2026-06-28T05:00:26Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/73312023-03-21T08:54:00Zcom_10259.4_2543com_10259_5086com_10259_2604com_10259_5841com_10259_4219col_10259.4_2544col_10259_5842col_10259_4220

00925njm 22002777a 4500 dc Sáiz Manzanares, María Consuelo author Alonso Martínez, Laura author Marticorena Sánchez, Raúl author 2022-07 In recent years, research interest in human and non-human behavioral analysis has increased significantly. One key element in the resulting studies is the use of software that facilitates comparative analysis of behavioral patterns, such as using T-Pattern and T-String analysis -TPA- with THEME. Furthermore, all these studies use mixed methods research. Results from these studies have indicated a certain amount of similarity between the biological, temporal, and spatial patterns of human social interactions and the interactions between the contents of their constituent cells. TPA has become an important, widely-used technique in applied behavioral science research. The objectives of the current review were: (1) To identify the results of research over the last 4 years related to the concepts of T-Pattern, TPA, and THEME, since it is in this period in which more publications on these topics have been detected (2) To examine the key concepts and areas in the selected articles with respect to those concepts, applying data and text mining techniques. The results indicate that, over the last 4 years, 20% of the studies were laboratory focused with non-humans, 18% were in sports environments, 9% were in psychological therapy environments and 9% were in natural human contexts. There were also indications that TPA is beginning to be used in workplace environments, which is a very promising setting for future research in this area. http://hdl.handle.net/10259/7331 10.3389/fpsyg.2022.943907 1664-1078 Behavioral structure Similarity Systematic review THEME T-Pattern T-String T-System A Systematic Review of the Use of T-Pattern and T-String Analysis (TPA) With Theme: An Analysis Using Mixed Methods and Data Mining Techniques