cross-sectional studies, qualitative studies, mixed-method studies, etc. The diverse evidence in this discipline is crucial for understanding human behaviour towards the pandemic, and the short & long-term social effects of the pandemic. Even so, methods for retrieving evidence in this field is not entirely perfect, like in other disciplines. For example, in the same umbrella review mentioned earlier, the majority of the studies were carried out in countries with higher income, causing evidence to be under-representative of lower-income countries. Limitations arising from a lack of accessibility or pandemic-related restrictions can cause such flaws in the evidence.

Issues arising from an overabundance of evidence and information
With the COVID-19 pandemic happening at a time where social media is at its peak, evidence can be easily and rapidly broadcasted to the world. Websites dedicated to COVID-19 are updated daily, and health ministries employ social media channels to announce new numbers and data. Yet, as much as it seems to be a blessing, this immense connectivity can be a curse, leading to an information overload. It is not uncommon for people to feel overwhelmed due to the excessive amount of data online, adding to their unrest and anxiety towards the pandemic. Furthermore, information online can be easily manipulated, enabling the falsification of evidence, thus adding fuel to the fire. Overexaggerated headlines and statements regarding the number of cases and deaths have caused an “infodemic”. With data coming from multiple disciplines every day, the public needs to be critical with evidence they are presented with, prioritising quality over quantity.

Introduction To Engineering
Engineering can be defined as the study of using scientific principles to design and build machines, structures, and other things, including bridges, roads, vehicles, and buildings Evidence is commonly used in engineering to justify knowledge claims, gather feedback and motivate decision. It is a discipline where qualitative and quantitive data are combined perfectly to collect reliable data and to make a decision concerning production, creation and effectiveness of materials, to prevent incidents and disasters.

Advantages of Qualitative Data in Engeenering
Before considering the development of modification of a product, surveys, reviews should be held and predictions should be made. The most common types of reviews for engineering is design reviews, requirements review and performance review. Different questions concerning modification track, system development, cost, schedule and technical progress are asked during such reviews and surveys to collect social opinion, statistics and other data analytic measures. Such qualitative data is necessary for understanding needs and wants of customers and population in general; what is their percentage usage of technology, what type of technology/ engineer-made product is used most commonly, how efficient products are in real-life an etc.

Advantages Of Quantitative Data In Engeneering
Measurement, tests and mathematical models are an essential type of evidence for engineering. These tests help to identify outcomes of machinery failures, the effectiveness of technical models through changing of the variables, discrete set of indicators or any unpredictable causes. Quantitive approach to research if good for deductive approaches in which the inputs, the purest argument and the course of the narrowly defined re-search questions are justified by a theory or hypothesis. . The hypothesizes which arose from testing measurements and new research questions are then indicate how the final data will be collected (qualitatively by holding surveys, commercials or public opinions or quantitatively by further researches and scientific experiments)

Advantages Of Computational Gathering Of Evidence
These days autonomous computational networks are used commonly to receive and transmit information from other technical artefacts, sensors and apparatuses. Software applications replaced human data-gatherers because now they can filter and similarly process the acquired data. There are two different scenarios to have a concrete representation of this. In the first scenario, some data is gathered, filtered and processed by a chain of artefacts where at the end of the chain, evidentiary evidence is made by a human engineer who picks up the results and makes some judgment. In this case, engineer compares the data to some other set of evidence, analyze the variables, change them for a "perfect fit" and make an official conclusion such as sending an approval to the head office. In the second scenario, there is no presence of a human engineer at all, there is only a computer responsible for actions. The system compares the data to some other sample of facts, extracts some outlying data points, re-analyzes the data, and sends a statement to the head office that the evidence is x.