Optimal Number Of Topics Research Articles

Assessment of the sentiments expressed by traumatic brain injury patients and caregivers: A qualitative study based on in-depth interviews

IntroductionTraumatic Brain Injury (TBI) is a significant public health concern that causes death, disability, and economic burden. Its repercussions affect physical, cognitive, emotional, and behavioral aspects of long-term care needs. Despite improvements in communication among multidisciplinary teams, the management of TBI remains fragmented. ObjectiveThis study aimed to assess patients and caregivers’ experiences through sentiment analysis. Materials and methodsA qualitative cross-sectional study utilized structured topic modeling (STM) to analyze in-depth interview data. The study involved 29 patients with TBI and 27 caregivers in Madrid (Spain), using a survey design. The interviews were conducted, transcribed, and coded independently over 5 months. Sentiments such as anticipation, fear, and emotional concerns were analyzed using three dictionaries. The STM analysis identified four key concepts: desire for independence, potential improvement, need for injury information, and psychological consequences. STM diagnostic graphs were used to determine the number of topics relevant to the evaluation of patient and caregiver concerns. Furthermore, an analysis was conducted across four topics. ResultsThe average age of the patients was 44.2 ± 14.9 years (69 % males). Regarding TBI severity, 59 % of patients had severe TBI, whereas the remaining 41 % had experienced moderate TBI. Among the caregivers, the majority were parents (30 %), partners (24 %), or siblings (24 % each). Among the 51 participants, sentiments were analyzed using three dictionaries. While there were no significant age differences (Z = 0.24, p = 0.815), the STM model was adjusted for significant sex differences (p = 0.017) between patients and relatives. Anticipation and fear prevailed in both groups, highlighting the shared emotional patterns. DiscussionThe analysis of diagnostic graphs indicated the optimal number of topics for evaluation, emphasizing key concerns across different phases of TBI. Patients' main worries shifted from physical symptoms to limitations in daily life and independence. Caregivers highlighted the importance of staff interactions, misinformation challenges, and the need for psychological care. ConclusionKey patient concerns, including dependency on daily activities, limitations in autonomy, and caregiver burden, emphasize crucial areas for enhancement in multidisciplinary treatment. Moreover, the lack of long-term psychological support is a significant barrier to optimal patient and caregiver well-being.

Efficient topic identification for urgent MOOC Forum posts using BERTopic and traditional topic modeling techniques

AbstractMOOC platforms provide a means of communication through forums, allowing learners to express their difficulties and challenges while studying various courses. Within these forums, some posts require urgent attention from instructors. Failing to respond promptly to these posts can contribute to higher dropout rates and lower course completion rates. While existing research primarily focuses on identifying urgent posts through various classification techniques, it has not adequately addressed the underlying reasons behind them. This research aims to delve into these reasons and assess the extent to which they vary. By understanding the root causes of urgency, instructors can effectively address these issues and provide appropriate support and solutions. BERTopic utilizes the advanced language capabilities of transformer models and represents an advanced approach in topic modeling. In this study, a comparison was conducted to evaluate the performance of BERTopic in topic modeling on MOOCs discussion forums, alongside traditional topic models such as LDA, LSI, and NMF. The experimental results revealed that the NMF and BERTopic models outperformed the other models. Specifically, the NMF model demonstrated superior performance when a lower number of topics was required, whereas the BERTopic model excelled in generating topics with higher coherence when a larger number of topics was needed.The results considering all urgent posts from the dataset were as follows: Optimal number of topics is 6 for NMF and 50 for BERTopic; coherence scores is 0.66 for NMF and 0.616 for BERTopic; and IRBO scores is 1 for both models. This highlights the BERTopic model capability to distinguish and extract diverse topics comprehensively and coherently, aiding in the identification of various reasons behind MOOC Forum posts.

A data-driven analysis to determine the optimal number of topics 'K' for latent Dirichlet allocation model

Topic modeling is an unsupervised machine learning technique successfully used to classify and retrieve textual data. However, the performance of topic models is sensitive to selecting optimal hyperparameters, the number of topics 'K' and Dirichlet priors 'α' and 'β.' This data-driven analysis aims to determine the optimum number of topics, 'K,' within the latent Dirichlet allocation (LDA) model. This work utilizes three datasets, namely 20-Newsgroups news articles, Wikipedia articles, and Web of Science containing science articles, to assess and compare various 'K' values through the grid search approach. The grid search approach finds the best combination of hyperparameter values by trying all possible combinations to see which performs best. This research seeks to identify the 'K' that optimizes topic relevance, coherence, and model performance by leveraging statistical metrics, such as coherence scores, perplexity, and topic distribution quality. Through empirical analysis and rigorous evaluation, this work provides valuable insights for determining the ideal 'K' for LDA models.

Leveraging Topic Modelling to Analyze Biomedical Research Trends from the PubMed Database Using LDA Method

Biomedical research has become an essential entity in human life. However, finding trends related to research topics in the health sector contained in the repository is a challenging matter. In this study, we implemented topic modelling to analyze biomedical research trends using the LDA method. Topic modelling was carried out using data from 7000 articles from PubMed, which were processed with text processing such as lowercase, punctuation removal, tokenization, stop-word removal, and lemmatization. For topic modelling, the LDA with corpus conditions varied to 75% and 100% for validation. Alpha and beta parameters are also set with variations between 0.01, 0.31, 0.61, 0.91, symmetry, and asymmetry when the number of the corpus is changed. When the number of the corpus is 75%, the optimal number of topics is 7, with a coherence value of 0.52. Whereas when the number of the corpus is 100%, the optimal number of topics is 10 with a coherence value of 0.51. In addition, based on the results of article topic modelling, several topics are trending, including disease diagnosis, patient care, and genetic or cell research. Based on the classification of biomedical topics into seven categories, the optimal accuracy, precision, and recall values using the Random Forest algorithm were obtained, namely 85.57%, 87.36%, and 87.58%. The results of this study suggest that topic modelling using the LDA can be used to identify trends in biomedical research with high accuracy. This information can help stakeholders make informed decisions about the direction of future research.

Hybrid topic modeling method based on dirichlet multinomial mixture and fuzzy match algorithm for short text clustering

Topic modeling methods proved to be effective for inferring latent topics from short texts. Dealing with short texts is challenging yet helpful for many real-world applications, due to the sparse terms in the text and the high dimensionality representation. Most of the topic modeling methods require the number of topics to be defined earlier. Similarly, methods based on Dirichlet Multinomial Mixture (DMM) involve the maximum possible number of topics before execution which is hard to determine due to topic uncertainty, and many noises exist in the dataset. Hence, a new approach called the Topic Clustering algorithm based on Levenshtein Distance (TCLD) is introduced in this paper, TCLD combines DMM models and the Fuzzy matching algorithm to address two key challenges in topic modeling: (a) The outlier problem in topic modeling methods. (b) The problem of determining the optimal number of topics. TCLD uses the initial clustered topics generated by DMM models and then evaluates the semantic relationships between documents using Levenshtein Distance. Subsequently, it determines whether to keep the document in the same cluster, relocate it to another cluster, or mark it as an outlier. The results demonstrate the efficiency of the proposed approach across six English benchmark datasets, in comparison to seven topic modeling approaches, with 83% improvement in purity and 67% enhancement in Normalized Mutual Information (NMI) across all datasets. The proposed method was also applied to a collected Arabic tweet and the results showed that only 12% of the Arabic short texts were incorrectly clustered, according to human inspection.

Evolution of renewable energy laws and policies in China

This study employs Latent Dirichlet Allocation (LDA) topic modelling methodology to analyze documents related to renewable energy laws and policies at the central level in China. The objective is to investigate the development and evolution of renewable energy policies in China and to gain insights into the national-level attitudes towards renewable energy development. The study consists of two phases: initially, renewable energy policy documents undergo keyword analysis using word clouds and keyword co-occurrence network analysis to elucidate the focal areas and their interconnections within the legal and policy texts. Subsequently, after determining the optimal number of topics for modelling based on topic perplexity and consistency results, the text undergoes data cleaning to isolate words with practical significance. These words are then incorporated into the LDA topic model to analyze the distribution and content of potential topics within the policies. Lastly, by linearly segmenting the time frame, changes in topic intensity over time are visually examined using heat maps. The findings indicate that energy policies have consistently prioritized “development” and emphasized the significance of “new energy” in renewable energy policies. Moreover, as renewable energy has progressed, governments and policymakers have come to acknowledge the importance of comprehensive energy planning, transitioning to clean energy sources, and regulating the electricity market. This growing awareness has led to efforts to strengthen policy and regulatory measures to foster renewable energy's sustainable development and utilization. In summary, this study highlights the effectiveness of the LDA topic model in analyzing renewable energy policies, advancing its adoption and furthering research in the field.

Exploring Latent Dirichlet Allocation (LDA) in Topic Modeling: Theory, Applications, and Future Directions

In an era dominated by an unprecedented deluge of textual information, the need for effective methods to make sense of large datasets is more pressing than ever. This article takes a pragmatic approach to unraveling the intricacies of topic modeling, with a specific focus on the widely used Latent Dirichlet Allocation (LDA) algorithm. The initial segment of the article lays the groundwork by exploring the practical relevance of topic modeling in real-world scenarios. It addresses the everyday challenges faced by researchers and professionals dealing with vast amounts of unstructured text, emphasizing the potential of topic modeling to distill meaningful insights from seemingly chaotic data. Moving beyond theoretical abstraction, the article then delves into the mechanics of Latent Dirichlet Allocation. Developed in 2003 by Blei, Ng, and Jordan, LDA provides a probabilistic framework to identify latent topics within documents. The article takes a step-by-step approach to demystify LDA, offering a practical understanding of its components and the Bayesian principles governing its operation. A significant portion of the article is dedicated to the practical implementation of LDA. It provides insights into preprocessing steps, parameter tuning, and model evaluation, offering readers a hands-on guide to applying LDA in their own projects. Real-world examples and case studies showcase how LDA can be a valuable tool for tasks such as document clustering, topic summarization, and sentiment analysis. However, the journey through LDA is not without challenges, and the article candidly addresses these hurdles. Topics such as determining the optimal number of topics, the sensitivity of results to parameter settings, and the interpretability of outcomes are discussed. This realistic appraisal adds depth to the article, helping readers navigate the nuances and potential pitfalls of employing LDA in practice. Beyond the technical intricacies, the article explores the broad spectrum of applications where LDA has proven its efficacy. From text mining and information retrieval to social network analysis and healthcare informatics, LDA has left an indelible mark on diverse domains. Through practical examples, the article illustrates how LDA can be adapted to different contexts, showcasing its versatility as a tool for uncovering latent patterns. Keywords: Topic Modeling, Latent Dirichlet Allocation, Text Mining, Natural Language Processing, Document Clustering, Bayesian Inference.

Topic models with elements of neural networks: investigation of stability, coherence, and determining the optimal number of topics.

Topic modeling is a widely used instrument for the analysis of large text collections. In the last few years, neural topic models and models with word embeddings have been proposed to increase the quality of topic solutions. However, these models were not extensively tested in terms of stability and interpretability. Moreover, the question of selecting the number of topics (a model parameter) remains a challenging task. We aim to partially fill this gap by testing four well-known and available to a wide range of users topic models such as the embedded topic model (ETM), Gaussian Softmax distribution model (GSM), Wasserstein autoencoders with Dirichlet prior (W-LDA), and Wasserstein autoencoders with Gaussian Mixture prior (WTM-GMM). We demonstrate that W-LDA, WTM-GMM, and GSM possess poor stability that complicates their application in practice. ETM model with additionally trained embeddings demonstrates high coherence and rather good stability for large datasets, but the question of the number of topics remains unsolved for this model. We also propose a new topic model based on granulated sampling with word embeddings (GLDAW), demonstrating the highest stability and good coherence compared to other considered models. Moreover, the optimal number of topics in a dataset can be determined for this model.

Research on Text Value and Linguistic Characteristics in Ancient Literature Based on Text Mining Technology

Abstract By combining mining algorithms, this paper provides a new method for quantifying the characteristics of literary language, thereby reducing the subjectivity of the value and language discussion of ancient literature in Chinese. Firstly, the TF-IDF algorithm is improved, the methods of inter-class dispersion and intra-class dispersion are adopted, and the optimal number of topics K of the LDA topic model is determined by using the confusion degree, and the quantitative model of the value and language characteristics of ancient literary texts is constructed. Furthermore, the concept of maximum word-to-frequency ratio is introduced. It is integrated into the traditional information gain method, and an old academic text recognition algorithm based on XGBoost model is constructed. The model’s results were applied to the network corpus mining and analysis, and the results showed that the word “cherishing spring” ranked first with a frequency of 7085 occurrences, followed by “hurt autumn” with 4598 occurrences. Among the eight themes, “natural imagery” (Topic 3) accounted for the highest proportion, reaching 23.68%, followed by “landscape and pastoral” (Topic 7) and “euphemistic words” (Topic2), accounting for 16.29% and 14.54%, respectively. The method of this paper not only provides a new perspective and tool for the quantitative analysis of the linguistic characteristics of literary works, but also points out a new research direction for the in-depth discussion of textual value and linguistic characteristics in the future.

분노, 비판, 대립의 장이 되었던 트위터

This research involved the extensive collection and refinement of tweets related to the 10·29 Itaewon Tragedy, followed by analysis using DMR topic modeling. Through model validation, the optimal number of topics was determined to be 15, clustered into seven groups: informational, emotional, government criticism, political confrontation, secondary harm, empathetic responses, and miscellaneous. Examining the temporal distribution of topics revealed a decrease in the weight of topics such as 'Lack of government preparedness,' 'Accountability for the disaster,' and 'Condolences and memorials for victims' over time, while topics like 'Controversy over disrespect for victims' families' and 'Anger and Blame' increased. Further, dividing the overall period into three phases demonstrated variations in topic weights. Compared to the 'Mourning' phase, the 'Investigation' and 'Parliamentary Inquiry' phases indicated an increase in the weight of topics related to 'Investigation and detainment of those responsible' and 'Demand for the truth about the disaster.' The research concludes by discussing the implications for the politicization of disasters and community recovery.

Navigating the Landscape of Telemedicine Research: A Topic Modeling Approach for the Present and Future.

Introduction: Telemedicine, which is the provision of remote clinical services via telecommunication technology, has undergone an upsurge since the COVID-19 pandemic. To capture this paradigm, this study surveyed telemedicine literature, including postpandemic publications, to identify dominant research themes and temporal trends and suggest directions for future research. Methods: A corpus of 56,445 telemedicine studies is sourced from PubMed. Latent Dirichlet allocation (LDA) topic modeling performed using the Konstanz Information Miner platform. The textual data for topic modeling were processed by following standard procedures for natural language processing. Moreover, the term frequency-inverse document frequency approach was used to capture the importance of words within the corpus. We assessed perplexity, coherence, and the elbow method to determine the optimal number of topics for modeling. Results: The findings confirm the surge in telemedicine research after 2020, signifying its prominence. LDA topic modeling reveals seven distinct research themes, with the most prominent topic being "patient satisfaction" (21.38%) followed by "perspectives and challenges" (17.95%), and "smartphone apps" (14.32%). Furthermore, the results demonstrate a noticeable shift in topics from screening to therapeutic applications of telemedicine. Conclusions: This study serves as a guide for a broad range of telemedicine research topics. This synthesis of themes reflects the commitment of scholars to address the changing dynamics and health care needs, such as the COVID-19 pandemic, aging in place, smartphone usage, and technological advancement. The analysis also reveals flexible research responses to policy and contextual shifts, highlighting the collective drive to broaden the application of telemedicine in community health care.

Research on Mining Talent Demand for E-commerce Majors based on LDA Topic Model

[Purpose] Based on the text mining method, this study analyzes the job demand for domestic e-commerce industry positions in the Internet-oriented recruitment data, promotes the matching of e-commerce positions and talents, and promotes the good construction of the employment environment in the domestic e-commerce industry.[Method] Use the LDA(Latent dirichlet allocation) topic model for job competency requirements to calculate the similarity of the word segmentation results for professional talent requirements, determine the optimal number of topics, and output the visualization results of the LDA topic model.[Conclusion] In eastern China, there is a high demand for e-commerce jobs, and the majority of these positions only call for 1-3 years of experience. The basic requirement for the majority of jobs is a college degree. The Director of Operations and Director of Network Operations focus on e-commerce operations and management, the Network Promotion Specialist focuses on e-commerce advertising and product promotion, the Data Analyst and Market Analyst focus on data analysis and market research, and the Sales Representative focuses on sales. There is a correlation between the salary of the positions and education, experience and region; the higher the education, the more experience and the higher the level of regional development, the higher the salary level.

Topic Modeling for Text Structure Assessment: The case of Russian Academic Texts

Background: Automatic assessment of text complexity levels is viewed as an important task, primarily in education. The existing methods of computing text complexity employ simple surface text properties neglecting complexity of text content and structure. The current paradigm of complexity studies can no longer keep up with the challenges of automatic evaluation of text structure.
 Purpose: The aim of the paper is twofold: (1) it introduces a new notion, i.e. complexity of a text topical structure which we define as a quantifiable measure and combination of four parameters, i.e. number of topics, topic coherence, topic distribution, and topic weight. We hypothesize that these parameters are dependent variables of text complexity and aligned with the grade level; (2) the paper is also aimed at justifying applicability of the recently developed methods of topic modeling to measuring complexity of a text topical structure.
 Method: To test this hypothesis, we use Russian Academic Corpus comprising school textbooks, texts of Russian as a foreign language and fiction texts recommended for reading in different grades, and employ it in three versions: (i) Full Texts Corpus, (ii) Corpus of Segments, (iii) Corpus of Paragraphs. The software tools we implement include LDA (Latent Dirichlet Allocation), OnlineLDA and Additive Regularization Of Topic Models with Word2vec-based metric and Normalized Pairwise Mutual Information.
 Results: Our findings include the following: the optimal number of topics in educational texts varies around 20; topic coherence and topic distribution are identified to be functions of grade level complexity; text complexity is suggested to be estimated with structural organization parameters and viewed as a new algorithm complementing the classical approach of text complexity assessment based on linguistic features.
 Conclusion: The results reported and discussed in the article strongly suggest that the theoretical framework and the analytic algorithms used in the study might be fruitfully applied in education and provide a basis for assessing complexity of academic texts.

Seeded Sequential LDA: A Semi-Supervised Algorithm for Topic-Specific Analysis of Sentences

Topic models have been widely used by researchers across disciplines to automatically analyze large textual data. However, they often fail to automate content analysis, because the algorithms cannot accurately classify individual sentences into pre-defined topics. Aiming to make topic classification more theoretically grounded and content analysis in general more topic-specific, we have developed Seeded Sequential Latent Dirichlet allocation (LDA), extending the existing LDA algorithm, and implementing it in a widely accessible open-source package. Taking a large corpus of speeches delivered by delegates at the United Nations General Assembly as an example, we explain how our algorithm differs from the original algorithm; why it can classify sentences more accurately; how it accepts pre-defined topics in deductive or semi-deductive analysis; how such ex-ante topic mapping differs from ex-post topic mapping; how it enables topic-specific framing analysis in applied research. We also offer practical guidance on how to determine the optimal number of topics and select seed words for the algorithm.

Topic detection with recursive consensus clustering and semantic enrichment

Extracting meaningful information from short texts like tweets has proved to be a challenging task. Literature on topic detection focuses mostly on methods that try to guess the plausible words that describe topics whose number has been decided in advance. Topics change according to the initial setup of the algorithms and show a consistent instability with words moving from one topic to another one. In this paper we propose an iterative procedure for topic detection that searches for the most stable solutions in terms of words describing a topic. We use an iterative procedure based on clustering on the consensus matrix, and traditional topic detection, to find both a stable set of words and an optimal number of topics. We observe however that in several cases the procedure does not converge to a unique value but oscillates. We further enhance the methodology using semantic enrichment via Word Embedding with the aim of reducing noise and improving topic separation. We foresee the application of this set of techniques in an automatic topic discovery in noisy channels such as Twitter or social media.

Mapping and predicting research trends in international journal publications using graph and topic modeling

Researchers and journal managers need summary information, such as research maps and trends. Topic and words-based document content analysis alternative to science mapping and trend prediction based on bibliographic analysis. The data are a collection of journal articles/proceeding documents and metadata for 2011-2020 published by the International Journal of Electrical and Computer Engineering (IJECE). A combination of several techniques and methods is used, such as text mining, topic modeling, cosine similarity, network analysis, graph theory, and seasonal autoregressive integrated moving average (SARIMA). This research has produced research topics, mapping, trend prediction, and visualization according to its objectives. The results of the topic coherence test obtained the optimal number of topics, as many as 6. The results of the topics were evaluated by experts to be given labels and areas of focus. On the research map for each topic, information is found on trending research, the most popular research, research that is central to the research group, and critical research on the development of the group path. It also identifies the type of breakthrough, incremental, and research gap. Predictions of research trends obtained are based on topics and words that describe the development of research. Visualization is descriptive and predictive.

온라인 커뮤니티 분석을 통한 초등교사의 인공지능 교육 현황 및 관심사 분석

The purpose of this study was to explore the current status of elementary AI education, the interest shown by elementary teachers in AI education, and topics related to elementary AI education, to obtain basic data on the direction of AI education policy and ways to revitalize AI education. After deleting duplicate posts and posts unrelated to the research topic in the post data collected by IndiSchool, a representative online community for elementary school teachers was established, followed by conducting basic and text-mining analyses using 454 posts as the research subject.
 First, to explore the current status of elementary AI education, the trend according to the period of AI-related postings was examined. From analyzing the trend of posts related to AI education uploaded to the online community of elementary school teachers, it was evident that interest in AI education had increased since 2020.
 Next, to analyze the interest in AI education expressed by elementary school teachers, the frequency of words from text mining was examined and keywords related to AI education were selected. Based on these selected keywords, the class types and class activities in which AI education was mainly operated, its active use on educational platforms, and the importance of copyright issues were established.
 Finally, a topic modeling analysis of text mining was conducted to explore AI education-related topics within the online community of elementary school teachers and keywords for each topic. First, the optimal number of topics was determined as three, and after organizing the top 30 words according to topic, keywords for each topic were selected and the name of each topic was established: “design and operation of AI class,” “development of AI class competency,” and “utilization and development of AI class content.” Ultimately, examples of posts corresponding to each topic were presented.

Topic Modeling as an evaluation basis in literature research - A proposal for a new literature review method for machine-assisted source evaluation using the example of anthropology.

Topic modeling is a machine learning method that has been used in disciplines like social sciences or the industrial production sector. With topic modeling, a scientist can reduce many articles to a few topics to get an overview of a specific field (e.g., for a scoping review). The objectives of this paper were (1) to demonstrate the applicability of topic modeling to the field of anthropology by a new framework and (2) to present a new method for determining the optimal number of topics used. Subjects and methods: The documents used in this paper were collected from the database IEEE, using the search term "anthropology" to obtain a broad range of topics. Topic modeling was performed by Latent Dirichlet Allocation (LDA) method, using R. To determine the optimal candidate of topics (k), a mathematical formula based on the slope of the perplexity curve was established. Results: The application of the framework to the corpus of 518 documents was able to sort all documents into 15 research areas with little time investment by the researcher while using a standard laptop computer. The process of semantic validation was successfully done for all 15 topics. Conclusions: The presented framework with the optimal number of topics k enables scientists in the field of anthropology to perform a scoping review and thus spend less time to manually categorize documents. Topic modeling can be used by researchers in multidisciplinary projects to improve understanding content in a faster way.

Quantitative Analysis of Citi's ESG Reporting: LDA and TF-IDF Approaches

The rapid development of society is accompanied by significant changes in the environment. ESG is still in its nascent stages, and ESG policies have yet to be perfected. In this study, we examined Citibank's 2019 annual report and analyzed ESG factors using the Latent Dirichlet Allocation (LDA) method. We collected data through word clouds and identified words with the highest frequency through frequency analysis. Subsequently, we determined the optimal topic number from the perplexity curve obtained via the LDA method. Visualization charts further helped us distinguish these four ESG topics. Our findings indicate that both environmental and social factors are crucial in ESG, with corporate governance playing the most significant role.

미디어 사용 행태 토픽 분석을 통한 추천모델링

As online shopping and mobile media are recently activated, various feedbacks are built on users' preferences and purchases. Various approaches are being studied to improve the performance of recommendation considering individual characteristics. This study carries out clustering from media panel data through LDA topic modeling to imply the meaning of user's media use behavior, and applies a new rating, calculated by combining the existing rating and sentiment analysis on media use by cluster, to the recommendation system. We select households living with their children, and make a recommendation 31 smart devices that could be helpful for the members. We utilize the SAMC algorithm for exact inference of LDA topic modeling, and set the optimal number of topics through coherence and perplexity. We use BPR and collaborative filtering for the recommendation algorithm, and BPR provides the best performance by comparing with three performance indicators. It is indicated that the recommendation process suggested by this study provides a reasonable recommendation considering individual characteristics.