Using an Intelligence Driven System as an Alternative Approach to Data Analysis

2012 ◽  
Vol 18 (4) ◽  
pp. 453-454
Author(s):  
Mayes D. Mathews ◽  
Shannon O. Jackson
Keyword(s):  
Data Analysis ◽  
Alternative Approach ◽  
Driven System

IRTree Model: An Alternative Approach for Self-Reported Ordinal Data Analysis

2019 ◽  
Vol 32 (2) ◽  
pp. 303-323
Author(s):  
Yoonsun Jang ◽  
Meereem Kim ◽  
Juyeon Lee
Keyword(s):  
Data Analysis ◽  
Ordinal Data ◽  
Alternative Approach ◽  
Ordinal Data Analysis

scholarly journals Comments on: involving service users in the qualitative analysis of patient narratives to support healthcare quality improvement

2019 ◽  
Vol 5 (1) ◽  
Author(s):  
Marney Williams ◽  
Mike Etkind ◽  
Fran Husson ◽  
Della Ogunleye ◽  
John Norton
Keyword(s):  
Quality Improvement ◽  
Data Analysis ◽  
Healthcare Quality ◽  
English Summary ◽  
Service Users ◽  
Research Projects ◽  
Period Analysis ◽  
Patient Narratives ◽  
Time Period ◽  
Alternative Approach

Plain English summary Some previous researchers (Locock et al) have written about what may be the best way for public contributors to be involved in data analysis in research projects. Their experience has been that giving public contributors large amounts of text to read is not the best use of their time and experience. They have recommended that a better approach would be for a researcher to meet with a group of users at the start of analysis, to discuss what to look out for. However, as another patient group that has been involved in analysis, we think differently. The approach we used was to be more fully involved in the project over a longer time period. Analysis tasks were broken down into stages to make it easier for those taking part. We found that this allowed us to take part fully without placing too much burden on us. We found that our approach was workable and successful and see no reason why it could not be applied in other circumstances. Abstract In this journal, Locock et al. have suggested that service users should not be overburdened with large amounts of data, and that eliciting users’ reflections on their experience at the start of analysis and using this as a guide to direct researcher attention during the remainder of the process may work better. As public contributors that have been involved in analysis we suggest an alternative approach in this brief letter, based on our own experiences.

Data Analysis in Agricultural Experimentation. III. Multiple Comparisons

1993 ◽  
Vol 29 (1) ◽  
pp. 1-8 ◽  
Author(s):  
S. C. Pearce
Keyword(s):  
Cluster Analysis ◽  
Data Analysis ◽  
Bayesian Methods ◽  
Multiple Comparisons ◽  
Multiple Comparison ◽  
Comparison Methods ◽  
Alternative Approach ◽  
And Cluster Analysis ◽  
Multiple Comparison Methods

SummaryMultiple comparison methods are described. It is noted that they have always been controversial, partly because they emphasize testing at the expense of estimation, partly because they pay no regard to the purpose of the investigation, partly because there are so many competing forms and, not least, because they can lead to illogical conclusions. There are many identified instances where they have been found misleading.An alternative approach is to designate ‘contrasts of interest’ from the start and to concentrate estimation and testing upon them. In many experiments the approach is powerful and definite in use, but sometimes there is no reason to designate one contrast rather than another, for example, in the assessment of new strains or new chemicals. In such circumstances some have found multiple comparisons useful, especially when the problem is to ‘pick the winner’. Bayesian methods and cluster analysis are considered briefly as other alternatives.The current over-use of multiple comparisons is deplored. It is thought to arise in part from bad teaching and in part from the general reluctance of non-statisticians to venture into the unknown territory of specifying contrasts. A bad situation is made worse by the availability of software that carries out multiple comparisons as a matter of course.

scholarly journals Executing native Java code in R: an approach based on a local server

2020 ◽  
Vol 6 ◽  
pp. e300
Author(s):  
Mathieu Fortin
Keyword(s):  
Data Analysis ◽  
Object Oriented ◽  
R Package ◽  
Complex Object ◽  
Computationally Efficient ◽  
R Language ◽  
Alternative Approach ◽  
Socket Connection ◽  
Java Native Interface ◽  
Java Code

The R language is widely used for data analysis. However, it does not allow for complex object-oriented implementation and it tends to be slower than other languages such as Java, C and C++. Consequently, it can be more computationally efficient to run native Java code in R. To do this, there exist at least two approaches. One is based on the Java Native Interface (JNI) and it has been successfully implemented in the rJava package. An alternative approach consists of running a local server in Java and linking it to an R environment through a socket connection. This alternative approach has been implemented in an R package called J4R. This article shows how this approach makes it possible to simplify the calls to Java methods and to integrate the R vectorization. The downside is a loss of performance. However, if the vectorization is used in conjunction with multithreading, this loss of performance can be compensated for.

Ipsative measurement and the analysis of organizational values: an alternative approach for data analysis

2014 ◽  
Vol 49 (2) ◽  
pp. 559-579 ◽  
Author(s):  
Frans M. van Eijnatten ◽  
L. Andries van der Ark ◽  
Sjaña S. Holloway
Keyword(s):  
Data Analysis ◽  
Organizational Values ◽  
Alternative Approach

scholarly journals Indirect Reference Intervals: Harnessing the Power of Stored Laboratory Data.

2019 ◽  
Vol 40 (2) ◽  
pp. 99-111 ◽  
Keyword(s):  
Data Analysis ◽  
Traditional Approach ◽  
Laboratory Data ◽  
Age Groups ◽  
Reference Interval ◽  
Reference Intervals ◽  
Indirect Approach ◽  
Distribution Of Values ◽  
Alternative Approach ◽  
Indirect Technique

Reference intervals are relied upon by clinicians when interpreting their patients’ test results. Therefore, laboratorians directly contribute to patient care when they report accurate reference intervals. The traditional approach to establishing reference intervals is to perform a study on healthy volunteers. However, the practical aspects of the staff time and cost required to perform these studies make this approach difficult for clinical laboratories to routinely use. Indirect methods for deriving reference intervals, which utilise patient results stored in the laboratory’s database, provide an alternative approach that is quick and inexpensive to perform. Additionally, because large amounts of patient data can be used, the approach can provide more detailed reference interval information when multiple partitions are required, such as with different age-groups. However, if the indirect approach is to be used to derive accurate reference intervals, several considerations need to be addressed. The laboratorian must assess whether the assay and patient population were stable over the study period, whether data ‘clean-up’ steps should be used prior to data analysis and, often, how the distribution of values from healthy individuals should be modelled. The assumptions and potential pitfalls of the particular indirect technique chosen for data analysis also need to be considered. A comprehensive understanding of all aspects of the indirect approach to establishing reference intervals allows the laboratorian to harness the power of the data stored in their laboratory database and ensure the reference intervals they report are accurate.

scholarly journals Reflective Learning Views of Students, Teachers and Instructors: A Mixed-Methods Study

2019 ◽  
Vol 12 (3) ◽  
pp. 200
Author(s):  
Irfan Tosuncuoglu
Keyword(s):  
Data Analysis ◽  
English Language ◽  
Qualitative Data ◽  
Reflective Learning ◽  
Mixed Methods Study ◽  
Computer Assisted ◽  
English Classes ◽  
Alternative Approach ◽  
Language And Literature ◽  
Analysis Programme

Teachers need to modernize themselves and keep pace with changes in order to adapt themselves to the developing and changing system. At this juncture, the “reflective learning” model becomes an important element of teachers’ professional development. Since the concept of reflection was first used, it has increased its importance as an alternative approach to the existing models of learning that still hold sway. Nevertheless, when the studies that have been conducted over the years were consulted, it was determined that reflective learning is a difficult concept to define, because reflection is an abstract concept, the question how it is to be distinguished from one’s other thoughts and ideas assumes importance. For this reason, research with 102 participants has been undertaken in order to ascertain the awareness of teachers, students and instructors of reflective learning. The participants are broken down thus: 35 students receiving education in the English Language and Literature department of Karabük University; 31 instructors providing in-service English classes in the same university’s Preparatory unit and other Faculties; and 36 English teachers serving in the secondary level schools in the Karabük province in Turkey. The participants have responded to a survey consisting of 33 questions. In the data analysis, SPSS and nonparametric Mann-Whitney-U, Kruskal-Wallis and Student Newman Kleus tests have been used. In addition, 2 teachers have been interviewed, the technique of content analysis has been used and the data has been analyzed by means of NVivo 12 Pro – computer assisted qualitative data analysis programme.

scholarly journals Protecting against researcher bias in secondary data analysis: Challenges and solutions

2020 ◽  
Author(s):  
Jessie Baldwin ◽  
Jean-Baptiste Pingault ◽  
Tabea Schoeler ◽  
Hannah Sallis ◽  
Marcus Robert Munafo
Keyword(s):  
Data Analysis ◽  
Secondary Data ◽  
Secondary Data Analysis ◽  
Epidemiological Research ◽  
Alternative Approach ◽  
Researcher Bias

Protecting against researcher biases – both conscious and unconscious – can help to ensure robust findings and correct inferences in epidemiology. While pre-registration can be an effective way to achieve this, it brings several challenges for researchers analysing existing datasets. Here we describe these challenges, and propose solutions and alternatives. For each solution, we provide guidance, and highlight practical considerations for researchers. The adoption of these practices will allow researchers to effectively pre-register secondary data analysis studies, or use an alternative approach, in order to protect themselves against common human biases. In turn, this will increase the robustness and credibility of epidemiological research based on secondary data.

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