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Resumen En la investigación cualitativa, el análisis y la interpretación de los datos es una tarea de difícil manejo, incluso para los investigadores más experimentados. Si bien existen muchas técnicas disponibles para llevar a cabo el análisis de datos cualitativos, algunos autores relevantes del área proponen que es posible identificar un núcleo básico sin necesidad de hacer coincidir las distintas perspectivas del método cualitativo. Hacer foco en esta comunalidad permitirá hacer más comprensible la tarea de análisis para los investigadores noveles. El objetivo principal del presente trabajo es mostrar en qué consiste este núcleo básico de análisis, dando cuenta de los pasos necesarios para llevarlo a cabo. Además, se revisan técnicas concretas para la detección de temas, se presentan ejemplos haciendo uso del software Atlas.ti, y se muestran las formas posibles de presentación de los resultados.
Abstract Within the research process, the analysis of the data emerges as one of the most important steps. In qualitative research, the analysis of data is a difficult task for even the most experienced researchers and often brings up many doubts about the way to implement it. It is therefore necessary to have material that facilitates the analysis process. Even though there are numerous manuals that focus on the analysis of qualitative data, researchers often can be confused with the large number of names that this type of analysis receives (e.g. Thematic Analysis, Content Analysis) or with the various qualitative methods (e.g. Phenomenology, Grounded Theory) that are available. Each of these qualitative approaches presents a particular language to detail the research process, which makes it difficult to recognize common aspects shared by these methods. Recently, the American Psychological Association has emphasized the need to identify, within the various qualitative methods and procedures, shared standards for reporting this type of work. In agreement with the above, several qualitative researchers have pointed out that beyond the aforementioned diversity it is possible to identify a basic core with regard to qualitative analysis, without having to match the different perspectives of the qualitative method, such as Grounded Theory, Ethnography ore Phenomenology. Focusing on this communality will facilitate a simpler and clearer approach to the data analysis process. The analysis process mainly involves 1) data condensation, and 2) presentation of results. Following this line, the present manuscript aims to: (a) develop what the basic core of data analysis consists of, (b) show the necessary steps to carry out this analysis process, (c) review specific techniques for the detection of categories, (d) present examples using the Atlas.ti software, and (e) show the possible ways of presenting the results. Researchers have realized the importance of having methodological works that facilitate the analysis of qualitative data, and allow answering the question: What does qualitative analysis look like in practice?. The development of this type of work pretends on the one hand to facilitate the understanding of the process of qualitative data analysis and, on the other hand, serve to shape better and in a more standard way which was the data analysis procedure applied in the respective investigations. This material should be taken as a first step in the understanding of the process, and it should not be understood that the qualitative analysis is reduced only to what is developed in this article. For example, in the first level grouping step or first coding cycle, the researcher can make use of 25 different types or forms of coding (e.g., live coding). Even so, the development of works such as the present manuscript is intended to facilitate the understanding and reporting the process of qualitative data analysis. Beyond the name with which the researcher calls the analysis procedure carried out, it is relevant to report in his works the basic steps (i.e. Identification, First and Second Level of Categorization), and the specific techniques used to detect categories or topics (e.g. repetition or similarities). Likewise, it is advisable to follow the guidelines recently published by the APA for the publication of qualitative research. We hope that this material will be useful especially for new researchers who need an introductory text to carry out the qualitative data analysis.
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RESUMEN Introducción: Especialistas de la Facultad de Psicología de la Universidad de La Habana propusieron el cuestionario sobre Bienestar Humano Personal, Laboral y Social (BHPLS), que se aplicó a 135 trabajadores cubanos de tres grupos sociolaborales. Dada la variedad de respuestas, se impuso un análisis de contenido (AC) para la Pregunta 1 del cuestionario. Objetivo: Proponer e implementar un software que permita la categorización semiautomática en un AC para dicha pregunta. Material y Métodos: Se utilizó el índice de concordancia Kappa para evaluar el acuerdo entre expertos respecto al esquema de categorías. Se implementó un software en el lenguaje de programación Python para cumplir el objetivo, considerando las funcionalidades de softwares similares. Resultados: Se implementó, validó y registró un software "BHPLS data processing-UH®" que permite establecer las categorías, cargar los datos, categorizarlos semiautomáticamente y guardar el resultado, entre otras funcionalidades. La categorización manual con estudiantes de Psicología obtuvo un índice de concordancia Kappa negativo (bajo acuerdo entre expertos), mientras que usando el software propuesto, se alcanzó un Kappa global 0.7871 con p=0.00 (alta concordancia y alta significación estadística). Además, se propuso un algoritmo para la unificación de las categorizaciones de expertos y se ejecutó un Análisis de Correspondencias (ANACOR) sobre la combinación de categorizaciones obtenidas. Conclusiones: Dada la alta concordancia alcanzada, se recomienda el uso del software por su adaptabilidad, facilidad de uso y la "humanización" del AC. El ANACOR permitió observar similitudes entre los grupos sociolaborales. Las funcionalidades del software pueden aplicarse para el procesamiento de asociaciones libres en otros escenarios.
ABSTRACT Introduction: Experts of the Faculty of Psychology of the University of Havana proposed the Personal, Labor and Social Human Well-being questionnaire (BHPLS, in Spanish), that was applied to 135 Cuban workers of three social and occupational groups. Given the variety of responses, a content analysis (CA) was used for Question 1 of the mentioned questionnaire. Objective: To present and implement a software that allows a semi-automatic categorization in a CA used for this question. Material and Methods: The Kappa index test was used to evaluate experts´ agreement with respect to category schemes. We implemented a software with the Python programming language to achieve our objective, considering other similar software functionalities. Results: We implemented, validated and registered the software BHPLS data processing-UH® that allows to set up a categories system, load the collected data, categorize associations in a semi-automatic way, and save the results, among other functionalities. This software was validated by Psychology students and, when they performed the manual categorization, a negative Kappa agreement index (low categorization agreement between experts) was obtained whereas using the proposed software, a global Kappa index of 0.7871 with p=0.00 (high and statistically significant categorization agreement between experts) was obtained. Besides, we proposed a unified algorithm for expert's categorizations, and carried out a Correspondence Analysis (ANACOR) on the basis of the categorizations achieved. Conclusions: According to the high concordance attained, we recommend the software due to its adaptability, ease of use, and "humanization'' of the process. The CA allowed us to observe similarities in social and occupational groups. The software functionalities can be applied for processing free associations in other scenarios.
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Introducción: los contenidos de análisis de datos cualitativos de la residencia de Bioestadística están contemplados en su plan de estudio desde 1981. Sus temas y estrategias docentes han cambiado a lo largo el tiempo. Objetivo: explicar las variaciones ocurridas en su impartición en cuanto a contenidos y estrategias docentes empleadas a partir de la experiencia de su actual profesora principal en la sede de La Habana. Métodos: se realizó análisis de los contenidos del curso en los programas la residencia de Bioestadística aprobados a partir de 1981. Se consideraron las experiencias de la autora que ha sido la profesora principal del curso por más de 20 años. Resultados: se eliminaron temas como Una Variable de Respuesta, se completó el de Regresión Logística y se introdujo el Análisis de Correspondencia, se perfeccionaron las clases teóricas y prácticas por el diseño de monografías docentes y la introducción de paquetes computacionales. Conclusiones: los contenidos han variado con la finalidad de lograr mayor comprensión de las técnicas que se imparten en la solución de problemas de investigación en correspondencia con el desarrollo de software y las posibilidades de contar con más computadoras para la docencia. Las estrategias docentes han cambiado en función de las facilidades informáticas y la utilización de monografías diseñadas para la residencia sobre el tema con el propósito de perfeccionar las habilidades de los residentes en el procesamiento de datos y en su análisis(AU)
Introduction: The contents of qualitative data analysis of the Biostatistics residency have been considered in its plan of studies since 1981. Such topics and teaching strategies have changed over time. Objective: To explain the variations that occurred in its teaching in terms of content and teaching strategies used from the experience of its current main professor at the Havana campus. Methods: The course's subjects were analyzed in the Biostatistics residency programs approved since 1981. The experiences of the author, who has been the course's main profesor for more than 20 years, were considered. Results: Topics such as A response variable were eliminated, Logistics regression was completed, and Correspondence analysis was introduced. Theoretical and practical lessons were improved by the design of teaching monographs and the introduction of computational packages. Conclusions: The contents have varied in order to gain better understanding of the techniques taught in the solution of research problems in correspondence with the development of software and the possibilities of having more computers for teaching. The teaching strategies have changed according to computer facilities and the use of monographs designed for the residency on the subject, in order to improve the residents' skills in data processing and analysis(AU)
Subject(s)
Humans , Teaching , Evaluation Studies as Topic , Biostatistics/methods , Education, DistanceABSTRACT
Although computers cannot analyze textual data in the same way as they analyze numerical data, they can nevertheless be of great assistance to qualitative researchers. Thus, the use of computers in analyzing qualitative data has increased since the 1980s. The purpose of this article was to explore advantages and disadvanteges of using computers to analyze textual data and to suggest strategies to prevent problems of using computers. In additon, it illustrated characteristics and functions of softwares designed to analyze qualitative data to help researchers choose the program wisely. It also demonstrated precise functions and procedures of the NUDIST program which was designed to develop a conceptual framework or grounded theory from unstructured data. Major advantage of using computers in qualitative research is the management of huge amount of unstructured data. By managing overloaded data, researcher can keep track of the emerging ideas, arguments and theoretical concepts and can organize these tasks more efficiently than the traditional method of "cut-and-paste" technique. Additional advantages are the abilities to increase trustworthiness of research, transparency of research process, and intuitional creativity of the researcher, and to facilitate team and secondary research. On the other hand, disvantages of using computers were identified as worries that the machine could conquer the human understanding and as probability of these problems, it suggested strategies such as 1) deep understanding of orthodoxy in analytical process. To overcome philosophical and theoretical background of qualitative research method, 2) deep understanding of the data as a whole before using software, 3) use of software after familiarity with it, 4) continuous evaluation of software and feedback from them, and 5) continuous awareness of the limitation of the machine, that is computer, in the interpretive analysis.