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Introduction
Statistics are integral to how people generate scientific breakthroughs, data-driven judgments, and forecasts. Psychologists utilize statistical data to aid research methodology and provide more accurate readings to determine whether something is statistically significant. Capdevila et al. (2015a) insinuated that the statistical issues were influenced by variables intrinsic to the discipline and the greater frameworks in which the field progressed and were one indication of the growth.
The benefits of statistical studies include enabling researchers to identify trends, make assertions, and disseminate their findings using numerical information to analyze data. Additionally, understanding numerical analysis allows investigators to utilize the appropriate methods for data collection, conduct the appropriate analysis, and communicate the results effectively. The main purpose of this article is to answer whether it is worth using exclusively quantitative methods in research.
Relevance of Statistical Studies
The mission of quantitative research methods is to concretize data in a numerical version, that is, to measure a phenomenon or process. Using this category of tools, analyze the decrease/increase in indicators, compliance with current standards or deviation from them, and the nature of deviations. Using numerical information to analyze data allows scientists to recognize trends, make statements, and disseminate results. The effectiveness and exclusivity of this approach in research will consider in specific examples later.
Example 1. Social psychology
Numerical analysis in social psychology has proved important for understanding how a person perceives suffering and emotional empathy. Capdevila et al. (2015a) investigated social factors that influence how people assess the degree of pain experienced by others. In one of their studies, 89 Italian participants were asked to assess the intensity of personal and cognitive suffering experienced by men from one of three social categories. The team consisted of Marco Rossi (a fellow Italian), Edison Mendes (an Ecuadorian from the outgroup), and Bai Guo Ye (a Chinese from the outgroup) (Capdevila et al., 2015a). The results were coded on a scale from 0 (no pain) to 10 (severe pain). Physical suffering included being punched in the face or deprived of water, but social suffering included embarrassment in front of colleagues or abandonment by the bride. These results played a major role in the conclusions regarding humans individual and emotional pain levels.
Estimates of social distress, but not forecasts of physical discomfort, vary depending on the victims group affiliation. Thus, these statistics allow scientists to understand the basics of cognitive compassion among the population. Capdevila et al. (2015a) indicate a predisposition to the humiliation of people from different communities. People can sympathize with the bodily suffering of others just as they can sympathize with the physiological torture of pets. People tend to vividly feel aspects of social pain present in people, such as sadness at the end of a marriage, because they are members of the same team. The study conducted by Capdevila et al. (2015a) also includes the most important elements of social psychology. Perhaps the most fundamental significance of the study lies in its intention to explore how broader environmental mechanisms affect peoples everyday thoughts, feelings, and behavior.
In this situation, an individuals ability to identify with others may be affected by their involvement in broader networks of interactive relationships. In addition, Capdevila et al. (2015a) suggest that this approach has significant implications for solving various social and political problems. This example demonstrates the scale of problems that quantitative research methods can solve and benefit an individual community and the state. Such research is necessary, and it is difficult to imagine what can serve as an alternative for a more objective reflection of reality.
The collection of information through quantitative research methods is considered the clearest, most structured and most rigid. The results obtained during the implementation of algorithms are more accurate, reliable, and scientifically based. In addition, knowledge of quantitative analysis allows analysts to use appropriate sampling procedures, make an accurate assessment and effectively present the results.
Example 2. Multitasking
Numerous studies show that trying to perform two tasks at the same time, as a rule, reduces efficiency in one or both activities. Nevertheless, there is statistical evidence that with prolonged effort, people can sometimes learn to combine several actions quite well, performing them almost as well as if they were performed separately. Capdevila et al. (2015b) tested experienced typists and found that the shading of the statement transmitted through headphones did not significantly affect their ability to decipher visually presented information. In their experiment, Capdevila et al. (2015b) instructed two respondents, Diana and John, to understand short stories by writing down arbitrary keywords that spoke to them. By the end of the trial, after 17 weeks and 85 sessions, they could successfully combine the two tasks (Capdevila et al., 2015b). The data proved worth studying because the results provided evidence of multitasking among individuals.
Memorization of dictated phrases was rather weak at first, but by the end of the test, Diana and John could classify the terms according to their meaning with normal training and almost normal understanding. Capdevila et al. (2015b) claimed that their subjects successfully divided their concentration between two projects. The study conducted by Capdevila et al. (2015b) is an outstanding example of how practice can increase efficiency while performing multiple tasks simultaneously. In contrast to their earlier study, recent results obtained by Capdevila et al. ((2015 b) focused on simulated problems with better control of the finite accuracy of input data and reactions. The researchers investigated a concept known as the psychological refractory period (PRP), during which the respondent performs two tasks that require rapid feedback from personal sensory input.
People learn to do two activities simultaneously to such an extent that, at least at first glance, their performance seems comparable to when each job is performed independently. Tasks are waiting for their turn to gain access to central processing capabilities. Despite significant practice, there is no indication that PRP can be eradicated. This example again illustrates the importance and non-exclusivity of quantitative research methods because it is not only based on data that it is possible to deepen the study of the work of the human psyche.
Conclusion
In conclusion, it should note that statistics are a fundamental component of how people make scientific achievements, data-based judgments, and forecasts. Psychologists use factual analysis to help with research methodology and provide more accurate readings when assessing whether something is statistically significant. Using numerical information to analyze data allows researchers and academia to identify trends, make statements, and publish their findings, among other benefits.
The numerical analysis explores the social elements that influence how people assess the severity of pain experienced by others. In addition, quantitative understanding research allows researchers to implement appropriate sampling processes and make accurate estimates. The essay demonstrated that trying to do two tasks simultaneously often reduces the effectiveness of one or both tasks. People can learn to do multiple tasks simultaneously almost as well as if they were done individually with enough time and effort. Thus, such data turned out to be key to understanding knowledge about multitasking. To ensure that additional research and understanding are internalized and based on the findings of such studies, such data should merit analysis. As a certain part of science, statistics has an important applied value. It makes it possible to process the results of experimental studies and formulate sound theoretical and applied propositions using statistical criteria. It puts quantitative study methods a step above qualitative ones, so it is worth considering leaving only one way to analyze new information.
Reference List
Capdevila, R., Dixon, J., and Briggs, G. (2015a) Investigating psychology 2 from social to cognitive. Plymouth: The Open University.
Capdevila, R., Dixon, J., and Briggs, G. (2015b) Investigating psychology 2 from cognitive to biological. Plymouth: The Open University.
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