scholarly journals Can squirrel monkeys learn an ABnA grammar? A re-evaluation of Ravignani et al. (2013)

2017 ◽  
Author(s):  
Stefano Ghirlanda
Keyword(s):  
Data Analysis ◽  
Discrimination Task ◽  
Statistical Significance ◽  
Squirrel Monkeys ◽  
Response Variable ◽  
Auditory Sequence ◽  
Data Points

Ravignani et al. (2013) habituated squirrel monkeys to sound sequences conforming to an ABnB grammar, then tested them for the ability to identify novel grammatical sequences as well as non-grammatical ones. Although they conclude that the monkeys "consistently recognized and generalized the sequence ABnA," the data indicate very poor generalization. Pattern grammaticality accounted for at most 6% of the variance in responding. In addition, the statistical significance of results depends on specific choices of data analysis (dichotomization of the response variable and omission of certain data points) which appear to have a weak rationale. I also suggest that the task used by Ravignani et al. (2013) may be fruitfully analyzed as an auditory sequence discrimination task that does not require specific proto-linguistic abilities.

scholarly journals Can squirrel monkeys learn an ABnA grammar? A re-evaluation of Ravignani et al. (2013)

2017 ◽  
Author(s):  
Stefano Ghirlanda
Keyword(s):  
Data Analysis ◽  
Discrimination Task ◽  
Statistical Significance ◽  
Squirrel Monkeys ◽  
Response Variable ◽  
Auditory Sequence ◽  
Data Points

Ravignani et al. (2013) habituated squirrel monkeys to sound sequences conforming to an ABnB grammar, then tested them for the ability to identify novel grammatical sequences as well as non-grammatical ones. Although they conclude that the monkeys "consistently recognized and generalized the sequence ABnA," the data indicate very poor generalization. Pattern grammaticality accounted for at most 6% of the variance in responding. In addition, the statistical significance of results depends on specific choices of data analysis (dichotomization of the response variable and omission of certain data points) which appear to have a weak rationale. I also suggest that the task used by Ravignani et al. (2013) may be fruitfully analyzed as an auditory sequence discrimination task that does not require specific proto-linguistic abilities.

scholarly journals Can squirrel monkeys learn an ABnA grammar? A re-evaluation of Ravignani et al. (2013)

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e3806 ◽  
Author(s):  
Stefano Ghirlanda
Keyword(s):  
Data Analysis ◽  
Statistical Significance ◽  
Squirrel Monkeys ◽  
Perceptual Similarity ◽  
Specific Data ◽  
Response Variable ◽  
Data Points

Ravignani et al. (2013) habituated squirrel monkeys to sound sequences conforming to an ABnA grammar (n = 1, 2, 3), then tested them for their reactions to novel grammatical and non-grammatical sequences. Although they conclude that the monkeys “consistently recognized and generalized the sequence ABnA,” I remark that this conclusion is not robust. The statistical significance of results depends on specific choices of data analysis, namely dichotomization of the response variable and omission of specific data points. Additionally, there is little evidence of generalization to novel patterns (n = 4, 5), which is important to conclude that the monkeys recognized the ABnA grammar beyond the habituation patterns. Lastly, many test sequences were perceptually similar to habituation sequences, raising the possibility that the monkeys may have generalized based on perceptual similarity rather than based on grammaticality.

scholarly journals Bayesian modeling and significant features exploration in wavelet power spectra

2007 ◽  
Vol 14 (1) ◽  
pp. 79-88 ◽  
Author(s):  
D. V. Divine ◽  
F. Godtliebsen
Keyword(s):  
Data Analysis ◽  
Traditional Approach ◽  
Statistical Significance ◽  
Power Spectra ◽  
Simulated Data ◽  
Scale Space ◽  
Independent Random Variables ◽  
Discrete Wavelet ◽  
Wavelet Coefficients ◽  
Testing Procedures

Abstract. This study proposes and justifies a Bayesian approach to modeling wavelet coefficients and finding statistically significant features in wavelet power spectra. The approach utilizes ideas elaborated in scale-space smoothing methods and wavelet data analysis. We treat each scale of the discrete wavelet decomposition as a sequence of independent random variables and then apply Bayes' rule for constructing the posterior distribution of the smoothed wavelet coefficients. Samples drawn from the posterior are subsequently used for finding the estimate of the true wavelet spectrum at each scale. The method offers two different significance testing procedures for wavelet spectra. A traditional approach assesses the statistical significance against a red noise background. The second procedure tests for homoscedasticity of the wavelet power assessing whether the spectrum derivative significantly differs from zero at each particular point of the spectrum. Case studies with simulated data and climatic time-series prove the method to be a potentially useful tool in data analysis.

Improving Data Analysis in Political Science

1969 ◽  
Vol 21 (4) ◽  
pp. 641-654 ◽  
Author(s):  
Edward R. Tufte
Keyword(s):  
Data Analysis ◽  
Measurement Error ◽  
Political Science ◽  
Statistical Significance ◽  
Large Body ◽  
Small Collection

Students of politics use statistical and quantitative techniques to: summarize a large body of numbers into a small collection of typical values;confirm (and perhaps sanctify) the results of the analysis by using tests of statistical significance that help protect against sampling and measurement error;discover what's going on in their data and expose some new relationships; andinform their audience what's going on in the data.

scholarly journals Impact of the COVID-19 Outbreak on Emergency Resources – An Observational Multicenter Study Including All Hospitals in a Major Urban Center of the Rhein Ruhr Metropolitan Region

2020 ◽  
Author(s):  
Sebastian Bergrath ◽  
Tobias Strapatsas ◽  
Michael Tuemen ◽  
Thorsten Reith ◽  
Marc Deussen ◽  
...  
Keyword(s):  
Data Analysis ◽  
Multicenter Study ◽  
Social Life ◽  
Hospital Admissions ◽  
Statistical Significance ◽  
Urban Center ◽  
Medical Specialties ◽  
Work Related ◽  
Ed Visits ◽  
The Impact

Abstract Background: The outbreak of the coronavirus disease 2019 (COVID-19) caused by the severe respiratory distress syndrome coronavirus 2 (SARS-CoV-2) led to severe disruption in social life and economics. The present study should analyze the impact of the local COVID-19 epidemic on emergency resources for all hospitals in a major urban center (Moenchengladbach, Germany). Methods: An observational multicenter study was performed involving all four acute care hospitals. Systemic parameters department (ED) parameters from week 4 to 24 in 2020 were compared to the corresponding period in 2019 for each hospital and in a summative data analysis using a logistic regression model. Outcomes: ED visits, ED to hospital admission, ED to Intensive Care Unit (ICU) admission, medical specialties of admitted patients, work related accidents. Results: In week 9/2020 the first SARS-CoV-2 positive patients were detected in our region. All hospitals decided to minimize elective admissions to ensure operational capability for COVID-19 patients. The summative number of ED visits dropped from 34,659 to 28,008. Numbers decreased from week 8 on between 38% and 48% per week per hospital at the maximum and began to rise again from week 16 on. The pooled data analysis showed statistically significant decreases in outpatient ED visits (20,152 vs. 16,477, p=<0.001), hospital admissions of ED patients (14,507 vs. 11,531, p=<0.001), and work-related accidents (2,290 vs. 1,468, p=<0.001). The decrease in admissions from ED to ICU did not reach statistical significance (2,093 vs. 1,566, p=0.255). The decline in ED cases was mainly caused by a decrease in non-trauma and non-surgical patients. Conclusion: The regional COVID-19 outbreak led to significantly reduced ED contacts after the first COVID-19 cases appeared. Even the admissions to the hospitals and the number of ED to ICU-admissions decreased, which is potentially dangerous, because the ratio of emergency outpatients vs. inpatients remained stable. Therefore, one can assume that patients with severe medical problems did not seek ED care in many cases. The decline of patients was earlier than in other German hospitals and in contrast to the findings in the U.S. and Italy where ED visits and hospital admissions in medical disciplines increased.

Analyzing the Performance and Efficiency of IT-Compliant Audit Module Using Clustering Methods

2020 ◽  
pp. 351-376 ◽  
Author(s):  
Soobia Saeed ◽  
N. Z. Jhanjhi ◽  
Mehmood Naqvi ◽  
Mamoona Humayun ◽  
Vasaki Ponnusamy
Keyword(s):  
Data Analysis ◽  
Service Quality ◽  
Statistical Significance ◽  
Human Beings ◽  
Clustering Methods ◽  
Minimum Period ◽  
The Impact

Human beings have a knack for errors. Counter-effective actions rendered to specify and rectify such errors in a minimum period of time are required when effectiveness and swift advancement depends on the capability of acknowledging the faults and errors and repair quickly. The software as audit module application in IT complaint is in review in this commentary as is another significant instrument created in the field of data analysis that digs deep into quickly and successfully assessing the imprecisions or grievances identified by the users in a certain company. The target of this study is to evaluate the statistical significance in relationship between client reporting attitude and client reliability and to evaluate the impact of strong responsiveness on client reliability, to measure the statistically noteworthy effect of client grievance conduct on service quality, and to test the impact of service quality on client dedication.

Anomaly Detection for Inferring Social Structure

2010 ◽  
pp. 1797-1803
Author(s):  
Lisa Friedland
Keyword(s):  
Data Analysis ◽  
Anomaly Detection ◽  
Social Structure ◽  
Small Groups ◽  
Analysis Data ◽  
Data Sets ◽  
Complex Data ◽  
Detection Approach ◽  
Complex Data Sets ◽  
Data Points

In traditional data analysis, data points lie in a Cartesian space, and an analyst asks certain questions: (1) What distribution can I fit to the data? (2) Which points are outliers? (3) Are there distinct clusters or substructure? Today, data mining treats richer and richer types of data. Social networks encode information about people and their communities; relational data sets incorporate multiple types of entities and links; and temporal information describes the dynamics of these systems. With such semantically complex data sets, a greater variety of patterns can be described and views constructed of the data. This article describes a specific social structure that may be present in such data sources and presents a framework for detecting it. The goal is to identify tribes, or small groups of individuals that intentionally coordinate their behavior—individuals with enough in common that they are unlikely to be acting independently. While this task can only be conceived of in a domain of interacting entities, the solution techniques return to the traditional data analysis questions. In order to find hidden structure (3), we use an anomaly detection approach: develop a model to describe the data (1), then identify outliers (2).

The Role and Characteristics of Tactile Graphics in Secondary Mathematics and Science Textbooks in Braille

2012 ◽  
Vol 106 (9) ◽  
pp. 543-554 ◽  
Author(s):  
Derrick W. Smith ◽  
Sinikka M. Smothers
Keyword(s):  
Content Analysis ◽  
Data Analysis ◽  
Comparative Analysis ◽  
Secondary Mathematics ◽  
Likert Scale ◽  
Science Textbooks ◽  
Data Points ◽  
Strongly Agree ◽  
Mathematics And Science ◽  
Tactile Graphics

IntroductionThe purpose of the study presented here was to determine how well tactile graphics (specifically data analysis graphs) in secondary mathematics and science braille textbooks correlated with the print graphics.MethodA content analysis was conducted on 598 separate data analysis graphics from 10 mathematics and science textbooks. The researchers (the authors) cross-validated the findings through a comparative analysis of the tactile graphics of five shared textbooks.ResultsDiscrepancies were found between the print graphic and the tactile graphic in 12.5% of the sample. The most common discrepancy was differences in how data lines and data points were individualized in the print graphic compared to the tactile graphic. On the basis of the reviews of the graphics, the researchers answered a 5-point Likert-scale question (from 1 = strongly disagree to 5 = strongly agree) asking if the “tactile graphic is a valid representation of the print graphic.” The overall score for the sample was 3.71 (SD = 1.60), with a Krippendorff alpha of 0.6328 (the measure of disagreement and alpha > 0.70 are consider moderate).DiscussionThe findings demonstrate that while the majority of tactile graphics have good correlations to their print counterparts, there is still room for improvement. Some transcribers omitted a tactile graphic without providing a reason. Forty graphics (6.7%) were omitted from the braille transcription. Two textbooks were missing more than 85% of the tactile graphics of the data graphs.Implications for PractitionersTactile graphics in math and science books are important for a student to understand. Although most transcribers do an excellent job of creating valid tactile graphics, problems with many graphics still exist in textbooks. Practitioners need constantly to review the tactile graphics that are used in all classrooms and be prepared to create their own if needed.

scholarly journals A User’s Guide to Topological Data Analysis

2017 ◽  
Vol 4 (2) ◽  
Author(s):  
Elizabeth Munch
Keyword(s):  
Data Analysis ◽  
Topological Data Analysis ◽  
Dimensional Structure ◽  
Persistence Diagram ◽  
Data Points ◽  
Good For ◽  
Topological Data

Topological data analysis (TDA) is a collection of powerful tools that can quantify shape and structure in data in order to answer questions from the data’s domain. This is done by representing some aspect of the structure of the data in a simplified topological signature. In this article, we introduce two of the most commonly used topological signatures. First, the persistence diagram represents loops and holes in the space by considering connectivity of the data points for a continuum of values rather than a single fixed value. The second topological signature, the mapper graph, returns a 1-dimensional structure representing the shape of the data, and is particularly good for exploration and visualization of the data. While these techniques are based on very sophisticated mathematics, the current ubiquity of available software means that these tools are more accessible than ever to be applied to data by researchers in education and learning, as well as all domain scientists.

Financial development and self-employment

2020 ◽  
Vol 19 (3) ◽  
pp. 339-357
Author(s):  
Papar Kananurak ◽  
Aeggarchat Sirisankanan
Keyword(s):  
Data Analysis ◽  
Panel Data ◽  
Financial Development ◽  
Panel Data Analysis ◽  
Statistical Significance ◽  
Random Effect Model ◽  
Direct Examination ◽  
Content Type ◽  
Self Employment ◽  
The Impact

Purpose There are several different factors that can influence self-employment. However, there is little evidence stemming from direct examination of the impact of financial development (FD) on self-employment. This study aims to formulate empirical specification models to examine the effect of FD on self-employment. Design/methodology/approach Panel data analysis of 136 sample countries was performed during the period from 2000 to 2017. This study initially implemented the new financial index developed by the International Monetary Fund (IMF) to examine the impact of FD on self-employment. Panel data analysis including the pooled model, fixed effect and random effect model has been carried out. Findings The empirical results show that the financial institutions index has a negative significant impact on self-employment by a considerable magnitude, whereas the financial markets index does not show any statistical significance. The results also find that the government effectiveness index is negative and statistically significant on self-employment. Originality/value There are several different factors which can influence self-employment. Nevertheless, there is little evidence for the direct examination of the impact of FD on self-employment. This study investigated the impact of FD on self-employment by using the new FD index created by the IMF. The finding may help policymakers to implement FD along with other institutional policies to control self-employment.

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