Exploring new areas
The fundamental assumption in questionnaire-based scientific research is to make conclusions based on the answers given by the respondents.
Working in new, not yet explored, areas of science can provide the opportunity to discover completely novel phenomena or develop innovative approaches to problems. The challenges of solving complex issues and constantly pushing the boundaries of knowledge are among the most important drivers for people in the scientific world.
Vivelio allows more than just the end-response layer to be included in the final analysis. The tool opens up the possibility to conduct analysis related to the decision-making process and the way the questionnaire was completed. At the same time, by presenting indexed values for the responses, access to new layers of information is structured, reliable and functional.
Reaching the behavioral layer in questionnaire research is of particular importance in fields where the context of behavior is of particular relevance. Psychological research, psychiatry, pedagogy, sociology, political science and health sciences are just a few examples of fields where in-depth analyses of responses allow new, previously unknown information to be obtained.
Through in-depth psychological and sociological research, factors influencing people’s behaviour and decision-making can be identified. This will make it possible to create more effective strategies and tools that can help solve social problems.
Also in psychiatry, the knowledge that can be obtained through questionnaire studies will be expanded to include additional aspects such as decision changes, hesitation, intentional responses. There is great value in being able to verify whether the way in which answers were given was deliberate and certain, or whether they were random, chaotic, randomly given answers. This becomes particularly important when the information forms the basis of diagnostic decisions or treatment planning for patients.
Vivelio Science’s solution makes it possible to reach completely new previously unknown layers of information in research. This is an additional layer of knowledge that does not require increased research effort. It also enables reliable and precise inference based on artificial intelligence-based modelling.
Although surveys are widely used in research, conducting them requires facing certain challenges and difficulties. One of the most common limitations is to analyse only the final ones. That is, the entire decision-making process preceding the selection of the final answer is not taken into account. Consequently, important information such as hesitation, change of mind, firmness or reliability of the respondent’s answer is ignored.
The relevance of such data cannot be overestimated. During a survey, some respondents may give firm answers, while others are pondering, may change or consider their decision. This may indicate that the respondent is unsure of their opinions or that their opinions have changed. This information can be valuable in understanding the complexity of the decision-making process, which can completely change the way in which the answers received should be interpreted. Unfortunately, it is lost in the traditional approach to survey research.
Vivelio Science is a groundbreaking mechanism for in-depth analysis of the answers given, based on Vivelio Behavioral Indexes. It allows us to determine whether the answers given are reliable, whether a given answer was given in a decisive manner (without hesitation) and whether the decision-making process itself was focused or chaotic. It also uncovers layers of intentional responses, i.e. responses that the respondent considered but did not ultimately give. Furthermore, it identifies factors that have a significant influence on the respondent’s behaviour. All of this together opens up the possibility of exploring entirely new areas of research in the fields of psychology, psychiatry, sociology, political science or medical science.
Avoidance of response bias
One of the challenges in survey research is the phenomenon of response bias, understood as the difference between the answer obtained in the survey and the answer the respondent considered. Many factors can contribute to such a difference, such as the tendency of participants to answer in a way that is consistent with the surveyor’s expectations or that relates to certain stereotypes or biases. The result of this phenomenon may be a reduction in the quality of the study – while information will be obtained, its usefulness in formulating conclusions will be poor.
The respondent’s reflections on the choice of answers and his/her decision-making process are ignored in standard survey tools. At the same time, only the final answers are considered for analysis. The weighting of answers given with conviction and decisiveness is the same as those given with hesitation or changes of mind.
Thanks to the in-depth behavioural analysis, Vivelio Science makes it possible to arrive at an objective layer of responses, undisturbed by external factors, e.g. the influence of the researcher’s opinion, the desire to answer in accordance with the researcher’s expectation or the social norm. This makes it possible to reconstruct the course of the response decision-making process, including possible fluctuations, changes of opinion. Taking into account, among other things, the response weights depending on the Behavioral Index values obtained, the system also carries out modelling based on artificial intelligence.
Solution for incomplete data
Conducting research is a process that requires a lot of time, effort and resources and faces a variety of challenges. A special case in point is survey research, where obtaining incomplete feedback from respondents is a common problem.
The result of this situation is often the need to make inferences based on heterogeneous and incomplete survey results. Relying on such data is difficult and also poses the risk of inaccurate conclusions. This situation can occur, for example, when respondents do not provide all the answers in a survey or do not turn up for a scheduled survey. Dropout from research projects is a common phenomenon and affects the process of drawing conclusions from the survey results obtained. At high levels of dropout, it is difficult to draw statistically significant conclusions about the survey population.
Heterogeneous data structure can also be a challenge in conducting analyses. If a study requires analysis of data of different types, e.g. numerical, symbolic or logical, their combined interpretation may require conversion, e.g. to numerical data. Such conversions can sometimes distort reality by introducing similarities between dissimilar data.
Vivelio Science, thanks to its implemented inference mechanisms of Graph AI, enables reliable conclusions based on incomplete data, without the need for imputation or estimation methods. The tool also allows for a more streamlined analysis of the results of the study by being able to conduct an overall analysis of heterogeneous data, without having to perform separate analyses for individual data categories.
Collaborative research projects have a number of benefits, but the organisation of collaboration in a research project involving researchers from different research centres remains a difficult issue.
Skutec efficient management of surveys is crucial to the success of a project. Conducting a survey involves various stages, from questionnaire design to distribution to analysis and reporting.
One of the key challenges is the efficient allocation of the time of those involved in survey projects. The more efficient the time-consuming stages associated with the data collection or reporting phase, the more time and energy is left for the researcher to prepare the interpretation of the results obtained in the survey.
It is also important to ensure effective communication and cooperation between those involved in the project: members of the research team, research participants, and the project manager. This requires effective communication and coordination throughout the project, especially difficult when a multicentre study is being conducted and data are coming in on an ongoing basis.
The benefits of using Vivelio Science in research projects include effective collaboration within a research group within the same organisation and between organisations. By increasing the number of results produced within a single project, there is a better allocation of the researcher’s time. The tool enables efficient data collection through the use of multiple survey distribution streams, both anonymous and personalised. It also offers very efficient and intuitive management of project results within a research group.
Nowadays, with data and information playing an increasingly important role in many areas of life, predictive analytics is rising to become one of the most necessary research methods. It is even indispensable when it comes to making strategic decisions or long-term planning or preparing opinions. It has scientific applications. When used in psychometrics, it allows for more precise survey results, which is important for the process of psychological diagnosis and therapy.
Questionnaire surveys are a basic research tool in the field of psychometric research. The results obtained from questionnaire surveys can be applied in predictive analysis by using survey responses to predict future behaviour or events.
To address this challenge, it is necessary to use advanced statistical methods such as multiple regression analysis or time series analysis to account for various factors that may affect the accuracy of the predictions. In addition, follow-up surveys may be necessary to confirm the accuracy of forecasts and adjust predictive models accordingly.
The methodology of conducting surveys excludes many layers of information from modelling, which are valuable carriers of information.
Vivelio Science, as an advanced questionnaire survey solution based on artificial intelligence, allows for predictive analysis, ready for use in psychology, psychiatry, sociology, political science and medical research. By utilising previously unknown layers of information in questionnaire research, it opens up a new dimension of psychometric research, and provides the possibility of creating customised Behavioural Indexes. This opens up the field of research in areas hitherto not possible, such as assessing the impact of selected factors on how visitors are given, or gaining more information from research you are already conducting.