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Text-mining in macroeconomics: the wealth of words

Azqueta Gavaldon, Andres (2020) Text-mining in macroeconomics: the wealth of words. PhD thesis, University of Glasgow.

The coming to life of the Royal Society in 1660 surely represented an important milestone in the history of science, not least in Economics. Yet, its founding motto, ``Nullius in verba'', could be somewhat misleading. Words in fact may play an important role in Economics. In order to extract relevant information that words provide, this thesis relies on state-of-the-art methods from the information retrieval and computer science communities.

Chapter 1 shows how policy uncertainty indices can be constructed via unsupervised machine learning models. Using unsupervised algorithms proves useful in terms of the time and resources needed to compute these indices. The unsupervised machine learning algorithm, called Latent Dirichlet Allocation (LDA), allows obtaining the different themes in documents without any prior information about their context. Given that this algorithm is widely used throughout this thesis, this chapter offers a detailed while intuitive description of its underlying mechanics.

Chapter 2 uses the LDA algorithm to categorize the political uncertainty embedded in the Scottish media. In particular, it models the uncertainty regarding Brexit and the Scottish referendum for independence. These referendum-related indices are compared with the Google search queries ``Scottish independence'' and ``Brexit'', showing strong similarities. The second part of the chapter examines the relationship of these indices on investment in a longitudinal panel dataset of 2,589 Scottish firms over the period 2008-2017. It presents evidence of greater sensitivity for firms that are financially constrained or whose investment is to a greater degree irreversible. Additionally, it is found that Scottish companies located on the border with England have a stronger negative correlation with Scottish political uncertainty than those operating in the rest of the country. Contrary to expectations, we notice that investment coming from manufacturing companies appears less sensitive to political uncertainty.

Chapter 3 builds eight different policy-related uncertainty indicators for the four largest euro area countries using press-media in German, French, Italian and Spanish from January 2000 until May 2019. This is done in two steps. Firstly, a continuous bag of word model is used to obtain semantically similar words to ``economy'' and ``uncertainty'' across the four languages and contexts. This allows for the retrieval of all news-articles relevant to economic uncertainty. Secondly, LDA is again employed to model the different sources of uncertainty for each country, highlighting how easily LDA can adapt to different languages and contexts. Using a Bayesian Structural Vector Autoregressive set up (BSVAR) a strong heterogeneity in the relationship between uncertainty and investment in machinery and equipment is then documented. For example, while investment in France, Italy and Spain reacts heavily to political uncertainty shocks, in Germany it is more sensitive to trade uncertainty shocks.

Finally, Chapter 4 analyses English language media from Europe, India and the United States, augmented by a sentiment analysis to study how different narratives concerning cryptocurrencies influence their prices. The time span ranges from April 2013 to December 2018 a period where cryptocurrency prices experienced a parabolic behaviour. In addition, this case study is motivated by Shiller's belief that narratives around cryptocurrencies might have led to this price behaviour. Nonetheless, the relationship between narratives and prices ought to be driven by complex interactions. For example, articles written in the media about a specific phenomenon will attract or detract new investors depending on their content and tone (sentiment). Moreover, the press might also react to price changes by increasing the coverage of a given topic. For this reason, a recent causal model, Convergent Cross Mapping (CCM), suited to discovering causal relationships in complex dynamical ecosystems is used. I find bidirectional causal relationships between narratives concerning investment and regulation while a mild unidirectional causal association exists in narratives that relate technology and security to prices.

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• 03 July 2019

Text mining facilitates materials discovery

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Olexandr Isayev is in the Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA.

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The total number of materials that can potentially be made — sometimes referred to as materials space — is vast, because there are countless combinations of components and structures from which materials can be fabricated. The accumulation of experimental data that represent pockets of this space has created a foundation for the emerging field of materials informatics, which integrates high-throughput experiments, computations and data-driven methods into a tight feedback loop that enables rational materials design. Writing in Nature , Tshitoyan et al . 1 report that knowledge of materials science ‘hidden’ in the text of published papers can be mined effectively by computers without any guidance from humans.

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Nature 571 , 42-43 (2019)

doi: https://doi.org/10.1038/d41586-019-01978-x

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PhDs with Industry Partners – Assessing Collaboration and Topic Distribution Using a Text Mining Methodology

• First Online: 22 February 2022

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• Kilian Buehling 6 &
• Matthias Geissler 7  

Part of the book series: International Studies in Entrepreneurship ((ISEN,volume 52))

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Collaboration between universities and industry partners is thought to facilitate knowledge diffusion and provide resources and new ideas for academic researchers. However, recent evidence also suggests a possible trade-off or cost with regard to individual productivity. Given its focus on quantitative output, the literature is rather silent on possible qualitative shifts in researchers’ agendas when engaging with industry partners. We contribute to a discussion on potential negative effects of university-industry engagement by comparing the topic distributions of PhD theses based on collaborative and noncollaborative research. The results indicate little difference between the two kinds of dissertation projects. We conclude that fears of agenda setting in collaborative research are unwarranted.

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We are aware that our second restriction leaves room for discussion and leads to a somewhat blurred understanding of “collaboration”. However, constructing the training sample for the classification algorithm and subsequently doing a fair amount of manual classification convinced us that donating sample materials or allowing the use of equipment did not necessarily involve an element of knowledge exchange. Firms also engage in these “giveaways” for strategic reasons, for example to expand the diffusion of specific materials/equipment among the scientific community. Notwithstanding the fact that firms may benefit from this kind of activities to some extent, we believe it is not justified to label these actions as “collaboration”.

For topic distributions following “Cao” the results did not significantly differ from 0. As the three procedures were based on different methods to determine the optimal topic number, the “Cao” procedure seems to systematically estimate a lower number. The mean number of topics according to the “Cao” procedure was 5.5 in our setting, whereas the “Griffith” and “Arun” procedures presented an average of 14.3 and 14.1 topics, respectively.

In an unreported variant, we employed a university fixed-effect model with no significant effect on the estimated coefficients.

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Acknowledgments

This article benefitted from the comments and suggestions made by participants of the Technology Transfer Society Conference 2018 in Valencia, Spain, the 12th Workshop on Organisation, Economics and Policy of Scientific Research 2018 in Bath, UK and several appreciated colleagues throughout the research process. The Stifterverband fuer die Deutsche Wissenschaft provided research funding for Kilian Buehling via its INNcentive grant, which we gratefully acknowledge. The authors would also like to thank the reviewers for very helpful comments.

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Buehling, K., Geissler, M. (2022). PhDs with Industry Partners – Assessing Collaboration and Topic Distribution Using a Text Mining Methodology. In: Azagra-Caro, J.M., D'Este, P., Barberá-Tomás, D. (eds) University-Industry Knowledge Interactions. International Studies in Entrepreneurship, vol 52. Springer, Cham. https://doi.org/10.1007/978-3-030-84669-5_2

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Text mining analysis on Pubmed data base

Due to the increasing amount of unstructured text data, information retrieval from large volumes of data has become highly important. Applying most of the algorithms, such as classification and clustering, is challenging because of the high dimensionality of the text data.

This study investigates a novel, co-occurrence model of text data to help reduce the dimension of the data set. We present a graph-based text mining approach for discovering similar documents in a scientific corpus and use it in a search engine that is built into the R Shiny web application. The Biological Scientific Corpus (BSC) is a collection of 764,213 PubMed-indexed English abstracts of research papers and proceedings papers, chosen to reflect the widest range of abstracts of scientific works published in 2012. Analysis of the co-occurrence matrix helps to understand the feature of interconnection between the words. Applying the community detection method, we discovered hubs and strong communities in the co-occurrence network and use them to reduce the dimension of the network.

After dimension reduction, we produced meaningful clusters of the data set. To see whether or not the clustering is correct we investigated the distribution of the authors of the papers over the clusters and the results were satisfactory. Finally, we used a hierarchal approach to develop a search engine on the data set that accepts a query from a user and responds with a set of retrieved documents. The main advantage of this search engine is the ability to take long text, and abstracts, as a query.

Another part of this work is to reproduce the well-known Elastic Map algorithm in R as an open resource for data visualization. We used the R Elastic Map package we developed to present a zoomable and rotatable visualization of a map fitted to clustered data in a two and three-dimensional space.

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Dynamic Lexical Features of PhD Theses across Disciplines: A Text Mining Approach

2020, Journal of Quantitative Linguistics

This study employed a text mining method to investigate the lexical features and their dynamic changes of PhD theses across the natural sciences, social sciences and humanities. Four quantitative indices, i.e. TTR, h-point, R1 and writer's view, were employed to analyze 150 PhD theses (50 theses from each discipline). Although h-point and writer's view were found counter-intuitively to show insignificant variation across disciplines, the results of TTR and R1 did reveal sharp contrasts between theses in humanities and natural sciences. While the second half of humanities theses showed a significantly higher level of lexical diversity, indicated by higher TTR, theses in natural sciences tended to be richer in content words in the first half, indicated by a higher R1. Meanwhile, theses in social sciences seemed to be more moderate, with features lying in the middle position. This study has implications not only for the widening of applications of quantitative linguistic methods but also for academic writing (especially PhD thesis writing) instruction and practice.

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As an innovative and systematic genre in the academic community, Ph.D. theses have been heatedly researched in the field of English for Academic Purposes. Although research on the functional and formal features of Ph.D. theses has been abundant, their stylometric traits regarding textual activity have not been explored. Accordingly, this study explored the textual activity of Ph.D. theses and its dynamic changes across natural sciences, social sciences and humanities. A total of 150 Ph.D. theses (50 from each discipline) were analyzed, and the and χ 2 values were calculated to determine the textual activity of theses as well as its dynamic changes with the progression of texts. The results showed that, although the theses were found to be active in general, significant differences across disciplines do exist, in that the theses in natural sciences and humanities were more active while those in social sciences were more likely to lean towards the descriptive mode. This study has implications for widening the scope of cross-disciplinary academic genre analyses from an innovative quantitative linguistic perspective.

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The past few decades have witnessed an upsurge of scholarly interest in the generic descriptions of PhD theses following Swales' seminal Genre Analysis. Fitted within the Create A Research Space (CARS) model, the thesis introduction plays a key role in justifying research originality/significance, where novice writers engage with academic communities through "establishing a research territory" (Move 1), "establishing a niche" (Move 2), and "occupying the niche" (Move 3). As the hinge of the CARS model, Move 2 (hereinafter EN) is of strategic importance as it enables writers to "sell" their ideas by pointing to the gap/ niche in the "marketplace" of previous research, which is typically realized through the co-occurrences of negation alongside other interpersonal language resources. Negation, as a disclaim marker within Martin and White's appraisal framework, is a prominent linguistic indicator of EN. Nevertheless, little research has systematically examined the use of negation in ENs of PhD thesis introductions. Accordingly, the study investigated negation via the appraisal framework addressing subtypes of negation (disalignment, cautious detachment, unfulfilled expectation) within ENs in the introduction sections of 120 PhD theses. The results showed that disalignment is the most frequent subtype of negation, while "not" and "no" are commonly used as indicators of negation. Our findings also revealed intriguing co-occurrences of negation sub-categories alongside other relevant appraisal resources. The corpus-informed results are expected to shed light on the nature and practice of PhD theses that educators may take into account during thesis writing instruction.

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In measuring the quality of written text, especially academic writing, lexical features are as important as grammatical features and should not be ignored. The highly computable nature of lexicons can make them a good criterion for determining and measuring the quality of text. In this article three lexical features: lexical density, complexity, and formality are reviewed and justified as measurement tools of academic texts. Furthermore, a measurement method is offered to evaluate lexical complexity level of an academic text.

Dr. Musarrat Azher (Fulbrighter)

With the concept of language variation, it has become utmost important to analyze linguistic patterns across register. Pakistani academic writing like other registers in Pakistan is an area that still seeks the attention of the researchers and linguists. This target register needs to be fully described in terms of linguistic characteristics to strengthen the distinct identity of Pakistani academic writing as a register. The present research strives to explore linguistic variation across research sections of Pakistani academic writing as a register along with five new textual dimensions explored through the technique of Multidimensional analysis (Azher & Mehmood, 2016). The research is based on the corpus of 235 M. Phil and PhD research dissertations taken from different universities all over Pakistan. The corpus was further divided into five research sections and was tagged for 189 linguistic features. The ANOVA results on variation among research sections indicate that there lie statistically significant differences among research sections of Pakistani Academic Writing on all the new textual dimensions.

chan swee heng

The present paper reviews the use of lexical bundles in academic writing from two different viewpoints namely linguistic and discipline, directed at how academic writers belonging to different disciplines or linguistic backgrounds construct their discourses through lexical bundles. As cohesive devices, lexical bundles are an indispensible part of the text and play a crucial role in shaping propositions, evolving the text, guiding readers through the flow of information and gaining the writer's proffered meaning. By using lexical bundles, academic writers are able to attain naturalness in their writings and create a more reader-friendly approach to the unfolding text. Bearing the significance of lexical bundles in mind, this review paper aims to examine the effect of disciplinary variation and linguistic differences on the use of lexical bundles in academic writing. Most researchers believe that the frequency as well as the use of lexical bundles is different across disciplines a...

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This paper presents a contrastive analysis of the introduction sections of 20 PhD theses in Turkish and in English in the field of English Language Teaching (ELT). The main aim of the study is to explore whether the authors from different academic institutions within the same discourse community performed the same rhetorical strategies in the introduction parts of doctorate dissertations. The study proceeded on a qualitative research design, through a content analysis including both genre and discourse analysis, which was carried on the basis of CARS model 2004 version under the scope of Swalesian approach. In general, the findings on the employment frequencies of each rhetorical strategy show that, although the dissertations have been written by the authors within the same discourse community (the realm of ELT), and context-Turkish context-, English thesis introductions have a more complex rhetorical organization than the introductory parts of Turkish theses.

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LATEST TEXT MINING RESEARCH NOTIONS

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Dynamic Lexical Features of PhD Theses across Disciplines: A Text Mining Approach

Author: xiao, wei, author: sun, shuyi.

This study employed a text mining method to investigate the lexical features and their dynamic changes of PhD theses across the natural sciences, social sciences and humanities. Four quantitative indices, i.e. TTR, h-point, R1 and writer’s view, were employed to analyze 150 PhD theses (50 theses from each discipline). Although h-point and writer’s view were found counter-intuitively to show insignificant variation across disciplines, the results of TTR and R1 did reveal sharp contrasts between theses in humanities and natural sciences. While the second half of humanities theses showed a significantly higher level of lexical diversity, indicated by higher TTR, theses in natural sciences tended to be richer in content words in the first half, indicated by a higher R1. Meanwhile, theses in social sciences seemed to be more moderate, with features lying in the middle position. This study has implications not only for the widening of applications of quantitative linguistic methods but also for academic writing (especially PhD thesis writing) instruction and practice.

PhD Research Topics in Text Mining

Text mining is a crucial extraction of hidden and useful information from large datasets. It opens up the research on automatic and semantic knowledge discovery .

PhD research topics in text mining  are a hub of a terrific amount of creative ideas for PhD/MS scholars. We help you to work on each corner of the research. It ends up in promising findings and execution.

Recent PhD/MS Concepts in Text Mining

Text preprocessing.

• Perceptual grouping
• PoS tagging and stemming
• Full and shallow parsing
• Entity and relation extraction
• Conference resolution
• Syntactic/semantic analysis

Feature extraction and selection

• Fuzzy theory
• Entropy method
• Mutual Information theory
• BFO, ACO, BFO

Classification

• Supervised ( like ANN, SVM, Multi-kernel regression and also CNN)
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• Reinforcement learning

“ Web 3.0 ” is a new wave of the internet that uses semantic text mining. To address this growth, our  PhD research topics in text mining  cover all aspects of this area. And, we assure you that we direct you on the right path to complete your research.

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• Social sentiment analysis
• Opinion and also in frequent item mining
• Mining from complex lexical structures
• Tools for profiling digital games
• Ontology and also in corpus based mining
• Secure and also in privacy preservation
• Also in Information retrieval from cloud/fog

We know that as a novice, shaping your research scope will be a big deal for you. To draw your research scope, join with us at any point. Once you get to connect with us, then we will predict your scope of the research. Since we also have been working with thousands of researchers.

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Take a glance over following new ideas of PhD research topics in text mining,

An effectual function for Drug-Drug Interaction Extraction Based on Transfer Weight Matrix and Memory Network system

The novel mechanism for Category Theory-Based Mobile User Interface Pattern Recommendation Method

An efficient mechanism for Business Process Analytics and Big Data Systems

The new-fangled mechanism for Fast genre classification of web images using global and local features

An innovative performance for Integrating Deep Learning Approaches for Identifying News Reprint Relation scheme

An inventive scheme for Sensitive Information Topics-Based on Sentiment Analysis Method for Big Data

An effectual process for Semantic-aware Visual Abstraction of Large-scale Social Media Data with Geo-Tags

An innovative performance for Social Media Based on Topic Modeling Correlation Analysis Method

An effective performance for Survey of Sentiment Analysis Based on Transfer Learning scheme

An effectual function for Detecting Regions of Maximal Divergence intended for Spatio-Temporal Anomaly Detection method

The novel approach for Multi-Scale Attentive Interaction Networks system

An inventive process for Multiple-Perspective Semantics-Crossover Model for Matching Sentences system

An innovative performance for  Bootstrapping Approach With CRF and Deep Learning Models for Improving the Biomedical Named Entity Recognition in Multi-Domains

An Unsupervised Approach of Truth Discovery from Multi-Sourced Text Data scheme

An innovative mechanism for Artificial Intelligence Driven Multi-Feature Extraction Scheme aimed at Big Data Detection

An effectual source for Gastroenterology Ontology Construction By Synonym Identification and Relation Extraction

A new mechanism for Trajectory big data processing based on frequent activity

An inventive process for Active Learning for Uneven Noisy Labeled Data in Mention-Level Relation Mining

An effectual function for Semantic Clustering-Based on Deep Hypergraph Model aimed at Online Reviews Semantic Classification in Cyber-Physical-Social Systems

The novel mechanism for Automatic Knowledge Discovery in Lecturing Videos via Deep Representation scheme

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UKnowledge > College of Engineering > Mining Engineering > Theses & Dissertations

Theses and Dissertations--Mining Engineering

Theses/dissertations from 2024 2024.

THE METHODOLOGY FOR INTEGRATING ROBOTIC SYSTEMS IN UNDEGROUND MINING MACHINES , Peter Kolapo

DISCRETE ELEMENT MODELING TO PREDICT MUCKPILE PROFILES FROM CAST BLASTING , Russell Lamont

AUTONOMOUS SHUTTLE CAR DOCKING TO A CONTINUOUS MINER USING RGB-DEPTH IMAGERY , Sky Rose

Theses/Dissertations from 2023 2023

ASSESSMENT OF AIR OVERPRESSURE FROM BLASTING USING COMPUTATIONAL FLUID DYNAMICS , Cecilia Estefania Aramayo

RECOVERY OF VALUABLE METALS FROM ELECTRONIC WASTE USING A NOVEL AMMONIA-BASED HYDROMETALLURGICAL PROCESS , Peijia Lin

AN ACID BAKING APPROACH TO ENHANCE RARE EARTH ELEMENT RECOVERY FROM BITUMINOUS COAL SOURCES , Ahmad Nawab

PREDICTION OF DYNAMIC SUBSIDENCE IN THE PROXIMITY OF LONGWALL PANEL BOUNDARIES , JESUS DAVID ROMERO BENITEZ

Prediction of Blast-Induced Ground Vibrations: A Comparison Between Empirical and Artificial-Neural-Network Approaches , Luis F. Velasquez

A LABORATORY AND NUMERICAL INVESTIGATION OF THE STRENGTH OF IRREGULARLY SHAPED PILLARS , Zachary Wedding

Theses/Dissertations from 2022 2022

DEVELOPMENT OF UNIVARIATE AND MULTIVARIATE FORECASTING MODELS FOR METHANE GAS EMISSIONS IN UNDERGROUND COAL MINES , Juan Diaz

PARAMETRIC NUMERICAL ANALYSIS OF INCLINED COAL PILLARS , Robin Flattery

Strain Energy Analysis Related To Strata Failure During Caving Operations , Caroline Gerwig

LAPTOP RECYCLING CASE STUDY: ESTIMATING THE CONTAINED VALUE AND VALUE RECOVERY PROCESS FEASIBILITY OF END-OF-LIFE CONSUMER ELECTRONICS , Zebulon Hart

INVESTIGATION INTO, & ANALYSIS OF TEMPERATURE & STRAIN DATA FOR COAL MINE SEAL MATERIAL DURING CURING , Stephanus Jaco van den Berg

Theses/Dissertations from 2021 2021

DEVELOPMENT OF AN AUTONOMOUS NAVIGATION SYSTEM FOR THE SHUTTLE CAR IN UNDERGROUND ROOM & PILLAR COAL MINES , Vasileios Androulakis

Investigation of Coal Burst Potential Using Numerical Modeling and Rock Burst Indices , Cristian David Cardenas Triana

Capture of Respirable Dust using Maintenance Free Impingement Screen , Neeraj Kumar Gupta

OXIDATION PRETREATMENT FOR ENHANCED LEACHABILITY OF RARE EARTH ELEMENTS FROM BITUMINOUS COAL SOURCES , Tushar Gupta

AN APPROACH FOR PREDICTING FLOW CHARACTERISTICS AT THE CONTINUOUS MINER FACE , Kayla Henderson

CONCEPTS FOR DEVELOPMENT OF SHUTTLE CAR AUTONOMOUS DOCKING WITH CONTINUOUS MINER USING 3-D DEPTH CAMERA , Sibley Miller

MODELING OF RARE EARTH SOLVENT EXTRACTION PROCESS FOR FLOWSHEET DESIGN AND OPTIMIZATION , Vaibhav Kumar Srivastava

Application of a Novel Ventilation Simplification Algorithm , Caitlin V. Strong

A METHODOLOGY FOR AUTONOMOUS ROOF BOLT INSTALLATION USING INDUSTRIAL ROBOTICS , Anastasia Xenaki

Theses/Dissertations from 2020 2020

NUMERICAL APPROXIMATION OF THE GROUND REACTION AND SUPPORT REACTION CURVES FOR UNDERGROUND LIMESTONE MINES , Jesus Castillo Gomez

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• Open access
• Published: 08 May 2024

Measurement and analysis of change in research scholars’ knowledge and attitudes toward statistics after PhD coursework

• Mariyamma Philip 1  

BMC Medical Education volume  24 , Article number:  512 ( 2024 ) Cite this article

102 Accesses

Metrics details

Knowledge of statistics is highly important for research scholars, as they are expected to submit a thesis based on original research as part of a PhD program. As statistics play a major role in the analysis and interpretation of scientific data, intensive training at the beginning of a PhD programme is essential. PhD coursework is mandatory in universities and higher education institutes in India. This study aimed to compare the scores of knowledge in statistics and attitudes towards statistics among the research scholars of an institute of medical higher education in South India at different time points of their PhD (i.e., before, soon after and 2–3 years after the coursework) to determine whether intensive training programs such as PhD coursework can change their knowledge or attitudes toward statistics.

One hundred and thirty research scholars who had completed PhD coursework in the last three years were invited by e-mail to be part of the study. Knowledge and attitudes toward statistics before and soon after the coursework were already assessed as part of the coursework module. Knowledge and attitudes towards statistics 2–3 years after the coursework were assessed using Google forms. Participation was voluntary, and informed consent was also sought.

Knowledge and attitude scores improved significantly subsequent to the coursework (i.e., soon after, percentage of change: 77%, 43% respectively). However, there was significant reduction in knowledge and attitude scores 2–3 years after coursework compared to the scores soon after coursework; knowledge and attitude scores have decreased by 10%, 37% respectively.

The study concluded that the coursework program was beneficial for improving research scholars’ knowledge and attitudes toward statistics. A refresher program 2–3 years after the coursework would greatly benefit the research scholars. Statistics educators must be empathetic to understanding scholars’ anxiety and attitudes toward statistics and its influence on learning outcomes.

Peer Review reports

A PhD degree is a research degree, and research scholars submit a thesis based on original research in their chosen field. Doctor of Philosophy (PhD) degrees are awarded in a wide range of academic disciplines, and the PhD students are usually referred as research scholars. A comprehensive understanding of statistics allows research scholars to add rigour to their research. This approach helps them evaluate the current practices and draw informed conclusions from studies that were undertaken to generate their own hypotheses and to design, analyse and interpret complex clinical decisions. Therefore, intensive training at the beginning of the PhD journey is essential, as intensive training in research methodology and statistics in the early stages of research helps scholars design and plan their studies efficiently.

The University Grants Commission of India has taken various initiatives to introduce academic reforms to higher education institutions in India and mandated in 2009 that coursework be treated as a prerequisite for PhD preparation and that a minimum of four credits be assigned to one or more courses on research methodology, which could cover areas such as quantitative methods, computer applications, and research ethics. UGC also clearly states that all candidates admitted to PhD programmes shall be required to complete the prescribed coursework during the initial two semesters [ 1 ]. National Institute of Mental Health and Neurosciences (NIMHANS) at Bangalore, a tertiary care hospital and medical higher education institute in South India, that trains students in higher education in clinical fields, also introduced coursework in the PhD program for research scholars from various backgrounds, such as basic, behavioral and neurosciences, as per the UGC mandate. Research scholars undertake coursework programs soon after admission, which consist of several modules that include research methodology and statistical software training, among others.

Most scholars approach a course in statistics with the prejudice that statistics is uninteresting, demanding, complex or involve much mathematics and, most importantly, it is not relevant to their career goals. They approach statistics with considerable apprehension and negative attitudes, probably because of their inability to grasp the relevance of the application of the methods in their fields of study. This could be resolved by providing sufficient and relevant examples of the application of statistical techniques from various fields of medical research and by providing hands-on experience to learn how these techniques are applied and interpreted on real data. Hence, research methodology and statistical methods and the application of statistical methods using software have been given much importance and are taught as two modules, named Research Methodology and Statistics and Statistical Software Training, at this institute of medical higher education that trains research scholars in fields as diverse as basic, behavioural and neurosciences. Approximately 50% of the coursework curriculum focused on these two modules. Research scholars were thus given an opportunity to understand the theoretical aspects of the research methodology and statistical methods. They were also given hands-on training on statistical software to analyse the data using these methods and to interpret the findings. The coursework program was designed in this specific manner, as this intensive training would enable the research scholars to design their research studies more effectively and analyse their data in a better manner.

It is important to study attitudes toward statistics because attitudes are known to impact the learning process. Also, most importantly, these scholars are expected to utilize the skills in statistics and research methods to design research projects or guide postgraduate students and research scholars in the near future. Several authors have assessed attitudes toward statistics among various students and examined how attitudes affect academic achievement, how attitudes are correlated with knowledge in statistics and how attitudes change after a training program. There are studies on attitudes toward statistics among graduate [ 2 , 3 , 4 ] and postgraduate [ 5 ] medical students, politics, sociology, ( 6 – 7 ) psychology [ 8 , 9 , 10 ], social work [ 11 ], and management students [ 12 ]. However, there is a dearth of related literature on research scholars, and there are only two studies on the attitudes of research scholars. In their study of doctoral students in education-related fields, Cook & Catanzaro (2022) investigated the factors that contribute to statistics anxiety and attitudes toward statistics and how anxiety, attitudes and plans for future research use are connected among doctoral students [ 13 ]. Another study by Sohrabi et al. (2018) on research scholars assessed the change in knowledge and attitude towards teaching and educational design of basic science PhD students at a Medical University after a two-day workshop on empowerment and familiarity with the teaching and learning principles [ 14 ]. There were no studies that assessed changes in the attitudes or knowledge of research scholars across the PhD training period or after intensive training programmes such as PhD coursework. Even though PhD coursework has been established in institutes of higher education in India for more than a decade, there are no published research on the effectiveness of coursework from Indian universities or institutes of higher education.

This study aimed to determine the effectiveness of PhD coursework and whether intensive training programs such as PhD coursework can influence the knowledge and attitudes toward statistics of research scholars. Additionally, it would be interesting to know if the acquired knowledge could be retained longer, especially 2–3 years after the coursework, the crucial time of PhD data analysis. Hence, this study compares the scores of knowledge in statistics and attitude toward statistics of the research scholars at different time points of their PhD training, i.e., before, soon after and 2–3 years after the coursework.

Participants

This is an observational study of single group with repeated assessments. The institute offers a three-month coursework program consisting of seven modules, the first module is ethics; the fifth is research methodology and statistics; and the last is neurosciences. The study was conducted in January 2020. All research scholars of the institute who had completed PhD coursework in the last three years were considered for this study ( n  = 130). Knowledge and attitudes toward statistics before and soon after the coursework module were assessed as part of the coursework program. They were collected on the first and last day of the program respectively. The author who was also the coordinator of the research methodology and statistics module of the coursework have obtained the necessary permission to use the data for this study. The scholars invited to be part of the study by e-mail. Knowledge and attitude towards statistics 2–3 years after the coursework were assessed online using Google forms. They were also administered a semi structured questionnaire to elicit details about the usefulness of coursework. Participation was voluntary, and consent was also sought online. The confidentiality of the data was assured. Data were not collected from research scholars of Biostatistics or from research scholars who had more than a decade of experience or who had been working in the institute as faculty, assuming that their scores could be higher and could bias the findings. This non funded study was reviewed and approved by the Institute Ethics Committee.

Instruments

Knowledge in Statistics was assessed by a questionnaire prepared by the author and was used as part of the coursework evaluation. The survey included 25 questions that assessed the knowledge of statistics on areas such as descriptive statistics, sampling methods, study design, parametric and nonparametric tests and multivariate analyses. Right answers were assigned a score of 1, and wrong answers were assigned a score of 0. Total scores ranged from 0 to 25. Statistics attitudes were assessed by the Survey of Attitudes toward Statistics (SATS) scale. The SATS is a 36-item scale that measures 6 domains of attitudes towards statistics. The possible range of scores for each item is between 1 and 7. The total score was calculated by dividing the summed score by the number of items. Higher scores indicate more positive attitudes. The SAT-36 is a copyrighted scale, and researchers are allowed to use it only with prior permission. ( 15 – 16 ) The author obtained permission for use in the coursework evaluation and this study. A semi structured questionnaire was also used to elicit details about the usefulness of coursework.

Statistical analysis

Descriptive statistics such as mean, standard deviation, number and percentages were used to describe the socio-demographic data. General Linear Model Repeated Measures of Analysis of variance was used to compare knowledge and attitude scores across assessments. Categorical data from the semi structured questionnaire are presented as percentages. All the statistical tests were two-tailed, and a p value < 0.05 was set a priori as the threshold for statistical significance. IBM SPSS (28.0) was used to analyse the data.

One hundred and thirty research scholars who had completed coursework (CW) in the last 2–3 years were considered for the study. These scholars were sent Google forms to assess their knowledge and attitudes 2–3 years after coursework. 81 scholars responded (62%), and 4 scholars did not consent to participate in the study. The data of 77 scholars were merged with the data obtained during the coursework program (before and soon after CW). Socio-demographic characteristics of the scholars are presented in Table  1 .

The age of the respondents ranged from 23 to 36 years, with an average of 28.7 years (3.01), and the majority of the respondents were females (65%). Years of experience (i.e., after masters) before joining a PhD programme ranged from 0.5 to 9 years, and half of them had less than three years of experience before joining the PhD programme (median-3). More than half of those who responded were research scholars from the behavioural sciences (55%), while approximately 30% were from the basic sciences (29%).

General Linear Model Repeated Measures of Analysis of variance was used to compare the knowledge and attitude scores of scholars before, soon after and 2–3 after the coursework (will now be referred as “later the CW”), and the results are presented below (Table  2 ; Fig.  1 ).

Comparison of knowledge and attitude scores across the assessments. Later the CW – 2–3 years after the coursework

The scores for knowledge and attitude differed significantly across time. Scores of knowledge and attitude increased soon after the coursework; the percentage of change was 77% and 43% respectively. However, significant reductions in knowledge and attitude scores were observed 2–3 years after the coursework compared to scores soon after the coursework. The reduction was higher for attitude scores; knowledge and attitude scores have decreased by 10% and 37% respectively. The change in scores across assessments is evident from the graph, and clearly the effect size is higher for attitude than knowledge.

The scores of knowledge or attitude before the coursework did not significantly differ with respect to gender or age or were not correlated with years of experience. Hence, they were not considered as covariates in the above analysis.

A semi structured questionnaire with open ended questions was also administered to elicit in-depth information about the usefulness of the coursework programme, in which they were also asked to self- rate their knowledge. The data were mostly categorical or narratives. Research scholars’ self-rated knowledge scores (on a scale of 0–10) also showed similar changes; knowledge improved significantly and was retained even after the training (Fig.  2 ).

Self-rated knowledge scores of research scholars over time. Later the CW – 2–3 years after the coursework

The response to the question “ How has coursework changed your attitude toward statistics?”, is presented in Fig.  3 . The responses were Yes, positively, Yes - Negatively, No change – still apprehensive, No change – still appreciate, No change – still hate statistics. The majority of the scholars (70%) reported a positive change in their attitude toward statistics. Moreover, none of the scholars reported negative changes. Approximately 9% of the scholars reported that they were still apprehensive about statistics or hate statistics after the coursework.

How has coursework changed your attitude toward statistics?

Those scholars who reported that they were apprehensive about statistics or hate statistics noted the complexity of the subject, lack of clarity, improper instructions and fear of mathematics as major reasons for their attitude. Some responses are listed below.

“The statistical concepts were not taught in an understandable manner from the UG level” , “I am weak in mathematical concepts. The equations and formulae in statistics scare me”. “Lack of knowledge about the importance of statistics and fear of mathematical equations”. “The preconceived notion that Statistics is difficult to learn” . “In most of the places, it is not taught properly and conceptual clarity is not focused on, and because of this an avoidance builds up, which might be a reason for the negative attitude”.

Majority of the scholars (92%) felt that coursework has helped them in their PhD, and they were happy to recommend it for other research scholars (97%). The responses of the scholars to the question “ How was coursework helpful in your PhD journey ?”, are listed below.

“Course work gave a fair idea on various things related to research as well as statistics” . “Creating the best design while planning methodology, which is learnt form course work, will increase efficiency in completing the thesis, thereby making it faster”. “Course work give better idea of how to proceed in many areas like literature search, referencing, choosing statistical methods, and learning about research procedures”. “Course work gave a good idea of research methodology, biostatistics and ethics. This would help in writing a better protocol and a better thesis”. “It helps us to plan our research well and to formulate, collect and plan for analysis”. “It makes people to plan their statistical analysis well in advance” .

This study evaluated the effectiveness of the existing coursework programme in an institution of higher medical education, and investigated whether the coursework programme benefits research scholars by improving their knowledge of statistics and attitudes towards statistics. The study concluded that the coursework program was beneficial for improving scholars’ knowledge about statistics and attitudes toward statistics.

Unlike other studies that have assessed attitudes toward statistics, the study participants in this study were research scholars. Research scholars need extensive training in statistics, as they need to apply statistical tests and use statistical reasoning in their research thesis, and in their profession to design research projects or their future student dissertations. Notably, no studies have assessed the attitudes or knowledge of research scholars in statistics either across the PhD training period or after intensive statistics training programs. However, the findings of this study are consistent with the findings of a study that compared the knowledge and attitudes toward teaching and education design of PhD students after a two-day educational course and instructional design workshop [ 14 ].

Statistics educators need not only impart knowledge but they should also motivate the learners to appreciate the role of statistics and to continue to learn the quantitative skills that is needed in their professional lives. Therefore, the role of learners’ attitudes toward statistics requires special attention. Since PhD coursework is possibly a major contributor to creating a statistically literate research community, scholars’ attitudes toward statistics need to be considered important and given special attention. Passionate and engaging statistics educators who have adequate experience in illustrating relatable examples could help scholars feel less anxious and build competence and better attitudes toward statistics. Statistics educators should be aware of scholars’ anxiety, fears and attitudes toward statistics and about its influence on learning outcomes and further interest in the subject.

Strengths and limitations

Analysis of changes in knowledge and attitudes scores across various time points of PhD training is the major strength of the study. Additionally, this study evaluates the effectiveness of intensive statistical courses for research scholars in terms of changes in knowledge and attitudes. This study has its own limitations: the data were collected through online platforms, and the nonresponse rate was about 38%. Ability in mathematics or prior learning experience in statistics, interest in the subject, statistics anxiety or performance in coursework were not assessed; hence, their influence could not be studied. The reliability and validity of the knowledge questionnaire have not been established at the time of this study. However, author who had prepared the questionnaire had ensured questions from different areas of statistics that were covered during the coursework, it has also been used as part of the coursework evaluation. Despite these limitations, this study highlights the changes in attitudes and knowledge following an intensive training program. Future research could investigate the roles of age, sex, mathematical ability, achievement or performance outcomes and statistics anxiety.

The study concluded that a rigorous and intensive training program such as PhD coursework was beneficial for improving knowledge about statistics and attitudes toward statistics. However, the significant reduction in attitude and knowledge scores after 2–3 years of coursework indicates that a refresher program might be helpful for research scholars as they approach the analysis stage of their thesis. Statistics educators must develop innovative methods to teach research scholars from nonstatistical backgrounds. They also must be empathetic to understanding scholars’ anxiety, fears and attitudes toward statistics and to understand its influence on learning outcomes and further interest in the subject.

Data availability

The data that support the findings of this study are available from the corresponding author upon request.

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Acknowledgements

The author would like to thank the participants of the study and peers and experts who examined the content of the questionnaire for their time and effort.

This research did not receive any grants from funding agencies in the public, commercial, or not-for-profit sectors.

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This study used data already collected data (before and soon after coursework). The data pertaining to knowledge and attitude towards statistics 2–3 years after coursework were collected from research scholars through the online survey platform Google forms. The participants were invited to participate in the survey through e-mail. The study was explained in detail, and participation in the study was completely voluntary. Informed consent was obtained online in the form of a statement of consent. The confidentiality of the data was assured, even though identifiable personal information was not collected. This non-funded study was reviewed and approved by NIMHANS Institute Ethics Committee (No. NIMHANS/21st IEC (BS&NS Div.)

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Philip, M. Measurement and analysis of change in research scholars’ knowledge and attitudes toward statistics after PhD coursework. BMC Med Educ 24 , 512 (2024). https://doi.org/10.1186/s12909-024-05487-y

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Received : 27 October 2023

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DOI : https://doi.org/10.1186/s12909-024-05487-y

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