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Data-driven methodologies for bearing vibration analysis and vibration based fault diagnosis

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phd thesis on vibration analysis

  • Al-Bugharbee, Hussein Razzaq.
  • Strathclyde Thesis Copyright
  • University of Strathclyde
  • Doctoral (Postgraduate)
  • Doctor of Philosophy (PhD)
  • Department of Mechanical and Aerospace Engineering.
  • Rolling element bearings (REBs) are one of the most critical mechanical components. Their failures can lead for catastrophic failures which might include great loss in economy or even in the lives of people. REBs are inherently dynamic and they demonstrate complex vibration behaviour where conventional vibration -based fault diagnosis methods might not give sensitive indicators of the presence of the defects. This thesis investigates the singular spectrum analysis (SSA) capabilities as completely data-based fault diagnosis method in REBs. The SSA is used to decompose the bearing vibration acceleration signals in a certain number of principal components having the trend, periodical components and structure-less noise. This thesis develops two methodologies to use SSA in different ways and for different purposes. The first methodology uses the SSA (i.e only the decomposition stage) to create a baseline space from healthy bearing vibration signals. Then, any new signals are projected onto this baseline space. From these projections, features are made and used for fault diagnosis purposes. In the second methodology, the SSA contributes to the development of an advanced signal pretreatment that efficiently improves representing the nonstationary bearing vibration signals by linear time invariant autoregressive (LTIVAR) model. Then the coefficients of LTIVAR model are used as features for fault diagnosis purposes.The two methodologies have been validated by using experimental data obtained from three different bearing test rigs. The data used in the analysis covers different defect locations and different defect severities. The results of both methodologies, in terms of correct classification, were compared to some other recent methodologies. In comparison, it is shown that both methodologies have a very good performance and they are superior to those methodologies.The thesis offers simple and efficient methodologies for a complete fault diagnosis in terms of fault detection, identification and severity estimation. Thus, these methodologies have a potential possibility for automation of the entire process of each method.
  • Doctoral thesis
  • 10.48730/vzwp-v296
  • 9912536893002996
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Vibration analysis of cracked aluminium plates

Israr, Asif (2008) Vibration analysis of cracked aluminium plates. PhD thesis, University of Glasgow.

This research is concerned with analytical modelling of the effects of cracks in structural plates and panels within aerospace systems such as aeroplane fuselage, wing, and tail-plane structures, and, as such, is part of a larger body of research into damage detection methodologies in such systems. This study is based on generating a so-called reduced order analytical model of the behaviour of the plate panel, within which a crack with some arbitrary characteristics is present, and which is subjected to a force that causes it to vibrate. In practice such a scenario is potentially extremely dangerous as it can lead to failure, with obvious consequences. The equation that is obtained is in the form of the classical Duffing equation, in this case, the coefficients within the equation contain information about the geometrical and mass properties of the plate, the loading and boundary conditions, and the geometry, location, and potentially the orientation of the crack. This equation has been known for just over a century and has in the last few decades received very considerable attention from both the analytical dynamics community and also from the dynamical systems researchers, in particular the work of Ueda, Thompson, in the 1970s and 1980s, and Thomsen in the 1990s and beyond. An approximate analytical solution is obtained by means of the perturbation method of multiple scales. This powerful method was popularized in the 1970s by Ali H.Nayfeh, and discussed in his famous books, ‘Perturbation Methods’ (1974) and ‘Nonlinear Oscillations’ (1979, with D.T.Mook), and also by J.Murdock (1990), and M.P.Cartmell et al. (2003) and has been shown to be immensely useful for a wide range of nonlinear vibration problems. In this work it is shown that different boundary conditions can be admitted for the plate and that the modal natural frequencies are sensitive to the crack geometry. Bifurcatory behaviour of the cracked plate has then been examined numerically, for a range of parameters. The model has been tested against experimental work and against a Finite Element model, with good corroboration from both. In all events, this is a significant new result in the field and one that if implemented within a larger damage detection strategy, could be of considerable practical use.

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The University of Glasgow is a registered Scottish charity: Registration Number SC004401

The University of Texas at Arlington

  • Publications
  • Dissertation and Thesis

Abe Davis

I am a computer scientist specializing in computer graphics and vision, human-computer interaction (HCI), and a breadth of other topics. 

Research Interests

I work on a range of topics in graphics, vision, and HCI, with most of my research focusing on how to apply work in these fields to new problems and application spaces. 

Selected Publications

2020 Zhiqiu Lin, Jin Sun, Abe Davis, and Noah Snavely. Visual chirality. IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2015), (Selected for Oral, Best Paper Nominee). 2018 Abe Davis and Maneesh Agrawala. Visual rhythm and beat. SIGGRAPH 2018.

2017 Mackenzie Leake, Abe Davis, Anh Truong, and Maneesh Agrawala. Computational video editing for dialogue-driven scenes. SIGGRAPH 2017.

2017 Abe Davis, Katherine L. Bouman (co-first author), Justin G. Chen, Michael Rubinstein, Oral Buyukozturk, Fredo Durand, and William T. Freeman. Visual vibrometry: Estimating material properties from small motions in video. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI).

2017 Abe Davis, Justin G. Chen, Oral Buyukozturk, Frédo Durand, and Doug L. James. Structural health monitoring from the window seat of a passenger airplane. 11th International Workshop on Structural Health Monitoring (IWSHM 2017).

2016 Neal Wadhwa, Hao-Yu Wu, Abe Davis, Michael Rubinstein, Eugene Shih, Gautham J. Mysore, Justin G. Chen, Oral Buyukozturk, John V. Guttag, William T. Freeman, and Frédo Durand. Eulerian video magnification and analysis. Communications of the ACM.

2016 Lukas Murmann, Abe Davis, Jan Kautz, and Frédo Durand. Computational bounce flash for indoor portraits. SIGGRAPH Asia 2016.

2016 Abe Davis. Visual Vibration Analysis. PhD thesis, Massachusetts Institute of Technology, Sep 2016, *MIT Sprowls Award | *Runner-up, SIGGRAPH Dissertation Award*.

2016 Oral Buyukozturk, Justin G Chen, Neal Wadhwa, Abe Davis, Frédo Durand, and William T Freeman. Smaller than the eye can see: Vibration analysis with video cameras. 19th World Conference on Non-Destructive Testing (WCNDT 2016).

2015 Abe Davis, Justin G. Chen, and Frédo Durand. Image-space modal bases for plausible manipulation of objects in video. SIGGRAPH Asia 2015.

2015 Abe Davis, Katherine L. Bouman (co-first author), Justin G. Chen, Michael Rubinstein, Fredo Durand, and William T. Freeman. Visual vibrometry: Estimating material properties from small motion in video. IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2015), (Selected for Oral Presentation).

2015 Justin G Chen, Neal Wadhwa, Abe Davis, Frédo Freeman Durand, T William, and Oral Buyukozturk. Long distance video camera measurements of structures. 10th International Workshop on Structural Health Monitoring (IWSHM 2015).

2015 Justin G Chen, Abe Davis, Neal Wadhwa, Frédo Durand, William T. Freeman, and Oral Buyukozturk. Video camera-based vibration measurement for condition assessment of civil infrastructure. International Symposium Non-Destructive Testing in Civil Engineering (NDT-CE 2015).

2014 Lixin Shi, Haitham Hassanieh, Abe Davis, Dina Katabi, and Fredo Durand. Light field reconstruction using sparsity in the continuous fourier domain. ACM TOG | SIGGRAPH 2015.

2014 Abe Davis, Michael Rubinstein, Neal Wadhwa, Gautham J. Mysore, Frédo Durand, and William T. Freeman. The visual microphone: Passive recovery of sound from video. SIGGRAPH 2014.

2013 Abe Davis. Unstructured light fields. Master’s thesis, Massachusetts Institute of Technology, Sep 2013.

2012 YiChang Shih, Abe Davis, Samuel W. Hasinoff, Frédo Durand, and William T. Freeman. Laser speckle photography for surface tampering detection. IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2012), *Google Student Travel Award*.

2012 Abe Davis, Marc Levoy, and Fredo Durand. Unstructured light fields. Computer Graphics Forum | Eurographics 2012.

2010 Andrew Adams, Jongmin Baek, and Abe Davis. Fast high-dimensional filtering using the permutohedral lattice. Computer Graphics Forum | Eurographics 2010.

Selected Awards and Honors

2018 Brown Institute for Innovation in Media Magic Grant for "Paraframe".

2017 ACM SIGGRAPH Dissertation Award, (Runner-up). 2017 Brown Institute for Innovation in Media Magic Grant for "Visual Beat".

2017 IWSHM 2017 Structural Health Monitoring in Action Award.

2016 George M. Sprowls Award for Best PhD Thesis in Computer Science at MIT.

2016 MIT 100K Pitch Competition, (Finalist).

2016 Forbes "30 under 30".

2016 Business Insider "The 8 most innovative scientists in tech and engineering".

2011 NSF Graduate Reasearch Fellow.

2011 Mathworks Fellow.

2011 Optical Society of America Color Constancy Competition, (3rd Place).

2010 Eurographics 2010, Second Best Paper.

2009 Stanford CS348B Annual Rendering Competition, (Grand Prize). *Featured in the textbook Physically Based Rendering: From Theory to Implementation

2006 Intel Science Talent Search, (7th Place).

2020-Present Assistant Professor, Cornell University Department of Computer Science.

2019-2020 Postdoc, Cornell Tech.

2016-2019 Postdoc, Stanford University. { Adviser: Maneesh Agrawala { Funding: Brown Institute for Media Innovation Magic Grants

2010-2016 PhD, Electrical Engineering and Computer Science, Massachusetts Institute of Technology. { Adviser: Frédo Durand { Thesis: "Visual Vibration Analysis" { Funding: Mathworks Fellowship, National Science Foundation Graduate Research Fellowship

2010–2012 MS, Electrical Engineering and Computer Science, Massachusetts Institute of Technology. { Adviser: Frédo Durand { Thesis: "Unstructured Light Fields"

2006–2010 BS, Computer Science, Stanford University, (with honors). { Thesis: "Interactive Hand-held Light Field Capture"

  • 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

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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.

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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 ).

figure 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 ).

figure 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.

figure 3

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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Department of Biostatistics, Dr. M.V. Govindaswamy Centre, National Institute of Mental Health and Neurosciences (NIMHANS), Bangalore, 560 029, India

Mariyamma Philip

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Mariyamma Philip: Conceptualization, Methodology, Validation, Investigation, Writing- Original draft, Reviewing and Editing.

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Correspondence to Mariyamma Philip .

Ethics declarations

Ethics approval and consent to participate.

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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Not applicable because there is no personal information or images that could lead to the identification of a study participant.

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The authors declare no competing interests.

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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

Accepted : 29 April 2024

Published : 08 May 2024

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

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phd thesis on vibration analysis

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    Dissertation and Thesis in the Vibro-Acoustics and Sound Quality Research Laboratory. Skip to main content. ... T. Peng, Ph.D., 2010, Coupled Multi-body Dynamic and Vibration Analysis of Hypoid and Bevel Geared Rotor System, University of Cincinnati. 16: X. Hua, M.Sc., 2010, Hypoid and Spiral Bevel Gear Dynamics with Emphasis on Gear-Shaft ...

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    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 ...